R introduction – E-Learning@VIB

E-Learning

R introduction

This material is linked to an online, remote training. Most of the theory can be found here on the e-learning system. The online sessions repeat the theory and are mostly intended to get practical experience. Please visit https://training.vib.be to check for an upcoming course.

Material

Tutorial with the exercises

Folder with the data sets

Software

R

RStudio

Install R and RStudio on Mac: video tutorial created by Gunesh Shanbhag on installing R and RStudio on Mac and short overview of the RStudio user interface on Mac.

External links

online documentation

Google for R

programming rules for R

RStudio keyboard shortcuts

External links for graphing

hexadecimal color codes

names of the colors

names and numbers of lines and shapes

examples of graphs (all graphing systems)

online book on ggplot2

ggplot2 tutorial

Course Content

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Script editor and console

Files tab: setting the working directory

Packages tab: installing packages

Help tab: getting help

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Logical operators

Combine logical operations

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Create variables by assigning a value

Create variables by running a function

Using multiple functions after each other

Functions with the same name from different packages

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Create a vector

Retrieve elements from a vector

Change or remove elements from a vector

Logical and arithmetic operations on variables

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Create a data frame

Retrieve elements from a data frame

Remove columns from a data frame

Add and reorder columns in a data frame

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Mathematical functions for vectors and tables

Functions for tables: names of rows/columns and merging

Functions that work on text

Functions that work on factors

Sort versus order

Functions that return indices

Functions to check and change the type of variables

Functions for handling missing values

Operations on rows of tables are hard

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Calculate descriptive statistics

apply() as a substitute for for-loops in R

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Define the file you want to read

The read functions

Arguments of the read functions

Reading compressed files

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Define what you want to plot

Define what you want to plot for a plot with 2 factors

The philosophy behind ggplot2

Plot statistics with stat_summary()

Why factor() is important in plots

Plot the data with geoms

Arguments to set colors, shapes, line types, position…

Plots with points: scatter, bubble, hexbin and beeswarm plots

Plots with bars and points

Plot distributions: histogram, QQ, box, violin and ridge plots

Adding lines to a plot

Adding text labels to a plot

Adding titles to a plot

Scales define what the axes and the legend look like

Themes define all remaining characteristics of a plot

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Adding regression lines to a plot

Doing the regression yourself

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Facets: multiple plots of the same type on one graph

patchwork: combining plots and tables on one graph

ggpubr: alternative package to combine plots and tables

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Heat maps with ggplot2

Heat maps with pheatmap

Doing the clustering yourself

Line plot with ggplot2: prep

Line plot with ggplot2: the code

Visualize a phylogenetic tree with ggtree

PCA plots with ggfortify

Plotly for 3D plots and interactive plots

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Checking assumptions of parametric tests

Comparing two groups

lm() for comparing groups

Checking normality of residuals

Multiple tests, one per row

Add p-values to a plot

Pairwise comparisons of groups after one way ANOVA

Kruskal Wallis: non parametric counterpart of one way ANOVA

Two main ANOVA: check main effects

Two way ANOVA: check interaction

The use of lm() for modeling

Other functions for modeling and comparing groups

Comparisons of unranked categorical data

Randomness in R

Survival analysis

1 of 2

Contributors: Janick Mathys, Joris De Wolf

Created by Janick Mathys

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