Appendix A: Install R & RStudio

This manual covers the installation of both R and RStudio for three different operating systems: Windows, macOS and Ubuntu. You only need to follow the one that you are using on your computer.

Differences between R and RStudio

  • R is the backbone of R programming. Once R is installed, you can use it via its built-in R Console (self-contained), terminal or any third-party integrated development environment (IDE), e.g., RStudio.

  • RStudio is a multi-faceted and user-friendly IDE that can make R programming and data analysis in one place and easy to manage. We recommend using RStudio and only demonstrate with it, while you are free to use any other alternative.

Acknowledgements This manual is adapted and updated from the materials produced by Xiunan Fang and other team members in Dr Joshua Ho’s lab.

A.1 Install R (>=4.3.1)

R on Windows

  • Open an internet browser and go to https://cran.r-project.org/.
  • Click on the Download R for Windows link at the top of the page.
  • Choose the base and then Click on the Download R 4.4.1 for Windows link at the top of the page (or a new version if this manual is outdated).
  • Once the download is finished, you will obtain a file named R-4.4.1-win.exe or similar depending on the version that you download.
  • Most of the time, you will likely want to go with the defaults, so click the button Next until the process is complete.

R on macOS

  • Open an internet browser and go to https://cran.r-project.org/.
  • Click on the Download R for macOS link at the top of the page.
  • Click on the file containing the latest version of R under the Latest release.
  • Save the R-4.4.1-**.pkg file, double-click it to open, and follow the installation instructions.

Note, there are two versions of the .pkg installation file according to the CPU model: Intel Macs (Intel-based) or M1/M2 Macs (ARM-based). Please choose accordingly.

R on Linux (Ubuntu)

As commonly used in Ubuntu, prior to installing R, let us update the system package index and upgrade all our installed packages using the following two commands:

sudo apt update
sudo apt -y upgrade

After that, all that you have to do is run the following in the command line to install base R.

sudo apt -y install r-base

A.2 Install RStudio

Now that R is installed, you need to download and install RStudio. The installation of RStudio is more straightforward and very similar across the three Operating Systems.

  • Go to https://posit.co/download/rstudio-desktop/#download. We are using `RStudio Desktop Free version.
  • Click on the right file for your OS (e.g., .exe file for Windows or .dmg for MacOS)
  • The installation process is very straightforward as the figure below.

A.3 Use R inside RStudio

R studio

RStudio is a very powerful IDE and provides many useful tools through a four-pane workspace. By default, the four panels are placed as follows (you can also change the setting for your own preference):

  1. Top-left panel: Your scripts of the R codes, script is good to keep a record of your work and also convenient for command execution.

You can create a new script by File –> New –> R Script

  1. Bottom-left panel: R console for R commands, where you actually run the R codes.

  2. Top-right panel: Workspace tab: All the data(more specifically, R objects) you have created in the Workspace and all previous commands you previously ran in the History.

  3. Bottom-right panel: Files in your working directory(you probably should also set your working directory) in Files, and the plots you have created in Plots.

Set working directory

  • Create a folder named “biof_Rdir” in your preferred directory
  • Create a “data” folder in the “biof_Rdir”
  • From RStudio, use the menu to change your working directory under Session > Set Working Directory > Choose Directory
  • Choose the directory to “biof_Rdir”

Or you can type in the console:

setwd("/yourdirectory/biof_Rdir")

For Windows, the command might look like :

setwd("c:/yourdirectory/biof_Rdir")

Some general knowledge

  • R is case-sensitive
  • Type enter to run R code in the console pane
  • Ctrl-enter or Cmd-return if the code is in the scripts pane.
  • Comments come after # will not be treated as codes
  • R has some pre-loaded data sets, to see the list of pre-loaded data, type data()
  • In R, a function is an object, a basic syntax of an R function looks like something below:
function_name <- function(arg_1, arg_2, ...) {
   actual function codes
}

For example:

my_average <- function(x){
  sum(x)/length(x)
}

my_average(c(1, 4, 5, 7))
#> [1] 4.25

R contains a lot of built-in functions, you can use ? or help() to see the documentation of a function, there are also a lot of external libraries with specific functions. To use a library, we do:

install.packages('package_name')
library(package_name)

Install packages

There are several packages used in this workshop, in the R console, type:

install.packages('ggplot2')
install.packages('pheatmap')
install.packages('aod')

A4. Cloud computing

In case you have limited computing power, you can still use cloud computing to finish this course. There can be multiple options and here we mainly recommend RStudio cloud (https://posit.cloud; previously known as https://rstudio.cloud/). You can explore directly from their website.