ANU BDSIworkshop Introduction to R programming

Current learning objective

-Conduct elementary arithmetic operations using R
-Navigate the RStudio interactive development environment (IDE)
-Install external packages in R to extend functionality
-Comprehend various object types in R
-Manipulate lists, matrices, and vectors in R
-Compute basic summary statistics including mean, median, quartiles, and standard deviation using R
-Grasp the concept of missing values within the R environment
Import and export data in R
-Create basic functions, employ conditional statements, and utilize for loops in R
-Decipher error messages and do basic troubleshooting

Data are stored as a file, which points to a block of computer memory.
A file format signals a way to interpret the information stored in the computer memory.
A file with the extension “csv” (comma-separated values) uses a comma as a delimiter while “tsv” uses tabs as a delimiter.

data.csv

len, supp, dose
4.2, VC, 0.5
11.5, VC, 0.5
...

Your file has to be in the right location to be read!
You may use a relative path (e.g. data/data.csv) or an absolute path (e.g. C:\\user/myproject/data.csv) to point to the right location of the data
You should avoid using absolute path! Why?
You can get and set the current path using getwd() and setwd(), respectively.

Your folder structure depends on the project, but it is generally a good idea to have a folder on its own for each project.

Within the project, it is also good to have a separate folder for:
- data
- script/analysis
- report/paper
- figures/images.

Within RStudio, you can create a project file (with an .Rproj extension).
Double clicking on this project file launches RStudio Desktop with the current working directory set to the location of the project file.
You can create this project file by going to RStudio > File > New Project …

Data can also be stored as a binary format (e.g. .RData, .rda or rds).
.RData, .rda or rds saves R objects so you don’t need the data to be in a data.frame.

Data can also come in a propriety format (e.g. xls and xlsx) – these require special ways to open/view/read it.

In RStudio Desktop, you can click on the file for importing via GUI.

Unless you are responsible for entering the data, you should never modify the original, stored data (note: exceptions do apply).
For scientific integrity, any modification to the original data should be recorded in a reproducible manner (e.g. by programming in R!) so that you can trace the exact modifications.

You can use readr::read_csv() to read CSV files.
You can use readxl::read_xlsx() to read Excel files.
Save a single R object using saveRDS() (recommended) and multiple objects using save().
In RStudio Desktop, you can click on the file for importing via GUI.
Set up R Projects and use relative path to data files.