Lesson1.Rmd

---
params:
  lesson: "Lesson 1"
  title: "Intro to plotting data in R with `ggplot`"
  bookchapter_name: "Section 3.1.1"    
  bookchapter_section: "https://r4ds.had.co.nz/data-visualisation.html"
  functions: "`?`, `str`, `glimpse`, `summary`, `table`, `min`, `max`, `ggplot`, `geom_point`, `geom_smooth`, `theme_minimal`, `theme_classic`, `theme_tufte`"
  packages: "`tidyverse`, `ggplot2`, `dplyr`"      
  # end inputs ---------------------------------------------------------------
header-includes: \usepackage{float}
always_allow_html: yes
output:
  html_document:
    code_folding: show
    code_download: true
---
  
```{r, setup, echo = FALSE, cache = FALSE, include = FALSE}
options(width=100)
knitr::opts_chunk$set(
  eval = FALSE, # run all code
  echo = TRUE, # show code chunks in output 
  tidy = TRUE, # make output as tidy
  message = FALSE,  # mask all messages
  warning = FALSE, # mask all warnings 
  comment = "",
  tidy.opts=list(width.cutoff=100), # set width of code chunks in output
  size="small" # set code chunk size
  )
```
\

<!-- install packages -->
```{r, load packages, eval=T, include=T, cache=F, message=F, warning=F, results='hide',echo=F}
packages <- c("ggplot2","ggthemes","dplyr","tidyverse","zoo","RColorBrewer","viridis","plyr")   
if (require(packages)) {
    install.packages(packages,dependencies = T)
    require(packages)
    # load tvthemes
    # devtools::install_github("Ryo-N7/tvthemes")
}
lapply(packages,library,character.only=T)  
```

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# `r paste0(params$lesson,": ",params$title)`    
\  

Functions for `r params$lesson`  
`r params$functions`    
\    

Packages for `r params$lesson`          
`r params$packages`        
\    

# Agenda  

[Data visualisation in `R` for Data Science, `r params$bookchapter_name`](`r params$bookchapter_section`).    

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* Intro to the `R`environment (IDE)  
* Loading packages, e.g. `tidyverse`  
* Using built-in `R` data: the `mpg` dataset   
* Using ggplot with the built-in data set (to make scatterplots)    
* Modifying plot aesthetics  
* Reading in outside data: Airbnb data  
* Plotting Airbnb data with ggplot      

\  

# Intro to the `R` environment (IDE)  

The `RStudio` integrated development environment (IDE) and what you can do with it.  
\    

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<div align="center">
  <img src="img/rstudio1.jpg" style=width:100%>
</div>
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A more complete example of what you can acheive with the interface.  
\  

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<div align="center">
  <img src="img/rstudio2.jpg" style=width:100%>
</div>
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# Loading packages, e.g. `tidyverse`  

How to load packages in `R`.  

```{r}
install.packages("tidyverse")  # install package
library(tidyverse) # load the package library
require(tidyverse) # same as library    

# We are typing in an R Script. Things with # in front make them comments and notes to ourselves
# Command Return to execute the line/ "run the code"
```
\  

# Using built-in `R` data: the `mpg` dataset   
[Section 3.2.1](https://r4ds.had.co.nz/data-visualisation.html#the-mpg-data-frame)  

We'll use a built-in tidyverse dataset called `mpg` with data about cars and gas-mileage.

```{r}
mpg
# run help page with '?'
?mpg   
```

* This is a tibble (data frame) that we've "printed" out. It's like R's version of an excel spreadsheet, but much better. 
* A tibble will show us the first 10 rows, rows containing the data, column names, and the class of data within each column, such as numeric, integer, or character.    

## Summarising data        
```{r}
str(mpg) # structure of data
glimpse(mpg) # preview of data 
summary(mpg) # basic summary stats  
table(mpg$manufacturer) # counts of each column
head(mpg) # visualise first 6 rows of data
tail(mpg,10) # visualise last 10 (or N) rows of data 
names(mpg) # get column names
class(mpg) # class of data frame
class(mpg$manufacturer) # class of data column
mpg$displ # print a column
mpg$hwy # print a column
```
\    

# Creating a plot with `ggplot`  
[Section 3.2.2](https://r4ds.had.co.nz/data-visualisation.html#creating-a-ggplot)     

* `ggplot()` Creates a coordinate system for us--basically an empty graph.  
* `geom_point()` Adds a "layer", e.g. geom_point (but there are many for different kinds of graphs).  

Plot two of the data columns  
```{r, eval = T}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))
```
\  

Changing the data column inputs for the **x** and **y** axis of the plot  
```{r, eval = T}
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = class, y = drv))
```
\  

Assign data to variables to create dynamic inputs  
```{r, eval = T}
my_data <- mpg # create own variable using a name of your choice  

ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy))
```
\  

## Themes  

Change plot style. Link for more [ggplot themes](https://www.datanovia.com/en/blog/ggplot-themes-gallery/).        
```{r, eval = T}
require(ggthemes)

# classic theme
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  theme_minimal()

# minimal theme
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  theme_tufte()  

# assign theme to variable  
my_theme <- theme_classic() 
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  my_theme # apply your chosen theme  

```
\  

# Aesthetic mapping  
[Section 3.3](https://r4ds.had.co.nz/data-visualisation.html#aesthetic-mappings)  

`colour`. Change the colour of the data points.
`size`. Change the size of the data points.  
`alpha`. Change the transparency of the data points.   

## Colour

Colour by colour name. 
```{r, eval = T}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy), colour = "light blue") +
  my_theme
  
```
\   

Colour by a [hex code](https://htmlcolorcodes.com/color-picker/) in quotes.  
```{r, eval = T}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy), colour = "#BB5C42") +
  my_theme
```
\  

colour by data column  
```{r, eval = T}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy, colour = class)) +
  my_theme
```
\  

Inside versus outside the `aes`  
```{r, eval = T}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy, colour = "blue")) +
  my_theme
```
\  

## Size 

Size by integer  
```{r, eval = T}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = 5)) +
  my_theme
```
\  

Size by data column      
```{r, eval = T}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class)) +
  my_theme
```
\  

We get a warning, but this is okay.  
\  

## Transparency 
```{r, eval = T}
# map classe column to different transparencies
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class)) + 
  my_theme
```
\  

## Shape  
```{r, eval = T}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class)) +
  my_theme
```
\  

Any warnings? Yes, because shape maxes out at six levels.
\  

## Manually changing aesthetic properties
  
But we can *set* the aesthetic properties manually, instead of having ggplot do the scaling automatically. For example, we can make our ggplot points all blue like this. This time, putting colour OUTSIDE the `aes` argument.

```{r, eval = T}
ggplot(data = my_data) +  
  geom_point(mapping = aes(x = displ, y = hwy), colour = "blue") +
  my_theme
```
\  

Using colour both inside and outside the aes  
```{r, eval = T}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy, colour = class), colour = "#AE42BB") +
  my_theme
```
\  

**The inner one is overridden.**     
\  

Putting it all together as a snapshot of what's possible  
```{r, eval = T}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = displ, y = hwy, colour = class, size = class, alpha = class)) +
  my_theme
```

## Aesthetics you can manually set  

  * The name of a colour as a character string.
  * The size of a point in mm.
  * The shape of a point as a number, as shown in Figure 3.1. 
  
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<div align="center">
  <img src="img/shapes.png" style=width:100%>
</div>
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`R` has 25 built in shapes that are identified by numbers. There are some seeming duplicates: for example, 0, 15, and 22 are all squares. The difference comes from the interaction of the `colour` and `fill` aesthetics. The hollow shapes (0--14) have a border determined by `colour`; the solid shapes (15--18) are filled with `colour`; the filled shapes (21--24) have a border of `colour` and are filled with `fill`.  

\newpage      

# Further plotting examples    
[Section 3.3.1](https://r4ds.had.co.nz/data-visualisation.html#exercises-1)  

The online reference contains further examples of how to visualise your data.    

# Reading in outside data: NYC Airbnb data  

```{r, eval = T, echo = F}
library(tidyverse) # includes package "readr"
# All Airbnb data (106 cols)
url <- "http://data.insideairbnb.com/united-states/ny/new-york-city/2021-04-07/data/listings.csv.gz"

nyc_full <- read_csv(url) # reads in data
head(nyc_full)
```
\  

Using a smaller dataset 
```{r, eval = T, results="hide"}
# smaller csv file (16 cols)
url <- "http://data.insideairbnb.com/united-states/ny/new-york-city/2021-04-07/data/listings.csv.gz"

nyc <-  read_csv(url)
nyc <- nyc[nyc$id < 20000,] # get smaller subet of data
length(nyc$id) # print length of 'id' column
head(nyc)
``` 
\    

# Plotting AirBnB data with ggplot  

Using the above plotting functions to visualise the AirBnB data  

```{r, eval = T}
# plot neighborhood_group vs price
ggplot(data = nyc) + 
  geom_point(mapping = aes(x = neighbourhood_group_cleansed, y = price, colour =
                             neighbourhood_group_cleansed), shape = 21, stroke = 1) +
  my_theme
```

```{r, eval=T}
# plot minimum_nights vs price
ggplot(data = nyc) + 
  geom_point(mapping = aes(x = minimum_nights, y = price, 
                           colour = neighbourhood_group_cleansed), shape = 20, size = 3, stroke = 1) +
  my_theme
```

```{r, eval=T}
# availability_365 vs price
ggplot(data = nyc) + 
  geom_point(mapping = aes(x = availability_365, y = price, 
                           colour = neighbourhood_group_cleansed), shape = 21, stroke = 1) +
  my_theme
```
    
```{r, eval=T}
# plot longitude vs price
ggplot(data = nyc) + 
  geom_point(mapping = aes(x = longitude, y = price, 
                           colour = neighbourhood_group_cleansed), shape = 21, stroke = 1) +
  my_theme
```


Try your own plot using the other variables in the dataset    
```{r}
# plot neighborhood_group vs price
names(airbnb)
glimpse(airbnb)

my_data <- NULL
x <- NULL
y <- NULL
colour <- NULL
shape <- NULL
stroke <- NULL
```

```{r, echo=F}
ggplot(data = my_data) + 
  geom_point(mapping = aes(x = x, y = y, colour = colour), shape = shape, stroke = stroke)
```

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