MIC training: Modern data analysis in R/RStudio

Presentation, the code, and exercises to accompany the MIC training at UniBe, November 3, 2020.

File description

• presentation-main.Rpres and html - the main presentation with workshop lecture and exercises,
• examples - code snippets and examples for the presentation,
• practicals - data, presentation, and code for the practical session.

Required software

Please download the latest versions of the following:

• R for macOS/Linux/Windows from here, version 4.0x.
• The latest RStudio from here, version 1.3.x.

Required R packages

The workflow is written in R and takes advantage of RStudio notebooks. The analysis uses the following packages that need to be present in your RStudio installation:

• R.utils for directly reading comressed files
• magrittr for pipes (typically loaded by other packages)
• data.table for fast processing of large datasets
• readxl for reading Excel files
• ggplot2 for plotting
• imputeTS for data imputation such as interpolation of NA's
• ggthemes for additional color schemes in ggplot
• plotly for interactive plots
• RColorBrewer for extended colour palettes
• pheatmap for heatmap
• heatmaply for interactive heatmaps
• scales for percentages on y-axis in ggplots
• factoextra for extracting and visualisation of the results of multivariate data analyses
• NbClust for determining the best number of clusters
• testthat for unit testing
• profvis for profiling
• foreach with extension of the for loop
• doParallel for parallel computations
• optparse for command-line parameters

Install these packages by typing the following line in the R command-line interface:

install.packages(c(
  "data.table", "tidyverse",
  "readxl", "R.utils",
  "ggplot2", "plotly",
  "pheatmap", "heatmaply",
  "dendextend", "RColorBrewer", "scales",
  "imputeTS", 
  "factoextra", "NbClust",
  "testthat", "profvis",
  "foreach", "doParallel",
  "optparse")) 

Alternatively, you can install packages using RStudio GUI by going to Tools > Install packages...