Working with R Projects #

The Bigger Picture #

In this document we learn how to use projects to help work in R Studio more efficiently. This skill has broad application in many aread of the data visualisation and communication workflow.

References #

This content refers to the Tidyverse LinkedIn Learning tutorial 2.1 - 2.3

What are R projects? #

R projects are a type of file which function with RStudio
They have the .Rproj file extension
R projects are each associated with a directory
They are useful when working with many files for one purpose, hence the name “project”
A great feature is that they “know” which files are relevant to a project, when you open the project RStudio will load those files automatically.

What do they do? #

If we are working in an R project, we automatically have our working directory set as the folder that the project is associated with
The RMarkdown file used to produce these notes is within a project, so we can call on images from the image folder without specifying the full file path

knitr::include_graphics("images/RStudio-Ball.png")

In this example, we only need to specify the sub-folder “images” and the image “Rstudio-Ball.png”

How to create projects #

At the top right of our RStudio workspace, there is a project button
You can select an existing project or create a new one
This button will also tell you which project is currently open

If we are creating a new project, we may select a new or existing directory

How to navigate existing projects #

The “Files” tab of RStudio will automatically view the project directory when the project is loaded
From here we can navigate files

.R files (R script files) can be created in our project directory as normal
Clicking a .R file in the project viewer opens it
.Rmd files (RMarkdown files) can likewise be created and opened

Structuring R projects #

Keeping files neat and tidy in R is an art form
It is suggested folders are used to keep separate components of data separate
In this example our directory separates raw and tidied data
One can still access files from these folders without issue

save() and load() #

Notice that in this project, we keep raw and tidy versions of the same data in different folders
This is possible because we can save R objects in one file and reload them in another file
For example, the raw_data folder contains a file called survey.txt which looks like this:

library("readr")
read_file("raw_data/survey.txt")

## [1] "Who in my family likes cereal? June 18/06/2019\r\n\r\nMe No\r\nMum Yes\r\nDad No\r\nSister Yes\r\nBrother No\r\nGrandma Yes\r\nGrandpa Yes"

In the file data_tidying.R (in which the data was tidied up), the save() function was used as below:

save(data, file = "tidy_data/data.rdata")

Note the first argument is the object to save (data) and the second is the file path
The file must be named the same as the name of the object
The .rdata extension must be included
We may want to load this data later in another file (for example, this .Rmd file) with load()

library("tidyverse")
load("tidy_data/survey_data.rdata")
survey_data

## # A tibble: 7 x 2
##   family_member cereal_preference
##   <chr>         <chr>            
## 1 Me            No               
## 2 Mum           Yes              
## 3 Dad           No               
## 4 Sister        Yes              
## 5 Brother       No               
## 6 Grandma       Yes              
## 7 Grandpa       Yes

The loaded file automatically takes the name of the original file

Side note: in practice never name your data “data”. It may lead to errors when loading the file, and you should use something more descriptive (such as “fam_cereal”. We only used it here for demonstration purposes.

So to summarise, with save() and load() we can split up our R projects into several script files for different purposes.