This workshop is beginner-level introduction to programming in R. The course is designed to be taught in one session of 4 hours and is focused on the application of R to the analysis of tabular data from medical datasets.
- No previous programming experience is required.
- Please review the following topics if you're not comfortable with them:
- basic linear algebra (operations with vectors and metrices)
- mathematical operations (exponentials, logarithms)
- basic statistics (mean, variance, median, standard deviation)
- logical statements (AND, OR, NOT)
Once you have setup R and RStudio copy the code below to install the packages required for the workshop.
install.packages(c("data.table","datasets","devtools","dplyr","ggplot2","plyr","medicaldata","gapminder","RColorBrewer","rmarkdown","stringr","tidyr","tidyverse","viridis"))
In the first module of the workshop, the goals are to (1) familiarize with the language and the logic behind it; (2) Get started with R studio and create your first project; (3) Configure the working directory with a common standard structure; (4) Create your first .R file to write down the live code; (5) Compute arithmetic operations; (6) Use logical operators; (7) Get fluent in R's console; (8) Learn how to ask for help within R ; and (9) get comfortabble with installing packages.
Module content:
- Syntax
- Aritmetic Operations
- Creating variables
- Logical operators
- Seeking help
- Installing packages
- Hands on: basics
In this section participants will (1) understand the differences between classes, objects and data types in R; (2) create objects of different types, learn about their attributes and apply some built-in functions in R; (3) Subset and index objects; and (4) get comfortable with vectorized operations.
Module content:
- Vectors
- Lists
- Factors
- Data frames
- Arrays
- Coercion
- Hands-on: data types
In this module participants will (1) learn how to read/write data to/from files with different formats (.tsv, .csv); (2) become familiar with basic data-frame operations; (3) index and subset data frames using base R; (4) manipulate individual data frame columns; and (5) learn how to join columns and rows of different data frames using base R.
Module content:
- Reading/writing data
- Exploring data frames
- Hands-on: basic data manipulation
The fourth module participants will (1) familiarize with the dplyr syntax; (2) create pipes with the operator %>%; (3) perform operations on data frames using dplyr and tidyr functions; and (4) implement functions from external packages by reading their documentation in R
Module content:
- Handling data frames with dplyr
- Other useful packages
- Hands-on: advanced data manipulation
This section will show participants how to (1) Create basic plots using base R functions; (2) Understand how to connect data frames with ggplot2; (3) create basic graphs with ggplot2; (4) use factors to customize graphics in ggplot2; (5) use RMarkdown to generate customized reports.
Module content:
- Figures with base R
- Graphics with
ggplot2
This hands-on activity will familiarize participants with a real-life use of R in the pharma industry environment and encorage them to apply the knowledge from previous modules to create an analysis pipeline.
To conclude the workshop, participants will (1) acquire good coding practices; (2) be familiar with code documentation standatds; (3) know what to avoid when programming in R; (4) learn how to debug and troubbleshoot their own code.
Module content:
- Good coding practices
- Style guide
- Debugging and troubleshooting
The materials for this workshop were based on the following sources:
Workshop created as part of the McGill Initiative in Computational Medicine