From 636c4a8471e4d76ae31b6f90df7ff97a381570be Mon Sep 17 00:00:00 2001 From: emunkhtsetseg Date: Thu, 14 Nov 2024 18:26:03 +0900 Subject: [PATCH 1/3] first page --- data-tidy.qmd | 3 ++- data-transform.qmd | 11 +++++++---- index.qmd | 43 +++++++++++++++++++++++-------------------- 3 files changed, 32 insertions(+), 25 deletions(-) diff --git a/data-tidy.qmd b/data-tidy.qmd index 0aa24f838..0552365c0 100644 --- a/data-tidy.qmd +++ b/data-tidy.qmd @@ -209,7 +209,8 @@ billboard |> The number of rows is now much lower, indicating that many rows with `NA`s were dropped. You might also wonder what happens if a song is in the top 100 for more than 76 weeks? -We can't tell from this data, but you might guess that additional columns `wk77`, `wk78`, ... would be added to the dataset. +We can't tell from this data, but you might guess that additional columns `wk77`, `wk78`, ... +would be added to the dataset. This data is now tidy, but we could make future computation a bit easier by converting values of `week` from character strings to numbers using `mutate()` and `readr::parse_number()`. `parse_number()` is a handy function that will extract the first number from a string, ignoring all other text. diff --git a/data-transform.qmd b/data-transform.qmd index b0d0a7c45..5fb9a9fa6 100644 --- a/data-transform.qmd +++ b/data-transform.qmd @@ -15,7 +15,8 @@ You'll learn how to do all that (and more!) in this chapter, which will introduc The goal of this chapter is to give you an overview of all the key tools for transforming a data frame. We'll start with functions that operate on rows and then columns of a data frame, then circle back to talk more about the pipe, an important tool that you use to combine verbs. We will then introduce the ability to work with groups. -We will end the chapter with a case study that showcases these functions in action. In later chapters, we'll return to the functions in more detail as we start to dig into specific types of data (e.g., numbers, strings, dates). +We will end the chapter with a case study that showcases these functions in action. +In later chapters, we'll return to the functions in more detail as we start to dig into specific types of data (e.g., numbers, strings, dates). ### Prerequisites @@ -86,14 +87,15 @@ flights |> ``` dplyr's verbs are organized into four groups based on what they operate on: **rows**, **columns**, **groups**, or **tables**. -In the following sections, you'll learn the most important verbs for rows, columns, and groups. Then, we'll return to the join verbs that work on tables in @sec-joins. +In the following sections, you'll learn the most important verbs for rows, columns, and groups. +Then, we'll return to the join verbs that work on tables in @sec-joins. Let's dive in! ## Rows The most important verbs that operate on rows of a dataset are `filter()`, which changes which rows are present without changing their order, and `arrange()`, which changes the order of the rows without changing which are present. Both functions only affect the rows, and the columns are left unchanged. -We'll also discuss `distinct()` which finds rows with unique values. +We'll also discuss `distinct()` which finds rows with unique values. Unlike `arrange()` and `filter()` it can also optionally modify the columns. ### `filter()` @@ -214,7 +216,8 @@ flights |> It's not a coincidence that all of these distinct flights are on January 1: `distinct()` will find the first occurrence of a unique row in the dataset and discard the rest. -If you want to find the number of occurrences instead, you're better off swapping `distinct()` for `count()`. With the `sort = TRUE` argument, you can arrange them in descending order of the number of occurrences. +If you want to find the number of occurrences instead, you're better off swapping `distinct()` for `count()`. +With the `sort = TRUE` argument, you can arrange them in descending order of the number of occurrences. You'll learn more about count in @sec-counts. ```{r} diff --git a/index.qmd b/index.qmd index b42f33def..42f17760d 100644 --- a/index.qmd +++ b/index.qmd @@ -1,29 +1,32 @@ -# Welcome {.unnumbered} +# тавтай морил {.numbered} -This is the website for the 2nd edition of **"R for Data Science"**. -This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it and visualize. +Энэ бол **"R for Data Science"** сэтгүүлийн 2 дахь хэвлэлд зориулагдсан вэбсайт юм. +Энэхүү ном нь танд R-ээр өгөгдлийн шинжлэх ухаан хэрхэн хийхийг заах болно: Та өөрийн өгөгдлийг R-д хэрхэн оруулах, түүнийг хамгийн хэрэгцээтэй бүтцэд оруулах, хувиргах, дүрслэн харуулах аргад суралцах болно. -In this book, you will find a practicum of skills for data science. -Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots---and many other things besides. -These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. -You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. -You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualizing, and exploring data. +Энэ номноос та мэдээллийн шинжлэх ухааны ур чадварын практикийг олох болно. +Химийн эмч туршилтын хоолойг хэрхэн цэвэрлэж, лаборатори нөөцлөх талаар сурдаг шиг та өгөгдлийг хэрхэн цэвэрлэж, график зурах талаар сурах болно. +Эдгээр нь өгөгдлийн шинжлэх ухааныг бий болгох боломжийг олгодог ур чадварууд бөгөөд эндээс та R-тэй эдгээр зүйл бүрийг хийх шилдэг туршлагуудыг олох болно. +Та цаг хэмнэхийн тулд график, бичиг үсэгт тайлагдсан програмчлал, хуулбарлах судалгааны дүрмийг хэрхэн ашиглах талаар суралцах болно. +Мөн та мэдээлэл солилцох, дүрслэх, судлах явцад нээлтийг хөнгөвчлөх танин мэдэхүйн нөөцийг хэрхэн удирдах талаар суралцах болно. -This website is and will always be free, licensed under the [CC BY-NC-ND 3.0](https://creativecommons.org/licenses/by-nc-nd/3.0/us/) License. -If you'd like a physical copy of the book, you can order it on [Amazon](https://www.amazon.com/dp/1492097403?&tag=hadlwick-20). -If you appreciate reading the book for free and would like to give back, please make a donation to [Kākāpō Recovery](https://www.doc.govt.nz/kakapo-donate): the [kākāpō](https://www.youtube.com/watch?v=9T1vfsHYiKY) (which appears on the cover of R4DS) is a critically endangered parrot native to New Zealand; there are only 248 left. +Энэ вэб сайт нь [CC BY-NC-ND 3.0](https://creativecommons.org/licenses/by-nc-nd/3.0/us/) лицензийн дагуу лицензтэй бөгөөд үргэлж үнэ төлбөргүй байх болно. +Хэрэв та номын биет хуулбарыг авахыг хүсвэл \[Amazon\] (https://www.amazon.com/dp/1492097403?&tag=hadlwick-20) дээр захиалж болно. +Хэрэв та энэ номыг үнэ төлбөргүй уншиж байгаад талархаж байгаа бөгөөд буцааж өгөхийг хүсвэл [Kākāpō Recovery](https://www.doc.govt.nz/kakapo-donate): [kākāpō](https:/)-д хандив өргөөрэй. +/www.youtube.com/watch?v +=9T1vfsHYiKY) (R4DS-ийн нүүрэн дээр гардаг) нь шүүмжлэлтэй ханддаг. +Шинэ Зеландаас гаралтай ховордсон тоть; ердөө 248 үлдсэн. -If you speak another language, you might be interested in the freely available translations of the 1st edition: +Хэрэв та өөр хэлээр ярьдаг бол 1-р хэвлэлд үнэгүй орчуулагдсан орчуулгыг сонирхож магадгүй юм. -- [Spanish](http://es.r4ds.hadley.nz) -- [Italian](https://it.r4ds.hadley.nz) -- [Turkish](https://tr.r4ds.hadley.nz) +- [Испани](http://es.r4ds.hadley.nz) +- [Итали](https://it.r4ds.hadley.nz) +- [Турк](https://tr.r4ds.hadley.nz) -You can find suggested answers to exercises in the book at . +Та дээрх номноос дасгалын санал болгож буй хариултуудыг олох боломжтой. -Please note that R4DS uses a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). -By contributing to this book, you agree to abide by its terms. +R4DS нь [Contributor-ийн ёс зүйн дүрмийг](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html) ашигладаг болохыг анхаарна уу. +Энэ номонд хувь нэмрээ оруулснаар та түүний нөхцлийг дагаж мөрдөхийг зөвшөөрч байна. -## Acknowledgements {.unnumbered} +## Талархал {.numbered} -R4DS is hosted by as part of their support of open source software and communities. +R4DS-ийг нээлттэй эхийн программ хангамж болон нийгэмлэгүүдийг дэмжих нэг хэсэг болгон зохион байгуулдаг. 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Contributor Covenant Code of Conduct

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Our Pledge

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We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

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We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.

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Our Standards

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Examples of behavior that contributes to a positive environment for our community include:

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  • Demonstrating empathy and kindness toward other people
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  • Being respectful of differing opinions, viewpoints, and experiences
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  • Giving and gracefully accepting constructive feedback
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  • Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
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  • Focusing on what is best not just for us as individuals, but for the overall community
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Examples of unacceptable behavior include:

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  • The use of sexualized language or imagery, and sexual attention or advances of any kind
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  • Trolling, insulting or derogatory comments, and personal or political attacks
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  • Public or private harassment
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  • Publishing others’ private information, such as a physical or email address, without their explicit permission
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  • Other conduct which could reasonably be considered inappropriate in a professional setting
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Enforcement Responsibilities

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Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.

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Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.

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Scope

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This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.

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Enforcement

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Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at [INSERT CONTACT METHOD]. All complaints will be reviewed and investigated promptly and fairly.

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All community leaders are obligated to respect the privacy and security of the reporter of any incident.

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Enforcement Guidelines

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Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:

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1. Correction

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Community Impact: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.

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Consequence: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.

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2. Warning

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Community Impact: A violation through a single incident or series of actions.

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Consequence: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.

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3. Temporary Ban

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Community Impact: A serious violation of community standards, including sustained inappropriate behavior.

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Consequence: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.

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4. Permanent Ban

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Community Impact: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.

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Consequence: A permanent ban from any sort of public interaction within the community.

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Attribution

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This Code of Conduct is adapted from the Contributor Covenant, version 2.0, available at https://www.contributor-covenant.org/version/2/0/ code_of_conduct.html.

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Community Impact Guidelines were inspired by Mozilla’s code of conduct enforcement ladder.

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For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https:// www.contributor-covenant.org/translations.

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Exploratory data analysis

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Introduction

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This chapter will show you how to use visualization and transformation to explore your data in a systematic way, a task that statisticians call exploratory data analysis, or EDA for short. EDA is an iterative cycle. You:

+
    +

  1. Generate questions about your data.

    2. +

  2. Search for answers by visualizing, transforming, and modelling your data.

    3. +

  3. Use what you learn to refine your questions and/or generate new questions.

    4. +
+

EDA is not a formal process with a strict set of rules. More than anything, EDA is a state of mind. During the initial phases of EDA you should feel free to investigate every idea that occurs to you. Some of these ideas will pan out, and some will be dead ends. As your exploration continues, you will home in on a few particularly productive insights that you’ll eventually write up and communicate to others.

+

EDA is an important part of any data analysis, even if the primary research questions are handed to you on a platter, because you always need to investigate the quality of your data. Data cleaning is just one application of EDA: you ask questions about whether your data meets your expectations or not. To do data cleaning, you’ll need to deploy all the tools of EDA: visualization, transformation, and modelling.

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+

Prerequisites

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In this chapter we’ll combine what you’ve learned about dplyr and ggplot2 to interactively ask questions, answer them with data, and then ask new questions.

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+
library(tidyverse)
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+
+
+
+

Questions

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“There are no routine statistical questions, only questionable statistical routines.” — Sir David Cox

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“Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.” — John Tukey

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Your goal during EDA is to develop an understanding of your data. The easiest way to do this is to use questions as tools to guide your investigation. When you ask a question, the question focuses your attention on a specific part of your dataset and helps you decide which graphs, models, or transformations to make.

+

EDA is fundamentally a creative process. And like most creative processes, the key to asking quality questions is to generate a large quantity of questions. It is difficult to ask revealing questions at the start of your analysis because you do not know what insights can be gleaned from your dataset. On the other hand, each new question that you ask will expose you to a new aspect of your data and increase your chance of making a discovery. You can quickly drill down into the most interesting parts of your data—and develop a set of thought-provoking questions—if you follow up each question with a new question based on what you find.

+

There is no rule about which questions you should ask to guide your research. However, two types of questions will always be useful for making discoveries within your data. You can loosely word these questions as:

+
    +

  1. What type of variation occurs within my variables?

    2. +

  2. What type of covariation occurs between my variables?

    3. +
+

The rest of this chapter will look at these two questions. We’ll explain what variation and covariation are, and we’ll show you several ways to answer each question.

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+
+

Variation

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Variation is the tendency of the values of a variable to change from measurement to measurement. You can see variation easily in real life; if you measure any continuous variable twice, you will get two different results. This is true even if you measure quantities that are constant, like the speed of light. Each of your measurements will include a small amount of error that varies from measurement to measurement. Variables can also vary if you measure across different subjects (e.g., the eye colors of different people) or at different times (e.g., the energy levels of an electron at different moments). Every variable has its own pattern of variation, which can reveal interesting information about how that it varies between measurements on the same observation as well as across observations. The best way to understand that pattern is to visualize the distribution of the variable’s values, which you’ve learned about in ?@sec-data-visualization.

+

We’ll start our exploration by visualizing the distribution of weights (carat) of ~54,000 diamonds from the diamonds dataset. Since carat is a numerical variable, we can use a histogram:

+
+
ggplot(diamonds, aes(x = carat)) +
+  geom_histogram(binwidth = 0.5)
+
+
+
+

A histogram of carats of diamonds, with the x-axis ranging from 0 to 4.5 and the y-axis ranging from 0 to 30000. The distribution is right skewed with very few diamonds in the bin centered at 0, almost 30000 diamonds in the bin centered at 0.5, approximately 15000 diamonds in the bin centered at 1, and much fewer, approximately 5000 diamonds in the bin centered at 1.5. Beyond this, there's a trailing tail.

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+
+
+
+

Now that you can visualize variation, what should you look for in your plots? And what type of follow-up questions should you ask? We’ve put together a list below of the most useful types of information that you will find in your graphs, along with some follow-up questions for each type of information. The key to asking good follow-up questions will be to rely on your curiosity (What do you want to learn more about?) as well as your skepticism (How could this be misleading?).

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Typical values

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In both bar charts and histograms, tall bars show the common values of a variable, and shorter bars show less-common values. Places that do not have bars reveal values that were not seen in your data. To turn this information into useful questions, look for anything unexpected:

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    +

  • Which values are the most common? Why?

    • +

  • Which values are rare? Why? Does that match your expectations?

    • +

  • Can you see any unusual patterns? What might explain them?

    • +
+

Let’s take a look at the distribution of carat for smaller diamonds.

+
+
smaller <- diamonds |> 
+  filter(carat < 3)
+
+ggplot(smaller, aes(x = carat)) +
+  geom_histogram(binwidth = 0.01)
+
+
+
+

A histogram of carats of diamonds, with the x-axis ranging from 0 to 3 and the y-axis ranging from 0 to roughly 2500. The binwidth is quite narrow (0.01), resulting in a very large number of skinny bars. The distribution is right skewed, with many peaks followed by bars in decreasing heights, until a sharp increase at the next peak.

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+
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This histogram suggests several interesting questions:

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    +

  • Why are there more diamonds at whole carats and common fractions of carats?

    • +

  • Why are there more diamonds slightly to the right of each peak than there are slightly to the left of each peak?

    • +
+

Visualizations can also reveal clusters, which suggest that subgroups exist in your data. To understand the subgroups, ask:

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    +

  • How are the observations within each subgroup similar to each other?

    • +

  • How are the observations in separate clusters different from each other?

    • +

  • How can you explain or describe the clusters?

    • +

  • Why might the appearance of clusters be misleading?

    • +
+

Some of these questions can be answered with the data while some will require domain expertise about the data. Many of them will prompt you to explore a relationship between variables, for example, to see if the values of one variable can explain the behavior of another variable. We’ll get to that shortly.

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+

Unusual values

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Outliers are observations that are unusual; data points that don’t seem to fit the pattern. Sometimes outliers are data entry errors, sometimes they are simply values at the extremes that happened to be observed in this data collection, and other times they suggest important new discoveries. When you have a lot of data, outliers are sometimes difficult to see in a histogram. For example, take the distribution of the y variable from the diamonds dataset. The only evidence of outliers is the unusually wide limits on the x-axis.

+
+
ggplot(diamonds, aes(x = y)) + 
+  geom_histogram(binwidth = 0.5)
+
+
+
+

A histogram of lengths of diamonds. The x-axis ranges from 0 to 60 and the y-axis ranges from 0 to 12000. There is a peak around 5, and the data appear to be completely clustered around the peak.

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+
+
+

There are so many observations in the common bins that the rare bins are very short, making it very difficult to see them (although maybe if you stare intently at 0 you’ll spot something). To make it easy to see the unusual values, we need to zoom to small values of the y-axis with coord_cartesian():

+
+
ggplot(diamonds, aes(x = y)) + 
+  geom_histogram(binwidth = 0.5) +
+  coord_cartesian(ylim = c(0, 50))
+
+
+
+

A histogram of lengths of diamonds. The x-axis ranges from 0 to 60 and the y-axis ranges from 0 to 50. There is a peak around 5, and the data appear to be completely clustered around the peak. Other than those data, there is one bin at 0 with a height of about 8, one a little over 30 with a height of 1 and another one a little below 60 with a height of 1.

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+
+
+

coord_cartesian() also has an xlim() argument for when you need to zoom into the x-axis. ggplot2 also has xlim() and ylim() functions that work slightly differently: they throw away the data outside the limits.

+

This allows us to see that there are three unusual values: 0, ~30, and ~60. We pluck them out with dplyr:

+
+
unusual <- diamonds |> 
+  filter(y < 3 | y > 20) |> 
+  select(price, x, y, z) |>
+  arrange(y)
+unusual
+#> # A tibble: 9 × 4
+#>   price     x     y     z
+#>   <int> <dbl> <dbl> <dbl>
+#> 1  5139  0      0    0   
+#> 2  6381  0      0    0   
+#> 3 12800  0      0    0   
+#> 4 15686  0      0    0   
+#> 5 18034  0      0    0   
+#> 6  2130  0      0    0   
+#> 7  2130  0      0    0   
+#> 8  2075  5.15  31.8  5.12
+#> 9 12210  8.09  58.9  8.06
+
+

The y variable measures one of the three dimensions of these diamonds, in mm. We know that diamonds can’t have a width of 0mm, so these values must be incorrect. By doing EDA, we have discovered missing data that was coded as 0, which we never would have found by simply searching for NAs. Going forward we might choose to re-code these values as NAs in order to prevent misleading calculations. We might also suspect that measurements of 32mm and 59mm are implausible: those diamonds are over an inch long, but don’t cost hundreds of thousands of dollars!

+

It’s good practice to repeat your analysis with and without the outliers. If they have minimal effect on the results, and you can’t figure out why they’re there, it’s reasonable to omit them, and move on. However, if they have a substantial effect on your results, you shouldn’t drop them without justification. You’ll need to figure out what caused them (e.g., a data entry error) and disclose that you removed them in your write-up.

+
+
+

Exercises

+
    +

  1. Explore the distribution of each of the x, y, and z variables in diamonds. What do you learn? Think about a diamond and how you might decide which dimension is the length, width, and depth.

    2. +

  2. Explore the distribution of price. Do you discover anything unusual or surprising? (Hint: Carefully think about the binwidth and make sure you try a wide range of values.)

    3. +

  3. How many diamonds are 0.99 carat? How many are 1 carat? What do you think is the cause of the difference?

    4. +

  4. Compare and contrast coord_cartesian() vs. xlim() or ylim() when zooming in on a histogram. What happens if you leave binwidth unset? What happens if you try and zoom so only half a bar shows?

    5. +
+
+
+
+

Unusual values

+

If you’ve encountered unusual values in your dataset, and simply want to move on to the rest of your analysis, you have two options.

+
    +

  1. Drop the entire row with the strange values:

    +
    +
    diamonds2 <- diamonds |> 
+  filter(between(y, 3, 20))
    +
    +

    We don’t recommend this option because one invalid value doesn’t imply that all the other values for that observation are also invalid. Additionally, if you have low quality data, by the time that you’ve applied this approach to every variable you might find that you don’t have any data left!

    2. +

  2. Instead, we recommend replacing the unusual values with missing values. The easiest way to do this is to use mutate() to replace the variable with a modified copy. You can use the if_else() function to replace unusual values with NA:

    +
    +
    diamonds2 <- diamonds |> 
+  mutate(y = if_else(y < 3 | y > 20, NA, y))
    +
    3. +
+

It’s not obvious where you should plot missing values, so ggplot2 doesn’t include them in the plot, but it does warn that they’ve been removed:

+
+
ggplot(diamonds2, aes(x = x, y = y)) + 
+  geom_point()
+#> Warning: Removed 9 rows containing missing values or values outside the scale range
+#> (`geom_point()`).
+
+
+
+

A scatterplot of widths vs. lengths of diamonds. There is a strong, linear association between the two variables. All but one of the diamonds has length greater than 3. The one outlier has a length of 0 and a width of about 6.5.

+
+
+
+
+

To suppress that warning, set na.rm = TRUE:

+
+
ggplot(diamonds2, aes(x = x, y = y)) + 
+  geom_point(na.rm = TRUE)
+
+

Other times you want to understand what makes observations with missing values different to observations with recorded values. For example, in nycflights13::flights1, missing values in the dep_time variable indicate that the flight was cancelled. So you might want to compare the scheduled departure times for cancelled and non-cancelled times. You can do this by making a new variable, using is.na() to check if dep_time is missing.

+
+
nycflights13::flights |> 
+  mutate(
+    cancelled = is.na(dep_time),
+    sched_hour = sched_dep_time %/% 100,
+    sched_min = sched_dep_time %% 100,
+    sched_dep_time = sched_hour + (sched_min / 60)
+  ) |> 
+  ggplot(aes(x = sched_dep_time)) + 
+  geom_freqpoly(aes(color = cancelled), binwidth = 1/4)
+
+
+
+

A frequency polygon of scheduled departure times of flights. Two lines represent flights that are cancelled and not cancelled. The x-axis ranges from 0 to 25 minutes and the y-axis ranges from 0 to 10000. The number of flights not cancelled are much higher than those cancelled.

+
+
+
+
+

However this plot isn’t great because there are many more non-cancelled flights than cancelled flights. In the next section we’ll explore some techniques for improving this comparison.

+
+

Exercises

+
    +

  1. What happens to missing values in a histogram? What happens to missing values in a bar chart? Why is there a difference in how missing values are handled in histograms and bar charts?

    2. +

  2. What does na.rm = TRUE do in mean() and sum()?

    3. +

  3. Recreate the frequency plot of scheduled_dep_time colored by whether the flight was cancelled or not. Also facet by the cancelled variable. Experiment with different values of the scales variable in the faceting function to mitigate the effect of more non-cancelled flights than cancelled flights.

    4. +
+
+
+
+

Covariation

+

If variation describes the behavior within a variable, covariation describes the behavior between variables. Covariation is the tendency for the values of two or more variables to vary together in a related way. The best way to spot covariation is to visualize the relationship between two or more variables.

+
+

A categorical and a numerical variable

+

For example, let’s explore how the price of a diamond varies with its quality (measured by cut) using geom_freqpoly():

+
+
ggplot(diamonds, aes(x = price)) + 
+  geom_freqpoly(aes(color = cut), binwidth = 500, linewidth = 0.75)
+
+
+
+

A frequency polygon of prices of diamonds where each cut of carat (Fair, Good, Very Good, Premium, and Ideal) is represented with a different color line. The x-axis ranges from 0 to 30000 and the y-axis ranges from 0 to 5000. The lines overlap a great deal, suggesting similar frequency distributions of prices of diamonds. One notable feature is that Ideal diamonds have the highest peak around 1500.

+
+
+
+
+

Note that ggplot2 uses an ordered color scale for cut because it’s defined as an ordered factor variable in the data. You’ll learn more about these in ?@sec-ordered-factors.

+

The default appearance of geom_freqpoly() is not that useful here because the height, determined by the overall count, differs so much across cuts, making it hard to see the differences in the shapes of their distributions.

+

To make the comparison easier we need to swap what is displayed on the y-axis. Instead of displaying count, we’ll display the density, which is the count standardized so that the area under each frequency polygon is one.

+
+
ggplot(diamonds, aes(x = price, y = after_stat(density))) + 
+  geom_freqpoly(aes(color = cut), binwidth = 500, linewidth = 0.75)
+
+
+
+

A frequency polygon of densities of prices of diamonds where each cut of carat (Fair, Good, Very Good, Premium, and Ideal) is represented with a different color line. The x-axis ranges from 0 to 20000. The lines overlap a great deal, suggesting similar density distributions of prices of diamonds. One notable feature is that all but Fair diamonds have high peaks around a price of 1500 and Fair diamonds have a higher mean than others.

+
+
+
+
+

Note that we’re mapping the density to y, but since density is not a variable in the diamonds dataset, we need to first calculate it. We use the after_stat() function to do so.

+

There’s something rather surprising about this plot - it appears that fair diamonds (the lowest quality) have the highest average price! But maybe that’s because frequency polygons are a little hard to interpret - there’s a lot going on in this plot.

+

A visually simpler plot for exploring this relationship is using side-by-side boxplots.

+
+
ggplot(diamonds, aes(x = cut, y = price)) +
+  geom_boxplot()
+
+
+
+

Side-by-side boxplots of prices of diamonds by cut. The distribution of prices is right skewed for each cut (Fair, Good, Very Good, Premium, and Ideal). The medians are close to each other, with the median for Ideal diamonds lowest and that for Fair highest.

+
+
+
+
+

We see much less information about the distribution, but the boxplots are much more compact so we can more easily compare them (and fit more on one plot). It supports the counter-intuitive finding that better quality diamonds are typically cheaper! In the exercises, you’ll be challenged to figure out why.

+

cut is an ordered factor: fair is worse than good, which is worse than very good and so on. Many categorical variables don’t have such an intrinsic order, so you might want to reorder them to make a more informative display. One way to do that is with fct_reorder(). You’ll learn more about that function in ?@sec-modifying-factor-order, but we want to give you a quick preview here because it’s so useful. For example, take the class variable in the mpg dataset. You might be interested to know how highway mileage varies across classes:

+
+
ggplot(mpg, aes(x = class, y = hwy)) +
+  geom_boxplot()
+
+
+
+

Side-by-side boxplots of highway mileages of cars by class. Classes are on the x-axis (2seaters, compact, midsize, minivan, pickup, subcompact, and suv).

+
+
+
+
+

To make the trend easier to see, we can reorder class based on the median value of hwy:

+
+
ggplot(mpg, aes(x = fct_reorder(class, hwy, median), y = hwy)) +
+  geom_boxplot()
+
+
+
+

Side-by-side boxplots of highway mileages of cars by class. Classes are on the x-axis and ordered by increasing median highway mileage (pickup, suv, minivan, 2seater, subcompact, compact, and midsize).

+
+
+
+
+

If you have long variable names, geom_boxplot() will work better if you flip it 90°. You can do that by exchanging the x and y aesthetic mappings.

+
+
ggplot(mpg, aes(x = hwy, y = fct_reorder(class, hwy, median))) +
+  geom_boxplot()
+
+
+
+

Side-by-side boxplots of highway mileages of cars by class. Classes are on the y-axis and ordered by increasing median highway mileage.

+
+
+
+
+
+

Exercises

+
    +

  1. Use what you’ve learned to improve the visualization of the departure times of cancelled vs. non-cancelled flights.

    2. +

  2. Based on EDA, what variable in the diamonds dataset appears to be most important for predicting the price of a diamond? How is that variable correlated with cut? Why does the combination of those two relationships lead to lower quality diamonds being more expensive?

    3. +

  3. Instead of exchanging the x and y variables, add coord_flip() as a new layer to the vertical boxplot to create a horizontal one. How does this compare to exchanging the variables?

    4. +

  4. One problem with boxplots is that they were developed in an era of much smaller datasets and tend to display a prohibitively large number of “outlying values”. One approach to remedy this problem is the letter value plot. Install the lvplot package, and try using geom_lv() to display the distribution of price vs. cut. What do you learn? How do you interpret the plots?

    5. +

  5. Create a visualization of diamond prices vs. a categorical variable from the diamonds dataset using geom_violin(), then a faceted geom_histogram(), then a colored geom_freqpoly(), and then a colored geom_density(). Compare and contrast the four plots. What are the pros and cons of each method of visualizing the distribution of a numerical variable based on the levels of a categorical variable?

    6. +

  6. If you have a small dataset, it’s sometimes useful to use geom_jitter() to avoid overplotting to more easily see the relationship between a continuous and categorical variable. The ggbeeswarm package provides a number of methods similar to geom_jitter(). List them and briefly describe what each one does.

    7. +
+
+
+
+

Two categorical variables

+

To visualize the covariation between categorical variables, you’ll need to count the number of observations for each combination of levels of these categorical variables. One way to do that is to rely on the built-in geom_count():

+
+
ggplot(diamonds, aes(x = cut, y = color)) +
+  geom_count()
+
+
+
+

A scatterplot of color vs. cut of diamonds. There is one point for each combination of levels of cut (Fair, Good, Very Good, Premium, and Ideal) and color (D, E, F, G, G, I, and J). The sizes of the points represent the number of observations for that combination. The legend indicates that these sizes range between 1000 and 4000.

+
+
+
+
+

The size of each circle in the plot displays how many observations occurred at each combination of values. Covariation will appear as a strong correlation between specific x values and specific y values.

+

Another approach for exploring the relationship between these variables is computing the counts with dplyr:

+
+
diamonds |> 
+  count(color, cut)
+#> # A tibble: 35 × 3
+#>   color cut           n
+#>   <ord> <ord>     <int>
+#> 1 D     Fair        163
+#> 2 D     Good        662
+#> 3 D     Very Good  1513
+#> 4 D     Premium    1603
+#> 5 D     Ideal      2834
+#> 6 E     Fair        224
+#> # ℹ 29 more rows
+
+

Then visualize with geom_tile() and the fill aesthetic:

+
+
diamonds |> 
+  count(color, cut) |>  
+  ggplot(aes(x = color, y = cut)) +
+  geom_tile(aes(fill = n))
+
+
+
+

A tile plot of cut vs. color of diamonds. Each tile represents a cut/color combination and tiles are colored according to the number of observations in each tile. There are more Ideal diamonds than other cuts, with the highest number being Ideal diamonds with color G. Fair diamonds and diamonds with color I are the lowest in frequency.

+
+
+
+
+

If the categorical variables are unordered, you might want to use the seriation package to simultaneously reorder the rows and columns in order to more clearly reveal interesting patterns. For larger plots, you might want to try the heatmaply package, which creates interactive plots.

+
+

Exercises

+
    +

  1. How could you rescale the count dataset above to more clearly show the distribution of cut within color, or color within cut?

    2. +

  2. What different data insights do you get with a segmented bar chart if color is mapped to the x aesthetic and cut is mapped to the fill aesthetic? Calculate the counts that fall into each of the segments.

    3. +

  3. Use geom_tile() together with dplyr to explore how average flight departure delays vary by destination and month of year. What makes the plot difficult to read? How could you improve it?

    4. +
+
+
+
+

Two numerical variables

+

You’ve already seen one great way to visualize the covariation between two numerical variables: draw a scatterplot with geom_point(). You can see covariation as a pattern in the points. For example, you can see a positive relationship between the carat size and price of a diamond: diamonds with more carats have a higher price. The relationship is exponential.

+
+
ggplot(smaller, aes(x = carat, y = price)) +
+  geom_point()
+
+
+
+

A scatterplot of price vs. carat. The relationship is positive, somewhat strong, and exponential.

+
+
+
+
+

(In this section we’ll use the smaller dataset to stay focused on the bulk of the diamonds that are smaller than 3 carats)

+

Scatterplots become less useful as the size of your dataset grows, because points begin to overplot, and pile up into areas of uniform black, making it hard to judge differences in the density of the data across the 2-dimensional space as well as making it hard to spot the trend. You’ve already seen one way to fix the problem: using the alpha aesthetic to add transparency.

+
+
ggplot(smaller, aes(x = carat, y = price)) + 
+  geom_point(alpha = 1 / 100)
+
+
+
+

A scatterplot of price vs. carat. The relationship is positive, somewhat strong, and exponential. The points are transparent, showing clusters where the number of points is higher than other areas, The most obvious clusters are for diamonds with 1, 1.5, and 2 carats.

+
+
+
+
+

But using transparency can be challenging for very large datasets. Another solution is to use bin. Previously you used geom_histogram() and geom_freqpoly() to bin in one dimension. Now you’ll learn how to use geom_bin2d() and geom_hex() to bin in two dimensions.

+

geom_bin2d() and geom_hex() divide the coordinate plane into 2d bins and then use a fill color to display how many points fall into each bin. geom_bin2d() creates rectangular bins. geom_hex() creates hexagonal bins. You will need to install the hexbin package to use geom_hex().

+
ggplot(smaller, aes(x = carat, y = price)) +
+  geom_bin2d()
+
+# install.packages("hexbin")
+ggplot(smaller, aes(x = carat, y = price)) +
+  geom_hex()
+#> Warning: Computation failed in `stat_binhex()`.
+#> Caused by error in `compute_group()`:
+#> ! The package "hexbin" is required for `stat_bin_hex()`.
+
+
+
+

Plot 1: A binned density plot of price vs. carat. Plot 2: A hexagonal bin plot of price vs. carat. Both plots show that the highest density of diamonds have low carats and low prices.

+
+
+

Plot 1: A binned density plot of price vs. carat. Plot 2: A hexagonal bin plot of price vs. carat. Both plots show that the highest density of diamonds have low carats and low prices.

+
+
+
+

Another option is to bin one continuous variable so it acts like a categorical variable. Then you can use one of the techniques for visualizing the combination of a categorical and a continuous variable that you learned about. For example, you could bin carat and then for each group, display a boxplot:

+
+
ggplot(smaller, aes(x = carat, y = price)) + 
+  geom_boxplot(aes(group = cut_width(carat, 0.1)))
+
+
+
+

Side-by-side box plots of price by carat. Each box plot represents diamonds that are 0.1 carats apart in weight. The box plots show that as carat increases the median price increases as well. Additionally, diamonds with 1.5 carats or lower have right skewed price distributions, 1.5 to 2 have roughly symmetric price distributions, and diamonds that weigh more have left skewed distributions. Cheaper, smaller diamonds have outliers on the higher end, more expensive, bigger diamonds have outliers on the lower end.

+
+
+
+
+

cut_width(x, width), as used above, divides x into bins of width width. By default, boxplots look roughly the same (apart from number of outliers) regardless of how many observations there are, so it’s difficult to tell that each boxplot summarizes a different number of points. One way to show that is to make the width of the boxplot proportional to the number of points with varwidth = TRUE.

+
+

Exercises

+
    +

  1. Instead of summarizing the conditional distribution with a boxplot, you could use a frequency polygon. What do you need to consider when using cut_width() vs. cut_number()? How does that impact a visualization of the 2d distribution of carat and price?

    2. +

  2. Visualize the distribution of carat, partitioned by price.

    3. +

  3. How does the price distribution of very large diamonds compare to small diamonds? Is it as you expect, or does it surprise you?

    4. +

  4. Combine two of the techniques you’ve learned to visualize the combined distribution of cut, carat, and price.

    5. +

  5. Two dimensional plots reveal outliers that are not visible in one dimensional plots. For example, some points in the following plot have an unusual combination of x and y values, which makes the points outliers even though their x and y values appear normal when examined separately. Why is a scatterplot a better display than a binned plot for this case?

    +
    +
    diamonds |> 
+  filter(x >= 4) |> 
+  ggplot(aes(x = x, y = y)) +
+  geom_point() +
+  coord_cartesian(xlim = c(4, 11), ylim = c(4, 11))
    +
    6. +

  6. Instead of creating boxes of equal width with cut_width(), we could create boxes that contain roughly equal number of points with cut_number(). What are the advantages and disadvantages of this approach?

    +
    +
    ggplot(smaller, aes(x = carat, y = price)) + 
+  geom_boxplot(aes(group = cut_number(carat, 20)))
    +
    7. +
+
+
+
+
+

Patterns and models

+

If a systematic relationship exists between two variables it will appear as a pattern in the data. If you spot a pattern, ask yourself:

+
    +

  • Could this pattern be due to coincidence (i.e. random chance)?

    • +

  • How can you describe the relationship implied by the pattern?

    • +

  • How strong is the relationship implied by the pattern?

    • +

  • What other variables might affect the relationship?

    • +

  • Does the relationship change if you look at individual subgroups of the data?

    • +
+

Patterns in your data provide clues about relationships, i.e., they reveal covariation. If you think of variation as a phenomenon that creates uncertainty, covariation is a phenomenon that reduces it. If two variables covary, you can use the values of one variable to make better predictions about the values of the second. If the covariation is due to a causal relationship (a special case), then you can use the value of one variable to control the value of the second.

+

Models are a tool for extracting patterns out of data. For example, consider the diamonds data. It’s hard to understand the relationship between cut and price, because cut and carat, and carat and price are tightly related. It’s possible to use a model to remove the very strong relationship between price and carat so we can explore the subtleties that remain. The following code fits a model that predicts price from carat and then computes the residuals (the difference between the predicted value and the actual value). The residuals give us a view of the price of the diamond, once the effect of carat has been removed. Note that instead of using the raw values of price and carat, we log transform them first, and fit a model to the log-transformed values. Then, we exponentiate the residuals to put them back in the scale of raw prices.

+
+
library(tidymodels)
+
+diamonds <- diamonds |>
+  mutate(
+    log_price = log(price),
+    log_carat = log(carat)
+  )
+
+diamonds_fit <- linear_reg() |>
+  fit(log_price ~ log_carat, data = diamonds)
+
+diamonds_aug <- augment(diamonds_fit, new_data = diamonds) |>
+  mutate(.resid = exp(.resid))
+
+ggplot(diamonds_aug, aes(x = carat, y = .resid)) + 
+  geom_point()
+
+
+
+

A scatterplot of residuals vs. carat of diamonds. The x-axis ranges from 0 to 5, the y-axis ranges from 0 to almost 4. Much of the data are clustered around low values of carat and residuals. There is a clear, curved pattern showing decrease in residuals as carat increases.

+
+
+
+
+

Once you’ve removed the strong relationship between carat and price, you can see what you expect in the relationship between cut and price: relative to their size, better quality diamonds are more expensive.

+
+
ggplot(diamonds_aug, aes(x = cut, y = .resid)) + 
+  geom_boxplot()
+
+
+
+

Side-by-side box plots of residuals by cut. The x-axis displays the various cuts (Fair to Ideal), the y-axis ranges from 0 to almost 5. The medians are quite similar, between roughly 0.75 to 1.25. Each of the distributions of residuals is right skewed, with many outliers on the higher end.

+
+
+
+
+

We’re not discussing modelling in this book because understanding what models are and how they work is easiest once you have tools of data wrangling and programming in hand.

+
+
+

Summary

+

In this chapter you’ve learned a variety of tools to help you understand the variation within your data. You’ve seen techniques that work with a single variable at a time and with a pair of variables. This might seem painfully restrictive if you have tens or hundreds of variables in your data, but they’re the foundation upon which all other techniques are built.

+

In the next chapter, we’ll focus on the tools we can use to communicate our results.

+ + +
+
+
+
+
    +

  1. Remember that when we need to be explicit about where a function (or dataset) comes from, we’ll use the special form package::function() or package::dataset.↩︎

    2. +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/arrow.html b/docs/arrow.html new file mode 100644 index 000000000..f7b503ec7 --- /dev/null +++ b/docs/arrow.html @@ -0,0 +1,827 @@ + + + + + + + + + + +arrow + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + + + +
+

Arrow

+
+

Introduction

+

CSV files are designed to be easily read by humans. They’re a good interchange format because they’re very simple and they can be read by every tool under the sun. But CSV files aren’t very efficient: you have to do quite a lot of work to read the data into R. In this chapter, you’ll learn about a powerful alternative: the parquet format, an open standards-based format widely used by big data systems.

+

We’ll pair parquet files with Apache Arrow, a multi-language toolbox designed for efficient analysis and transport of large datasets. We’ll use Apache Arrow via the arrow package, which provides a dplyr backend allowing you to analyze larger-than-memory datasets using familiar dplyr syntax. As an additional benefit, arrow is extremely fast: you’ll see some examples later in the chapter.

+

Both arrow and dbplyr provide dplyr backends, so you might wonder when to use each. In many cases, the choice is made for you, as the data is already in a database or in parquet files, and you’ll want to work with it as is. But if you’re starting with your own data (perhaps CSV files), you can either load it into a database or convert it to parquet. In general, it’s hard to know what will work best, so in the early stages of your analysis we’d encourage you to try both and pick the one that works the best for you.

+

(A big thanks to Danielle Navarro who contributed the initial version of this chapter.)

+
+

Prerequisites

+

In this chapter, we’ll continue to use the tidyverse, particularly dplyr, but we’ll pair it with the arrow package which is designed specifically for working with large data.

+
+
library(tidyverse)
+library(arrow)
+
+

Later in the chapter, we’ll also see some connections between arrow and duckdb, so we’ll also need dbplyr and duckdb.

+
+
library(dbplyr, warn.conflicts = FALSE)
+library(duckdb)
+#> Loading required package: DBI
+
+
+
+
+

Getting the data

+

We begin by getting a dataset worthy of these tools: a dataset of item checkouts from Seattle public libraries, available online at data.seattle.gov/Community/Checkouts-by-Title/tmmm-ytt6. This dataset contains 41,389,465 rows that tell you how many times each book was checked out each month from April 2005 to October 2022.

+

The following code will get you a cached copy of the data. The data is a 9GB CSV file, so it will take some time to download. I highly recommend using curl::multi_download() to get very large files as it’s built for exactly this purpose: it gives you a progress bar and it can resume the download if its interrupted.

+
+
dir.create("data", showWarnings = FALSE)
+
+curl::multi_download(
+  "https://r4ds.s3.us-west-2.amazonaws.com/seattle-library-checkouts.csv",
+  "data/seattle-library-checkouts.csv",
+  resume = TRUE
+)
+#> # A tibble: 1 × 10
+#>   success status_code resumefrom url                    destfile        error
+#>   <lgl>         <int>      <dbl> <chr>                  <chr>           <chr>
+#> 1 TRUE            200          0 https://r4ds.s3.us-we… data/seattle-l… <NA> 
+#> # ℹ 4 more variables: type <chr>, modified <dttm>, time <dbl>,
+#> #   headers <list>
+
+
+
+

Opening a dataset

+

Let’s start by taking a look at the data. At 9 GB, this file is large enough that we probably don’t want to load the whole thing into memory. A good rule of thumb is that you usually want at least twice as much memory as the size of the data, and many laptops top out at 16 GB. This means we want to avoid read_csv() and instead use the arrow::open_dataset():

+
+
seattle_csv <- open_dataset(
+  sources = "data/seattle-library-checkouts.csv", 
+  col_types = schema(ISBN = string()),
+  format = "csv"
+)
+
+

What happens when this code is run? open_dataset() will scan a few thousand rows to figure out the structure of the dataset. The ISBN column contains blank values for the first 80,000 rows, so we have to specify the column type to help arrow work out the data structure. Once the data has been scanned by open_dataset(), it records what it’s found and stops; it will only read further rows as you specifically request them. This metadata is what we see if we print seattle_csv:

+
+
seattle_csv
+#> FileSystemDataset with 1 csv file
+#> UsageClass: string
+#> CheckoutType: string
+#> MaterialType: string
+#> CheckoutYear: int64
+#> CheckoutMonth: int64
+#> Checkouts: int64
+#> Title: string
+#> ISBN: string
+#> Creator: string
+#> Subjects: string
+#> Publisher: string
+#> PublicationYear: string
+
+

The first line in the output tells you that seattle_csv is stored locally on-disk as a single CSV file; it will only be loaded into memory as needed. The remainder of the output tells you the column type that arrow has imputed for each column.

+

We can see what’s actually in with glimpse(). This reveals that there are ~41 million rows and 12 columns, and shows us a few values.

+
+
seattle_csv |> glimpse()
+#> FileSystemDataset with 1 csv file
+#> 41,389,465 rows x 12 columns
+#> $ UsageClass      <string> "Physical", "Physical", "Digital", "Physical", "Ph…
+#> $ CheckoutType    <string> "Horizon", "Horizon", "OverDrive", "Horizon", "Hor…
+#> $ MaterialType    <string> "BOOK", "BOOK", "EBOOK", "BOOK", "SOUNDDISC", "BOO…
+#> $ CheckoutYear     <int64> 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 20…
+#> $ CheckoutMonth    <int64> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,…
+#> $ Checkouts        <int64> 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 2, 3, 2, 1, 3, 2,…
+#> $ Title           <string> "Super rich : a guide to having it all / Russell S…
+#> $ ISBN            <string> "", "", "", "", "", "", "", "", "", "", "", "", ""…
+#> $ Creator         <string> "Simmons, Russell", "Barclay, James, 1965-", "Tim …
+#> $ Subjects        <string> "Self realization, Conduct of life, Attitude Psych…
+#> $ Publisher       <string> "Gotham Books,", "Pyr,", "Random House, Inc.", "Di…
+#> $ PublicationYear <string> "c2011.", "2010.", "2015", "2005.", "c2004.", "c20…
+
+

We can start to use this dataset with dplyr verbs, using collect() to force arrow to perform the computation and return some data. For example, this code tells us the total number of checkouts per year:

+
+
seattle_csv |> 
+  group_by(CheckoutYear) |> 
+  summarise(Checkouts = sum(Checkouts)) |> 
+  arrange(CheckoutYear) |> 
+  collect()
+#> # A tibble: 18 × 2
+#>   CheckoutYear Checkouts
+#>          <int>     <int>
+#> 1         2005   3798685
+#> 2         2006   6599318
+#> 3         2007   7126627
+#> 4         2008   8438486
+#> 5         2009   9135167
+#> 6         2010   8608966
+#> # ℹ 12 more rows
+
+

Thanks to arrow, this code will work regardless of how large the underlying dataset is. But it’s currently rather slow: on Hadley’s computer, it took ~10s to run. That’s not terrible given how much data we have, but we can make it much faster by switching to a better format.

+
+
+

The parquet format

+

To make this data easier to work with, let’s switch to the parquet file format and split it up into multiple files. The following sections will first introduce you to parquet and partitioning, and then apply what we learned to the Seattle library data.

+
+

Advantages of parquet

+

Like CSV, parquet is used for rectangular data, but instead of being a text format that you can read with any file editor, it’s a custom binary format designed specifically for the needs of big data. This means that:

+
    +

  • Parquet files are usually smaller than the equivalent CSV file. Parquet relies on efficient encodings to keep file size down, and supports file compression. This helps make parquet files fast because there’s less data to move from disk to memory.

    • +

  • Parquet files have a rich type system. As we talked about in ?@sec-col-types, a CSV file does not provide any information about column types. For example, a CSV reader has to guess whether "08-10-2022" should be parsed as a string or a date. In contrast, parquet files store data in a way that records the type along with the data.

    • +

  • Parquet files are “column-oriented”. This means that they’re organized column-by-column, much like R’s data frame. This typically leads to better performance for data analysis tasks compared to CSV files, which are organized row-by-row.

    • +

  • Parquet files are “chunked”, which makes it possible to work on different parts of the file at the same time, and, if you’re lucky, to skip some chunks altogether.

    • +
+

There’s one primary disadvantage to parquet files: they are no longer “human readable”, i.e. if you look at a parquet file using readr::read_file(), you’ll just see a bunch of gibberish.

+
+
+

Partitioning

+

As datasets get larger and larger, storing all the data in a single file gets increasingly painful and it’s often useful to split large datasets across many files. When this structuring is done intelligently, this strategy can lead to significant improvements in performance because many analyses will only require a subset of the files.

+

There are no hard and fast rules about how to partition your dataset: the results will depend on your data, access patterns, and the systems that read the data. You’re likely to need to do some experimentation before you find the ideal partitioning for your situation. As a rough guide, arrow suggests that you avoid files smaller than 20MB and larger than 2GB and avoid partitions that produce more than 10,000 files. You should also try to partition by variables that you filter by; as you’ll see shortly, that allows arrow to skip a lot of work by reading only the relevant files.

+
+
+

Rewriting the Seattle library data

+

Let’s apply these ideas to the Seattle library data to see how they play out in practice. We’re going to partition by CheckoutYear, since it’s likely some analyses will only want to look at recent data and partitioning by year yields 18 chunks of a reasonable size.

+

To rewrite the data we define the partition using dplyr::group_by() and then save the partitions to a directory with arrow::write_dataset(). write_dataset() has two important arguments: a directory where we’ll create the files and the format we’ll use.

+
+
pq_path <- "data/seattle-library-checkouts"
+
+
+
seattle_csv |>
+  group_by(CheckoutYear) |>
+  write_dataset(path = pq_path, format = "parquet")
+
+

This takes about a minute to run; as we’ll see shortly this is an initial investment that pays off by making future operations much much faster.

+

Let’s take a look at what we just produced:

+
+
tibble(
+  files = list.files(pq_path, recursive = TRUE),
+  size_MB = file.size(file.path(pq_path, files)) / 1024^2
+)
+#> # A tibble: 18 × 2
+#>   files                            size_MB
+#>   <chr>                              <dbl>
+#> 1 CheckoutYear=2005/part-0.parquet    109.
+#> 2 CheckoutYear=2006/part-0.parquet    164.
+#> 3 CheckoutYear=2007/part-0.parquet    178.
+#> 4 CheckoutYear=2008/part-0.parquet    195.
+#> 5 CheckoutYear=2009/part-0.parquet    214.
+#> 6 CheckoutYear=2010/part-0.parquet    222.
+#> # ℹ 12 more rows
+
+

Our single 9GB CSV file has been rewritten into 18 parquet files. The file names use a “self-describing” convention used by the Apache Hive project. Hive-style partitions name folders with a “key=value” convention, so as you might guess, the CheckoutYear=2005 directory contains all the data where CheckoutYear is 2005. Each file is between 100 and 300 MB and the total size is now around 4 GB, a little over half the size of the original CSV file. This is as we expect since parquet is a much more efficient format.

+
+
+
+

Using dplyr with arrow

+

Now we’ve created these parquet files, we’ll need to read them in again. We use open_dataset() again, but this time we give it a directory:

+
+
seattle_pq <- open_dataset(pq_path)
+
+

Now we can write our dplyr pipeline. For example, we could count the total number of books checked out in each month for the last five years:

+
+
query <- seattle_pq |> 
+  filter(CheckoutYear >= 2018, MaterialType == "BOOK") |>
+  group_by(CheckoutYear, CheckoutMonth) |>
+  summarize(TotalCheckouts = sum(Checkouts)) |>
+  arrange(CheckoutYear, CheckoutMonth)
+
+

Writing dplyr code for arrow data is conceptually similar to dbplyr, ?@sec-import-databases: you write dplyr code, which is automatically transformed into a query that the Apache Arrow C++ library understands, which is then executed when you call collect(). If we print out the query object we can see a little information about what we expect Arrow to return when the execution takes place:

+
+
query
+#> FileSystemDataset (query)
+#> CheckoutYear: int32
+#> CheckoutMonth: int64
+#> TotalCheckouts: int64
+#> 
+#> * Grouped by CheckoutYear
+#> * Sorted by CheckoutYear [asc], CheckoutMonth [asc]
+#> See $.data for the source Arrow object
+
+

And we can get the results by calling collect():

+
+
query |> collect()
+#> # A tibble: 58 × 3
+#> # Groups:   CheckoutYear [5]
+#>   CheckoutYear CheckoutMonth TotalCheckouts
+#>          <int>         <int>          <int>
+#> 1         2018             1         355101
+#> 2         2018             2         309813
+#> 3         2018             3         344487
+#> 4         2018             4         330988
+#> 5         2018             5         318049
+#> 6         2018             6         341825
+#> # ℹ 52 more rows
+
+

Like dbplyr, arrow only understands some R expressions, so you may not be able to write exactly the same code you usually would. However, the list of operations and functions supported is fairly extensive and continues to grow; find a complete list of currently supported functions in ?acero.

+
+

Performance

+

Let’s take a quick look at the performance impact of switching from CSV to parquet. First, let’s time how long it takes to calculate the number of books checked out in each month of 2021, when the data is stored as a single large csv:

+
+
seattle_csv |> 
+  filter(CheckoutYear == 2021, MaterialType == "BOOK") |>
+  group_by(CheckoutMonth) |>
+  summarize(TotalCheckouts = sum(Checkouts)) |>
+  arrange(desc(CheckoutMonth)) |>
+  collect() |> 
+  system.time()
+#>    user  system elapsed 
+#>  11.951   1.297  11.387
+
+

Now let’s use our new version of the dataset in which the Seattle library checkout data has been partitioned into 18 smaller parquet files:

+
+
seattle_pq |> 
+  filter(CheckoutYear == 2021, MaterialType == "BOOK") |>
+  group_by(CheckoutMonth) |>
+  summarize(TotalCheckouts = sum(Checkouts)) |>
+  arrange(desc(CheckoutMonth)) |>
+  collect() |> 
+  system.time()
+#>    user  system elapsed 
+#>   0.263   0.058   0.063
+
+

The ~100x speedup in performance is attributable to two factors: the multi-file partitioning, and the format of individual files:

+
    +
  • Partitioning improves performance because this query uses CheckoutYear == 2021 to filter the data, and arrow is smart enough to recognize that it only needs to read 1 of the 18 parquet files.
    • +
  • The parquet format improves performance by storing data in a binary format that can be read more directly into memory. The column-wise format and rich metadata means that arrow only needs to read the four columns actually used in the query (CheckoutYear, MaterialType, CheckoutMonth, and Checkouts).
    • +
+

This massive difference in performance is why it pays off to convert large CSVs to parquet!

+
+
+

Using duckdb with arrow

+

There’s one last advantage of parquet and arrow — it’s very easy to turn an arrow dataset into a DuckDB database (?@sec-import-databases) by calling arrow::to_duckdb():

+
+
seattle_pq |> 
+  to_duckdb() |>
+  filter(CheckoutYear >= 2018, MaterialType == "BOOK") |>
+  group_by(CheckoutYear) |>
+  summarize(TotalCheckouts = sum(Checkouts)) |>
+  arrange(desc(CheckoutYear)) |>
+  collect()
+#> Warning: Missing values are always removed in SQL aggregation functions.
+#> Use `na.rm = TRUE` to silence this warning
+#> This warning is displayed once every 8 hours.
+#> # A tibble: 5 × 2
+#>   CheckoutYear TotalCheckouts
+#>          <int>          <dbl>
+#> 1         2022        2431502
+#> 2         2021        2266438
+#> 3         2020        1241999
+#> 4         2019        3931688
+#> 5         2018        3987569
+
+

The neat thing about to_duckdb() is that the transfer doesn’t involve any memory copying, and speaks to the goals of the arrow ecosystem: enabling seamless transitions from one computing environment to another.

+
+
+

Exercises

+
    +
  1. Figure out the most popular book each year.
    2. +
  2. Which author has the most books in the Seattle library system?
    3. +
  3. How has checkouts of books vs ebooks changed over the last 10 years?
    4. +
+
+
+
+

Summary

+

In this chapter, you’ve been given a taste of the arrow package, which provides a dplyr backend for working with large on-disk datasets. It can work with CSV files, and it’s much much faster if you convert your data to parquet. Parquet is a binary data format that’s designed specifically for data analysis on modern computers. Far fewer tools can work with parquet files compared to CSV, but its partitioned, compressed, and columnar structure makes it much more efficient to analyze.

+

Next up you’ll learn about your first non-rectangular data source, which you’ll handle using tools provided by the tidyr package. We’ll focus on data that comes from JSON files, but the general principles apply to tree-like data regardless of its source.

+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/base-R.html b/docs/base-R.html new file mode 100644 index 000000000..66108ff31 --- /dev/null +++ b/docs/base-R.html @@ -0,0 +1,1028 @@ + + + + + + + + + + +base-r + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + + + +
+

A field guide to base R

+
+

Introduction

+

To finish off the programming section, we’re going to give you a quick tour of the most important base R functions that we don’t otherwise discuss in the book. These tools are particularly useful as you do more programming and will help you read code you’ll encounter in the wild.

+

This is a good place to remind you that the tidyverse is not the only way to solve data science problems. We teach the tidyverse in this book because tidyverse packages share a common design philosophy, increasing the consistency across functions, and making each new function or package a little easier to learn and use. It’s not possible to use the tidyverse without using base R, so we’ve actually already taught you a lot of base R functions: from library() to load packages, to sum() and mean() for numeric summaries, to the factor, date, and POSIXct data types, and of course all the basic operators like +, -, /, *, |, &, and !. What we haven’t focused on so far is base R workflows, so we will highlight a few of those in this chapter.

+

After you read this book, you’ll learn other approaches to the same problems using base R, data.table, and other packages. You’ll undoubtedly encounter these other approaches when you start reading R code written by others, particularly if you’re using StackOverflow. It’s 100% okay to write code that uses a mix of approaches, and don’t let anyone tell you otherwise!

+

In this chapter, we’ll focus on four big topics: subsetting with [, subsetting with [[ and $, the apply family of functions, and for loops. To finish off, we’ll briefly discuss two essential plotting functions.

+
+

Prerequisites

+

This package focuses on base R so doesn’t have any real prerequisites, but we’ll load the tidyverse in order to explain some of the differences.

+
+
library(tidyverse)
+
+
+
+
+

Selecting multiple elements with [

+

[ is used to extract sub-components from vectors and data frames, and is called like x[i] or x[i, j]. In this section, we’ll introduce you to the power of [, first showing you how you can use it with vectors, then how the same principles extend in a straightforward way to two-dimensional (2d) structures like data frames. We’ll then help you cement that knowledge by showing how various dplyr verbs are special cases of [.

+
+

Subsetting vectors

+

There are five main types of things that you can subset a vector with, i.e., that can be the i in x[i]:

+
    +

  1. A vector of positive integers. Subsetting with positive integers keeps the elements at those positions:

    +
    +
    x <- c("one", "two", "three", "four", "five")
+x[c(3, 2, 5)]
+#> [1] "three" "two"   "five"
    +
    +

    By repeating a position, you can actually make a longer output than input, making the term “subsetting” a bit of a misnomer.

    +
    +
    x[c(1, 1, 5, 5, 5, 2)]
+#> [1] "one"  "one"  "five" "five" "five" "two"
    +
    2. +

  2. A vector of negative integers. Negative values drop the elements at the specified positions:

    +
    +
    x[c(-1, -3, -5)]
+#> [1] "two"  "four"
    +
    3. +

  3. A logical vector. Subsetting with a logical vector keeps all values corresponding to a TRUE value. This is most often useful in conjunction with the comparison functions.

    +
    +
    x <- c(10, 3, NA, 5, 8, 1, NA)
+
+# All non-missing values of x
+x[!is.na(x)]
+#> [1] 10  3  5  8  1
+
+# All even (or missing!) values of x
+x[x %% 2 == 0]
+#> [1] 10 NA  8 NA
    +
    +

    Unlike filter(), NA indices will be included in the output as NAs.

    4. +

  4. A character vector. If you have a named vector, you can subset it with a character vector:

    +
    +
    x <- c(abc = 1, def = 2, xyz = 5)
+x[c("xyz", "def")]
+#> xyz def 
+#>   5   2
    +
    +

    As with subsetting with positive integers, you can use a character vector to duplicate individual entries.

    5. +

  5. Nothing. The final type of subsetting is nothing, x[], which returns the complete x. This is not useful for subsetting vectors, but as we’ll see shortly, it is useful when subsetting 2d structures like tibbles.

    6. +
+
+
+

Subsetting data frames

+

There are quite a few different ways1 that you can use [ with a data frame, but the most important way is to select rows and columns independently with df[rows, cols]. Here rows and cols are vectors as described above. For example, df[rows, ] and df[, cols] select just rows or just columns, using the empty subset to preserve the other dimension.

+

Here are a couple of examples:

+
+
df <- tibble(
+  x = 1:3, 
+  y = c("a", "e", "f"), 
+  z = runif(3)
+)
+
+# Select first row and second column
+df[1, 2]
+#> # A tibble: 1 × 1
+#>   y    
+#>   <chr>
+#> 1 a
+
+# Select all rows and columns x and y
+df[, c("x" , "y")]
+#> # A tibble: 3 × 2
+#>       x y    
+#>   <int> <chr>
+#> 1     1 a    
+#> 2     2 e    
+#> 3     3 f
+
+# Select rows where `x` is greater than 1 and all columns
+df[df$x > 1, ]
+#> # A tibble: 2 × 3
+#>       x y         z
+#>   <int> <chr> <dbl>
+#> 1     2 e     0.834
+#> 2     3 f     0.601
+
+

We’ll come back to $ shortly, but you should be able to guess what df$x does from the context: it extracts the x variable from df. We need to use it here because [ doesn’t use tidy evaluation, so you need to be explicit about the source of the x variable.

+

There’s an important difference between tibbles and data frames when it comes to [. In this book, we’ve mainly used tibbles, which are data frames, but they tweak some behaviors to make your life a little easier. In most places, you can use “tibble” and “data frame” interchangeably, so when we want to draw particular attention to R’s built-in data frame, we’ll write data.frame. If df is a data.frame, then df[, cols] will return a vector if col selects a single column and a data frame if it selects more than one column. If df is a tibble, then [ will always return a tibble.

+
+
df1 <- data.frame(x = 1:3)
+df1[, "x"]
+#> [1] 1 2 3
+
+df2 <- tibble(x = 1:3)
+df2[, "x"]
+#> # A tibble: 3 × 1
+#>       x
+#>   <int>
+#> 1     1
+#> 2     2
+#> 3     3
+
+

One way to avoid this ambiguity with data.frames is to explicitly specify drop = FALSE:

+
+
df1[, "x" , drop = FALSE]
+#>   x
+#> 1 1
+#> 2 2
+#> 3 3
+
+
+
+

dplyr equivalents

+

Several dplyr verbs are special cases of [:

+
    +

  • filter() is equivalent to subsetting the rows with a logical vector, taking care to exclude missing values:

    +
    +
    df <- tibble(
+  x = c(2, 3, 1, 1, NA), 
+  y = letters[1:5], 
+  z = runif(5)
+)
+df |> filter(x > 1)
+
+# same as
+df[!is.na(df$x) & df$x > 1, ]
    +
    +

    Another common technique in the wild is to use which() for its side-effect of dropping missing values: df[which(df$x > 1), ].

    • +

  • arrange() is equivalent to subsetting the rows with an integer vector, usually created with order():

    +
    +
    df |> arrange(x, y)
+
+# same as
+df[order(df$x, df$y), ]
    +
    +

    You can use order(decreasing = TRUE) to sort all columns in descending order or -rank(col) to sort columns in decreasing order individually.

    • +

  • Both select() and relocate() are similar to subsetting the columns with a character vector:

    +
    +
    df |> select(x, z)
+
+# same as
+df[, c("x", "z")]
    +
    • +
+

Base R also provides a function that combines the features of filter() and select()2 called subset():

+
+
df |> 
+  filter(x > 1) |> 
+  select(y, z)
+#> # A tibble: 2 × 2
+#>   y           z
+#>   <chr>   <dbl>
+#> 1 a     0.157  
+#> 2 b     0.00740
+
+
+
# same as
+df |> subset(x > 1, c(y, z))
+
+

This function was the inspiration for much of dplyr’s syntax.

+
+
+

Exercises

+
    +

  1. Create functions that take a vector as input and return:

    +
      +
    1. The elements at even-numbered positions.
      2. +
    2. Every element except the last value.
      3. +
    3. Only even values (and no missing values).
      4. +
    2. +

  2. Why is x[-which(x > 0)] not the same as x[x <= 0]? Read the documentation for which() and do some experiments to figure it out.

    3. +
+
+
+
+

Selecting a single element with $ and [[

+

[, which selects many elements, is paired with [[ and $, which extract a single element. In this section, we’ll show you how to use [[ and $ to pull columns out of data frames, discuss a couple more differences between data.frames and tibbles, and emphasize some important differences between [ and [[ when used with lists.

+
+

Data frames

+

[[ and $ can be used to extract columns out of a data frame. [[ can access by position or by name, and $ is specialized for access by name:

+
+
tb <- tibble(
+  x = 1:4,
+  y = c(10, 4, 1, 21)
+)
+
+# by position
+tb[[1]]
+#> [1] 1 2 3 4
+
+# by name
+tb[["x"]]
+#> [1] 1 2 3 4
+tb$x
+#> [1] 1 2 3 4
+
+

They can also be used to create new columns, the base R equivalent of mutate():

+
+
tb$z <- tb$x + tb$y
+tb
+#> # A tibble: 4 × 3
+#>       x     y     z
+#>   <int> <dbl> <dbl>
+#> 1     1    10    11
+#> 2     2     4     6
+#> 3     3     1     4
+#> 4     4    21    25
+
+

There are several other base R approaches to creating new columns including with transform(), with(), and within(). Hadley collected a few examples at https://gist.github.com/hadley/1986a273e384fb2d4d752c18ed71bedf.

+

Using $ directly is convenient when performing quick summaries. For example, if you just want to find the size of the biggest diamond or the possible values of cut, there’s no need to use summarize():

+
+
max(diamonds$carat)
+#> [1] 5.01
+
+levels(diamonds$cut)
+#> [1] "Fair"      "Good"      "Very Good" "Premium"   "Ideal"
+
+

dplyr also provides an equivalent to [[/$ that we didn’t mention in ?@sec-data-transform: pull(). pull() takes either a variable name or variable position and returns just that column. That means we could rewrite the above code to use the pipe:

+
+
diamonds |> pull(carat) |> max()
+#> [1] 5.01
+
+diamonds |> pull(cut) |> levels()
+#> [1] "Fair"      "Good"      "Very Good" "Premium"   "Ideal"
+
+
+
+

Tibbles

+

There are a couple of important differences between tibbles and base data.frames when it comes to $. Data frames match the prefix of any variable names (so-called partial matching) and don’t complain if a column doesn’t exist:

+
+
df <- data.frame(x1 = 1)
+df$x
+#> [1] 1
+df$z
+#> NULL
+
+

Tibbles are more strict: they only ever match variable names exactly and they will generate a warning if the column you are trying to access doesn’t exist:

+
+
tb <- tibble(x1 = 1)
+
+tb$x
+#> Warning: Unknown or uninitialised column: `x`.
+#> NULL
+tb$z
+#> Warning: Unknown or uninitialised column: `z`.
+#> NULL
+
+

For this reason we sometimes joke that tibbles are lazy and surly: they do less and complain more.

+
+
+

Lists

+

[[ and $ are also really important for working with lists, and it’s important to understand how they differ from [. Let’s illustrate the differences with a list named l:

+
+
l <- list(
+  a = 1:3, 
+  b = "a string", 
+  c = pi, 
+  d = list(-1, -5)
+)
+
+
    +

  • [ extracts a sub-list. It doesn’t matter how many elements you extract, the result will always be a list.

    +
    +
    str(l[1:2])
+#> List of 2
+#>  $ a: int [1:3] 1 2 3
+#>  $ b: chr "a string"
+
+str(l[1])
+#> List of 1
+#>  $ a: int [1:3] 1 2 3
+
+str(l[4])
+#> List of 1
+#>  $ d:List of 2
+#>   ..$ : num -1
+#>   ..$ : num -5
    +
    +

    Like with vectors, you can subset with a logical, integer, or character vector.

    • +

  • [[ and $ extract a single component from a list. They remove a level of hierarchy from the list.

    +
    +
    str(l[[1]])
+#>  int [1:3] 1 2 3
+
+str(l[[4]])
+#> List of 2
+#>  $ : num -1
+#>  $ : num -5
+
+str(l$a)
+#>  int [1:3] 1 2 3
    +
    • +
+

The difference between [ and [[ is particularly important for lists because [[ drills down into the list while [ returns a new, smaller list. To help you remember the difference, take a look at the unusual pepper shaker shown in Figure 1. If this pepper shaker is your list pepper, then, pepper[1] is a pepper shaker containing a single pepper packet. pepper[2] would look the same, but would contain the second packet. pepper[1:2] would be a pepper shaker containing two pepper packets. pepper[[1]] would extract the pepper packet itself.

+
+
+
+
+
+Three photos. On the left is a photo of a glass pepper shaker. Instead of the pepper shaker containing pepper, it contains a single packet of pepper. In the middle is a photo of a single packet of pepper. On the right is a photo of the contents of a packet of pepper. +
+
+Figure 1: (Left) A pepper shaker that Hadley once found in his hotel room. (Middle) pepper[1]. (Right) pepper[[1]] +
+
+
+
+
+

This same principle applies when you use 1d [ with a data frame: df["x"] returns a one-column data frame and df[["x"]] returns a vector.

+
+
+

Exercises

+
    +

  1. What happens when you use [[ with a positive integer that’s bigger than the length of the vector? What happens when you subset with a name that doesn’t exist?

    2. +

  2. What would pepper[[1]][1] be? What about pepper[[1]][[1]]?

    3. +
+
+
+
+

Apply family

+

In ?@sec-iteration, you learned tidyverse techniques for iteration like dplyr::across() and the map family of functions. In this section, you’ll learn about their base equivalents, the apply family. In this context apply and map are synonyms because another way of saying “map a function over each element of a vector” is “apply a function over each element of a vector”. Here we’ll give you a quick overview of this family so you can recognize them in the wild.

+

The most important member of this family is lapply(), which is very similar to purrr::map()3. In fact, because we haven’t used any of map()’s more advanced features, you can replace every map() call in ?@sec-iteration with lapply().

+

There’s no exact base R equivalent to across() but you can get close by using [ with lapply(). This works because under the hood, data frames are lists of columns, so calling lapply() on a data frame applies the function to each column.

+
+
df <- tibble(a = 1, b = 2, c = "a", d = "b", e = 4)
+
+# First find numeric columns
+num_cols <- sapply(df, is.numeric)
+num_cols
+#>     a     b     c     d     e 
+#>  TRUE  TRUE FALSE FALSE  TRUE
+
+# Then transform each column with lapply() then replace the original values
+df[, num_cols] <- lapply(df[, num_cols, drop = FALSE], \(x) x * 2)
+df
+#> # A tibble: 1 × 5
+#>       a     b c     d         e
+#>   <dbl> <dbl> <chr> <chr> <dbl>
+#> 1     2     4 a     b         8
+
+

The code above uses a new function, sapply(). It’s similar to lapply() but it always tries to simplify the result, hence the s in its name, here producing a logical vector instead of a list. We don’t recommend using it for programming, because the simplification can fail and give you an unexpected type, but it’s usually fine for interactive use. purrr has a similar function called map_vec() that we didn’t mention in ?@sec-iteration.

+

Base R provides a stricter version of sapply() called vapply(), short for vector apply. It takes an additional argument that specifies the expected type, ensuring that simplification occurs the same way regardless of the input. For example, we could replace the sapply() call above with this vapply() where we specify that we expect is.numeric() to return a logical vector of length 1:

+
+
vapply(df, is.numeric, logical(1))
+#>     a     b     c     d     e 
+#>  TRUE  TRUE FALSE FALSE  TRUE
+
+

The distinction between sapply() and vapply() is really important when they’re inside a function (because it makes a big difference to the function’s robustness to unusual inputs), but it doesn’t usually matter in data analysis.

+

Another important member of the apply family is tapply() which computes a single grouped summary:

+
+
diamonds |> 
+  group_by(cut) |> 
+  summarize(price = mean(price))
+#> # A tibble: 5 × 2
+#>   cut       price
+#>   <ord>     <dbl>
+#> 1 Fair      4359.
+#> 2 Good      3929.
+#> 3 Very Good 3982.
+#> 4 Premium   4584.
+#> 5 Ideal     3458.
+
+tapply(diamonds$price, diamonds$cut, mean)
+#>      Fair      Good Very Good   Premium     Ideal 
+#>  4358.758  3928.864  3981.760  4584.258  3457.542
+
+

Unfortunately tapply() returns its results in a named vector which requires some gymnastics if you want to collect multiple summaries and grouping variables into a data frame (it’s certainly possible to not do this and just work with free floating vectors, but in our experience that just delays the work). If you want to see how you might use tapply() or other base techniques to perform other grouped summaries, Hadley has collected a few techniques in a gist.

+

The final member of the apply family is the titular apply(), which works with matrices and arrays. In particular, watch out for apply(df, 2, something), which is a slow and potentially dangerous way of doing lapply(df, something). This rarely comes up in data science because we usually work with data frames and not matrices.

+
+
+

for loops

+

for loops are the fundamental building block of iteration that both the apply and map families use under the hood. for loops are powerful and general tools that are important to learn as you become a more experienced R programmer. The basic structure of a for loop looks like this:

+
+
for (element in vector) {
+  # do something with element
+}
+
+

The most straightforward use of for loops is to achieve the same effect as walk(): call some function with a side-effect on each element of a list. For example, in ?@sec-save-database instead of using walk():

+
+
paths |> walk(append_file)
+
+

We could have used a for loop:

+
+
for (path in paths) {
+  append_file(path)
+}
+
+

Things get a little trickier if you want to save the output of the for loop, for example reading all of the excel files in a directory like we did in ?@sec-iteration:

+
+
paths <- dir("data/gapminder", pattern = "\\.xlsx$", full.names = TRUE)
+files <- map(paths, readxl::read_excel)
+
+

There are a few different techniques that you can use, but we recommend being explicit about what the output is going to look like upfront. In this case, we’re going to want a list the same length as paths, which we can create with vector():

+
+
files <- vector("list", length(paths))
+
+

Then instead of iterating over the elements of paths, we’ll iterate over their indices, using seq_along() to generate one index for each element of paths:

+
+
seq_along(paths)
+#>  [1]  1  2  3  4  5  6  7  8  9 10 11 12
+
+

Using the indices is important because it allows us to link to each position in the input with the corresponding position in the output:

+
+
for (i in seq_along(paths)) {
+  files[[i]] <- readxl::read_excel(paths[[i]])
+}
+
+

To combine the list of tibbles into a single tibble you can use do.call() + rbind():

+
+
do.call(rbind, files)
+#> # A tibble: 1,704 × 5
+#>   country     continent lifeExp      pop gdpPercap
+#>   <chr>       <chr>       <dbl>    <dbl>     <dbl>
+#> 1 Afghanistan Asia         28.8  8425333      779.
+#> 2 Albania     Europe       55.2  1282697     1601.
+#> 3 Algeria     Africa       43.1  9279525     2449.
+#> 4 Angola      Africa       30.0  4232095     3521.
+#> 5 Argentina   Americas     62.5 17876956     5911.
+#> 6 Australia   Oceania      69.1  8691212    10040.
+#> # ℹ 1,698 more rows
+
+

Rather than making a list and saving the results as we go, a simpler approach is to build up the data frame piece-by-piece:

+
+
out <- NULL
+for (path in paths) {
+  out <- rbind(out, readxl::read_excel(path))
+}
+
+

We recommend avoiding this pattern because it can become very slow when the vector is very long. This is the source of the persistent canard that for loops are slow: they’re not, but iteratively growing a vector is.

+
+
+

Plots

+

Many R users who don’t otherwise use the tidyverse prefer ggplot2 for plotting due to helpful features like sensible defaults, automatic legends, and a modern look. However, base R plotting functions can still be useful because they’re so concise — it takes very little typing to do a basic exploratory plot.

+

There are two main types of base plot you’ll see in the wild: scatterplots and histograms, produced with plot() and hist() respectively. Here’s a quick example from the diamonds dataset:

+
# Left
+hist(diamonds$carat)
+
+# Right
+plot(diamonds$carat, diamonds$price)
+
+
+
+

On the left, histogram of carats of diamonds, ranging from 0 to 5 carats. The distribution is unimodal and right-skewed. On the right, scatter plot of price vs. carat of diamonds, showing a positive relationship that fans out as both price and carat increases. The scatter plot shows very few diamonds bigger than 3 carats compared to diamonds between 0 to 3 carats.

+
+
+

On the left, histogram of carats of diamonds, ranging from 0 to 5 carats. The distribution is unimodal and right-skewed. On the right, scatter plot of price vs. carat of diamonds, showing a positive relationship that fans out as both price and carat increases. The scatter plot shows very few diamonds bigger than 3 carats compared to diamonds between 0 to 3 carats.

+
+
+
+

Note that base plotting functions work with vectors, so you need to pull columns out of the data frame using $ or some other technique.

+
+
+

Summary

+

In this chapter, we’ve shown you a selection of base R functions useful for subsetting and iteration. Compared to approaches discussed elsewhere in the book, these functions tend to have more of a “vector” flavor than a “data frame” flavor because base R functions tend to take individual vectors, rather than a data frame and some column specification. This often makes life easier for programming and so becomes more important as you write more functions and begin to write your own packages.

+

This chapter concludes the programming section of the book. You’ve made a solid start on your journey to becoming not just a data scientist who uses R, but a data scientist who can program in R. We hope these chapters have sparked your interest in programming and that you’re looking forward to learning more outside of this book.

+ + +
+
+
+
+
    +

  1. Read https://adv-r.hadley.nz/subsetting.html#subset-multiple to see how you can also subset a data frame like it is a 1d object and how you can subset it with a matrix.↩︎

    2. +

  2. But it doesn’t handle grouped data frames differently and it doesn’t support selection helper functions like starts_with().↩︎

    3. +

  3. It just lacks convenient features like progress bars and reporting which element caused the problem if there’s an error.↩︎

    4. +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/communicate.html b/docs/communicate.html new file mode 100644 index 000000000..21532b464 --- /dev/null +++ b/docs/communicate.html @@ -0,0 +1,541 @@ + + + + + + + + + + +communicate + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + + + +
+

Communicate

+

So far, you’ve learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand your data through transformation, and visualization. However, it doesn’t matter how great your analysis is unless you can explain it to others: you need to communicate your results.

+
+
+
+
+
+A diagram displaying the data science cycle with communicate highlighed in blue. +
+
+Figure 1: Communication is the final part of the data science process; if you can’t communicate your results to other humans, it doesn’t matter how great your analysis is. +
+
+
+
+
+

Communication is the theme of the following two chapters:

+
    +

  • In ?@sec-quarto, you will learn about Quarto, a tool for integrating prose, code, and results. You can use Quarto for analyst-to-analyst communication as well as analyst-to-decision-maker communication. Thanks to the power of Quarto formats, you can even use the same document for both purposes.

    • +

  • In ?@sec-quarto-formats, you’ll learn a little about the many other varieties of outputs you can produce using Quarto, including dashboards, websites, and books.

    • +
+

These chapters focus mostly on the technical mechanics of communication, not the really hard problems of communicating your thoughts to other humans. However, there are lot of other great books about communication, which we’ll point you to at the end of each chapter.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/contribute.html b/docs/contribute.html new file mode 100644 index 000000000..a82efcd16 --- /dev/null +++ b/docs/contribute.html @@ -0,0 +1,531 @@ + + + + + + + + + + +contribute + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + + + +
+

Contributing

+

This book has been developed in the open, and it wouldn’t be nearly as good without your contributions. There are a number of ways you can help make the book even better:

+
    +

  • If you don’t understand something, please let me know. Your feedback on what is confusing or hard to understand is valuable.

    • +

  • If you spot a typo, feel free to edit the underlying page and send a pull request. If you’ve never done this before, the process is very easy:

    +
      +

    • Click the edit this page on the sidebar.

      • +

    • Make the changes using GitHub’s in-page editor and save.

      • +

    • Submit a pull request and include a brief description of your changes. “Fixing typos” is perfectly adequate.

      • +

    • If you make significant changes, include the phrase “I assign the copyright of this contribution to Hadley Wickham” - I need this so I can publish the printed book.

      • +
    • +
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/data-import.html b/docs/data-import.html new file mode 100644 index 000000000..7294376fc --- /dev/null +++ b/docs/data-import.html @@ -0,0 +1,1147 @@ + + + + + + + + + + +data-import + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + + + +
+

Data import

+
+

Introduction

+

Working with data provided by R packages is a great way to learn data science tools, but you want to apply what you’ve learned to your own data at some point. In this chapter, you’ll learn the basics of reading data files into R.

+

Specifically, this chapter will focus on reading plain-text rectangular files. We’ll start with practical advice for handling features like column names, types, and missing data. You will then learn about reading data from multiple files at once and writing data from R to a file. Finally, you’ll learn how to handcraft data frames in R.

+
+

Prerequisites

+

In this chapter, you’ll learn how to load flat files in R with the readr package, which is part of the core tidyverse.

+
+
library(tidyverse)
+
+
+
+
+

Reading data from a file

+

To begin, we’ll focus on the most common rectangular data file type: CSV, which is short for comma-separated values. Here is what a simple CSV file looks like. The first row, commonly called the header row, gives the column names, and the following six rows provide the data. The columns are separated, aka delimited, by commas.

+
+
Student ID,Full Name,favourite.food,mealPlan,AGE
+1,Sunil Huffmann,Strawberry yoghurt,Lunch only,4
+2,Barclay Lynn,French fries,Lunch only,5
+3,Jayendra Lyne,N/A,Breakfast and lunch,7
+4,Leon Rossini,Anchovies,Lunch only,
+5,Chidiegwu Dunkel,Pizza,Breakfast and lunch,five
+6,Güvenç Attila,Ice cream,Lunch only,6
+
+

Table 1 shows a representation of the same data as a table.

+
+
+
+
+Table 1: Data from the students.csv file as a table. +
+
+
+ +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Student IDFull Namefavourite.foodmealPlanAGE
1Sunil HuffmannStrawberry yoghurtLunch only4
2Barclay LynnFrench friesLunch only5
3Jayendra LyneN/ABreakfast and lunch7
4Leon RossiniAnchoviesLunch onlyNA
5Chidiegwu DunkelPizzaBreakfast and lunchfive
6Güvenç AttilaIce creamLunch only6
+
+
+
+
+
+

We can read this file into R using read_csv(). The first argument is the most important: the path to the file. You can think about the path as the address of the file: the file is called students.csv and that it lives in the data folder.

+
+
students <- read_csv("data/students.csv")
+#> Rows: 6 Columns: 5
+#> ── Column specification ─────────────────────────────────────────────────────
+#> Delimiter: ","
+#> chr (4): Full Name, favourite.food, mealPlan, AGE
+#> dbl (1): Student ID
+#> 
+#> ℹ Use `spec()` to retrieve the full column specification for this data.
+#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
+
+

The code above will work if you have the students.csv file in a data folder in your project. You can download the students.csv file from https://pos.it/r4ds-students-csv or you can read it directly from that URL with:

+
+
students <- read_csv("https://pos.it/r4ds-students-csv")
+
+

When you run read_csv(), it prints out a message telling you the number of rows and columns of data, the delimiter that was used, and the column specifications (names of columns organized by the type of data the column contains). It also prints out some information about retrieving the full column specification and how to quiet this message. This message is an integral part of readr, and we’ll return to it in Section 1.3.

+
+

Practical advice

+

Once you read data in, the first step usually involves transforming it in some way to make it easier to work with in the rest of your analysis. Let’s take another look at the students data with that in mind.

+
+
students
+#> # A tibble: 6 × 5
+#>   `Student ID` `Full Name`      favourite.food     mealPlan            AGE  
+#>          <dbl> <chr>            <chr>              <chr>               <chr>
+#> 1            1 Sunil Huffmann   Strawberry yoghurt Lunch only          4    
+#> 2            2 Barclay Lynn     French fries       Lunch only          5    
+#> 3            3 Jayendra Lyne    N/A                Breakfast and lunch 7    
+#> 4            4 Leon Rossini     Anchovies          Lunch only          <NA> 
+#> 5            5 Chidiegwu Dunkel Pizza              Breakfast and lunch five 
+#> 6            6 Güvenç Attila    Ice cream          Lunch only          6
+
+

In the favourite.food column, there are a bunch of food items, and then the character string N/A, which should have been a real NA that R will recognize as “not available”. This is something we can address using the na argument. By default, read_csv() only recognizes empty strings ("") in this dataset as NAs, and we want it to also recognize the character string "N/A".

+
+
students <- read_csv("data/students.csv", na = c("N/A", ""))
+
+students
+#> # A tibble: 6 × 5
+#>   `Student ID` `Full Name`      favourite.food     mealPlan            AGE  
+#>          <dbl> <chr>            <chr>              <chr>               <chr>
+#> 1            1 Sunil Huffmann   Strawberry yoghurt Lunch only          4    
+#> 2            2 Barclay Lynn     French fries       Lunch only          5    
+#> 3            3 Jayendra Lyne    <NA>               Breakfast and lunch 7    
+#> 4            4 Leon Rossini     Anchovies          Lunch only          <NA> 
+#> 5            5 Chidiegwu Dunkel Pizza              Breakfast and lunch five 
+#> 6            6 Güvenç Attila    Ice cream          Lunch only          6
+
+

You might also notice that the Student ID and Full Name columns are surrounded by backticks. That’s because they contain spaces, breaking R’s usual rules for variable names; they’re non-syntactic names. To refer to these variables, you need to surround them with backticks, `:

+
+
students |> 
+  rename(
+    student_id = `Student ID`,
+    full_name = `Full Name`
+  )
+#> # A tibble: 6 × 5
+#>   student_id full_name        favourite.food     mealPlan            AGE  
+#>        <dbl> <chr>            <chr>              <chr>               <chr>
+#> 1          1 Sunil Huffmann   Strawberry yoghurt Lunch only          4    
+#> 2          2 Barclay Lynn     French fries       Lunch only          5    
+#> 3          3 Jayendra Lyne    <NA>               Breakfast and lunch 7    
+#> 4          4 Leon Rossini     Anchovies          Lunch only          <NA> 
+#> 5          5 Chidiegwu Dunkel Pizza              Breakfast and lunch five 
+#> 6          6 Güvenç Attila    Ice cream          Lunch only          6
+
+

An alternative approach is to use janitor::clean_names() to use some heuristics to turn them all into snake case at once1.

+
+
students |> janitor::clean_names()
+#> # A tibble: 6 × 5
+#>   student_id full_name        favourite_food     meal_plan           age  
+#>        <dbl> <chr>            <chr>              <chr>               <chr>
+#> 1          1 Sunil Huffmann   Strawberry yoghurt Lunch only          4    
+#> 2          2 Barclay Lynn     French fries       Lunch only          5    
+#> 3          3 Jayendra Lyne    <NA>               Breakfast and lunch 7    
+#> 4          4 Leon Rossini     Anchovies          Lunch only          <NA> 
+#> 5          5 Chidiegwu Dunkel Pizza              Breakfast and lunch five 
+#> 6          6 Güvenç Attila    Ice cream          Lunch only          6
+
+

Another common task after reading in data is to consider variable types. For example, meal_plan is a categorical variable with a known set of possible values, which in R should be represented as a factor:

+
+
students |>
+  janitor::clean_names() |>
+  mutate(meal_plan = factor(meal_plan))
+#> # A tibble: 6 × 5
+#>   student_id full_name        favourite_food     meal_plan           age  
+#>        <dbl> <chr>            <chr>              <fct>               <chr>
+#> 1          1 Sunil Huffmann   Strawberry yoghurt Lunch only          4    
+#> 2          2 Barclay Lynn     French fries       Lunch only          5    
+#> 3          3 Jayendra Lyne    <NA>               Breakfast and lunch 7    
+#> 4          4 Leon Rossini     Anchovies          Lunch only          <NA> 
+#> 5          5 Chidiegwu Dunkel Pizza              Breakfast and lunch five 
+#> 6          6 Güvenç Attila    Ice cream          Lunch only          6
+
+

Note that the values in the meal_plan variable have stayed the same, but the type of variable denoted underneath the variable name has changed from character (<chr>) to factor (<fct>). You’ll learn more about factors in ?@sec-factors.

+

Before you analyze these data, you’ll probably want to fix the age column. Currently, age is a character variable because one of the observations is typed out as five instead of a numeric 5. We discuss the details of fixing this issue in ?@sec-import-spreadsheets.

+
+
students <- students |>
+  janitor::clean_names() |>
+  mutate(
+    meal_plan = factor(meal_plan),
+    age = parse_number(if_else(age == "five", "5", age))
+  )
+
+students
+#> # A tibble: 6 × 5
+#>   student_id full_name        favourite_food     meal_plan             age
+#>        <dbl> <chr>            <chr>              <fct>               <dbl>
+#> 1          1 Sunil Huffmann   Strawberry yoghurt Lunch only              4
+#> 2          2 Barclay Lynn     French fries       Lunch only              5
+#> 3          3 Jayendra Lyne    <NA>               Breakfast and lunch     7
+#> 4          4 Leon Rossini     Anchovies          Lunch only             NA
+#> 5          5 Chidiegwu Dunkel Pizza              Breakfast and lunch     5
+#> 6          6 Güvenç Attila    Ice cream          Lunch only              6
+
+

A new function here is if_else(), which has three arguments. The first argument test should be a logical vector. The result will contain the value of the second argument, yes, when test is TRUE, and the value of the third argument, no, when it is FALSE. Here we’re saying if age is the character string "five", make it "5", and if not leave it as age. You will learn more about if_else() and logical vectors in ?@sec-logicals.

+
+
+

Other arguments

+

There are a couple of other important arguments that we need to mention, and they’ll be easier to demonstrate if we first show you a handy trick: read_csv() can read text strings that you’ve created and formatted like a CSV file:

+
+
read_csv(
+  "a,b,c
+  1,2,3
+  4,5,6"
+)
+#> # A tibble: 2 × 3
+#>       a     b     c
+#>   <dbl> <dbl> <dbl>
+#> 1     1     2     3
+#> 2     4     5     6
+
+

Usually, read_csv() uses the first line of the data for the column names, which is a very common convention. But it’s not uncommon for a few lines of metadata to be included at the top of the file. You can use skip = n to skip the first n lines or use comment = "#" to drop all lines that start with (e.g.) #:

+
+
read_csv(
+  "The first line of metadata
+  The second line of metadata
+  x,y,z
+  1,2,3",
+  skip = 2
+)
+#> # A tibble: 1 × 3
+#>       x     y     z
+#>   <dbl> <dbl> <dbl>
+#> 1     1     2     3
+
+read_csv(
+  "# A comment I want to skip
+  x,y,z
+  1,2,3",
+  comment = "#"
+)
+#> # A tibble: 1 × 3
+#>       x     y     z
+#>   <dbl> <dbl> <dbl>
+#> 1     1     2     3
+
+

In other cases, the data might not have column names. You can use col_names = FALSE to tell read_csv() not to treat the first row as headings and instead label them sequentially from X1 to Xn:

+
+
read_csv(
+  "1,2,3
+  4,5,6",
+  col_names = FALSE
+)
+#> # A tibble: 2 × 3
+#>      X1    X2    X3
+#>   <dbl> <dbl> <dbl>
+#> 1     1     2     3
+#> 2     4     5     6
+
+

Alternatively, you can pass col_names a character vector which will be used as the column names:

+
+
read_csv(
+  "1,2,3
+  4,5,6",
+  col_names = c("x", "y", "z")
+)
+#> # A tibble: 2 × 3
+#>       x     y     z
+#>   <dbl> <dbl> <dbl>
+#> 1     1     2     3
+#> 2     4     5     6
+
+

These arguments are all you need to know to read the majority of CSV files that you’ll encounter in practice. (For the rest, you’ll need to carefully inspect your .csv file and read the documentation for read_csv()’s many other arguments.)

+
+
+

Other file types

+

Once you’ve mastered read_csv(), using readr’s other functions is straightforward; it’s just a matter of knowing which function to reach for:

+
    +

  • read_csv2() reads semicolon-separated files. These use ; instead of , to separate fields and are common in countries that use , as the decimal marker.

    • +

  • read_tsv() reads tab-delimited files.

    • +

  • read_delim() reads in files with any delimiter, attempting to automatically guess the delimiter if you don’t specify it.

    • +

  • read_fwf() reads fixed-width files. You can specify fields by their widths with fwf_widths() or by their positions with fwf_positions().

    • +

  • read_table() reads a common variation of fixed-width files where columns are separated by white space.

    • +

  • read_log() reads Apache-style log files.

    • +
+
+
+

Exercises

+
    +

  1. What function would you use to read a file where fields were separated with “|”?

    2. +

  2. Apart from file, skip, and comment, what other arguments do read_csv() and read_tsv() have in common?

    3. +

  3. What are the most important arguments to read_fwf()?

    4. +

  4. Sometimes strings in a CSV file contain commas. To prevent them from causing problems, they need to be surrounded by a quoting character, like " or '. By default, read_csv() assumes that the quoting character will be ". To read the following text into a data frame, what argument to read_csv() do you need to specify?

    +
    +
    "x,y\n1,'a,b'"
    +
    5. +

  5. Identify what is wrong with each of the following inline CSV files. What happens when you run the code?

    +
    +
    read_csv("a,b\n1,2,3\n4,5,6")
+read_csv("a,b,c\n1,2\n1,2,3,4")
+read_csv("a,b\n\"1")
+read_csv("a,b\n1,2\na,b")
+read_csv("a;b\n1;3")
    +
    6. +

  6. Practice referring to non-syntactic names in the following data frame by:

    +
      +
    1. Extracting the variable called 1.
      2. +
    2. Plotting a scatterplot of 1 vs. 2.
      3. +
    3. Creating a new column called 3, which is 2 divided by 1.
      4. +
    4. Renaming the columns to one, two, and three.
      5. +
    +
    +
    annoying <- tibble(
+  `1` = 1:10,
+  `2` = `1` * 2 + rnorm(length(`1`))
+)
    +
    7. +
+
+
+
+

Controlling column types

+

A CSV file doesn’t contain any information about the type of each variable (i.e. whether it’s a logical, number, string, etc.), so readr will try to guess the type. This section describes how the guessing process works, how to resolve some common problems that cause it to fail, and, if needed, how to supply the column types yourself. Finally, we’ll mention a few general strategies that are useful if readr is failing catastrophically and you need to get more insight into the structure of your file.

+
+

Guessing types

+

readr uses a heuristic to figure out the column types. For each column, it pulls the values of 1,0002 rows spaced evenly from the first row to the last, ignoring missing values. It then works through the following questions:

+
    +
  • Does it contain only F, T, FALSE, or TRUE (ignoring case)? If so, it’s a logical.
    • +
  • Does it contain only numbers (e.g., 1, -4.5, 5e6, Inf)? If so, it’s a number.
    • +
  • Does it match the ISO8601 standard? If so, it’s a date or date-time. (We’ll return to date-times in more detail in ?@sec-creating-datetimes).
    • +
  • Otherwise, it must be a string.
    • +
+

You can see that behavior in action in this simple example:

+
+
read_csv("
+  logical,numeric,date,string
+  TRUE,1,2021-01-15,abc
+  false,4.5,2021-02-15,def
+  T,Inf,2021-02-16,ghi
+")
+#> # A tibble: 3 × 4
+#>   logical numeric date       string
+#>   <lgl>     <dbl> <date>     <chr> 
+#> 1 TRUE        1   2021-01-15 abc   
+#> 2 FALSE       4.5 2021-02-15 def   
+#> 3 TRUE      Inf   2021-02-16 ghi
+
+

This heuristic works well if you have a clean dataset, but in real life, you’ll encounter a selection of weird and beautiful failures.

+
+
+

Missing values, column types, and problems

+

The most common way column detection fails is that a column contains unexpected values, and you get a character column instead of a more specific type. One of the most common causes for this is a missing value, recorded using something other than the NA that readr expects.

+

Take this simple 1 column CSV file as an example:

+
+
simple_csv <- "
+  x
+  10
+  .
+  20
+  30"
+
+

If we read it without any additional arguments, x becomes a character column:

+
+
read_csv(simple_csv)
+#> # A tibble: 4 × 1
+#>   x    
+#>   <chr>
+#> 1 10   
+#> 2 .    
+#> 3 20   
+#> 4 30
+
+

In this very small case, you can easily see the missing value .. But what happens if you have thousands of rows with only a few missing values represented by .s sprinkled among them? One approach is to tell readr that x is a numeric column, and then see where it fails. You can do that with the col_types argument, which takes a named list where the names match the column names in the CSV file:

+
+
df <- read_csv(
+  simple_csv, 
+  col_types = list(x = col_double())
+)
+#> Warning: One or more parsing issues, call `problems()` on your data frame for
+#> details, e.g.:
+#>   dat <- vroom(...)
+#>   problems(dat)
+
+

Now read_csv() reports that there was a problem, and tells us we can find out more with problems():

+
+
problems(df)
+#> # A tibble: 1 × 5
+#>     row   col expected actual file                                           
+#>   <int> <int> <chr>    <chr>  <chr>                                          
+#> 1     3     1 a double .      /private/var/folders/9f/nn2jnl8n1lj1sk3y391hyd…
+
+

This tells us that there was a problem in row 3, col 1 where readr expected a double but got a .. That suggests this dataset uses . for missing values. So then we set na = ".", the automatic guessing succeeds, giving us the numeric column that we want:

+
+
read_csv(simple_csv, na = ".")
+#> # A tibble: 4 × 1
+#>       x
+#>   <dbl>
+#> 1    10
+#> 2    NA
+#> 3    20
+#> 4    30
+
+
+
+

Column types

+

readr provides a total of nine column types for you to use:

+
    +
  • col_logical() and col_double() read logicals and real numbers. They’re relatively rarely needed (except as above), since readr will usually guess them for you.
    • +
  • col_integer() reads integers. We seldom distinguish integers and doubles in this book because they’re functionally equivalent, but reading integers explicitly can occasionally be useful because they occupy half the memory of doubles.
    • +
  • col_character() reads strings. This can be useful to specify explicitly when you have a column that is a numeric identifier, i.e., long series of digits that identifies an object but doesn’t make sense to apply mathematical operations to. Examples include phone numbers, social security numbers, credit card numbers, etc.
    • +
  • col_factor(), col_date(), and col_datetime() create factors, dates, and date-times respectively; you’ll learn more about those when we get to those data types in ?@sec-factors and ?@sec-dates-and-times.
    • +
  • col_number() is a permissive numeric parser that will ignore non-numeric components, and is particularly useful for currencies. You’ll learn more about it in ?@sec-numbers.
    • +
  • col_skip() skips a column so it’s not included in the result, which can be useful for speeding up reading the data if you have a large CSV file and you only want to use some of the columns.
    • +
+

It’s also possible to override the default column by switching from list() to cols() and specifying .default:

+
+
another_csv <- "
+x,y,z
+1,2,3"
+
+read_csv(
+  another_csv, 
+  col_types = cols(.default = col_character())
+)
+#> # A tibble: 1 × 3
+#>   x     y     z    
+#>   <chr> <chr> <chr>
+#> 1 1     2     3
+
+

Another useful helper is cols_only() which will read in only the columns you specify:

+
+
read_csv(
+  another_csv,
+  col_types = cols_only(x = col_character())
+)
+#> # A tibble: 1 × 1
+#>   x    
+#>   <chr>
+#> 1 1
+
+
+
+
+

Reading data from multiple files

+

Sometimes your data is split across multiple files instead of being contained in a single file. For example, you might have sales data for multiple months, with each month’s data in a separate file: 01-sales.csv for January, 02-sales.csv for February, and 03-sales.csv for March. With read_csv() you can read these data in at once and stack them on top of each other in a single data frame.

+
+
sales_files <- c("data/01-sales.csv", "data/02-sales.csv", "data/03-sales.csv")
+read_csv(sales_files, id = "file")
+#> # A tibble: 19 × 6
+#>   file              month    year brand  item     n
+#>   <chr>             <chr>   <dbl> <dbl> <dbl> <dbl>
+#> 1 data/01-sales.csv January  2019     1  1234     3
+#> 2 data/01-sales.csv January  2019     1  8721     9
+#> 3 data/01-sales.csv January  2019     1  1822     2
+#> 4 data/01-sales.csv January  2019     2  3333     1
+#> 5 data/01-sales.csv January  2019     2  2156     9
+#> 6 data/01-sales.csv January  2019     2  3987     6
+#> # ℹ 13 more rows
+
+

Once again, the code above will work if you have the CSV files in a data folder in your project. You can download these files from https://pos.it/r4ds-01-sales, https://pos.it/r4ds-02-sales, and https://pos.it/r4ds-03-sales or you can read them directly with:

+
+
sales_files <- c(
+  "https://pos.it/r4ds-01-sales",
+  "https://pos.it/r4ds-02-sales",
+  "https://pos.it/r4ds-03-sales"
+)
+read_csv(sales_files, id = "file")
+
+

The id argument adds a new column called file to the resulting data frame that identifies the file the data come from. This is especially helpful in circumstances where the files you’re reading in do not have an identifying column that can help you trace the observations back to their original sources.

+

If you have many files you want to read in, it can get cumbersome to write out their names as a list. Instead, you can use the base list.files() function to find the files for you by matching a pattern in the file names. You’ll learn more about these patterns in ?@sec-regular-expressions.

+
+
sales_files <- list.files("data", pattern = "sales\\.csv$", full.names = TRUE)
+sales_files
+#> [1] "data/01-sales.csv" "data/02-sales.csv" "data/03-sales.csv"
+
+
+
+

Writing to a file

+

readr also comes with two useful functions for writing data back to disk: write_csv() and write_tsv(). The most important arguments to these functions are x (the data frame to save) and file (the location to save it). You can also specify how missing values are written with na, and if you want to append to an existing file.

+
+
write_csv(students, "students.csv")
+
+

Now let’s read that csv file back in. Note that the variable type information that you just set up is lost when you save to CSV because you’re starting over with reading from a plain text file again:

+
+
students
+#> # A tibble: 6 × 5
+#>   student_id full_name        favourite_food     meal_plan             age
+#>        <dbl> <chr>            <chr>              <fct>               <dbl>
+#> 1          1 Sunil Huffmann   Strawberry yoghurt Lunch only              4
+#> 2          2 Barclay Lynn     French fries       Lunch only              5
+#> 3          3 Jayendra Lyne    <NA>               Breakfast and lunch     7
+#> 4          4 Leon Rossini     Anchovies          Lunch only             NA
+#> 5          5 Chidiegwu Dunkel Pizza              Breakfast and lunch     5
+#> 6          6 Güvenç Attila    Ice cream          Lunch only              6
+write_csv(students, "students-2.csv")
+read_csv("students-2.csv")
+#> # A tibble: 6 × 5
+#>   student_id full_name        favourite_food     meal_plan             age
+#>        <dbl> <chr>            <chr>              <chr>               <dbl>
+#> 1          1 Sunil Huffmann   Strawberry yoghurt Lunch only              4
+#> 2          2 Barclay Lynn     French fries       Lunch only              5
+#> 3          3 Jayendra Lyne    <NA>               Breakfast and lunch     7
+#> 4          4 Leon Rossini     Anchovies          Lunch only             NA
+#> 5          5 Chidiegwu Dunkel Pizza              Breakfast and lunch     5
+#> 6          6 Güvenç Attila    Ice cream          Lunch only              6
+
+

This makes CSVs a little unreliable for caching interim results—you need to recreate the column specification every time you load in. There are two main alternatives:

+
    +

  1. write_rds() and read_rds() are uniform wrappers around the base functions readRDS() and saveRDS(). These store data in R’s custom binary format called RDS. This means that when you reload the object, you are loading the exact same R object that you stored.

    +
    +
    write_rds(students, "students.rds")
+read_rds("students.rds")
+#> # A tibble: 6 × 5
+#>   student_id full_name        favourite_food     meal_plan             age
+#>        <dbl> <chr>            <chr>              <fct>               <dbl>
+#> 1          1 Sunil Huffmann   Strawberry yoghurt Lunch only              4
+#> 2          2 Barclay Lynn     French fries       Lunch only              5
+#> 3          3 Jayendra Lyne    <NA>               Breakfast and lunch     7
+#> 4          4 Leon Rossini     Anchovies          Lunch only             NA
+#> 5          5 Chidiegwu Dunkel Pizza              Breakfast and lunch     5
+#> 6          6 Güvenç Attila    Ice cream          Lunch only              6
    +
    2. +

  2. The arrow package allows you to read and write parquet files, a fast binary file format that can be shared across programming languages. We’ll return to arrow in more depth in ?@sec-arrow.

    +
    +
    library(arrow)
+write_parquet(students, "students.parquet")
+read_parquet("students.parquet")
+#> # A tibble: 6 × 5
+#>   student_id full_name        favourite_food     meal_plan             age
+#>        <dbl> <chr>            <chr>              <fct>               <dbl>
+#> 1          1 Sunil Huffmann   Strawberry yoghurt Lunch only              4
+#> 2          2 Barclay Lynn     French fries       Lunch only              5
+#> 3          3 Jayendra Lyne    NA                 Breakfast and lunch     7
+#> 4          4 Leon Rossini     Anchovies          Lunch only             NA
+#> 5          5 Chidiegwu Dunkel Pizza              Breakfast and lunch     5
+#> 6          6 Güvenç Attila    Ice cream          Lunch only              6
    +
    3. +
+

Parquet tends to be much faster than RDS and is usable outside of R, but does require the arrow package.

+
+
+

Data entry

+

Sometimes you’ll need to assemble a tibble “by hand” doing a little data entry in your R script. There are two useful functions to help you do this which differ in whether you layout the tibble by columns or by rows. tibble() works by column:

+
+
tibble(
+  x = c(1, 2, 5), 
+  y = c("h", "m", "g"),
+  z = c(0.08, 0.83, 0.60)
+)
+#> # A tibble: 3 × 3
+#>       x y         z
+#>   <dbl> <chr> <dbl>
+#> 1     1 h      0.08
+#> 2     2 m      0.83
+#> 3     5 g      0.6
+
+

Laying out the data by column can make it hard to see how the rows are related, so an alternative is tribble(), short for transposed tibble, which lets you lay out your data row by row. tribble() is customized for data entry in code: column headings start with ~ and entries are separated by commas. This makes it possible to lay out small amounts of data in an easy to read form:

+
+
tribble(
+  ~x, ~y, ~z,
+  1, "h", 0.08,
+  2, "m", 0.83,
+  5, "g", 0.60
+)
+#> # A tibble: 3 × 3
+#>       x y         z
+#>   <dbl> <chr> <dbl>
+#> 1     1 h      0.08
+#> 2     2 m      0.83
+#> 3     5 g      0.6
+
+
+
+

Summary

+

In this chapter, you’ve learned how to load CSV files with read_csv() and to do your own data entry with tibble() and tribble(). You’ve learned how csv files work, some of the problems you might encounter, and how to overcome them. We’ll come to data import a few times in this book: ?@sec-import-spreadsheets from Excel and Google Sheets, ?@sec-import-databases will show you how to load data from databases, ?@sec-arrow from parquet files, ?@sec-rectangling from JSON, and ?@sec-scraping from websites.

+

We’re just about at the end of this section of the book, but there’s one important last topic to cover: how to get help. So in the next chapter, you’ll learn some good places to look for help, how to create a reprex to maximize your chances of getting good help, and some general advice on keeping up with the world of R.

+ + +
+
+
+
+
    +

  1. The janitor package is not part of the tidyverse, but it offers handy functions for data cleaning and works well within data pipelines that use |>.↩︎

    2. +

  2. You can override the default of 1000 with the guess_max argument.↩︎

    3. +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/data-tidy.html b/docs/data-tidy.html new file mode 100644 index 000000000..09c11756b --- /dev/null +++ b/docs/data-tidy.html @@ -0,0 +1,1187 @@ + + + + + + + + + + +data-tidy + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + + + +
+

Data tidying

+
+

Introduction

+
+

“Happy families are all alike; every unhappy family is unhappy in its own way.”
+— Leo Tolstoy

+
+
+

“Tidy datasets are all alike, but every messy dataset is messy in its own way.”
+— Hadley Wickham

+
+

In this chapter, you will learn a consistent way to organize your data in R using a system called tidy data. Getting your data into this format requires some work up front, but that work pays off in the long term. Once you have tidy data and the tidy tools provided by packages in the tidyverse, you will spend much less time munging data from one representation to another, allowing you to spend more time on the data questions you care about.

+

In this chapter, you’ll first learn the definition of tidy data and see it applied to a simple toy dataset. Then we’ll dive into the primary tool you’ll use for tidying data: pivoting. Pivoting allows you to change the form of your data without changing any of the values.

+
+

Prerequisites

+

In this chapter, we’ll focus on tidyr, a package that provides a bunch of tools to help tidy up your messy datasets. tidyr is a member of the core tidyverse.

+
+
library(tidyverse)
+
+

From this chapter on, we’ll suppress the loading message from library(tidyverse).

+
+
+
+

Tidy data

+

You can represent the same underlying data in multiple ways. The example below shows the same data organized in three different ways. Each dataset shows the same values of four variables: country, year, population, and number of documented cases of TB (tuberculosis), but each dataset organizes the values in a different way.

+
+
table1
+#> # A tibble: 6 × 4
+#>   country      year  cases population
+#>   <chr>       <dbl>  <dbl>      <dbl>
+#> 1 Afghanistan  1999    745   19987071
+#> 2 Afghanistan  2000   2666   20595360
+#> 3 Brazil       1999  37737  172006362
+#> 4 Brazil       2000  80488  174504898
+#> 5 China        1999 212258 1272915272
+#> 6 China        2000 213766 1280428583
+
+table2
+#> # A tibble: 12 × 4
+#>   country      year type           count
+#>   <chr>       <dbl> <chr>          <dbl>
+#> 1 Afghanistan  1999 cases            745
+#> 2 Afghanistan  1999 population  19987071
+#> 3 Afghanistan  2000 cases           2666
+#> 4 Afghanistan  2000 population  20595360
+#> 5 Brazil       1999 cases          37737
+#> 6 Brazil       1999 population 172006362
+#> # ℹ 6 more rows
+
+table3
+#> # A tibble: 6 × 3
+#>   country      year rate             
+#>   <chr>       <dbl> <chr>            
+#> 1 Afghanistan  1999 745/19987071     
+#> 2 Afghanistan  2000 2666/20595360    
+#> 3 Brazil       1999 37737/172006362  
+#> 4 Brazil       2000 80488/174504898  
+#> 5 China        1999 212258/1272915272
+#> 6 China        2000 213766/1280428583
+
+

These are all representations of the same underlying data, but they are not equally easy to use. One of them, table1, will be much easier to work with inside the tidyverse because it’s tidy.

+

There are three interrelated rules that make a dataset tidy:

+
    +
  1. Each variable is a column; each column is a variable.
    2. +
  2. Each observation is a row; each row is an observation.
    3. +
  3. Each value is a cell; each cell is a single value.
    4. +
+

Figure 1 shows the rules visually.

+
+
+
+
+
+Three panels, each representing a tidy data frame. The first panel shows that each variable is a column. The second panel shows that each observation is a row. The third panel shows that each value is a cell. +
+
+Figure 1: The following three rules make a dataset tidy: variables are columns, observations are rows, and values are cells. +
+
+
+
+
+

Why ensure that your data is tidy? There are two main advantages:

+
    +

  1. There’s a general advantage to picking one consistent way of storing data. If you have a consistent data structure, it’s easier to learn the tools that work with it because they have an underlying uniformity.

    2. +

  2. There’s a specific advantage to placing variables in columns because it allows R’s vectorized nature to shine. As you learned in ?@sec-mutate and ?@sec-summarize, most built-in R functions work with vectors of values. That makes transforming tidy data feel particularly natural.

    3. +
+

dplyr, ggplot2, and all the other packages in the tidyverse are designed to work with tidy data. Here are a few small examples showing how you might work with table1.

+
+
# Compute rate per 10,000
+table1 |>
+  mutate(rate = cases / population * 10000)
+#> # A tibble: 6 × 5
+#>   country      year  cases population  rate
+#>   <chr>       <dbl>  <dbl>      <dbl> <dbl>
+#> 1 Afghanistan  1999    745   19987071 0.373
+#> 2 Afghanistan  2000   2666   20595360 1.29 
+#> 3 Brazil       1999  37737  172006362 2.19 
+#> 4 Brazil       2000  80488  174504898 4.61 
+#> 5 China        1999 212258 1272915272 1.67 
+#> 6 China        2000 213766 1280428583 1.67
+
+# Compute total cases per year
+table1 |> 
+  group_by(year) |> 
+  summarize(total_cases = sum(cases))
+#> # A tibble: 2 × 2
+#>    year total_cases
+#>   <dbl>       <dbl>
+#> 1  1999      250740
+#> 2  2000      296920
+
+# Visualize changes over time
+ggplot(table1, aes(x = year, y = cases)) +
+  geom_line(aes(group = country), color = "grey50") +
+  geom_point(aes(color = country, shape = country)) +
+  scale_x_continuous(breaks = c(1999, 2000)) # x-axis breaks at 1999 and 2000
+
+
+
+

This figure shows the number of cases in 1999 and 2000 for Afghanistan, Brazil, and China, with year on the x-axis and number of cases on the y-axis. Each point on the plot represents the number of cases in a given country in a given year. The points for each country are differentiated from others by color and shape and connected with a line, resulting in three, non-parallel, non-intersecting lines. The numbers of cases in China are highest for both 1999 and 2000, with values above 200,000 for both years. The number of cases in Brazil is approximately 40,000 in 1999 and approximately 75,000 in 2000. The numbers of cases in Afghanistan are lowest for both 1999 and 2000, with values that appear to be very close to 0 on this scale.

+
+
+
+
+
+

Exercises

+
    +

  1. For each of the sample tables, describe what each observation and each column represents.

    2. +

  2. Sketch out the process you’d use to calculate the rate for table2 and table3. You will need to perform four operations:

    +
      +
    1. Extract the number of TB cases per country per year.
      2. +
    2. Extract the matching population per country per year.
      3. +
    3. Divide cases by population, and multiply by 10000.
      4. +
    4. Store back in the appropriate place.
      5. +
    +

    You haven’t yet learned all the functions you’d need to actually perform these operations, but you should still be able to think through the transformations you’d need.

    3. +
+
+
+
+

Lengthening data

+

The principles of tidy data might seem so obvious that you wonder if you’ll ever encounter a dataset that isn’t tidy. Unfortunately, however, most real data is untidy. There are two main reasons:

+
    +

  1. Data is often organized to facilitate some goal other than analysis. For example, it’s common for data to be structured to make data entry, not analysis, easy.

    2. +

  2. Most people aren’t familiar with the principles of tidy data, and it’s hard to derive them yourself unless you spend a lot of time working with data.

    3. +
+

This means that most real analyses will require at least a little tidying. You’ll begin by figuring out what the underlying variables and observations are. Sometimes this is easy; other times you’ll need to consult with the people who originally generated the data. Next, you’ll pivot your data into a tidy form, with variables in the columns and observations in the rows.

+

tidyr provides two functions for pivoting data: pivot_longer() and pivot_wider(). We’ll first start with pivot_longer() because it’s the most common case. Let’s dive into some examples.

+
+

Data in column names

+

The billboard dataset records the billboard rank of songs in the year 2000:

+
+
billboard
+#> # A tibble: 317 × 79
+#>   artist       track               date.entered   wk1   wk2   wk3   wk4   wk5
+#>   <chr>        <chr>               <date>       <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 2 Pac        Baby Don't Cry (Ke… 2000-02-26      87    82    72    77    87
+#> 2 2Ge+her      The Hardest Part O… 2000-09-02      91    87    92    NA    NA
+#> 3 3 Doors Down Kryptonite          2000-04-08      81    70    68    67    66
+#> 4 3 Doors Down Loser               2000-10-21      76    76    72    69    67
+#> 5 504 Boyz     Wobble Wobble       2000-04-15      57    34    25    17    17
+#> 6 98^0         Give Me Just One N… 2000-08-19      51    39    34    26    26
+#> # ℹ 311 more rows
+#> # ℹ 71 more variables: wk6 <dbl>, wk7 <dbl>, wk8 <dbl>, wk9 <dbl>, …
+
+

In this dataset, each observation is a song. The first three columns (artist, track and date.entered) are variables that describe the song. Then we have 76 columns (wk1-wk76) that describe the rank of the song in each week1. Here, the column names are one variable (the week) and the cell values are another (the rank).

+

To tidy this data, we’ll use pivot_longer():

+
+
billboard |> 
+  pivot_longer(
+    cols = starts_with("wk"), 
+    names_to = "week", 
+    values_to = "rank"
+  )
+#> # A tibble: 24,092 × 5
+#>    artist track                   date.entered week   rank
+#>    <chr>  <chr>                   <date>       <chr> <dbl>
+#>  1 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk1      87
+#>  2 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk2      82
+#>  3 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk3      72
+#>  4 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk4      77
+#>  5 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk5      87
+#>  6 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk6      94
+#>  7 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk7      99
+#>  8 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk8      NA
+#>  9 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk9      NA
+#> 10 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk10     NA
+#> # ℹ 24,082 more rows
+
+

After the data, there are three key arguments:

+
    +
  • cols specifies which columns need to be pivoted, i.e. which columns aren’t variables. This argument uses the same syntax as select() so here we could use !c(artist, track, date.entered) or starts_with("wk").
    • +
  • names_to names the variable stored in the column names, we named that variable week.
    • +
  • values_to names the variable stored in the cell values, we named that variable rank.
    • +
+

Note that in the code "week" and "rank" are quoted because those are new variables we’re creating, they don’t yet exist in the data when we run the pivot_longer() call.

+

Now let’s turn our attention to the resulting, longer data frame. What happens if a song is in the top 100 for less than 76 weeks? Take 2 Pac’s “Baby Don’t Cry”, for example. The above output suggests that it was only in the top 100 for 7 weeks, and all the remaining weeks are filled in with missing values. These NAs don’t really represent unknown observations; they were forced to exist by the structure of the dataset2, so we can ask pivot_longer() to get rid of them by setting values_drop_na = TRUE:

+
+
billboard |> 
+  pivot_longer(
+    cols = starts_with("wk"), 
+    names_to = "week", 
+    values_to = "rank",
+    values_drop_na = TRUE
+  )
+#> # A tibble: 5,307 × 5
+#>   artist track                   date.entered week   rank
+#>   <chr>  <chr>                   <date>       <chr> <dbl>
+#> 1 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk1      87
+#> 2 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk2      82
+#> 3 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk3      72
+#> 4 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk4      77
+#> 5 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk5      87
+#> 6 2 Pac  Baby Don't Cry (Keep... 2000-02-26   wk6      94
+#> # ℹ 5,301 more rows
+
+

The number of rows is now much lower, indicating that many rows with NAs were dropped.

+

You might also wonder what happens if a song is in the top 100 for more than 76 weeks? We can’t tell from this data, but you might guess that additional columns wk77, wk78, … would be added to the dataset.

+

This data is now tidy, but we could make future computation a bit easier by converting values of week from character strings to numbers using mutate() and readr::parse_number(). parse_number() is a handy function that will extract the first number from a string, ignoring all other text.

+
+
billboard_longer <- billboard |> 
+  pivot_longer(
+    cols = starts_with("wk"), 
+    names_to = "week", 
+    values_to = "rank",
+    values_drop_na = TRUE
+  ) |> 
+  mutate(
+    week = parse_number(week)
+  )
+billboard_longer
+#> # A tibble: 5,307 × 5
+#>   artist track                   date.entered  week  rank
+#>   <chr>  <chr>                   <date>       <dbl> <dbl>
+#> 1 2 Pac  Baby Don't Cry (Keep... 2000-02-26       1    87
+#> 2 2 Pac  Baby Don't Cry (Keep... 2000-02-26       2    82
+#> 3 2 Pac  Baby Don't Cry (Keep... 2000-02-26       3    72
+#> 4 2 Pac  Baby Don't Cry (Keep... 2000-02-26       4    77
+#> 5 2 Pac  Baby Don't Cry (Keep... 2000-02-26       5    87
+#> 6 2 Pac  Baby Don't Cry (Keep... 2000-02-26       6    94
+#> # ℹ 5,301 more rows
+
+

Now that we have all the week numbers in one variable and all the rank values in another, we’re in a good position to visualize how song ranks vary over time. The code is shown below and the result is in Figure 2. We can see that very few songs stay in the top 100 for more than 20 weeks.

+
+
billboard_longer |> 
+  ggplot(aes(x = week, y = rank, group = track)) + 
+  geom_line(alpha = 0.25) + 
+  scale_y_reverse()
+
+
+
+
+A line plot with week on the x-axis and rank on the y-axis, where each line represents a song. Most songs appear to start at a high rank, rapidly accelerate to a low rank, and then decay again. There are surprisingly few tracks in the region when week is >20 and rank is >50. +
+
+Figure 2: A line plot showing how the rank of a song changes over time. +
+
+
+
+
+
+
+

How does pivoting work?

+

Now that you’ve seen how we can use pivoting to reshape our data, let’s take a little time to gain some intuition about what pivoting does to the data. Let’s start with a very simple dataset to make it easier to see what’s happening. Suppose we have three patients with ids A, B, and C, and we take two blood pressure measurements on each patient. We’ll create the data with tribble(), a handy function for constructing small tibbles by hand:

+
+
df <- tribble(
+  ~id,  ~bp1, ~bp2,
+   "A",  100,  120,
+   "B",  140,  115,
+   "C",  120,  125
+)
+
+

We want our new dataset to have three variables: id (already exists), measurement (the column names), and value (the cell values). To achieve this, we need to pivot df longer:

+
+
df |> 
+  pivot_longer(
+    cols = bp1:bp2,
+    names_to = "measurement",
+    values_to = "value"
+  )
+#> # A tibble: 6 × 3
+#>   id    measurement value
+#>   <chr> <chr>       <dbl>
+#> 1 A     bp1           100
+#> 2 A     bp2           120
+#> 3 B     bp1           140
+#> 4 B     bp2           115
+#> 5 C     bp1           120
+#> 6 C     bp2           125
+
+

How does the reshaping work? It’s easier to see if we think about it column by column. As shown in Figure 3, the values in a column that was already a variable in the original dataset (id) need to be repeated, once for each column that is pivoted.

+
+
+
+
+
+A diagram showing how `pivot_longer()` transforms a simple dataset, using color to highlight how the values in the `id` column ("A", "B", "C") are each repeated twice in the output because there are two columns being pivoted ("bp1" and "bp2"). +
+
+Figure 3: Columns that are already variables need to be repeated, once for each column that is pivoted. +
+
+
+
+
+

The column names become values in a new variable, whose name is defined by names_to, as shown in Figure 4. They need to be repeated once for each row in the original dataset.

+
+
+
+
+
+A diagram showing how `pivot_longer()` transforms a simple data set, using color to highlight how column names ("bp1" and "bp2") become the values in a new `measurement` column. They are repeated three times because there were three rows in the input. +
+
+Figure 4: The column names of pivoted columns become values in a new column. The values need to be repeated once for each row of the original dataset. +
+
+
+
+
+

The cell values also become values in a new variable, with a name defined by values_to. They are unwound row by row. Figure 5 illustrates the process.

+
+
+
+
+
+A diagram showing how `pivot_longer()` transforms data, using color to highlight how the cell values (blood pressure measurements) become the values in a new `value` column. They are unwound row-by-row, so the original rows (100,120), then (140,115), then (120,125), become a column running from 100 to 125. +
+
+Figure 5: The number of values is preserved (not repeated), but unwound row-by-row. +
+
+
+
+
+
+
+

Many variables in column names

+

A more challenging situation occurs when you have multiple pieces of information crammed into the column names, and you would like to store these in separate new variables. For example, take the who2 dataset, the source of table1 and friends that you saw above:

+
+
who2
+#> # A tibble: 7,240 × 58
+#>   country      year sp_m_014 sp_m_1524 sp_m_2534 sp_m_3544 sp_m_4554
+#>   <chr>       <dbl>    <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 Afghanistan  1980       NA        NA        NA        NA        NA
+#> 2 Afghanistan  1981       NA        NA        NA        NA        NA
+#> 3 Afghanistan  1982       NA        NA        NA        NA        NA
+#> 4 Afghanistan  1983       NA        NA        NA        NA        NA
+#> 5 Afghanistan  1984       NA        NA        NA        NA        NA
+#> 6 Afghanistan  1985       NA        NA        NA        NA        NA
+#> # ℹ 7,234 more rows
+#> # ℹ 51 more variables: sp_m_5564 <dbl>, sp_m_65 <dbl>, sp_f_014 <dbl>, …
+
+

This dataset, collected by the World Health Organisation, records information about tuberculosis diagnoses. There are two columns that are already variables and are easy to interpret: country and year. They are followed by 56 columns like sp_m_014, ep_m_4554, and rel_m_3544. If you stare at these columns for long enough, you’ll notice there’s a pattern. Each column name is made up of three pieces separated by _. The first piece, sp/rel/ep, describes the method used for the diagnosis, the second piece, m/f is the gender (coded as a binary variable in this dataset), and the third piece, 014/1524/2534/3544/4554/5564/65 is the age range (014 represents 0-14, for example).

+

So in this case we have six pieces of information recorded in who2: the country and the year (already columns); the method of diagnosis, the gender category, and the age range category (contained in the other column names); and the count of patients in that category (cell values). To organize these six pieces of information in six separate columns, we use pivot_longer() with a vector of column names for names_to and instructors for splitting the original variable names into pieces for names_sep as well as a column name for values_to:

+
+
who2 |> 
+  pivot_longer(
+    cols = !(country:year),
+    names_to = c("diagnosis", "gender", "age"), 
+    names_sep = "_",
+    values_to = "count"
+  )
+#> # A tibble: 405,440 × 6
+#>   country      year diagnosis gender age   count
+#>   <chr>       <dbl> <chr>     <chr>  <chr> <dbl>
+#> 1 Afghanistan  1980 sp        m      014      NA
+#> 2 Afghanistan  1980 sp        m      1524     NA
+#> 3 Afghanistan  1980 sp        m      2534     NA
+#> 4 Afghanistan  1980 sp        m      3544     NA
+#> 5 Afghanistan  1980 sp        m      4554     NA
+#> 6 Afghanistan  1980 sp        m      5564     NA
+#> # ℹ 405,434 more rows
+
+

An alternative to names_sep is names_pattern, which you can use to extract variables from more complicated naming scenarios, once you’ve learned about regular expressions in ?@sec-regular-expressions.

+

Conceptually, this is only a minor variation on the simpler case you’ve already seen. Figure 6 shows the basic idea: now, instead of the column names pivoting into a single column, they pivot into multiple columns. You can imagine this happening in two steps (first pivoting and then separating) but under the hood it happens in a single step because that’s faster.

+
+
+
+
+
+A diagram that uses color to illustrate how supplying `names_sep` and multiple `names_to` creates multiple variables in the output. The input has variable names "x_1" and "y_2" which are split up by "_" to create name and number columns in the output. This is is similar case with a single `names_to`, but what would have been a single output variable is now separated into multiple variables. +
+
+Figure 6: Pivoting columns with multiple pieces of information in the names means that each column name now fills in values in multiple output columns. +
+
+
+
+
+
+
+

Data and variable names in the column headers

+

The next step up in complexity is when the column names include a mix of variable values and variable names. For example, take the household dataset:

+
+
household
+#> # A tibble: 5 × 5
+#>   family dob_child1 dob_child2 name_child1 name_child2
+#>    <int> <date>     <date>     <chr>       <chr>      
+#> 1      1 1998-11-26 2000-01-29 Susan       Jose       
+#> 2      2 1996-06-22 NA         Mark        <NA>       
+#> 3      3 2002-07-11 2004-04-05 Sam         Seth       
+#> 4      4 2004-10-10 2009-08-27 Craig       Khai       
+#> 5      5 2000-12-05 2005-02-28 Parker      Gracie
+
+

This dataset contains data about five families, with the names and dates of birth of up to two children. The new challenge in this dataset is that the column names contain the names of two variables (dob, name) and the values of another (child, with values 1 or 2). To solve this problem we again need to supply a vector to names_to but this time we use the special ".value" sentinel; this isn’t the name of a variable but a unique value that tells pivot_longer() to do something different. This overrides the usual values_to argument to use the first component of the pivoted column name as a variable name in the output.

+
+
household |> 
+  pivot_longer(
+    cols = !family, 
+    names_to = c(".value", "child"), 
+    names_sep = "_", 
+    values_drop_na = TRUE
+  )
+#> # A tibble: 9 × 4
+#>   family child  dob        name 
+#>    <int> <chr>  <date>     <chr>
+#> 1      1 child1 1998-11-26 Susan
+#> 2      1 child2 2000-01-29 Jose 
+#> 3      2 child1 1996-06-22 Mark 
+#> 4      3 child1 2002-07-11 Sam  
+#> 5      3 child2 2004-04-05 Seth 
+#> 6      4 child1 2004-10-10 Craig
+#> # ℹ 3 more rows
+
+

We again use values_drop_na = TRUE, since the shape of the input forces the creation of explicit missing variables (e.g., for families with only one child).

+

Figure 7 illustrates the basic idea with a simpler example. When you use ".value" in names_to, the column names in the input contribute to both values and variable names in the output.

+
+
+
+
+
+A diagram that uses color to illustrate how the special ".value" sentinel works. The input has names "x_1", "x_2", "y_1", and "y_2", and we want to use the first component ("x", "y") as a variable name and the second ("1", "2") as the value for a new "num" column. +
+
+Figure 7: Pivoting with names_to = c(".value", "num") splits the column names into two components: the first part determines the output column name (x or y), and the second part determines the value of the num column. +
+
+
+
+
+
+
+
+

Widening data

+

So far we’ve used pivot_longer() to solve the common class of problems where values have ended up in column names. Next we’ll pivot (HA HA) to pivot_wider(), which makes datasets wider by increasing columns and reducing rows and helps when one observation is spread across multiple rows. This seems to arise less commonly in the wild, but it does seem to crop up a lot when dealing with governmental data.

+

We’ll start by looking at cms_patient_experience, a dataset from the Centers of Medicare and Medicaid services that collects data about patient experiences:

+
+
cms_patient_experience
+#> # A tibble: 500 × 5
+#>   org_pac_id org_nm                     measure_cd   measure_title   prf_rate
+#>   <chr>      <chr>                      <chr>        <chr>              <dbl>
+#> 1 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_1  CAHPS for MIPS…       63
+#> 2 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_2  CAHPS for MIPS…       87
+#> 3 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_3  CAHPS for MIPS…       86
+#> 4 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_5  CAHPS for MIPS…       57
+#> 5 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_8  CAHPS for MIPS…       85
+#> 6 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_12 CAHPS for MIPS…       24
+#> # ℹ 494 more rows
+
+

The core unit being studied is an organization, but each organization is spread across six rows, with one row for each measurement taken in the survey organization. We can see the complete set of values for measure_cd and measure_title by using distinct():

+
+
cms_patient_experience |> 
+  distinct(measure_cd, measure_title)
+#> # A tibble: 6 × 2
+#>   measure_cd   measure_title                                                 
+#>   <chr>        <chr>                                                         
+#> 1 CAHPS_GRP_1  CAHPS for MIPS SSM: Getting Timely Care, Appointments, and In…
+#> 2 CAHPS_GRP_2  CAHPS for MIPS SSM: How Well Providers Communicate            
+#> 3 CAHPS_GRP_3  CAHPS for MIPS SSM: Patient's Rating of Provider              
+#> 4 CAHPS_GRP_5  CAHPS for MIPS SSM: Health Promotion and Education            
+#> 5 CAHPS_GRP_8  CAHPS for MIPS SSM: Courteous and Helpful Office Staff        
+#> 6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources
+
+

Neither of these columns will make particularly great variable names: measure_cd doesn’t hint at the meaning of the variable and measure_title is a long sentence containing spaces. We’ll use measure_cd as the source for our new column names for now, but in a real analysis you might want to create your own variable names that are both short and meaningful.

+

pivot_wider() has the opposite interface to pivot_longer(): instead of choosing new column names, we need to provide the existing columns that define the values (values_from) and the column name (names_from):

+
+
cms_patient_experience |> 
+  pivot_wider(
+    names_from = measure_cd,
+    values_from = prf_rate
+  )
+#> # A tibble: 500 × 9
+#>   org_pac_id org_nm                   measure_title   CAHPS_GRP_1 CAHPS_GRP_2
+#>   <chr>      <chr>                    <chr>                 <dbl>       <dbl>
+#> 1 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS…          63          NA
+#> 2 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS…          NA          87
+#> 3 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS…          NA          NA
+#> 4 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS…          NA          NA
+#> 5 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS…          NA          NA
+#> 6 0446157747 USC CARE MEDICAL GROUP … CAHPS for MIPS…          NA          NA
+#> # ℹ 494 more rows
+#> # ℹ 4 more variables: CAHPS_GRP_3 <dbl>, CAHPS_GRP_5 <dbl>, …
+
+

The output doesn’t look quite right; we still seem to have multiple rows for each organization. That’s because, we also need to tell pivot_wider() which column or columns have values that uniquely identify each row; in this case those are the variables starting with "org":

+
+
cms_patient_experience |> 
+  pivot_wider(
+    id_cols = starts_with("org"),
+    names_from = measure_cd,
+    values_from = prf_rate
+  )
+#> # A tibble: 95 × 8
+#>   org_pac_id org_nm           CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5
+#>   <chr>      <chr>                  <dbl>       <dbl>       <dbl>       <dbl>
+#> 1 0446157747 USC CARE MEDICA…          63          87          86          57
+#> 2 0446162697 ASSOCIATION OF …          59          85          83          63
+#> 3 0547164295 BEAVER MEDICAL …          49          NA          75          44
+#> 4 0749333730 CAPE PHYSICIANS…          67          84          85          65
+#> 5 0840104360 ALLIANCE PHYSIC…          66          87          87          64
+#> 6 0840109864 REX HOSPITAL INC          73          87          84          67
+#> # ℹ 89 more rows
+#> # ℹ 2 more variables: CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>
+
+

This gives us the output that we’re looking for.

+
+

How does pivot_wider() work?

+

To understand how pivot_wider() works, let’s again start with a very simple dataset. This time we have two patients with ids A and B, we have three blood pressure measurements on patient A and two on patient B:

+
+
df <- tribble(
+  ~id, ~measurement, ~value,
+  "A",        "bp1",    100,
+  "B",        "bp1",    140,
+  "B",        "bp2",    115, 
+  "A",        "bp2",    120,
+  "A",        "bp3",    105
+)
+
+

We’ll take the values from the value column and the names from the measurement column:

+
+
df |> 
+  pivot_wider(
+    names_from = measurement,
+    values_from = value
+  )
+#> # A tibble: 2 × 4
+#>   id      bp1   bp2   bp3
+#>   <chr> <dbl> <dbl> <dbl>
+#> 1 A       100   120   105
+#> 2 B       140   115    NA
+
+

To begin the process pivot_wider() needs to first figure out what will go in the rows and columns. The new column names will be the unique values of measurement.

+
+
df |> 
+  distinct(measurement) |> 
+  pull()
+#> [1] "bp1" "bp2" "bp3"
+
+

By default, the rows in the output are determined by all the variables that aren’t going into the new names or values. These are called the id_cols. Here there is only one column, but in general there can be any number.

+
+
df |> 
+  select(-measurement, -value) |> 
+  distinct()
+#> # A tibble: 2 × 1
+#>   id   
+#>   <chr>
+#> 1 A    
+#> 2 B
+
+

pivot_wider() then combines these results to generate an empty data frame:

+
+
df |> 
+  select(-measurement, -value) |> 
+  distinct() |> 
+  mutate(x = NA, y = NA, z = NA)
+#> # A tibble: 2 × 4
+#>   id    x     y     z    
+#>   <chr> <lgl> <lgl> <lgl>
+#> 1 A     NA    NA    NA   
+#> 2 B     NA    NA    NA
+
+

It then fills in all the missing values using the data in the input. In this case, not every cell in the output has a corresponding value in the input as there’s no third blood pressure measurement for patient B, so that cell remains missing. We’ll come back to this idea that pivot_wider() can “make” missing values in ?@sec-missing-values.

+

You might also wonder what happens if there are multiple rows in the input that correspond to one cell in the output. The example below has two rows that correspond to id “A” and measurement “bp1”:

+
+
df <- tribble(
+  ~id, ~measurement, ~value,
+  "A",        "bp1",    100,
+  "A",        "bp1",    102,
+  "A",        "bp2",    120,
+  "B",        "bp1",    140, 
+  "B",        "bp2",    115
+)
+
+

If we attempt to pivot this we get an output that contains list-columns, which you’ll learn more about in ?@sec-rectangling:

+
+
df |>
+  pivot_wider(
+    names_from = measurement,
+    values_from = value
+  )
+#> Warning: Values from `value` are not uniquely identified; output will contain
+#> list-cols.
+#> • Use `values_fn = list` to suppress this warning.
+#> • Use `values_fn = {summary_fun}` to summarise duplicates.
+#> • Use the following dplyr code to identify duplicates.
+#>   {data} |>
+#>   dplyr::summarise(n = dplyr::n(), .by = c(id, measurement)) |>
+#>   dplyr::filter(n > 1L)
+#> # A tibble: 2 × 3
+#>   id    bp1       bp2      
+#>   <chr> <list>    <list>   
+#> 1 A     <dbl [2]> <dbl [1]>
+#> 2 B     <dbl [1]> <dbl [1]>
+
+

Since you don’t know how to work with this sort of data yet, you’ll want to follow the hint in the warning to figure out where the problem is:

+
+
df |> 
+  group_by(id, measurement) |> 
+  summarize(n = n(), .groups = "drop") |> 
+  filter(n > 1)
+#> # A tibble: 1 × 3
+#>   id    measurement     n
+#>   <chr> <chr>       <int>
+#> 1 A     bp1             2
+
+

It’s then up to you to figure out what’s gone wrong with your data and either repair the underlying damage or use your grouping and summarizing skills to ensure that each combination of row and column values only has a single row.

+
+
+
+

Summary

+

In this chapter you learned about tidy data: data that has variables in columns and observations in rows. Tidy data makes working in the tidyverse easier, because it’s a consistent structure understood by most functions, the main challenge is transforming the data from whatever structure you receive it in to a tidy format. To that end, you learned about pivot_longer() and pivot_wider() which allow you to tidy up many untidy datasets. The examples we presented here are a selection of those from vignette("pivot", package = "tidyr"), so if you encounter a problem that this chapter doesn’t help you with, that vignette is a good place to try next.

+

Another challenge is that, for a given dataset, it can be impossible to label the longer or the wider version as the “tidy” one. This is partly a reflection of our definition of tidy data, where we said tidy data has one variable in each column, but we didn’t actually define what a variable is (and it’s surprisingly hard to do so). It’s totally fine to be pragmatic and to say a variable is whatever makes your analysis easiest. So if you’re stuck figuring out how to do some computation, consider switching up the organisation of your data; don’t be afraid to untidy, transform, and re-tidy as needed!

+

If you enjoyed this chapter and want to learn more about the underlying theory, you can learn more about the history and theoretical underpinnings in the Tidy Data paper published in the Journal of Statistical Software.

+

Now that you’re writing a substantial amount of R code, it’s time to learn more about organizing your code into files and directories. In the next chapter, you’ll learn all about the advantages of scripts and projects, and some of the many tools that they provide to make your life easier.

+ + +
+
+
+
+
    +

  1. The song will be included as long as it was in the top 100 at some point in 2000, and is tracked for up to 72 weeks after it appears.↩︎

    2. +

  2. We’ll come back to this idea in ?@sec-missing-values.↩︎

    3. +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/data-transform.html b/docs/data-transform.html new file mode 100644 index 000000000..b3b9e0506 --- /dev/null +++ b/docs/data-transform.html @@ -0,0 +1,1469 @@ + + + + + + + + + + +data-transform + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + + + +
+

Data transformation

+
+

Introduction

+

Visualization is an important tool for generating insight, but it’s rare that you get the data in exactly the right form you need to make the graph you want. Often you’ll need to create some new variables or summaries to answer your questions with your data, or maybe you just want to rename the variables or reorder the observations to make the data a little easier to work with. You’ll learn how to do all that (and more!) in this chapter, which will introduce you to data transformation using the dplyr package and a new dataset on flights that departed from New York City in 2013.

+

The goal of this chapter is to give you an overview of all the key tools for transforming a data frame. We’ll start with functions that operate on rows and then columns of a data frame, then circle back to talk more about the pipe, an important tool that you use to combine verbs. We will then introduce the ability to work with groups. We will end the chapter with a case study that showcases these functions in action. In later chapters, we’ll return to the functions in more detail as we start to dig into specific types of data (e.g., numbers, strings, dates).

+
+

Prerequisites

+

In this chapter, we’ll focus on the dplyr package, another core member of the tidyverse. We’ll illustrate the key ideas using data from the nycflights13 package and use ggplot2 to help us understand the data.

+
+
library(nycflights13)
+library(tidyverse)
+#> ── Attaching core tidyverse packages ───────────────────── tidyverse 2.0.0 ──
+#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
+#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
+#> ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
+#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
+#> ✔ purrr     1.0.2     
+#> ── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
+#> ✖ dplyr::filter() masks stats::filter()
+#> ✖ dplyr::lag()    masks stats::lag()
+#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
+
+

Take careful note of the conflicts message that’s printed when you load the tidyverse. It tells you that dplyr overwrites some functions in base R. If you want to use the base version of these functions after loading dplyr, you’ll need to use their full names: stats::filter() and stats::lag(). So far, we’ve mostly ignored which package a function comes from because it doesn’t usually matter. However, knowing the package can help you find help and find related functions, so when we need to be precise about which package a function comes from, we’ll use the same syntax as R: packagename::functionname().

+
+
+

nycflights13

+

To explore the basic dplyr verbs, we will use nycflights13::flights. This dataset contains all 336,776 flights that departed from New York City in 2013. The data comes from the US Bureau of Transportation Statistics and is documented in ?flights.

+
+
flights
+#> # A tibble: 336,776 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 336,770 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

flights is a tibble, a special type of data frame used by the tidyverse to avoid some common gotchas. The most important difference between tibbles and data frames is the way tibbles print; they are designed for large datasets, so they only show the first few rows and only the columns that fit on one screen. There are a few options to see everything. If you’re using RStudio, the most convenient is probably View(flights), which opens an interactive, scrollable, and filterable view. Otherwise you can use print(flights, width = Inf) to show all columns, or use glimpse():

+
+
glimpse(flights)
+#> Rows: 336,776
+#> Columns: 19
+#> $ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013…
+#> $ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
+#> $ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
+#> $ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 55…
+#> $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 60…
+#> $ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2,…
+#> $ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 8…
+#> $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 8…
+#> $ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7,…
+#> $ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6"…
+#> $ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301…
+#> $ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N…
+#> $ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LG…
+#> $ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IA…
+#> $ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149…
+#> $ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 73…
+#> $ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6…
+#> $ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59…
+#> $ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-0…
+
+

In both views, the variable names are followed by abbreviations that tell you the type of each variable: <int> is short for integer, <dbl> is short for double (aka real numbers), <chr> for character (aka strings), and <dttm> for date-time. These are important because the operations you can perform on a column depend heavily on its “type.”

+
+
+

dplyr basics

+

You’re about to learn the primary dplyr verbs (functions), which will allow you to solve the vast majority of your data manipulation challenges. But before we discuss their individual differences, it’s worth stating what they have in common:

+
    +

  1. The first argument is always a data frame.

    2. +

  2. The subsequent arguments typically describe which columns to operate on using the variable names (without quotes).

    3. +

  3. The output is always a new data frame.

    4. +
+

Because each verb does one thing well, solving complex problems will usually require combining multiple verbs, and we’ll do so with the pipe, |>. We’ll discuss the pipe more in Section 1.4, but in brief, the pipe takes the thing on its left and passes it along to the function on its right so that x |> f(y) is equivalent to f(x, y), and x |> f(y) |> g(z) is equivalent to g(f(x, y), z). The easiest way to pronounce the pipe is “then”. That makes it possible to get a sense of the following code even though you haven’t yet learned the details:

+
+
flights |>
+  filter(dest == "IAH") |> 
+  group_by(year, month, day) |> 
+  summarize(
+    arr_delay = mean(arr_delay, na.rm = TRUE)
+  )
+
+

dplyr’s verbs are organized into four groups based on what they operate on: rows, columns, groups, or tables. In the following sections, you’ll learn the most important verbs for rows, columns, and groups. Then, we’ll return to the join verbs that work on tables in ?@sec-joins. Let’s dive in!

+
+
+
+

Rows

+

The most important verbs that operate on rows of a dataset are filter(), which changes which rows are present without changing their order, and arrange(), which changes the order of the rows without changing which are present. Both functions only affect the rows, and the columns are left unchanged. We’ll also discuss distinct() which finds rows with unique values. Unlike arrange() and filter() it can also optionally modify the columns.

+
+

filter()

+

filter() allows you to keep rows based on the values of the columns1. The first argument is the data frame. The second and subsequent arguments are the conditions that must be true to keep the row. For example, we could find all flights that departed more than 120 minutes (two hours) late:

+
+
flights |> 
+  filter(dep_delay > 120)
+#> # A tibble: 9,723 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      848           1835       853     1001           1950
+#> 2  2013     1     1      957            733       144     1056            853
+#> 3  2013     1     1     1114            900       134     1447           1222
+#> 4  2013     1     1     1540           1338       122     2020           1825
+#> 5  2013     1     1     1815           1325       290     2120           1542
+#> 6  2013     1     1     1842           1422       260     1958           1535
+#> # ℹ 9,717 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

As well as > (greater than), you can use >= (greater than or equal to), < (less than), <= (less than or equal to), == (equal to), and != (not equal to). You can also combine conditions with & or , to indicate “and” (check for both conditions) or with | to indicate “or” (check for either condition):

+
+
# Flights that departed on January 1
+flights |> 
+  filter(month == 1 & day == 1)
+#> # A tibble: 842 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 836 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+# Flights that departed in January or February
+flights |> 
+  filter(month == 1 | month == 2)
+#> # A tibble: 51,955 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 51,949 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

There’s a useful shortcut when you’re combining | and ==: %in%. It keeps rows where the variable equals one of the values on the right:

+
+
# A shorter way to select flights that departed in January or February
+flights |> 
+  filter(month %in% c(1, 2))
+#> # A tibble: 51,955 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 51,949 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

We’ll come back to these comparisons and logical operators in more detail in ?@sec-logicals.

+

When you run filter() dplyr executes the filtering operation, creating a new data frame, and then prints it. It doesn’t modify the existing flights dataset because dplyr functions never modify their inputs. To save the result, you need to use the assignment operator, <-:

+
+
jan1 <- flights |> 
+  filter(month == 1 & day == 1)
+
+
+
+

Common mistakes

+

When you’re starting out with R, the easiest mistake to make is to use = instead of == when testing for equality. filter() will let you know when this happens:

+
+
flights |> 
+  filter(month = 1)
+#> Error in `filter()`:
+#> ! We detected a named input.
+#> ℹ This usually means that you've used `=` instead of `==`.
+#> ℹ Did you mean `month == 1`?
+
+

Another mistakes is you write “or” statements like you would in English:

+
+
flights |> 
+  filter(month == 1 | 2)
+
+

This “works”, in the sense that it doesn’t throw an error, but it doesn’t do what you want because | first checks the condition month == 1 and then checks the condition 2, which is not a sensible condition to check. We’ll learn more about what’s happening here and why in ?@sec-order-operations-boolean.

+
+
+

arrange()

+

arrange() changes the order of the rows based on the value of the columns. It takes a data frame and a set of column names (or more complicated expressions) to order by. If you provide more than one column name, each additional column will be used to break ties in the values of the preceding columns. For example, the following code sorts by the departure time, which is spread over four columns. We get the earliest years first, then within a year, the earliest months, etc.

+
+
flights |> 
+  arrange(year, month, day, dep_time)
+#> # A tibble: 336,776 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 336,770 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

You can use desc() on a column inside of arrange() to re-order the data frame based on that column in descending (big-to-small) order. For example, this code orders flights from most to least delayed:

+
+
flights |> 
+  arrange(desc(dep_delay))
+#> # A tibble: 336,776 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     9      641            900      1301     1242           1530
+#> 2  2013     6    15     1432           1935      1137     1607           2120
+#> 3  2013     1    10     1121           1635      1126     1239           1810
+#> 4  2013     9    20     1139           1845      1014     1457           2210
+#> 5  2013     7    22      845           1600      1005     1044           1815
+#> 6  2013     4    10     1100           1900       960     1342           2211
+#> # ℹ 336,770 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

Note that the number of rows has not changed – we’re only arranging the data, we’re not filtering it.

+
+
+

distinct()

+

distinct() finds all the unique rows in a dataset, so technically, it primarily operates on the rows. Most of the time, however, you’ll want the distinct combination of some variables, so you can also optionally supply column names:

+
+
# Remove duplicate rows, if any
+flights |> 
+  distinct()
+#> # A tibble: 336,776 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 336,770 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+# Find all unique origin and destination pairs
+flights |> 
+  distinct(origin, dest)
+#> # A tibble: 224 × 2
+#>   origin dest 
+#>   <chr>  <chr>
+#> 1 EWR    IAH  
+#> 2 LGA    IAH  
+#> 3 JFK    MIA  
+#> 4 JFK    BQN  
+#> 5 LGA    ATL  
+#> 6 EWR    ORD  
+#> # ℹ 218 more rows
+
+

Alternatively, if you want to keep other columns when filtering for unique rows, you can use the .keep_all = TRUE option.

+
+
flights |> 
+  distinct(origin, dest, .keep_all = TRUE)
+#> # A tibble: 224 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 218 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

It’s not a coincidence that all of these distinct flights are on January 1: distinct() will find the first occurrence of a unique row in the dataset and discard the rest.

+

If you want to find the number of occurrences instead, you’re better off swapping distinct() for count(). With the sort = TRUE argument, you can arrange them in descending order of the number of occurrences. You’ll learn more about count in ?@sec-counts.

+
+
flights |>
+  count(origin, dest, sort = TRUE)
+#> # A tibble: 224 × 3
+#>   origin dest      n
+#>   <chr>  <chr> <int>
+#> 1 JFK    LAX   11262
+#> 2 LGA    ATL   10263
+#> 3 LGA    ORD    8857
+#> 4 JFK    SFO    8204
+#> 5 LGA    CLT    6168
+#> 6 EWR    ORD    6100
+#> # ℹ 218 more rows
+
+
+
+

Exercises

+
    +

  1. In a single pipeline for each condition, find all flights that meet the condition:

    +
      +
    • Had an arrival delay of two or more hours
      • +
    • Flew to Houston (IAH or HOU)
      • +
    • Were operated by United, American, or Delta
      • +
    • Departed in summer (July, August, and September)
      • +
    • Arrived more than two hours late but didn’t leave late
      • +
    • Were delayed by at least an hour, but made up over 30 minutes in flight
      • +
    2. +

  2. Sort flights to find the flights with the longest departure delays. Find the flights that left earliest in the morning.

    3. +

  3. Sort flights to find the fastest flights. (Hint: Try including a math calculation inside of your function.)

    4. +

  4. Was there a flight on every day of 2013?

    5. +

  5. Which flights traveled the farthest distance? Which traveled the least distance?

    6. +

  6. Does it matter what order you used filter() and arrange() if you’re using both? Why/why not? Think about the results and how much work the functions would have to do.

    7. +
+
+
+
+

Columns

+

There are four important verbs that affect the columns without changing the rows: mutate() creates new columns that are derived from the existing columns, select() changes which columns are present, rename() changes the names of the columns, and relocate() changes the positions of the columns.

+
+

mutate()

+

The job of mutate() is to add new columns that are calculated from the existing columns. In the transform chapters, you’ll learn a large set of functions that you can use to manipulate different types of variables. For now, we’ll stick with basic algebra, which allows us to compute the gain, how much time a delayed flight made up in the air, and the speed in miles per hour:

+
+
flights |> 
+  mutate(
+    gain = dep_delay - arr_delay,
+    speed = distance / air_time * 60
+  )
+#> # A tibble: 336,776 × 21
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 336,770 more rows
+#> # ℹ 13 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

By default, mutate() adds new columns on the right-hand side of your dataset, which makes it difficult to see what’s happening here. We can use the .before argument to instead add the variables to the left-hand side2:

+
+
flights |> 
+  mutate(
+    gain = dep_delay - arr_delay,
+    speed = distance / air_time * 60,
+    .before = 1
+  )
+#> # A tibble: 336,776 × 21
+#>    gain speed  year month   day dep_time sched_dep_time dep_delay arr_time
+#>   <dbl> <dbl> <int> <int> <int>    <int>          <int>     <dbl>    <int>
+#> 1    -9  370.  2013     1     1      517            515         2      830
+#> 2   -16  374.  2013     1     1      533            529         4      850
+#> 3   -31  408.  2013     1     1      542            540         2      923
+#> 4    17  517.  2013     1     1      544            545        -1     1004
+#> 5    19  394.  2013     1     1      554            600        -6      812
+#> 6   -16  288.  2013     1     1      554            558        -4      740
+#> # ℹ 336,770 more rows
+#> # ℹ 12 more variables: sched_arr_time <int>, arr_delay <dbl>, …
+
+

The . indicates that .before is an argument to the function, not the name of a third new variable we are creating. You can also use .after to add after a variable, and in both .before and .after you can use the variable name instead of a position. For example, we could add the new variables after day:

+
+
flights |> 
+  mutate(
+    gain = dep_delay - arr_delay,
+    speed = distance / air_time * 60,
+    .after = day
+  )
+
+

Alternatively, you can control which variables are kept with the .keep argument. A particularly useful argument is "used" which specifies that we only keep the columns that were involved or created in the mutate() step. For example, the following output will contain only the variables dep_delay, arr_delay, air_time, gain, hours, and gain_per_hour.

+
+
flights |> 
+  mutate(
+    gain = dep_delay - arr_delay,
+    hours = air_time / 60,
+    gain_per_hour = gain / hours,
+    .keep = "used"
+  )
+
+

Note that since we haven’t assigned the result of the above computation back to flights, the new variables gain, hours, and gain_per_hour will only be printed but will not be stored in a data frame. And if we want them to be available in a data frame for future use, we should think carefully about whether we want the result to be assigned back to flights, overwriting the original data frame with many more variables, or to a new object. Often, the right answer is a new object that is named informatively to indicate its contents, e.g., delay_gain, but you might also have good reasons for overwriting flights.

+
+
+

select()

+

It’s not uncommon to get datasets with hundreds or even thousands of variables. In this situation, the first challenge is often just focusing on the variables you’re interested in. select() allows you to rapidly zoom in on a useful subset using operations based on the names of the variables:

+
    +

  • Select columns by name:

    +
    +
    flights |> 
+  select(year, month, day)
    +
    • +

  • Select all columns between year and day (inclusive):

    +
    +
    flights |> 
+  select(year:day)
    +
    • +

  • Select all columns except those from year to day (inclusive):

    +
    +
    flights |> 
+  select(!year:day)
    +
    +

    Historically this operation was done with - instead of !, so you’re likely to see that in the wild. These two operators serve the same purpose but with subtle differences in behavior. We recommend using ! because it reads as “not” and combines well with & and |.

    • +

  • Select all columns that are characters:

    +
    +
    flights |> 
+  select(where(is.character))
    +
    • +
+

There are a number of helper functions you can use within select():

+
    +
  • starts_with("abc"): matches names that begin with “abc”.
    • +
  • ends_with("xyz"): matches names that end with “xyz”.
    • +
  • contains("ijk"): matches names that contain “ijk”.
    • +
  • num_range("x", 1:3): matches x1, x2 and x3.
    • +
+

See ?select for more details. Once you know regular expressions (the topic of ?@sec-regular-expressions) you’ll also be able to use matches() to select variables that match a pattern.

+

You can rename variables as you select() them by using =. The new name appears on the left-hand side of the =, and the old variable appears on the right-hand side:

+
+
flights |> 
+  select(tail_num = tailnum)
+#> # A tibble: 336,776 × 1
+#>   tail_num
+#>   <chr>   
+#> 1 N14228  
+#> 2 N24211  
+#> 3 N619AA  
+#> 4 N804JB  
+#> 5 N668DN  
+#> 6 N39463  
+#> # ℹ 336,770 more rows
+
+
+
+

rename()

+

If you want to keep all the existing variables and just want to rename a few, you can use rename() instead of select():

+
+
flights |> 
+  rename(tail_num = tailnum)
+#> # A tibble: 336,776 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 336,770 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

If you have a bunch of inconsistently named columns and it would be painful to fix them all by hand, check out janitor::clean_names() which provides some useful automated cleaning.

+
+
+

relocate()

+

Use relocate() to move variables around. You might want to collect related variables together or move important variables to the front. By default relocate() moves variables to the front:

+
+
flights |> 
+  relocate(time_hour, air_time)
+#> # A tibble: 336,776 × 19
+#>   time_hour           air_time  year month   day dep_time sched_dep_time
+#>   <dttm>                 <dbl> <int> <int> <int>    <int>          <int>
+#> 1 2013-01-01 05:00:00      227  2013     1     1      517            515
+#> 2 2013-01-01 05:00:00      227  2013     1     1      533            529
+#> 3 2013-01-01 05:00:00      160  2013     1     1      542            540
+#> 4 2013-01-01 05:00:00      183  2013     1     1      544            545
+#> 5 2013-01-01 06:00:00      116  2013     1     1      554            600
+#> 6 2013-01-01 05:00:00      150  2013     1     1      554            558
+#> # ℹ 336,770 more rows
+#> # ℹ 12 more variables: dep_delay <dbl>, arr_time <int>, …
+
+

You can also specify where to put them using the .before and .after arguments, just like in mutate():

+
+
flights |> 
+  relocate(year:dep_time, .after = time_hour)
+flights |> 
+  relocate(starts_with("arr"), .before = dep_time)
+
+
+
+

Exercises

+
    +

  1. Compare dep_time, sched_dep_time, and dep_delay. How would you expect those three numbers to be related?

    2. +

  2. Brainstorm as many ways as possible to select dep_time, dep_delay, arr_time, and arr_delay from flights.

    3. +

  3. What happens if you specify the name of the same variable multiple times in a select() call?

    4. +

  4. What does the any_of() function do? Why might it be helpful in conjunction with this vector?

    +
    +
    variables <- c("year", "month", "day", "dep_delay", "arr_delay")
    +
    5. +

  5. Does the result of running the following code surprise you? How do the select helpers deal with upper and lower case by default? How can you change that default?

    +
    +
    flights |> select(contains("TIME"))
    +
    6. +

  6. Rename air_time to air_time_min to indicate units of measurement and move it to the beginning of the data frame.

    7. +

  7. Why doesn’t the following work, and what does the error mean?

    +
    +
    flights |> 
+  select(tailnum) |> 
+  arrange(arr_delay)
+#> Error in `arrange()`:
+#> ℹ In argument: `..1 = arr_delay`.
+#> Caused by error:
+#> ! オブジェクト 'arr_delay' がありません
    +
    8. +
+
+
+
+

The pipe

+

We’ve shown you simple examples of the pipe above, but its real power arises when you start to combine multiple verbs. For example, imagine that you wanted to find the fastest flights to Houston’s IAH airport: you need to combine filter(), mutate(), select(), and arrange():

+
+
flights |> 
+  filter(dest == "IAH") |> 
+  mutate(speed = distance / air_time * 60) |> 
+  select(year:day, dep_time, carrier, flight, speed) |> 
+  arrange(desc(speed))
+#> # A tibble: 7,198 × 7
+#>    year month   day dep_time carrier flight speed
+#>   <int> <int> <int>    <int> <chr>    <int> <dbl>
+#> 1  2013     7     9      707 UA         226  522.
+#> 2  2013     8    27     1850 UA        1128  521.
+#> 3  2013     8    28      902 UA        1711  519.
+#> 4  2013     8    28     2122 UA        1022  519.
+#> 5  2013     6    11     1628 UA        1178  515.
+#> 6  2013     8    27     1017 UA         333  515.
+#> # ℹ 7,192 more rows
+
+

Even though this pipeline has four steps, it’s easy to skim because the verbs come at the start of each line: start with the flights data, then filter, then mutate, then select, then arrange.

+

What would happen if we didn’t have the pipe? We could nest each function call inside the previous call:

+
+
arrange(
+  select(
+    mutate(
+      filter(
+        flights, 
+        dest == "IAH"
+      ),
+      speed = distance / air_time * 60
+    ),
+    year:day, dep_time, carrier, flight, speed
+  ),
+  desc(speed)
+)
+
+

Or we could use a bunch of intermediate objects:

+
+
flights1 <- filter(flights, dest == "IAH")
+flights2 <- mutate(flights1, speed = distance / air_time * 60)
+flights3 <- select(flights2, year:day, dep_time, carrier, flight, speed)
+arrange(flights3, desc(speed))
+
+

While both forms have their time and place, the pipe generally produces data analysis code that is easier to write and read.

+

To add the pipe to your code, we recommend using the built-in keyboard shortcut Ctrl/Cmd + Shift + M. You’ll need to make one change to your RStudio options to use |> instead of %>% as shown in Figure 1; more on %>% shortly.

+
+
+
+
+
+Screenshot showing the "Use native pipe operator" option which can be found on the "Editing" panel of the "Code" options. +
+
+Figure 1: To insert |>, make sure the “Use native pipe operator” option is checked. +
+
+
+
+
+
+
+
+ +
+
+magrittr +
+
+
+

If you’ve been using the tidyverse for a while, you might be familiar with the %>% pipe provided by the magrittr package. The magrittr package is included in the core tidyverse, so you can use %>% whenever you load the tidyverse:

+
+
library(tidyverse)
+
+mtcars %>% 
+  group_by(cyl) %>%
+  summarize(n = n())
+
+

For simple cases, |> and %>% behave identically. So why do we recommend the base pipe? Firstly, because it’s part of base R, it’s always available for you to use, even when you’re not using the tidyverse. Secondly, |> is quite a bit simpler than %>%: in the time between the invention of %>% in 2014 and the inclusion of |> in R 4.1.0 in 2021, we gained a better understanding of the pipe. This allowed the base implementation to jettison infrequently used and less important features.

+
+
+
+
+

Groups

+

So far you’ve learned about functions that work with rows and columns. dplyr gets even more powerful when you add in the ability to work with groups. In this section, we’ll focus on the most important functions: group_by(), summarize(), and the slice family of functions.

+
+

group_by()

+

Use group_by() to divide your dataset into groups meaningful for your analysis:

+
+
flights |> 
+  group_by(month)
+#> # A tibble: 336,776 × 19
+#> # Groups:   month [12]
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 336,770 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

group_by() doesn’t change the data but, if you look closely at the output, you’ll notice that the output indicates that it is “grouped by” month (Groups: month [12]). This means subsequent operations will now work “by month”. group_by() adds this grouped feature (referred to as class) to the data frame, which changes the behavior of the subsequent verbs applied to the data.

+
+
+

summarize()

+

The most important grouped operation is a summary, which, if being used to calculate a single summary statistic, reduces the data frame to have a single row for each group. In dplyr, this operation is performed by summarize()3, as shown by the following example, which computes the average departure delay by month:

+
+
flights |> 
+  group_by(month) |> 
+  summarize(
+    avg_delay = mean(dep_delay)
+  )
+#> # A tibble: 12 × 2
+#>   month avg_delay
+#>   <int>     <dbl>
+#> 1     1        NA
+#> 2     2        NA
+#> 3     3        NA
+#> 4     4        NA
+#> 5     5        NA
+#> 6     6        NA
+#> # ℹ 6 more rows
+
+

Uh-oh! Something has gone wrong, and all of our results are NAs (pronounced “N-A”), R’s symbol for missing value. This happened because some of the observed flights had missing data in the delay column, and so when we calculated the mean including those values, we got an NA result. We’ll come back to discuss missing values in detail in ?@sec-missing-values, but for now, we’ll tell the mean() function to ignore all missing values by setting the argument na.rm to TRUE:

+
+
flights |> 
+  group_by(month) |> 
+  summarize(
+    avg_delay = mean(dep_delay, na.rm = TRUE)
+  )
+#> # A tibble: 12 × 2
+#>   month avg_delay
+#>   <int>     <dbl>
+#> 1     1      10.0
+#> 2     2      10.8
+#> 3     3      13.2
+#> 4     4      13.9
+#> 5     5      13.0
+#> 6     6      20.8
+#> # ℹ 6 more rows
+
+

You can create any number of summaries in a single call to summarize(). You’ll learn various useful summaries in the upcoming chapters, but one very useful summary is n(), which returns the number of rows in each group:

+
+
flights |> 
+  group_by(month) |> 
+  summarize(
+    avg_delay = mean(dep_delay, na.rm = TRUE), 
+    n = n()
+  )
+#> # A tibble: 12 × 3
+#>   month avg_delay     n
+#>   <int>     <dbl> <int>
+#> 1     1      10.0 27004
+#> 2     2      10.8 24951
+#> 3     3      13.2 28834
+#> 4     4      13.9 28330
+#> 5     5      13.0 28796
+#> 6     6      20.8 28243
+#> # ℹ 6 more rows
+
+

Means and counts can get you a surprisingly long way in data science!

+
+
+

The slice_ functions

+

There are five handy functions that allow you to extract specific rows within each group:

+
    +
  • df |> slice_head(n = 1) takes the first row from each group.
    • +
  • df |> slice_tail(n = 1) takes the last row in each group.
    • +
  • df |> slice_min(x, n = 1) takes the row with the smallest value of column x.
    • +
  • df |> slice_max(x, n = 1) takes the row with the largest value of column x.
    • +
  • df |> slice_sample(n = 1) takes one random row.
    • +
+

You can vary n to select more than one row, or instead of n =, you can use prop = 0.1 to select (e.g.) 10% of the rows in each group. For example, the following code finds the flights that are most delayed upon arrival at each destination:

+
+
flights |> 
+  group_by(dest) |> 
+  slice_max(arr_delay, n = 1) |>
+  relocate(dest)
+#> # A tibble: 108 × 19
+#> # Groups:   dest [105]
+#>   dest   year month   day dep_time sched_dep_time dep_delay arr_time
+#>   <chr> <int> <int> <int>    <int>          <int>     <dbl>    <int>
+#> 1 ABQ    2013     7    22     2145           2007        98      132
+#> 2 ACK    2013     7    23     1139            800       219     1250
+#> 3 ALB    2013     1    25      123           2000       323      229
+#> 4 ANC    2013     8    17     1740           1625        75     2042
+#> 5 ATL    2013     7    22     2257            759       898      121
+#> 6 AUS    2013     7    10     2056           1505       351     2347
+#> # ℹ 102 more rows
+#> # ℹ 11 more variables: sched_arr_time <int>, arr_delay <dbl>, …
+
+

Note that there are 105 destinations but we get 108 rows here. What’s up? slice_min() and slice_max() keep tied values so n = 1 means give us all rows with the highest value. If you want exactly one row per group you can set with_ties = FALSE.

+

This is similar to computing the max delay with summarize(), but you get the whole corresponding row (or rows if there’s a tie) instead of the single summary statistic.

+
+
+

Grouping by multiple variables

+

You can create groups using more than one variable. For example, we could make a group for each date.

+
+
daily <- flights |>  
+  group_by(year, month, day)
+daily
+#> # A tibble: 336,776 × 19
+#> # Groups:   year, month, day [365]
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 336,770 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

When you summarize a tibble grouped by more than one variable, each summary peels off the last group. In hindsight, this wasn’t a great way to make this function work, but it’s difficult to change without breaking existing code. To make it obvious what’s happening, dplyr displays a message that tells you how you can change this behavior:

+
+
daily_flights <- daily |> 
+  summarize(n = n())
+#> `summarise()` has grouped output by 'year', 'month'. You can override using
+#> the `.groups` argument.
+
+

If you’re happy with this behavior, you can explicitly request it in order to suppress the message:

+
+
daily_flights <- daily |> 
+  summarize(
+    n = n(), 
+    .groups = "drop_last"
+  )
+
+

Alternatively, change the default behavior by setting a different value, e.g., "drop" to drop all grouping or "keep" to preserve the same groups.

+
+
+

Ungrouping

+

You might also want to remove grouping from a data frame without using summarize(). You can do this with ungroup().

+
+
daily |> 
+  ungroup()
+#> # A tibble: 336,776 × 19
+#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
+#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
+#> 1  2013     1     1      517            515         2      830            819
+#> 2  2013     1     1      533            529         4      850            830
+#> 3  2013     1     1      542            540         2      923            850
+#> 4  2013     1     1      544            545        -1     1004           1022
+#> 5  2013     1     1      554            600        -6      812            837
+#> 6  2013     1     1      554            558        -4      740            728
+#> # ℹ 336,770 more rows
+#> # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, …
+
+

Now let’s see what happens when you summarize an ungrouped data frame.

+
+
daily |> 
+  ungroup() |>
+  summarize(
+    avg_delay = mean(dep_delay, na.rm = TRUE), 
+    flights = n()
+  )
+#> # A tibble: 1 × 2
+#>   avg_delay flights
+#>       <dbl>   <int>
+#> 1      12.6  336776
+
+

You get a single row back because dplyr treats all the rows in an ungrouped data frame as belonging to one group.

+
+
+

.by

+

dplyr 1.1.0 includes a new, experimental, syntax for per-operation grouping, the .by argument. group_by() and ungroup() aren’t going away, but you can now also use the .by argument to group within a single operation:

+
+
flights |> 
+  summarize(
+    delay = mean(dep_delay, na.rm = TRUE), 
+    n = n(),
+    .by = month
+  )
+
+

Or if you want to group by multiple variables:

+
+
flights |> 
+  summarize(
+    delay = mean(dep_delay, na.rm = TRUE), 
+    n = n(),
+    .by = c(origin, dest)
+  )
+
+

.by works with all verbs and has the advantage that you don’t need to use the .groups argument to suppress the grouping message or ungroup() when you’re done.

+

We didn’t focus on this syntax in this chapter because it was very new when we wrote the book. We did want to mention it because we think it has a lot of promise and it’s likely to be quite popular. You can learn more about it in the dplyr 1.1.0 blog post.

+
+
+

Exercises

+
    +

  1. Which carrier has the worst average delays? Challenge: can you disentangle the effects of bad airports vs. bad carriers? Why/why not? (Hint: think about flights |> group_by(carrier, dest) |> summarize(n()))

    2. +

  2. Find the flights that are most delayed upon departure from each destination.

    3. +

  3. How do delays vary over the course of the day? Illustrate your answer with a plot.

    4. +

  4. What happens if you supply a negative n to slice_min() and friends?

    5. +

  5. Explain what count() does in terms of the dplyr verbs you just learned. What does the sort argument to count() do?

    6. +

  6. Suppose we have the following tiny data frame:

    +
    +
    df <- tibble(
+  x = 1:5,
+  y = c("a", "b", "a", "a", "b"),
+  z = c("K", "K", "L", "L", "K")
+)
    +
    +
      +

    1. Write down what you think the output will look like, then check if you were correct, and describe what group_by() does.

      +
      +
      df |>
+  group_by(y)
      +
      2. +

    2. Write down what you think the output will look like, then check if you were correct, and describe what arrange() does. Also, comment on how it’s different from the group_by() in part (a).

      +
      +
      df |>
+  arrange(y)
      +
      3. +

    3. Write down what you think the output will look like, then check if you were correct, and describe what the pipeline does.

      +
      +
      df |>
+  group_by(y) |>
+  summarize(mean_x = mean(x))
      +
      4. +

    4. Write down what you think the output will look like, then check if you were correct, and describe what the pipeline does. Then, comment on what the message says.

      +
      +
      df |>
+  group_by(y, z) |>
+  summarize(mean_x = mean(x))
      +
      5. +

    5. Write down what you think the output will look like, then check if you were correct, and describe what the pipeline does. How is the output different from the one in part (d)?

      +
      +
      df |>
+  group_by(y, z) |>
+  summarize(mean_x = mean(x), .groups = "drop")
      +
      6. +

    6. Write down what you think the outputs will look like, then check if you were correct, and describe what each pipeline does. How are the outputs of the two pipelines different?

      +
      +
      df |>
+  group_by(y, z) |>
+  summarize(mean_x = mean(x))
+
+df |>
+  group_by(y, z) |>
+  mutate(mean_x = mean(x))
      +
      7. +
    7. +
+
+
+
+

Case study: aggregates and sample size

+

Whenever you do any aggregation, it’s always a good idea to include a count (n()). That way, you can ensure that you’re not drawing conclusions based on very small amounts of data. We’ll demonstrate this with some baseball data from the Lahman package. Specifically, we will compare what proportion of times a player gets a hit (H) vs. the number of times they try to put the ball in play (AB):

+
+
batters <- Lahman::Batting |> 
+  group_by(playerID) |> 
+  summarize(
+    performance = sum(H, na.rm = TRUE) / sum(AB, na.rm = TRUE),
+    n = sum(AB, na.rm = TRUE)
+  )
+batters
+#> # A tibble: 20,730 × 3
+#>   playerID  performance     n
+#>   <chr>           <dbl> <int>
+#> 1 aardsda01      0          4
+#> 2 aaronha01      0.305  12364
+#> 3 aaronto01      0.229    944
+#> 4 aasedo01       0          5
+#> 5 abadan01       0.0952    21
+#> 6 abadfe01       0.111      9
+#> # ℹ 20,724 more rows
+
+

When we plot the skill of the batter (measured by the batting average, performance) against the number of opportunities to hit the ball (measured by times at bat, n), you see two patterns:

+
    +

  1. The variation in performance is larger among players with fewer at-bats. The shape of this plot is very characteristic: whenever you plot a mean (or other summary statistics) vs. group size, you’ll see that the variation decreases as the sample size increases4.

    2. +

  2. There’s a positive correlation between skill (performance) and opportunities to hit the ball (n) because teams want to give their best batters the most opportunities to hit the ball.

    3. +
+
+
batters |> 
+  filter(n > 100) |> 
+  ggplot(aes(x = n, y = performance)) +
+  geom_point(alpha = 1 / 10) + 
+  geom_smooth(se = FALSE)
+
+
+
+

A scatterplot of the number of batting performances vs. batting opportunities overlaid with a smoothed line. Average performance increases sharply from 0.2 at when n is ~100 to 0.25 when n is ~1000. Average performance continues to increase linearly at a much shallower slope, reaching 0.3 when n is ~12,000.

+
+
+
+
+

Note the handy pattern for combining ggplot2 and dplyr. You just have to remember to switch from |>, for dataset processing, to + for adding layers to your plot.

+

This also has important implications for ranking. If you naively sort on desc(performance), the people with the best batting averages are clearly the ones who tried to put the ball in play very few times and happened to get a hit, they’re not necessarily the most skilled players:

+
+
batters |> 
+  arrange(desc(performance))
+#> # A tibble: 20,730 × 3
+#>   playerID  performance     n
+#>   <chr>           <dbl> <int>
+#> 1 abramge01           1     1
+#> 2 alberan01           1     1
+#> 3 banisje01           1     1
+#> 4 bartocl01           1     1
+#> 5 bassdo01            1     1
+#> 6 birasst01           1     2
+#> # ℹ 20,724 more rows
+
+

You can find a good explanation of this problem and how to overcome it at http://varianceexplained.org/r/empirical_bayes_baseball/ and https://www.evanmiller.org/how-not-to-sort-by-average-rating.html.

+
+
+

Summary

+

In this chapter, you’ve learned the tools that dplyr provides for working with data frames. The tools are roughly grouped into three categories: those that manipulate the rows (like filter() and arrange()), those that manipulate the columns (like select() and mutate()) and those that manipulate groups (like group_by() and summarize()). In this chapter, we’ve focused on these “whole data frame” tools, but you haven’t yet learned much about what you can do with the individual variable. We’ll return to that in the Transform part of the book, where each chapter provides tools for a specific type of variable.

+

In the next chapter, we’ll pivot back to workflow to discuss the importance of code style and keeping your code well organized to make it easy for you and others to read and understand.

+ + +
+
+
+
+
    +

  1. Later, you’ll learn about the slice_*() family, which allows you to choose rows based on their positions.↩︎

    2. +

  2. Remember that in RStudio, the easiest way to see a dataset with many columns is View().↩︎

    3. +

  3. Or summarise(), if you prefer British English.↩︎

    4. +

  4. *cough* the law of large numbers *cough*.↩︎

    5. +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/data-visualize.html b/docs/data-visualize.html new file mode 100644 index 000000000..c10a5385c --- /dev/null +++ b/docs/data-visualize.html @@ -0,0 +1,1202 @@ + + + + + + + + + + +data-visualize + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + + + +
+

Data visualization

+
+

Introduction

+
+

“The simple graph has brought more information to the data analyst’s mind than any other device.” — John Tukey

+
+

R has several systems for making graphs, but ggplot2 is one of the most elegant and most versatile. ggplot2 implements the grammar of graphics, a coherent system for describing and building graphs. With ggplot2, you can do more and faster by learning one system and applying it in many places.

+

This chapter will teach you how to visualize your data using ggplot2. We will start by creating a simple scatterplot and use that to introduce aesthetic mappings and geometric objects – the fundamental building blocks of ggplot2. We will then walk you through visualizing distributions of single variables as well as visualizing relationships between two or more variables. We’ll finish off with saving your plots and troubleshooting tips.

+
+

Prerequisites

+

This chapter focuses on ggplot2, one of the core packages in the tidyverse. To access the datasets, help pages, and functions used in this chapter, load the tidyverse by running:

+
+
library(tidyverse)
+#> ── Attaching core tidyverse packages ───────────────────── tidyverse 2.0.0 ──
+#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
+#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
+#> ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
+#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
+#> ✔ purrr     1.0.2     
+#> ── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
+#> ✖ dplyr::filter() masks stats::filter()
+#> ✖ dplyr::lag()    masks stats::lag()
+#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
+
+

That one line of code loads the core tidyverse; the packages that you will use in almost every data analysis. It also tells you which functions from the tidyverse conflict with functions in base R (or from other packages you might have loaded)1.

+

If you run this code and get the error message there is no package called 'tidyverse', you’ll need to first install it, then run library() once again.

+
+
install.packages("tidyverse")
+library(tidyverse)
+
+

You only need to install a package once, but you need to load it every time you start a new session.

+

In addition to tidyverse, we will also use the palmerpenguins package, which includes the penguins dataset containing body measurements for penguins on three islands in the Palmer Archipelago, and the ggthemes package, which offers a colorblind safe color palette.

+
+
library(palmerpenguins)
+library(ggthemes)
+
+
+
+
+

First steps

+

Do penguins with longer flippers weigh more or less than penguins with shorter flippers? You probably already have an answer, but try to make your answer precise. What does the relationship between flipper length and body mass look like? Is it positive? Negative? Linear? Nonlinear? Does the relationship vary by the species of the penguin? How about by the island where the penguin lives? Let’s create visualizations that we can use to answer these questions.

+
+

The penguins data frame

+

You can test your answers to those questions with the penguins data frame found in palmerpenguins (a.k.a. palmerpenguins::penguins). A data frame is a rectangular collection of variables (in the columns) and observations (in the rows). penguins contains 344 observations collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER2.

+

To make the discussion easier, let’s define some terms:

+
    +

  • A variable is a quantity, quality, or property that you can measure.

    • +

  • A value is the state of a variable when you measure it. The value of a variable may change from measurement to measurement.

    • +

  • An observation is a set of measurements made under similar conditions (you usually make all of the measurements in an observation at the same time and on the same object). An observation will contain several values, each associated with a different variable. We’ll sometimes refer to an observation as a data point.

    • +

  • Tabular data is a set of values, each associated with a variable and an observation. Tabular data is tidy if each value is placed in its own “cell”, each variable in its own column, and each observation in its own row.

    • +
+

In this context, a variable refers to an attribute of all the penguins, and an observation refers to all the attributes of a single penguin.

+

Type the name of the data frame in the console and R will print a preview of its contents. Note that it says tibble on top of this preview. In the tidyverse, we use special data frames called tibbles that you will learn more about soon.

+
+
penguins
+#> # A tibble: 344 × 8
+#>   species island    bill_length_mm bill_depth_mm flipper_length_mm
+#>   <fct>   <fct>              <dbl>         <dbl>             <int>
+#> 1 Adelie  Torgersen           39.1          18.7               181
+#> 2 Adelie  Torgersen           39.5          17.4               186
+#> 3 Adelie  Torgersen           40.3          18                 195
+#> 4 Adelie  Torgersen           NA            NA                  NA
+#> 5 Adelie  Torgersen           36.7          19.3               193
+#> 6 Adelie  Torgersen           39.3          20.6               190
+#> # ℹ 338 more rows
+#> # ℹ 3 more variables: body_mass_g <int>, sex <fct>, year <int>
+
+

This data frame contains 8 columns. For an alternative view, where you can see all variables and the first few observations of each variable, use glimpse(). Or, if you’re in RStudio, run View(penguins) to open an interactive data viewer.

+
+
glimpse(penguins)
+#> Rows: 344
+#> Columns: 8
+#> $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, A…
+#> $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torge…
+#> $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.…
+#> $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.…
+#> $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, …
+#> $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 347…
+#> $ sex               <fct> male, female, female, NA, female, male, female, m…
+#> $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2…
+
+

Among the variables in penguins are:

+
    +

  1. species: a penguin’s species (Adelie, Chinstrap, or Gentoo).

    2. +

  2. flipper_length_mm: length of a penguin’s flipper, in millimeters.

    3. +

  3. body_mass_g: body mass of a penguin, in grams.

    4. +
+

To learn more about penguins, open its help page by running ?penguins.

+
+
+

Ultimate goal

+

Our ultimate goal in this chapter is to recreate the following visualization displaying the relationship between flipper lengths and body masses of these penguins, taking into consideration the species of the penguin.

+
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins, with a best fit line of the relationship between these two variables overlaid. The plot displays a positive, fairly linear, and relatively strong relationship between these two variables. Species (Adelie, Chinstrap, and Gentoo) are represented with different colors and shapes. The relationship between body mass and flipper length is roughly the same for these three species, and Gentoo penguins are larger than penguins from the other two species.

+
+
+
+
+
+
+

Creating a ggplot

+

Let’s recreate this plot step-by-step.

+

With ggplot2, you begin a plot with the function ggplot(), defining a plot object that you then add layers to. The first argument of ggplot() is the dataset to use in the graph and so ggplot(data = penguins) creates an empty graph that is primed to display the penguins data, but since we haven’t told it how to visualize it yet, for now it’s empty. This is not a very exciting plot, but you can think of it like an empty canvas you’ll paint the remaining layers of your plot onto.

+
+
ggplot(data = penguins)
+
+
+
+

A blank, gray plot area.

+
+
+
+
+

Next, we need to tell ggplot() how the information from our data will be visually represented. The mapping argument of the ggplot() function defines how variables in your dataset are mapped to visual properties (aesthetics) of your plot. The mapping argument is always defined in the aes() function, and the x and y arguments of aes() specify which variables to map to the x and y axes. For now, we will only map flipper length to the x aesthetic and body mass to the y aesthetic. ggplot2 looks for the mapped variables in the data argument, in this case, penguins.

+

The following plot shows the result of adding these mappings.

+
+
ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g)
+)
+
+
+
+

The plot shows flipper length on the x-axis, with values that range from 170 to 230, and body mass on the y-axis, with values that range from 3000 to 6000.

+
+
+
+
+

Our empty canvas now has more structure – it’s clear where flipper lengths will be displayed (on the x-axis) and where body masses will be displayed (on the y-axis). But the penguins themselves are not yet on the plot. This is because we have not yet articulated, in our code, how to represent the observations from our data frame on our plot.

+

To do so, we need to define a geom: the geometrical object that a plot uses to represent data. These geometric objects are made available in ggplot2 with functions that start with geom_. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms (geom_bar()), line charts use line geoms (geom_line()), boxplots use boxplot geoms (geom_boxplot()), scatterplots use point geoms (geom_point()), and so on.

+

The function geom_point() adds a layer of points to your plot, which creates a scatterplot. ggplot2 comes with many geom functions that each adds a different type of layer to a plot. You’ll learn a whole bunch of geoms throughout the book, particularly in ?@sec-layers.

+
+
ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+  geom_point()
+#> Warning: Removed 2 rows containing missing values or values outside the scale range
+#> (`geom_point()`).
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins. The plot displays a positive, linear, and relatively strong relationship between these two variables.

+
+
+
+
+

Now we have something that looks like what we might think of as a “scatterplot”. It doesn’t yet match our “ultimate goal” plot, but using this plot we can start answering the question that motivated our exploration: “What does the relationship between flipper length and body mass look like?” The relationship appears to be positive (as flipper length increases, so does body mass), fairly linear (the points are clustered around a line instead of a curve), and moderately strong (there isn’t too much scatter around such a line). Penguins with longer flippers are generally larger in terms of their body mass.

+

Before we add more layers to this plot, let’s pause for a moment and review the warning message we got:

+
+

Removed 2 rows containing missing values (geom_point()).

+
+

We’re seeing this message because there are two penguins in our dataset with missing body mass and/or flipper length values and ggplot2 has no way of representing them on the plot without both of these values. Like R, ggplot2 subscribes to the philosophy that missing values should never silently go missing. This type of warning is probably one of the most common types of warnings you will see when working with real data – missing values are a very common issue and you’ll learn more about them throughout the book, particularly in ?@sec-missing-values. For the remaining plots in this chapter we will suppress this warning so it’s not printed alongside every single plot we make.

+
+
+

Adding aesthetics and layers

+

Scatterplots are useful for displaying the relationship between two numerical variables, but it’s always a good idea to be skeptical of any apparent relationship between two variables and ask if there may be other variables that explain or change the nature of this apparent relationship. For example, does the relationship between flipper length and body mass differ by species? Let’s incorporate species into our plot and see if this reveals any additional insights into the apparent relationship between these variables. We will do this by representing species with different colored points.

+

To achieve this, will we need to modify the aesthetic or the geom? If you guessed “in the aesthetic mapping, inside of aes()”, you’re already getting the hang of creating data visualizations with ggplot2! And if not, don’t worry. Throughout the book you will make many more ggplots and have many more opportunities to check your intuition as you make them.

+
+
ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)
+) +
+  geom_point()
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins. The plot displays a positive, fairly linear, and relatively strong relationship between these two variables. Species (Adelie, Chinstrap, and Gentoo) are represented with different colors.

+
+
+
+
+

When a categorical variable is mapped to an aesthetic, ggplot2 will automatically assign a unique value of the aesthetic (here a unique color) to each unique level of the variable (each of the three species), a process known as scaling. ggplot2 will also add a legend that explains which values correspond to which levels.

+

Now let’s add one more layer: a smooth curve displaying the relationship between body mass and flipper length. Before you proceed, refer back to the code above, and think about how we can add this to our existing plot.

+

Since this is a new geometric object representing our data, we will add a new geom as a layer on top of our point geom: geom_smooth(). And we will specify that we want to draw the line of best fit based on a linear model with method = "lm".

+
+
ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)
+) +
+  geom_point() +
+  geom_smooth(method = "lm")
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins. Overlaid on the scatterplot are three smooth curves displaying the relationship between these variables for each species (Adelie, Chinstrap, and Gentoo). Different penguin species are plotted in different colors for the points and the smooth curves.

+
+
+
+
+

We have successfully added lines, but this plot doesn’t look like the plot from Section 1.2.2, which only has one line for the entire dataset as opposed to separate lines for each of the penguin species.

+

When aesthetic mappings are defined in ggplot(), at the global level, they’re passed down to each of the subsequent geom layers of the plot. However, each geom function in ggplot2 can also take a mapping argument, which allows for aesthetic mappings at the local level that are added to those inherited from the global level. Since we want points to be colored based on species but don’t want the lines to be separated out for them, we should specify color = species for geom_point() only.

+
+
ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+  geom_point(mapping = aes(color = species)) +
+  geom_smooth(method = "lm")
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins. Overlaid on the scatterplot is a single line of best fit displaying the relationship between these variables for each species (Adelie, Chinstrap, and Gentoo). Different penguin species are plotted in different colors for the points only.

+
+
+
+
+

Voila! We have something that looks very much like our ultimate goal, though it’s not yet perfect. We still need to use different shapes for each species of penguins and improve labels.

+

It’s generally not a good idea to represent information using only colors on a plot, as people perceive colors differently due to color blindness or other color vision differences. Therefore, in addition to color, we can also map species to the shape aesthetic.

+
+
ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+  geom_point(mapping = aes(color = species, shape = species)) +
+  geom_smooth(method = "lm")
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins. Overlaid on the scatterplot is a single line of best fit displaying the relationship between these variables for each species (Adelie, Chinstrap, and Gentoo). Different penguin species are plotted in different colors and shapes for the points only.

+
+
+
+
+

Note that the legend is automatically updated to reflect the different shapes of the points as well.

+

And finally, we can improve the labels of our plot using the labs() function in a new layer. Some of the arguments to labs() might be self explanatory: title adds a title and subtitle adds a subtitle to the plot. Other arguments match the aesthetic mappings, x is the x-axis label, y is the y-axis label, and color and shape define the label for the legend. In addition, we can improve the color palette to be colorblind safe with the scale_color_colorblind() function from the ggthemes package.

+
+
ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+  geom_point(aes(color = species, shape = species)) +
+  geom_smooth(method = "lm") +
+  labs(
+    title = "Body mass and flipper length",
+    subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
+    x = "Flipper length (mm)", y = "Body mass (g)",
+    color = "Species", shape = "Species"
+  ) +
+  scale_color_colorblind()
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins, with a line of best fit displaying the relationship between these two variables overlaid. The plot displays a positive, fairly linear, and relatively strong relationship between these two variables. Species (Adelie, Chinstrap, and Gentoo) are represented with different colors and shapes. The relationship between body mass and flipper length is roughly the same for these three species, and Gentoo penguins are larger than penguins from the other two species.

+
+
+
+
+

We finally have a plot that perfectly matches our “ultimate goal”!

+
+
+

Exercises

+
    +

  1. How many rows are in penguins? How many columns?

    2. +

  2. What does the bill_depth_mm variable in the penguins data frame describe? Read the help for ?penguins to find out.

    3. +

  3. Make a scatterplot of bill_depth_mm vs. bill_length_mm. That is, make a scatterplot with bill_depth_mm on the y-axis and bill_length_mm on the x-axis. Describe the relationship between these two variables.

    4. +

  4. What happens if you make a scatterplot of species vs. bill_depth_mm? What might be a better choice of geom?

    5. +

  5. Why does the following give an error and how would you fix it?

    +
    +
    ggplot(data = penguins) + 
+  geom_point()
    +
    6. +

  6. What does the na.rm argument do in geom_point()? What is the default value of the argument? Create a scatterplot where you successfully use this argument set to TRUE.

    7. +

  7. Add the following caption to the plot you made in the previous exercise: “Data come from the palmerpenguins package.” Hint: Take a look at the documentation for labs().

    8. +

  8. Recreate the following visualization. What aesthetic should bill_depth_mm be mapped to? And should it be mapped at the global level or at the geom level?

    +
    +
    +
    +
    +

    A scatterplot of body mass vs. flipper length of penguins, colored by bill depth. A smooth curve of the relationship between body mass and flipper length is overlaid. The relationship is positive, fairly linear, and moderately strong.

    +
    +
    +
    +
    9. +

  9. Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

    +
    +
    ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = island)
+) +
+  geom_point() +
+  geom_smooth(se = FALSE)
    +
    10. +

  10. Will these two graphs look different? Why/why not?

    +
    +
    ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+  geom_point() +
+  geom_smooth()
+
+ggplot() +
+  geom_point(
+    data = penguins,
+    mapping = aes(x = flipper_length_mm, y = body_mass_g)
+  ) +
+  geom_smooth(
+    data = penguins,
+    mapping = aes(x = flipper_length_mm, y = body_mass_g)
+  )
    +
    11. +
+
+
+
+

ggplot2 calls

+

As we move on from these introductory sections, we’ll transition to a more concise expression of ggplot2 code. So far we’ve been very explicit, which is helpful when you are learning:

+
+
ggplot(
+  data = penguins,
+  mapping = aes(x = flipper_length_mm, y = body_mass_g)
+) +
+  geom_point()
+
+

Typically, the first one or two arguments to a function are so important that you should know them by heart. The first two arguments to ggplot() are data and mapping, in the remainder of the book, we won’t supply those names. That saves typing, and, by reducing the amount of extra text, makes it easier to see what’s different between plots. That’s a really important programming concern that we’ll come back to in ?@sec-functions.

+

Rewriting the previous plot more concisely yields:

+
+
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) + 
+  geom_point()
+
+

In the future, you’ll also learn about the pipe, |>, which will allow you to create that plot with:

+
+
penguins |> 
+  ggplot(aes(x = flipper_length_mm, y = body_mass_g)) + 
+  geom_point()
+
+
+
+

Visualizing distributions

+

How you visualize the distribution of a variable depends on the type of variable: categorical or numerical.

+
+

A categorical variable

+

A variable is categorical if it can only take one of a small set of values. To examine the distribution of a categorical variable, you can use a bar chart. The height of the bars displays how many observations occurred with each x value.

+
+
ggplot(penguins, aes(x = species)) +
+  geom_bar()
+
+
+
+

A bar chart of frequencies of species of penguins: Adelie (approximately 150), Chinstrap (approximately 90), Gentoo (approximately 125).

+
+
+
+
+

In bar plots of categorical variables with non-ordered levels, like the penguin species above, it’s often preferable to reorder the bars based on their frequencies. Doing so requires transforming the variable to a factor (how R handles categorical data) and then reordering the levels of that factor.

+
+
ggplot(penguins, aes(x = fct_infreq(species))) +
+  geom_bar()
+
+
+
+

A bar chart of frequencies of species of penguins, where the bars are ordered in decreasing order of their heights (frequencies): Adelie (approximately 150), Gentoo (approximately 125), Chinstrap (approximately 90).

+
+
+
+
+

You will learn more about factors and functions for dealing with factors (like fct_infreq() shown above) in ?@sec-factors.

+
+
+

A numerical variable

+

A variable is numerical (or quantitative) if it can take on a wide range of numerical values, and it is sensible to add, subtract, or take averages with those values. Numerical variables can be continuous or discrete.

+

One commonly used visualization for distributions of continuous variables is a histogram.

+
+
ggplot(penguins, aes(x = body_mass_g)) +
+  geom_histogram(binwidth = 200)
+
+
+
+

A histogram of body masses of penguins. The distribution is unimodal and right skewed, ranging between approximately 2500 to 6500 grams.

+
+
+
+
+

A histogram divides the x-axis into equally spaced bins and then uses the height of a bar to display the number of observations that fall in each bin. In the graph above, the tallest bar shows that 39 observations have a body_mass_g value between 3,500 and 3,700 grams, which are the left and right edges of the bar.

+

You can set the width of the intervals in a histogram with the binwidth argument, which is measured in the units of the x variable. You should always explore a variety of binwidths when working with histograms, as different binwidths can reveal different patterns. In the plots below a binwidth of 20 is too narrow, resulting in too many bars, making it difficult to determine the shape of the distribution. Similarly, a binwidth of 2,000 is too high, resulting in all data being binned into only three bars, and also making it difficult to determine the shape of the distribution. A binwidth of 200 provides a sensible balance.

+
ggplot(penguins, aes(x = body_mass_g)) +
+  geom_histogram(binwidth = 20)
+ggplot(penguins, aes(x = body_mass_g)) +
+  geom_histogram(binwidth = 2000)
+
+
+
+

Two histograms of body masses of penguins, one with binwidth of 20 (left) and one with binwidth of 2000 (right). The histogram with binwidth of 20 shows lots of ups and downs in the heights of the bins, creating a jagged outline. The histogram with binwidth of 2000 shows only three bins.

+
+
+

Two histograms of body masses of penguins, one with binwidth of 20 (left) and one with binwidth of 2000 (right). The histogram with binwidth of 20 shows lots of ups and downs in the heights of the bins, creating a jagged outline. The histogram with binwidth of 2000 shows only three bins.

+
+
+
+

An alternative visualization for distributions of numerical variables is a density plot. A density plot is a smoothed-out version of a histogram and a practical alternative, particularly for continuous data that comes from an underlying smooth distribution. We won’t go into how geom_density() estimates the density (you can read more about that in the function documentation), but let’s explain how the density curve is drawn with an analogy. Imagine a histogram made out of wooden blocks. Then, imagine that you drop a cooked spaghetti string over it. The shape the spaghetti will take draped over blocks can be thought of as the shape of the density curve. It shows fewer details than a histogram but can make it easier to quickly glean the shape of the distribution, particularly with respect to modes and skewness.

+
+
ggplot(penguins, aes(x = body_mass_g)) +
+  geom_density()
+#> Warning: Removed 2 rows containing non-finite outside the scale range
+#> (`stat_density()`).
+
+
+
+

A density plot of body masses of penguins. The distribution is unimodal and right skewed, ranging between approximately 2500 to 6500 grams.

+
+
+
+
+
+
+

Exercises

+
    +

  1. Make a bar plot of species of penguins, where you assign species to the y aesthetic. How is this plot different?

    2. +

  2. How are the following two plots different? Which aesthetic, color or fill, is more useful for changing the color of bars?

    +
    +
    ggplot(penguins, aes(x = species)) +
+  geom_bar(color = "red")
+
+ggplot(penguins, aes(x = species)) +
+  geom_bar(fill = "red")
    +
    3. +

  3. What does the bins argument in geom_histogram() do?

    4. +

  4. Make a histogram of the carat variable in the diamonds dataset that is available when you load the tidyverse package. Experiment with different binwidths. What binwidth reveals the most interesting patterns?

    5. +
+
+
+
+

Visualizing relationships

+

To visualize a relationship we need to have at least two variables mapped to aesthetics of a plot. In the following sections you will learn about commonly used plots for visualizing relationships between two or more variables and the geoms used for creating them.

+
+

A numerical and a categorical variable

+

To visualize the relationship between a numerical and a categorical variable we can use side-by-side box plots. A boxplot is a type of visual shorthand for measures of position (percentiles) that describe a distribution. It is also useful for identifying potential outliers. As shown in Figure 1, each boxplot consists of:

+
    +

  • A box that indicates the range of the middle half of the data, a distance known as the interquartile range (IQR), stretching from the 25th percentile of the distribution to the 75th percentile. In the middle of the box is a line that displays the median, i.e. 50th percentile, of the distribution. These three lines give you a sense of the spread of the distribution and whether or not the distribution is symmetric about the median or skewed to one side.

    • +

  • Visual points that display observations that fall more than 1.5 times the IQR from either edge of the box. These outlying points are unusual so are plotted individually.

    • +

  • A line (or whisker) that extends from each end of the box and goes to the farthest non-outlier point in the distribution.

    • +
+
+
+
+
+
+A diagram depicting how a boxplot is created following the steps outlined above. +
+
+Figure 1: Diagram depicting how a boxplot is created. +
+
+
+
+
+

Let’s take a look at the distribution of body mass by species using geom_boxplot():

+
+
ggplot(penguins, aes(x = species, y = body_mass_g)) +
+  geom_boxplot()
+
+
+
+

Side-by-side box plots of distributions of body masses of Adelie, Chinstrap, and Gentoo penguins. The distribution of Adelie and Chinstrap penguins' body masses appear to be symmetric with medians around 3750 grams. The median body mass of Gentoo penguins is much higher, around 5000 grams, and the distribution of the body masses of these penguins appears to be somewhat right skewed.

+
+
+
+
+

Alternatively, we can make density plots with geom_density().

+
+
ggplot(penguins, aes(x = body_mass_g, color = species)) +
+  geom_density(linewidth = 0.75)
+
+
+
+

A density plot of body masses of penguins by species of penguins. Each species (Adelie, Chinstrap, and Gentoo) is represented with different colored outlines for the density curves.

+
+
+
+
+

We’ve also customized the thickness of the lines using the linewidth argument in order to make them stand out a bit more against the background.

+

Additionally, we can map species to both color and fill aesthetics and use the alpha aesthetic to add transparency to the filled density curves. This aesthetic takes values between 0 (completely transparent) and 1 (completely opaque). In the following plot it’s set to 0.5.

+
+
ggplot(penguins, aes(x = body_mass_g, color = species, fill = species)) +
+  geom_density(alpha = 0.5)
+
+
+
+

A density plot of body masses of penguins by species of penguins. Each species (Adelie, Chinstrap, and Gentoo) is represented in different colored outlines for the density curves. The density curves are also filled with the same colors, with some transparency added.

+
+
+
+
+

Note the terminology we have used here:

+
    +
  • We map variables to aesthetics if we want the visual attribute represented by that aesthetic to vary based on the values of that variable.
    • +
  • Otherwise, we set the value of an aesthetic.
    • +
+
+
+

Two categorical variables

+

We can use stacked bar plots to visualize the relationship between two categorical variables. For example, the following two stacked bar plots both display the relationship between island and species, or specifically, visualizing the distribution of species within each island.

+

The first plot shows the frequencies of each species of penguins on each island. The plot of frequencies shows that there are equal numbers of Adelies on each island. But we don’t have a good sense of the percentage balance within each island.

+
+
ggplot(penguins, aes(x = island, fill = species)) +
+  geom_bar()
+
+
+
+

Bar plots of penguin species by island (Biscoe, Dream, and Torgersen)

+
+
+
+
+

The second plot, a relative frequency plot created by setting position = "fill" in the geom, is more useful for comparing species distributions across islands since it’s not affected by the unequal numbers of penguins across the islands. Using this plot we can see that Gentoo penguins all live on Biscoe island and make up roughly 75% of the penguins on that island, Chinstrap all live on Dream island and make up roughly 50% of the penguins on that island, and Adelie live on all three islands and make up all of the penguins on Torgersen.

+
+
ggplot(penguins, aes(x = island, fill = species)) +
+  geom_bar(position = "fill")
+
+
+
+

Bar plots of penguin species by island (Biscoe, Dream, and Torgersen) the bars are scaled to the same height, making it a relative frequencies plot

+
+
+
+
+

In creating these bar charts, we map the variable that will be separated into bars to the x aesthetic, and the variable that will change the colors inside the bars to the fill aesthetic.

+
+
+

Two numerical variables

+

So far you’ve learned about scatterplots (created with geom_point()) and smooth curves (created with geom_smooth()) for visualizing the relationship between two numerical variables. A scatterplot is probably the most commonly used plot for visualizing the relationship between two numerical variables.

+
+
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
+  geom_point()
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins. The plot displays a positive, linear, relatively strong relationship between these two variables.

+
+
+
+
+
+
+

Three or more variables

+

As we saw in Section 1.2.4, we can incorporate more variables into a plot by mapping them to additional aesthetics. For example, in the following scatterplot the colors of points represent species and the shapes of points represent islands.

+
+
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
+  geom_point(aes(color = species, shape = island))
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins. The plot displays a positive, linear, relatively strong relationship between these two variables. The points are colored based on the species of the penguins and the shapes of the points represent islands (round points are Biscoe island, triangles are Dream island, and squared are Torgersen island). The plot is very busy and it's difficult to distinguish the shapes of the points.

+
+
+
+
+

However adding too many aesthetic mappings to a plot makes it cluttered and difficult to make sense of. Another way, which is particularly useful for categorical variables, is to split your plot into facets, subplots that each display one subset of the data.

+

To facet your plot by a single variable, use facet_wrap(). The first argument of facet_wrap() is a formula3, which you create with ~ followed by a variable name. The variable that you pass to facet_wrap() should be categorical.

+
+
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
+  geom_point(aes(color = species, shape = species)) +
+  facet_wrap(~island)
+
+
+
+

A scatterplot of body mass vs. flipper length of penguins. The shapes and colors of points represent species. Penguins from each island are on a separate facet. Within each facet, the relationship between body mass and flipper length is positive, linear, relatively strong.

+
+
+
+
+

You will learn about many other geoms for visualizing distributions of variables and relationships between them in ?@sec-layers.

+
+
+

Exercises

+
    +

  1. The mpg data frame that is bundled with the ggplot2 package contains 234 observations collected by the US Environmental Protection Agency on 38 car models. Which variables in mpg are categorical? Which variables are numerical? (Hint: Type ?mpg to read the documentation for the dataset.) How can you see this information when you run mpg?

    2. +

  2. Make a scatterplot of hwy vs. displ using the mpg data frame. Next, map a third, numerical variable to color, then size, then both color and size, then shape. How do these aesthetics behave differently for categorical vs. numerical variables?

    3. +

  3. In the scatterplot of hwy vs. displ, what happens if you map a third variable to linewidth?

    4. +

  4. What happens if you map the same variable to multiple aesthetics?

    5. +

  5. Make a scatterplot of bill_depth_mm vs. bill_length_mm and color the points by species. What does adding coloring by species reveal about the relationship between these two variables? What about faceting by species?

    6. +

  6. Why does the following yield two separate legends? How would you fix it to combine the two legends?

    +
    +
    ggplot(
+  data = penguins,
+  mapping = aes(
+    x = bill_length_mm, y = bill_depth_mm, 
+    color = species, shape = species
+  )
+) +
+  geom_point() +
+  labs(color = "Species")
    +
    7. +

  7. Create the two following stacked bar plots. Which question can you answer with the first one? Which question can you answer with the second one?

    +
    +
    ggplot(penguins, aes(x = island, fill = species)) +
+  geom_bar(position = "fill")
+ggplot(penguins, aes(x = species, fill = island)) +
+  geom_bar(position = "fill")
    +
    8. +
+
+
+
+

Saving your plots

+

Once you’ve made a plot, you might want to get it out of R by saving it as an image that you can use elsewhere. That’s the job of ggsave(), which will save the plot most recently created to disk:

+
+
ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
+  geom_point()
+ggsave(filename = "penguin-plot.png")
+
+

This will save your plot to your working directory, a concept you’ll learn more about in ?@sec-workflow-scripts-projects.

+

If you don’t specify the width and height they will be taken from the dimensions of the current plotting device. For reproducible code, you’ll want to specify them. You can learn more about ggsave() in the documentation.

+

Generally, however, we recommend that you assemble your final reports using Quarto, a reproducible authoring system that allows you to interleave your code and your prose and automatically include your plots in your write-ups. You will learn more about Quarto in ?@sec-quarto.

+
+

Exercises

+
    +

  1. Run the following lines of code. Which of the two plots is saved as mpg-plot.png? Why?

    +
    +
    ggplot(mpg, aes(x = class)) +
+  geom_bar()
+ggplot(mpg, aes(x = cty, y = hwy)) +
+  geom_point()
+ggsave("mpg-plot.png")
    +
    2. +

  2. What do you need to change in the code above to save the plot as a PDF instead of a PNG? How could you find out what types of image files would work in ggsave()?

    3. +
+
+
+
+

Common problems

+

As you start to run R code, you’re likely to run into problems. Don’t worry — it happens to everyone. We have all been writing R code for years, but every day we still write code that doesn’t work on the first try!

+

Start by carefully comparing the code that you’re running to the code in the book. R is extremely picky, and a misplaced character can make all the difference. Make sure that every ( is matched with a ) and every " is paired with another ". Sometimes you’ll run the code and nothing happens. Check the left-hand of your console: if it’s a +, it means that R doesn’t think you’ve typed a complete expression and it’s waiting for you to finish it. In this case, it’s usually easy to start from scratch again by pressing ESCAPE to abort processing the current command.

+

One common problem when creating ggplot2 graphics is to put the + in the wrong place: it has to come at the end of the line, not the start. In other words, make sure you haven’t accidentally written code like this:

+
+
ggplot(data = mpg) 
++ geom_point(mapping = aes(x = displ, y = hwy))
+
+

If you’re still stuck, try the help. You can get help about any R function by running ?function_name in the console, or highlighting the function name and pressing F1 in RStudio. Don’t worry if the help doesn’t seem that helpful - instead skip down to the examples and look for code that matches what you’re trying to do.

+

If that doesn’t help, carefully read the error message. Sometimes the answer will be buried there! But when you’re new to R, even if the answer is in the error message, you might not yet know how to understand it. Another great tool is Google: try googling the error message, as it’s likely someone else has had the same problem, and has gotten help online.

+
+
+

Summary

+

In this chapter, you’ve learned the basics of data visualization with ggplot2. We started with the basic idea that underpins ggplot2: a visualization is a mapping from variables in your data to aesthetic properties like position, color, size and shape. You then learned about increasing the complexity and improving the presentation of your plots layer-by-layer. You also learned about commonly used plots for visualizing the distribution of a single variable as well as for visualizing relationships between two or more variables, by leveraging additional aesthetic mappings and/or splitting your plot into small multiples using faceting.

+

We’ll use visualizations again and again throughout this book, introducing new techniques as we need them as well as do a deeper dive into creating visualizations with ggplot2 in ?@sec-layers through ?@sec-communication.

+

With the basics of visualization under your belt, in the next chapter we’re going to switch gears a little and give you some practical workflow advice. We intersperse workflow advice with data science tools throughout this part of the book because it’ll help you stay organized as you write increasing amounts of R code.

+ + +
+
+
+
+
    +

  1. You can eliminate that message and force conflict resolution to happen on demand by using the conflicted package, which becomes more important as you load more packages. You can learn more about conflicted at https://conflicted.r-lib.org.↩︎

    2. +

  2. Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. https://allisonhorst.github.io/palmerpenguins/. doi: 10.5281/zenodo.3960218.↩︎

    3. +

  3. Here “formula” is the name of the thing created by ~, not a synonym for “equation”.↩︎

    4. +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/docs/datetimes.html b/docs/datetimes.html new file mode 100644 index 000000000..0a2e20a97 --- /dev/null +++ b/docs/datetimes.html @@ -0,0 +1,1365 @@ + + + + + + + + + + +datetimes + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + + +
+ + + + +
+

Dates and times

+
+

Introduction

+

This chapter will show you how to work with dates and times in R. At first glance, dates and times seem simple. You use them all the time in your regular life, and they don’t seem to cause much confusion. However, the more you learn about dates and times, the more complicated they seem to get!

+

To warm up think about how many days there are in a year, and how many hours there are in a day. You probably remembered that most years have 365 days, but leap years have 366. Do you know the full rule for determining if a year is a leap year1? The number of hours in a day is a little less obvious: most days have 24 hours, but in places that use daylight saving time (DST), one day each year has 23 hours and another has 25.

+

Dates and times are hard because they have to reconcile two physical phenomena (the rotation of the Earth and its orbit around the sun) with a whole raft of geopolitical phenomena including months, time zones, and DST. This chapter won’t teach you every last detail about dates and times, but it will give you a solid grounding of practical skills that will help you with common data analysis challenges.

+

We’ll begin by showing you how to create date-times from various inputs, and then once you’ve got a date-time, how you can extract components like year, month, and day. We’ll then dive into the tricky topic of working with time spans, which come in a variety of flavors depending on what you’re trying to do. We’ll conclude with a brief discussion of the additional challenges posed by time zones.

+
+

Prerequisites

+

This chapter will focus on the lubridate package, which makes it easier to work with dates and times in R. As of the latest tidyverse release, lubridate is part of core tidyverse. We will also need nycflights13 for practice data.

+
+
library(tidyverse)
+library(nycflights13)
+
+
+
+
+

Creating date/times

+

There are three types of date/time data that refer to an instant in time:

+
    +

  • A date. Tibbles print this as <date>.

    • +

  • A time within a day. Tibbles print this as <time>.

    • +

  • A date-time is a date plus a time: it uniquely identifies an instant in time (typically to the nearest second). Tibbles print this as <dttm>. Base R calls these POSIXct, but that doesn’t exactly trip off the tongue.

    • +
+

In this chapter we are going to focus on dates and date-times as R doesn’t have a native class for storing times. If you need one, you can use the hms package.

+

You should always use the simplest possible data type that works for your needs. That means if you can use a date instead of a date-time, you should. Date-times are substantially more complicated because of the need to handle time zones, which we’ll come back to at the end of the chapter.

+

To get the current date or date-time you can use today() or now():

+
+
today()
+#> [1] "2024-11-14"
+now()
+#> [1] "2024-11-14 18:39:52 JST"
+
+

Otherwise, the following sections describe the four ways you’re likely to create a date/time:

+
    +
  • While reading a file with readr.
    • +
  • From a string.
    • +
  • From individual date-time components.
    • +
  • From an existing date/time object.
    • +
+
+

During import

+

If your CSV contains an ISO8601 date or date-time, you don’t need to do anything; readr will automatically recognize it:

+
+
csv <- "
+  date,datetime
+  2022-01-02,2022-01-02 05:12
+"
+read_csv(csv)
+#> # A tibble: 1 × 2
+#>   date       datetime           
+#>   <date>     <dttm>             
+#> 1 2022-01-02 2022-01-02 05:12:00
+
+

If you haven’t heard of ISO8601 before, it’s an international standard2 for writing dates where the components of a date are organized from biggest to smallest separated by -. For example, in ISO8601 May 3 2022 is 2022-05-03. ISO8601 dates can also include times, where hour, minute, and second are separated by :, and the date and time components are separated by either a T or a space. For example, you could write 4:26pm on May 3 2022 as either 2022-05-03 16:26 or 2022-05-03T16:26.

+

For other date-time formats, you’ll need to use col_types plus col_date() or col_datetime() along with a date-time format. The date-time format used by readr is a standard used across many programming languages, describing a date component with a % followed by a single character. For example, %Y-%m-%d specifies a date that’s a year, -, month (as number) -, day. Table Table 1 lists all the options.

+
+
+
+Table 1: All date formats understood by readr +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeCodeMeaningExample
Year%Y4 digit year2021
%y2 digit year21
Month%mNumber2
%bAbbreviated nameFeb
%BFull nameFebruary
Day%dOne or two digits2
%eTwo digits02
Time%H24-hour hour13
%I12-hour hour1
%pAM/PMpm
%MMinutes35
%SSeconds45
%OSSeconds with decimal component45.35
%ZTime zone nameAmerica/Chicago
%zOffset from UTC+0800
Other%.Skip one non-digit:
%*Skip any number of non-digits
+
+
+
+

And this code shows a few options applied to a very ambiguous date:

+
+
csv <- "
+  date
+  01/02/15
+"
+
+read_csv(csv, col_types = cols(date = col_date("%m/%d/%y")))
+#> # A tibble: 1 × 1
+#>   date      
+#>   <date>    
+#> 1 2015-01-02
+
+read_csv(csv, col_types = cols(date = col_date("%d/%m/%y")))
+#> # A tibble: 1 × 1
+#>   date      
+#>   <date>    
+#> 1 2015-02-01
+
+read_csv(csv, col_types = cols(date = col_date("%y/%m/%d")))
+#> # A tibble: 1 × 1
+#>   date      
+#>   <date>    
+#> 1 2001-02-15
+
+

Note that no matter how you specify the date format, it’s always displayed the same way once you get it into R.

+

If you’re using %b or %B and working with non-English dates, you’ll also need to provide a locale(). See the list of built-in languages in date_names_langs(), or create your own with date_names(),

+
+
+

From strings

+

The date-time specification language is powerful, but requires careful analysis of the date format. An alternative approach is to use lubridate’s helpers which attempt to automatically determine the format once you specify the order of the component. To use them, identify the order in which year, month, and day appear in your dates, then arrange “y”, “m”, and “d” in the same order. That gives you the name of the lubridate function that will parse your date. For example:

+
+
ymd("2017-01-31")
+#> [1] "2017-01-31"
+mdy("January 31st, 2017")
+#> [1] "2017-01-31"
+dmy("31-Jan-2017")
+#> [1] "2017-01-31"
+
+

ymd() and friends create dates. To create a date-time, add an underscore and one or more of “h”, “m”, and “s” to the name of the parsing function:

+
+
ymd_hms("2017-01-31 20:11:59")
+#> [1] "2017-01-31 20:11:59 UTC"
+mdy_hm("01/31/2017 08:01")
+#> [1] "2017-01-31 08:01:00 UTC"
+
+

You can also force the creation of a date-time from a date by supplying a timezone:

+
+
ymd("2017-01-31", tz = "UTC")
+#> [1] "2017-01-31 UTC"
+
+

Here I use the UTC3 timezone which you might also know as GMT, or Greenwich Mean Time, the time at 0° longitude4 . It doesn’t use daylight saving time, making it a bit easier to compute with .

+
+
+

From individual components

+

Instead of a single string, sometimes you’ll have the individual components of the date-time spread across multiple columns. This is what we have in the flights data:

+
+
flights |> 
+  select(year, month, day, hour, minute)
+#> # A tibble: 336,776 × 5
+#>    year month   day  hour minute
+#>   <int> <int> <int> <dbl>  <dbl>
+#> 1  2013     1     1     5     15
+#> 2  2013     1     1     5     29
+#> 3  2013     1     1     5     40
+#> 4  2013     1     1     5     45
+#> 5  2013     1     1     6      0
+#> 6  2013     1     1     5     58
+#> # ℹ 336,770 more rows
+
+

To create a date/time from this sort of input, use make_date() for dates, or make_datetime() for date-times:

+
+
flights |> 
+  select(year, month, day, hour, minute) |> 
+  mutate(departure = make_datetime(year, month, day, hour, minute))
+#> # A tibble: 336,776 × 6
+#>    year month   day  hour minute departure          
+#>   <int> <int> <int> <dbl>  <dbl> <dttm>             
+#> 1  2013     1     1     5     15 2013-01-01 05:15:00
+#> 2  2013     1     1     5     29 2013-01-01 05:29:00
+#> 3  2013     1     1     5     40 2013-01-01 05:40:00
+#> 4  2013     1     1     5     45 2013-01-01 05:45:00
+#> 5  2013     1     1     6      0 2013-01-01 06:00:00
+#> 6  2013     1     1     5     58 2013-01-01 05:58:00
+#> # ℹ 336,770 more rows
+
+

Let’s do the same thing for each of the four time columns in flights. The times are represented in a slightly odd format, so we use modulus arithmetic to pull out the hour and minute components. Once we’ve created the date-time variables, we focus in on the variables we’ll explore in the rest of the chapter.

+
+
make_datetime_100 <- function(year, month, day, time) {
+  make_datetime(year, month, day, time %/% 100, time %% 100)
+}
+
+flights_dt <- flights |> 
+  filter(!is.na(dep_time), !is.na(arr_time)) |> 
+  mutate(
+    dep_time = make_datetime_100(year, month, day, dep_time),
+    arr_time = make_datetime_100(year, month, day, arr_time),
+    sched_dep_time = make_datetime_100(year, month, day, sched_dep_time),
+    sched_arr_time = make_datetime_100(year, month, day, sched_arr_time)
+  ) |> 
+  select(origin, dest, ends_with("delay"), ends_with("time"))
+
+flights_dt
+#> # A tibble: 328,063 × 9
+#>   origin dest  dep_delay arr_delay dep_time            sched_dep_time     
+#>   <chr>  <chr>     <dbl>     <dbl> <dttm>              <dttm>             
+#> 1 EWR    IAH           2        11 2013-01-01 05:17:00 2013-01-01 05:15:00
+#> 2 LGA    IAH           4        20 2013-01-01 05:33:00 2013-01-01 05:29:00
+#> 3 JFK    MIA           2        33 2013-01-01 05:42:00 2013-01-01 05:40:00
+#> 4 JFK    BQN          -1       -18 2013-01-01 05:44:00 2013-01-01 05:45:00
+#> 5 LGA    ATL          -6       -25 2013-01-01 05:54:00 2013-01-01 06:00:00
+#> 6 EWR    ORD          -4        12 2013-01-01 05:54:00 2013-01-01 05:58:00
+#> # ℹ 328,057 more rows
+#> # ℹ 3 more variables: arr_time <dttm>, sched_arr_time <dttm>, …
+
+

With this data, we can visualize the distribution of departure times across the year:

+
+
flights_dt |> 
+  ggplot(aes(x = dep_time)) + 
+  geom_freqpoly(binwidth = 86400) # 86400 seconds = 1 day
+
+
+
+

A frequency polyon with departure time (Jan-Dec 2013) on the x-axis and number of flights on the y-axis (0-1000). The frequency polygon is binned by day so you see a time series of flights by day. The pattern is dominated by a weekly pattern; there are fewer flights on weekends. The are few days that stand out as having a surprisingly few flights in early February, early July, late November, and late December.

+
+
+
+
+

Or within a single day:

+
+
flights_dt |> 
+  filter(dep_time < ymd(20130102)) |> 
+  ggplot(aes(x = dep_time)) + 
+  geom_freqpoly(binwidth = 600) # 600 s = 10 minutes
+
+
+
+

A frequency polygon with departure time (6am - midnight Jan 1) on the x-axis, number of flights on the y-axis (0-17), binned into 10 minute increments. It's hard to see much pattern because of high variability, but most bins have 8-12 flights, and there are markedly fewer flights before 6am and after 8pm.

+
+
+
+
+

Note that when you use date-times in a numeric context (like in a histogram), 1 means 1 second, so a binwidth of 86400 means one day. For dates, 1 means 1 day.

+
+
+

From other types

+

You may want to switch between a date-time and a date. That’s the job of as_datetime() and as_date():

+
+
as_datetime(today())
+#> [1] "2024-11-14 UTC"
+as_date(now())
+#> [1] "2024-11-14"
+
+

Sometimes you’ll get date/times as numeric offsets from the “Unix Epoch”, 1970-01-01. If the offset is in seconds, use as_datetime(); if it’s in days, use as_date().

+
+
as_datetime(60 * 60 * 10)
+#> [1] "1970-01-01 10:00:00 UTC"
+as_date(365 * 10 + 2)
+#> [1] "1980-01-01"
+
+
+
+

Exercises

+
    +

  1. What happens if you parse a string that contains invalid dates?

    +
    +
    ymd(c("2010-10-10", "bananas"))
    +
    2. +

  2. What does the tzone argument to today() do? Why is it important?

    3. +

  3. For each of the following date-times, show how you’d parse it using a readr column specification and a lubridate function.

    +
    +
    d1 <- "January 1, 2010"
+d2 <- "2015-Mar-07"
+d3 <- "06-Jun-2017"
+d4 <- c("August 19 (2015)", "July 1 (2015)")
+d5 <- "12/30/14" # Dec 30, 2014
+t1 <- "1705"
+t2 <- "11:15:10.12 PM"
    +
    4. +
+
+
+
+

Date-time components

+

Now that you know how to get date-time data into R’s date-time data structures, let’s explore what you can do with them. This section will focus on the accessor functions that let you get and set individual components. The next section will look at how arithmetic works with date-times.

+
+

Getting components

+

You can pull out individual parts of the date with the accessor functions year(), month(), mday() (day of the month), yday() (day of the year), wday() (day of the week), hour(), minute(), and second(). These are effectively the opposites of make_datetime().

+
+
datetime <- ymd_hms("2026-07-08 12:34:56")
+
+year(datetime)
+#> [1] 2026
+month(datetime)
+#> [1] 7
+mday(datetime)
+#> [1] 8
+
+yday(datetime)
+#> [1] 189
+wday(datetime)
+#> [1] 4
+
+

For month() and wday() you can set label = TRUE to return the abbreviated name of the month or day of the week. Set abbr = FALSE to return the full name.

+
+
month(datetime, label = TRUE)
+#> [1] Jul
+#> 12 Levels: Jan < Feb < Mar < Apr < May < Jun < Jul < Aug < Sep < ... < Dec
+wday(datetime, label = TRUE, abbr = FALSE)
+#> [1] Wednesday
+#> 7 Levels: Sunday < Monday < Tuesday < Wednesday < Thursday < ... < Saturday
+
+

We can use wday() to see that more flights depart during the week than on the weekend:

+
+
flights_dt |> 
+  mutate(wday = wday(dep_time, label = TRUE)) |> 
+  ggplot(aes(x = wday)) +
+  geom_bar()
+
+
+
+

A bar chart with days of the week on the x-axis and number of flights on the y-axis. Monday-Friday have roughly the same number of flights, ~48,0000, decreasingly slightly over the course of the week. Sunday is a little lower (~45,000), and Saturday is much lower (~38,000).

+
+
+
+
+

We can also look at the average departure delay by minute within the hour. There’s an interesting pattern: flights leaving in minutes 20-30 and 50-60 have much lower delays than the rest of the hour!

+
+
flights_dt |> 
+  mutate(minute = minute(dep_time)) |> 
+  group_by(minute) |> 
+  summarize(
+    avg_delay = mean(dep_delay, na.rm = TRUE),
+    n = n()
+  ) |> 
+  ggplot(aes(x = minute, y = avg_delay)) +
+  geom_line()
+
+
+
+

A line chart with minute of actual departure (0-60) on the x-axis and average delay (4-20) on the y-axis. Average delay starts at (0, 12), steadily increases to (18, 20), then sharply drops, hitting at minimum at ~23 minute past the hour and 9 minutes of delay. It then increases again to (17, 35), and sharply decreases to (55, 4). It finishes off with an increase to (60, 9).

+
+
+
+
+

Interestingly, if we look at the scheduled departure time we don’t see such a strong pattern:

+
+
sched_dep <- flights_dt |> 
+  mutate(minute = minute(sched_dep_time)) |> 
+  group_by(minute) |> 
+  summarize(
+    avg_delay = mean(arr_delay, na.rm = TRUE),
+    n = n()
+  )
+
+ggplot(sched_dep, aes(x = minute, y = avg_delay)) +
+  geom_line()
+
+
+
+

A line chart with minute of scheduled departure (0-60) on the x-axis and average delay (4-16). There is relatively little pattern, just a small suggestion that the average delay decreases from maybe 10 minutes to 8 minutes over the course of the hour.

+
+
+
+
+

So why do we see that pattern with the actual departure times? Well, like much data collected by humans, there’s a strong bias towards flights leaving at “nice” departure times, as Figure 1 shows. Always be alert for this sort of pattern whenever you work with data that involves human judgement!

+
+
+
+
+
+A line plot with departure minute (0-60) on the x-axis and number of flights (0-60000) on the y-axis. Most flights are scheduled to depart on either the hour (~60,000) or the half hour (~35,000). Otherwise, all most all flights are scheduled to depart on multiples of five, with a few extra at 15, 45, and 55 minutes. +
+
+Figure 1: A frequency polygon showing the number of flights scheduled to depart each hour. You can see a strong preference for round numbers like 0 and 30 and generally for numbers that are a multiple of five. +
+
+
+
+
+
+
+

Rounding

+

An alternative approach to plotting individual components is to round the date to a nearby unit of time, with floor_date(), round_date(), and ceiling_date(). Each function takes a vector of dates to adjust and then the name of the unit to round down (floor), round up (ceiling), or round to. This, for example, allows us to plot the number of flights per week:

+
+
flights_dt |> 
+  count(week = floor_date(dep_time, "week")) |> 
+  ggplot(aes(x = week, y = n)) +
+  geom_line() + 
+  geom_point()
+
+
+
+

A line plot with week (Jan-Dec 2013) on the x-axis and number of flights (2,000-7,000) on the y-axis. The pattern is fairly flat from February to November with around 7,000 flights per week. There are far fewer flights on the first (approximately 4,500 flights) and last weeks of the year (approximately 2,500 flights).

+
+
+
+
+

You can use rounding to show the distribution of flights across the course of a day by computing the difference between dep_time and the earliest instant of that day:

+
+
flights_dt |> 
+  mutate(dep_hour = dep_time - floor_date(dep_time, "day")) |> 
+  ggplot(aes(x = dep_hour)) +
+  geom_freqpoly(binwidth = 60 * 30)
+#> Don't know how to automatically pick scale for object of type <difftime>.
+#> Defaulting to continuous.
+
+
+
+

A line plot with depature time on the x-axis. This is units of seconds since midnight so it's hard to interpret.

+
+
+
+
+

Computing the difference between a pair of date-times yields a difftime (more on that in Section 1.4.3). We can convert that to an hms object to get a more useful x-axis:

+
+
flights_dt |> 
+  mutate(dep_hour = hms::as_hms(dep_time - floor_date(dep_time, "day"))) |> 
+  ggplot(aes(x = dep_hour)) +
+  geom_freqpoly(binwidth = 60 * 30)
+
+
+
+

A line plot with depature time (midnight to midnight) on the x-axis and number of flights on the y-axis (0 to 15,000). There are very few (<100) flights before 5am. The number of flights then rises rapidly to 12,000 / hour, peaking at 15,000 at 9am, before falling to around 8,000 / hour for 10am to 2pm. Number of flights then increases to around 12,000 per hour until 8pm, when they rapidly drop again.

+
+
+
+
+
+
+

Modifying components

+

You can also use each accessor function to modify the components of a date/time. This doesn’t come up much in data analysis, but can be useful when cleaning data that has clearly incorrect dates.

+
+
(datetime <- ymd_hms("2026-07-08 12:34:56"))
+#> [1] "2026-07-08 12:34:56 UTC"
+
+year(datetime) <- 2030
+datetime
+#> [1] "2030-07-08 12:34:56 UTC"
+month(datetime) <- 01
+datetime
+#> [1] "2030-01-08 12:34:56 UTC"
+hour(datetime) <- hour(datetime) + 1
+datetime
+#> [1] "2030-01-08 13:34:56 UTC"
+
+

Alternatively, rather than modifying an existing variable, you can create a new date-time with update(). This also allows you to set multiple values in one step:

+
+
update(datetime, year = 2030, month = 2, mday = 2, hour = 2)
+#> [1] "2030-02-02 02:34:56 UTC"
+
+

If values are too big, they will roll-over:

+
+
update(ymd("2023-02-01"), mday = 30)
+#> [1] "2023-03-02"
+update(ymd("2023-02-01"), hour = 400)
+#> [1] "2023-02-17 16:00:00 UTC"
+
+
+
+

Exercises

+
    +

  1. How does the distribution of flight times within a day change over the course of the year?

    2. +

  2. Compare dep_time, sched_dep_time and dep_delay. Are they consistent? Explain your findings.

    3. +

  3. Compare air_time with the duration between the departure and arrival. Explain your findings. (Hint: consider the location of the airport.)

    4. +

  4. How does the average delay time change over the course of a day? Should you use dep_time or sched_dep_time? Why?

    5. +

  5. On what day of the week should you leave if you want to minimise the chance of a delay?

    6. +

  6. What makes the distribution of diamonds$carat and flights$sched_dep_time similar?

    7. +

  7. Confirm our hypothesis that the early departures of flights in minutes 20-30 and 50-60 are caused by scheduled flights that leave early. Hint: create a binary variable that tells you whether or not a flight was delayed.

    8. +
+
+
+
+

Time spans

+

Next you’ll learn about how arithmetic with dates works, including subtraction, addition, and division. Along the way, you’ll learn about three important classes that represent time spans:

+
    +
  • Durations, which represent an exact number of seconds.
    • +
  • Periods, which represent human units like weeks and months.
    • +
  • Intervals, which represent a starting and ending point.
    • +
+

How do you pick between duration, periods, and intervals? As always, pick the simplest data structure that solves your problem. If you only care about physical time, use a duration; if you need to add human times, use a period; if you need to figure out how long a span is in human units, use an interval.

+
+

Durations

+

In R, when you subtract two dates, you get a difftime object:

+
+
# How old is Hadley?
+h_age <- today() - ymd("1979-10-14")
+h_age
+#> Time difference of 16468 days
+
+

A difftime class object records a time span of seconds, minutes, hours, days, or weeks. This ambiguity can make difftimes a little painful to work with, so lubridate provides an alternative which always uses seconds: the duration.

+
+
as.duration(h_age)
+#> [1] "1422835200s (~45.09 years)"
+
+

Durations come with a bunch of convenient constructors:

+
+
dseconds(15)
+#> [1] "15s"
+dminutes(10)
+#> [1] "600s (~10 minutes)"
+dhours(c(12, 24))
+#> [1] "43200s (~12 hours)" "86400s (~1 days)"
+ddays(0:5)
+#> [1] "0s"                "86400s (~1 days)"  "172800s (~2 days)"
+#> [4] "259200s (~3 days)" "345600s (~4 days)" "432000s (~5 days)"
+dweeks(3)
+#> [1] "1814400s (~3 weeks)"
+dyears(1)
+#> [1] "31557600s (~1 years)"
+
+

Durations always record the time span in seconds. Larger units are created by converting minutes, hours, days, weeks, and years to seconds: 60 seconds in a minute, 60 minutes in an hour, 24 hours in a day, and 7 days in a week. Larger time units are more problematic. A year uses the “average” number of days in a year, i.e. 365.25. There’s no way to convert a month to a duration, because there’s just too much variation.

+

You can add and multiply durations:

+
+
2 * dyears(1)
+#> [1] "63115200s (~2 years)"
+dyears(1) + dweeks(12) + dhours(15)
+#> [1] "38869200s (~1.23 years)"
+
+

You can add and subtract durations to and from days:

+
+
tomorrow <- today() + ddays(1)
+last_year <- today() - dyears(1)
+
+

However, because durations represent an exact number of seconds, sometimes you might get an unexpected result:

+
+
one_am <- ymd_hms("2026-03-08 01:00:00", tz = "America/New_York")
+
+one_am
+#> [1] "2026-03-08 01:00:00 EST"
+one_am + ddays(1)
+#> [1] "2026-03-09 02:00:00 EDT"
+
+

Why is one day after 1am March 8, 2am March 9? If you look carefully at the date you might also notice that the time zones have changed. March 8 only has 23 hours because it’s when DST starts, so if we add a full days worth of seconds we end up with a different time.

+
+
+

Periods

+

To solve this problem, lubridate provides periods. Periods are time spans but don’t have a fixed length in seconds, instead they work with “human” times, like days and months. That allows them to work in a more intuitive way:

+
+
one_am
+#> [1] "2026-03-08 01:00:00 EST"
+one_am + days(1)
+#> [1] "2026-03-09 01:00:00 EDT"
+
+

Like durations, periods can be created with a number of friendly constructor functions.

+
+
hours(c(12, 24))
+#> [1] "12H 0M 0S" "24H 0M 0S"
+days(7)
+#> [1] "7d 0H 0M 0S"
+months(1:6)
+#> [1] "1m 0d 0H 0M 0S" "2m 0d 0H 0M 0S" "3m 0d 0H 0M 0S" "4m 0d 0H 0M 0S"
+#> [5] "5m 0d 0H 0M 0S" "6m 0d 0H 0M 0S"
+
+

You can add and multiply periods:

+
+
10 * (months(6) + days(1))
+#> [1] "60m 10d 0H 0M 0S"
+days(50) + hours(25) + minutes(2)
+#> [1] "50d 25H 2M 0S"
+
+

And of course, add them to dates. Compared to durations, periods are more likely to do what you expect:

+
+
# A leap year
+ymd("2024-01-01") + dyears(1)
+#> [1] "2024-12-31 06:00:00 UTC"
+ymd("2024-01-01") + years(1)
+#> [1] "2025-01-01"
+
+# Daylight saving time
+one_am + ddays(1)
+#> [1] "2026-03-09 02:00:00 EDT"
+one_am + days(1)
+#> [1] "2026-03-09 01:00:00 EDT"
+
+

Let’s use periods to fix an oddity related to our flight dates. Some planes appear to have arrived at their destination before they departed from New York City.

+
+
flights_dt |> 
+  filter(arr_time < dep_time) 
+#> # A tibble: 10,633 × 9
+#>   origin dest  dep_delay arr_delay dep_time            sched_dep_time     
+#>   <chr>  <chr>     <dbl>     <dbl> <dttm>              <dttm>             
+#> 1 EWR    BQN           9        -4 2013-01-01 19:29:00 2013-01-01 19:20:00
+#> 2 JFK    DFW          59        NA 2013-01-01 19:39:00 2013-01-01 18:40:00
+#> 3 EWR    TPA          -2         9 2013-01-01 20:58:00 2013-01-01 21:00:00
+#> 4 EWR    SJU          -6       -12 2013-01-01 21:02:00 2013-01-01 21:08:00
+#> 5 EWR    SFO          11       -14 2013-01-01 21:08:00 2013-01-01 20:57:00
+#> 6 LGA    FLL         -10        -2 2013-01-01 21:20:00 2013-01-01 21:30:00
+#> # ℹ 10,627 more rows
+#> # ℹ 3 more variables: arr_time <dttm>, sched_arr_time <dttm>, …
+
+

These are overnight flights. We used the same date information for both the departure and the arrival times, but these flights arrived on the following day. We can fix this by adding days(1) to the arrival time of each overnight flight.

+
+
flights_dt <- flights_dt |> 
+  mutate(
+    overnight = arr_time < dep_time,
+    arr_time = arr_time + days(overnight),
+    sched_arr_time = sched_arr_time + days(overnight)
+  )
+
+

Now all of our flights obey the laws of physics.

+
+
flights_dt |> 
+  filter(arr_time < dep_time) 
+#> # A tibble: 0 × 10
+#> # ℹ 10 variables: origin <chr>, dest <chr>, dep_delay <dbl>,
+#> #   arr_delay <dbl>, dep_time <dttm>, sched_dep_time <dttm>, …
+
+
+
+

Intervals

+

What does dyears(1) / ddays(365) return? It’s not quite one, because dyears() is defined as the number of seconds per average year, which is 365.25 days.

+

What does years(1) / days(1) return? Well, if the year was 2015 it should return 365, but if it was 2016, it should return 366! There’s not quite enough information for lubridate to give a single clear answer. What it does instead is give an estimate:

+
+
years(1) / days(1)
+#> [1] 365.25
+
+

If you want a more accurate measurement, you’ll have to use an interval. An interval is a pair of starting and ending date times, or you can think of it as a duration with a starting point.

+

You can create an interval by writing start %--% end:

+
+
y2023 <- ymd("2023-01-01") %--% ymd("2024-01-01")
+y2024 <- ymd("2024-01-01") %--% ymd("2025-01-01")
+
+y2023
+#> [1] 2023-01-01 UTC--2024-01-01 UTC
+y2024
+#> [1] 2024-01-01 UTC--2025-01-01 UTC
+
+

You could then divide it by days() to find out how many days fit in the year:

+
+
y2023 / days(1)
+#> [1] 365
+y2024 / days(1)
+#> [1] 366
+
+
+
+

Exercises

+
    +

  1. Explain days(!overnight) and days(overnight) to someone who has just started learning R. What is the key fact you need to know?

    2. +

  2. Create a vector of dates giving the first day of every month in 2015. Create a vector of dates giving the first day of every month in the current year.

    3. +

  3. Write a function that given your birthday (as a date), returns how old you are in years.

    4. +

  4. Why can’t (today() %--% (today() + years(1))) / months(1) work?

    5. +
+
+
+
+

Time zones

+

Time zones are an enormously complicated topic because of their interaction with geopolitical entities. Fortunately we don’t need to dig into all the details as they’re not all important for data analysis, but there are a few challenges we’ll need to tackle head on.

+ +

The first challenge is that everyday names of time zones tend to be ambiguous. For example, if you’re American you’re probably familiar with EST, or Eastern Standard Time. However, both Australia and Canada also have EST! To avoid confusion, R uses the international standard IANA time zones. These use a consistent naming scheme {area}/{location}, typically in the form {continent}/{city} or {ocean}/{city}. Examples include “America/New_York”, “Europe/Paris”, and “Pacific/Auckland”.

+

You might wonder why the time zone uses a city, when typically you think of time zones as associated with a country or region within a country. This is because the IANA database has to record decades worth of time zone rules. Over the course of decades, countries change names (or break apart) fairly frequently, but city names tend to stay the same. Another problem is that the name needs to reflect not only the current behavior, but also the complete history. For example, there are time zones for both “America/New_York” and “America/Detroit”. These cities both currently use Eastern Standard Time but in 1969-1972 Michigan (the state in which Detroit is located), did not follow DST, so it needs a different name. It’s worth reading the raw time zone database (available at https://www.iana.org/time-zones) just to read some of these stories!

+

You can find out what R thinks your current time zone is with Sys.timezone():

+
+
Sys.timezone()
+#> [1] "Asia/Tokyo"
+
+

(If R doesn’t know, you’ll get an NA.)

+

And see the complete list of all time zone names with OlsonNames():

+
+
length(OlsonNames())
+#> [1] 597
+head(OlsonNames())
+#> [1] "Africa/Abidjan"     "Africa/Accra"       "Africa/Addis_Ababa"
+#> [4] "Africa/Algiers"     "Africa/Asmara"      "Africa/Asmera"
+
+

In R, the time zone is an attribute of the date-time that only controls printing. For example, these three objects represent the same instant in time:

+
+
x1 <- ymd_hms("2024-06-01 12:00:00", tz = "America/New_York")
+x1
+#> [1] "2024-06-01 12:00:00 EDT"
+
+x2 <- ymd_hms("2024-06-01 18:00:00", tz = "Europe/Copenhagen")
+x2
+#> [1] "2024-06-01 18:00:00 CEST"
+
+x3 <- ymd_hms("2024-06-02 04:00:00", tz = "Pacific/Auckland")
+x3
+#> [1] "2024-06-02 04:00:00 NZST"
+
+

You can verify that they’re the same time using subtraction:

+
+
x1 - x2
+#> Time difference of 0 secs
+x1 - x3
+#> Time difference of 0 secs
+
+

Unless otherwise specified, lubridate always uses UTC. UTC (Coordinated Universal Time) is the standard time zone used by the scientific community and is roughly equivalent to GMT (Greenwich Mean Time). It does not have DST, which makes a convenient representation for computation. Operations that combine date-times, like c(), will often drop the time zone. In that case, the date-times will display in the time zone of the first element:

+
+
x4 <- c(x1, x2, x3)
+x4
+#> [1] "2024-06-01 12:00:00 EDT" "2024-06-01 12:00:00 EDT"
+#> [3] "2024-06-01 12:00:00 EDT"
+
+

You can change the time zone in two ways:

+
    +

  • Keep the instant in time the same, and change how it’s displayed. Use this when the instant is correct, but you want a more natural display.

    +
    +
    x4a <- with_tz(x4, tzone = "Australia/Lord_Howe")
+x4a
+#> [1] "2024-06-02 02:30:00 +1030" "2024-06-02 02:30:00 +1030"
+#> [3] "2024-06-02 02:30:00 +1030"
+x4a - x4
+#> Time differences in secs
+#> [1] 0 0 0
    +
    +

    (This also illustrates another challenge of times zones: they’re not all integer hour offsets!)

    • +

  • Change the underlying instant in time. Use this when you have an instant that has been labelled with the incorrect time zone, and you need to fix it.

    +
    +
    x4b <- force_tz(x4, tzone = "Australia/Lord_Howe")
+x4b
+#> [1] "2024-06-01 12:00:00 +1030" "2024-06-01 12:00:00 +1030"
+#> [3] "2024-06-01 12:00:00 +1030"
+x4b - x4
+#> Time differences in hours
+#> [1] -14.5 -14.5 -14.5
    +
    • +
+
+
+

Summary

+

This chapter has introduced you to the tools that lubridate provides to help you work with date-time data. Working with dates and times can seem harder than necessary, but hopefully this chapter has helped you see why — date-times are more complex than they seem at first glance, and handling every possible situation adds complexity. Even if your data never crosses a day light savings boundary or involves a leap year, the functions need to be able to handle it.

+

The next chapter gives a round up of missing values. You’ve seen them in a few places and have no doubt encounter in your own analysis, and it’s now time to provide a grab bag of useful techniques for dealing with them.

+ + +
+
+
+
+
    +

  1. A year is a leap year if it’s divisible by 4, unless it’s also divisible by 100, except if it’s also divisible by 400. In other words, in every set of 400 years, there’s 97 leap years.↩︎

    2. +

  2. https://xkcd.com/1179/↩︎

    3. +

  3. You might wonder what UTC stands for. It’s a compromise between the English “Coordinated Universal Time” and French “Temps Universel Coordonné”.↩︎

    4. +

  4. No prizes for guessing which country came up with the longitude system.↩︎

    5. +
+
+ +
+ +
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