This repository contains all the resources for the R-Workshop.
To get start, you can either clone or download the repository.
This is a two-day hands on workshop based on the book R for Data Science. You will learn how to visualize, transform, and model data in R and work with date-times, character strings, and untidy data formats. Along the way, you will learn and use many packages from the tidyverse including ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, lubridate, and forcats.
As I know everyone is busy with work, research, and classes, this workshop is not meant to teach you the basics of R, but to share some best practices in running statistical analysis in R. Hence if you want to learn more foundational R programming, please check out the pre-workshop notes to get started. However, the pre-workshop notes are not as comprehensive as the book; I extract the most fundamental elements that is most commonly used for this workshop.
Please ensure that you have the latest version of R and R studio before coming for the workshop! Download the most recent versions of R and RStudio for the appropriate OS using the links below:
In the unlikely event that there are problems with installing R and R Studio, you can use RStudio Cloud, but you'll need a laptop that can access the internet.
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The set of relevant R packages, which you can install by connecting to the internet, opening RStudio, and running:
`install.packages( c("remotes", "devtools", "rmarkdown", "knitr", "tidyverse", "kableExtra", "skimr", "haven", "readxl", "ggbeeswarm", "ggrepel", "xaringan", "DT") )
remotes::install_github('yihui/xaringan')
remotes::install_github("rstudio/fontawesome")`
In some cases for Windows users, you may encounter errors while installing some packages as you need Rtools installed. In that case, please install Rtools here.
If you're a new R user, it's possible that installing R will be challenging. In that case, feel free to ignore the instructions and just count on RStudio Cloud. You can also contact me before the workshop so we can troubleshoot together.
These are some resources to get you start or continue learning R. The list is non-exhaustive; as i find more interesting resource, I will put them here.
RStudio Cheat Sheets
UCLA's Statistics Consulting Group
R for Data Science
Our Coding Club
R for Psychological Science
Teacups, giraffes, and statistics
Based on a work at https://github.com/rstudio-education/master-the-tidyverse, https://github.com/cwickham/data-science-in-tidyverse, and https://github.com/AmeliaMN/IntroToR/