General useful information

R Packages

It is possible to add functions to R by writing a package, or by obtaining a package written by someone else. As of this writing, there are over 10,000 packages available on CRAN (the comprehensive R archive network). R and RStudio have functionality for managing packages:

You can see what packages are installed by typing installed.packages()
You can install packages by typing install.packages("packagename"), where packagename is the package name, in quotes.
You can update installed packages by typing update.packages()
You can remove a package with remove.packages("packagename")
You can make a package available for use with library(packagename)

Packages can also be viewed, loaded, and detached in the Packages tab of the lower right panel in RStudio. Clicking on this tab will display all of installed packages with a checkbox next to them. If the box next to a package name is checked, the package is loaded and if it is empty, the package is not loaded. Click an empty box to load that package and click a checked box to detach that package.

Packages can be installed and updated from the Package tab with the Install and Update buttons at the top of the tab.

Challenge 2

Clean up your working environment by deleting variables in it.
Solution to challenge 2

We can use the rm command to accomplish this task
rm()

Challenge 3

Install the following packages: ggplot2, plyr, gapminder
Solution to challenge 3

We can use the install.packages() command to install the required packages.
install.packages("ggplot2")
install.packages("plyr")
install.packages("gapminder")
An alternate solution, to install multiple packages with a single install.packages() command is:
install.packages(c("ggplot2", "plyr", "gapminder"))

Reading Help files

R, and every package, provide help files for functions. The general syntax to search for help on any function, “function_name”, from a specific function that is in a package loaded into your namespace (your interactive R session):

?function_name
help(function_name)

Getting help on packages

Many packages come with “vignettes”: tutorials and extended example documentation. Without any arguments, vignette() will list all vignettes for all installed packages; vignette(package="package-name") will list all available vignettes for package-name, and vignette("vignette-name") will open the specified vignette.

If a package doesn’t have any vignettes, you can usually find help by typing help("package-name").

RStudio also has a set of excellent cheatsheets for many packages.

When you kind of remember the function

If you’re not sure what package a function is in, or how it’s specifically spelled you can do a fuzzy search:

??function_name

A fuzzy search is when you search for an approximate string match. For example, you may remember that the function to set your working directory includes “set” in its name. You can do a fuzzy search to help you identify the function:

??set

When you have no idea where to begin

If you don’t know what function or package you need to use CRAN Task Views is a specially maintained list of packages grouped into fields. This can be a good starting point.

When your code doesn’t work: seeking help from your peers

If you’re having trouble using a function, 9 times out of 10, the answers you are seeking have already been answered on Stack Overflow. You can search using the [r] tag. Please make sure to see their page on how to ask a good question.

sessionInfo()

R version 4.5.1 (2025-06-13)
Platform: x86_64-apple-darwin20
Running under: macOS Sonoma 14.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/London
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] digest_0.6.37     coro_1.1.0        R6_2.6.1          fastmap_1.2.0    
 [5] xfun_0.53         magrittr_2.0.4    rappdirs_0.3.3    glue_1.8.0       
 [9] knitr_1.50        htmltools_0.5.8.1 rmarkdown_2.30    lifecycle_1.0.4  
[13] cli_3.6.5         S7_0.2.0          compiler_4.5.1    tools_4.5.1      
[17] rstudioapi_0.17.1 evaluate_1.0.5    httr2_1.2.1       ellmer_0.3.2     
[21] htmlwidgets_1.6.4 jsonlite_2.0.0    rlang_1.1.6

Will print out your current version of R, as well as any packages you have loaded. This can be useful for others to help reproduce and debug your issue.

Other ports of call

Save the data and directories

One of R’s most powerful features is its ability to deal with tabular data - such as you may already have in a spreadsheet or a CSV file. Let’s start by making a toy dataset called feline-data.csv:

cats <- data.frame(coat = c("calico", "black", "tabby"),
                    weight = c(2.1, 5.0, 3.2),
                    likes_string = c(1, 0, 1))
write.csv(x = cats, file = "feline-data.csv", row.names = FALSE)

The contents of the new file, feline-data.csv:

coat,weight,likes_string
calico,2.1,1
black,5.0,0
tabby,3.2,1

Tip: Editing Text files in R

Alternatively, you can create feline-data.csv using a text editor (Nano), or within RStudio with the File -> New File -> Text File menu item. {: .callout}

We can load this into R via the following:

cats <- read.csv(file = "feline-data.csv", stringsAsFactors = TRUE)
cats

    coat weight likes_string
1 calico    2.1            1
2  black    5.0            0
3  tabby    3.2            1

The read.table function is used for reading in tabular data stored in a text file where the columns of data are separated by punctuation characters such as CSV files (csv = comma-separated values). Tabs and commas are the most common punctuation characters used to separate or delimit data points in csv files. For convenience R provides 2 other versions of read.table. These are: read.csv for files where the data are separated with commas and read.delim for files where the data are separated with tabs. Of these three functions read.csv is the most commonly used. If needed it is possible to override the default delimiting punctuation marks for both read.csv and read.delim.

Explore getting working directory and setting working directory as well as try out storing your data in different folders and retrieving it, making sure that you understand what a path is, directory/file relationships how to change them.