Welcome to the R Coding Club

 

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.

   

Organisers

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

   

 

Location

Emory University
Room 2052
Wayne Rollins Building
(Click on marker for room directions)
 

 

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.
  • If you’re asked to choose a CRAN mirror, just choose whichever is closest to your location.
  • Install RStudio Desktop
  • 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.
install.packages("tidyverse")
library(tidyverse)

   


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 free online text to guide us through learning R.


The dataset

We’ll also be using open-access data of housing listings from AirBnb as our toy dataset. Once we’ve completed the above R for Data Science exercises, we’ll repeat the plotting exercise using these data.
 

We’ll be using this dataset for the rest of the club sessions, so feel free to explore it in your own time.
   

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.

# 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.

   

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.