index.Rmd

---
Title: "Welcome to the `R` Coding Club"
header-includes: \usepackage{float}
always_allow_html: yes
output:
  html_document
params: 
  lesson: "Index"
  title: "Welcome to the `R` Coding Club" 
---

<!-- ---------------------------------------------------------------- -->
<!-- ----------------------- rmd settings --------------------------- -->
<!-- ---------------------------------------------------------------- -->

```{r klippy, echo=FALSE, include=TRUE}
# klippy::klippy()
# rmarkdown::render_site() # render entire site 
```

<!-- rmd settings -->
```{r, set-options, echo = FALSE, cache = FALSE}
options(width=100)
knitr::opts_chunk$set(
 eval = TRUE, # run all code
 # echo = FALSE, # show code chunks in output 
 comment = "",
 tidy.opts=list(width.cutoff=100), # set width of code chunks in output
 tidy=TRUE, # make output as tidy
 message = FALSE,  # mask all messages
 warning = FALSE, # mask all warnings 
 size="small", # set code chunk size
 knitr::opts_chunk$set(fig.pos = 'H')
)
```

<!-- install packages -->
```{r, load packages, include=T, cache=F, message=F, warning=F, results='hide',echo=F}
packages <- c("ggplot2","dplyr","tidyverse","zoo","RColorBrewer","viridis","plyr","leaflet")   
if (require(packages)) {
    install.packages(packages,dependencies = T)
    require(packages)
}
lapply(packages,library,character.only=T)
```

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<!-- ____________________________________________________________________________ -->
<!-- ____________________________________________________________________________ -->
<!-- start body -->  

# `r params$title`      
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## Overview    
This page contains all the documents, links, and information for the monthly `R` coding club held at Emory University, Atlanta. We meet monthly and teach new coding concepts and techniques in a casual environment. The club is half lesson half workshop style and caters for all coding and `R` levels.      

Since Covid-19, the club has been virtual and all material remains online and free to distribute.  

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## Organisers  

**Matt Malishev, Department of Biology**    
**Desiree de Leon, Yerkes National Primate Research Center**  
**Hasse Walum, Yerkes National Primate Research Center**    

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<!-- ----------------------- image --------------------------- -->

```{r, echo = FALSE, out.width = "33%", fig.align = "center", out.extra = "style = 'display: inline;'"}

knitr::include_graphics(c("img/mmm.jpg", "img/ddl.jpeg", "img/hw.jpg"))

```

<!-- ----------------------- image --------------------------- -->
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<!-- ## Date for next session   -->
<!-- Time and day TBC -->

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## Location   
Emory University  
Room 2052    
Wayne Rollins Building     
(Click on marker for room directions)          
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```{r,echo=F}
require(leaflet)
require(dplyr)
custom_tile <- "http://b.sm.mapstack.stamen.com/((mapbox-water,$f2f7ff[hsl-color]),(positron,$f2f7ff[hsl-color]),(buildings,$f2f7ff[hsl-color]),(parks,$2c403b[hsl-color]))/{z}/{x}/{y}.png"

latlon <- c(33.79667, -84.32346) # emory
ecc_label <- "Room 2052, Wayne Rollins Building"
ecc_popup <- "Take the elevator to the second floor, \nthen exit left and take the first left."

map <- leaflet() %>%  # initiate the leaflet map object
  setView(latlon[2],latlon[1],zoom=15) %>%
  addTiles(custom_tile) %>%  # add map tiles to the leaflet object
  addMarkers(latlon[2],latlon[1],label=ecc_label,popup=ecc_popup) 
map 


```


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# Before you arrive
Everyone needs to have the following software and packages loaded before you come to the first session. That way, we can get straight into it.     
  

* [Install `R` from CRAN](https://www.r-project.org/). 
 + If you're asked to choose a CRAN mirror, just choose whichever is closest to your location.  
* [Install `RStudio` Desktop](https://www.rstudio.com/products/rstudio/download/)
* Install the `tidyverse` package and then load it by copying and pasting the following code into the *Console* of `RStudio` and then pressing *Enter* to execute it.

```{r, eval= FALSE}
install.packages("tidyverse")
library(tidyverse)
```

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<br>

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<a id="sec3"></a>  

# Learning Objectives

The Coding Club will use the `R` tidyverse suite and packages. We will use base `R` for some of the fundamentals, with the idea of implementing tidyverse functions to learn more versatile, reproducible, and efficient code.    

# Materials    

We will use the [R for Data Science](https://r4ds.had.co.nz/index.html) free online text to guide us through learning `R`.


<!-- ----------------------- image --------------------------- -->
<div align="center"; text-align:center>
  <img src="img/cover.png" style=width:33%>
</div>
<!-- ----------------------- image --------------------------- -->

<br>

# The dataset      

We'll also be using [open-access data of housing listings from AirBnb](http://insideairbnb.com/new-york-city/) as our toy dataset. Once we've completed the above **R for Data Science exercises**, we'll repeat the plotting exercise using these data.      
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We'll be using this dataset for the rest of the club sessions, so feel free to explore it in your own time.      
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<!-- ----------------------- image --------------------------- -->
<div align="center"; text-align:center>
  <img src="img/nyc.jpg" style=width:100%>
</div>
<!-- ----------------------- image --------------------------- -->  
  

This is a really big data set (it has ~50k rows !!), so it will take a moment to run. To import the AirBnb data from the URL, copy and paste the lines of code below in your `R` console and press *Enter*.   

```{r, eval = F}
# require(readr)
# url <- "http://data.insideairbnb.com/united-states/ny/new-york-city/2021-04-07/data/listings.csv.gz"
# 
# nyc_full <-  read_csv(url)
# 
# # 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 < 1000000,] # truncate dataset 
# 
# head(nyc[,1:5])

```

If everything worked correctly, you should see the below ouput printed in your `R` console.  

```{r, echo=F, eval=T}
# 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 < 1000000,] # truncate dataset 
# 
# head(nyc[,1:5])

```
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#### Save and use the above code snippet whenever you need to load our two datasets, `nyc_full` and `nyc`, as we'll be using them regularly throughout the club sessions.         

```{r, echo =FALSE, }
# devtools::install_github('yihui/xfun')

# a single RmD file
# xfun::embed_file('example.Rmd')

# embed a single csv file
 # r xfun::embed_file('data.csv')` 

```