# 1 Course objectives

In this course you will learn how to visualize your data in R. In particular, you will learn:

• how to format the data necessary for ggplot
• how to make bar-charts, box-plots and others using ggplot
• how to make PCA plots in ggplot
• how to use R packages for heatmaps
• how to plot data on maps using R (optional)
• how to plot and handle phylogenetic trees in R (optional)
• how to make interactive plots in R using Rshiny
• how to host a Rshiny app in one of the available ser

# 2 Page Info

This page contains links to different lectures (slides) and practical exercises (labs) that are part of this workshop. The links below are similar to that under Schedule but organised here by topic.

Input code blocks are displayed like shown below. The code language is displayed above the block. Shell scripts (SH) are to be executed in the linux terminal. R scripts are to be run in R either through the terminal, RGui or RStudio.

sh
command

This topic covers retrieving data needed for all the exercises in the entire course can be found in the following link.

Some pre-course R warm-up below, if you fancy that:

R warmup

# 3 Introduction

Most of the analyses is carried out in R and it will be useful to learn some basics in R that are very much necessary for this course. Even if you have learnt these things already, it is good to freshen up your memory.

Introduction to R

# 4 Main lab

## 4.1 Data

In most of the exercises, we will use RNA-seq data (Illumina short reads) from the human A431 cell line. It is an epidermoid carcinoma cell line which is often used to study cancer and the cell cycle, and as a sort of positive control of epidermal growth factor receptor (EGFR) expression. A431 cells express very high levels of EGFR, in contrast to normal human fibroblasts.

The A431 cells were treated with gefinitib, which is an EGFR inhibitor and is used (under the trade name Iressa) as a drug to treat cancers with mutated and overactive EGFR. In the experiment, RNA was extracted at four time points: before the gefinitib treatment (t=0), and two, six and twenty-four hours after treatment (t=2, t=6, t=24, respectively), and sequenced using an Illumina HiSeq instrument in triplicates (thus there are 3x4=12 samples).

This data is part of the transcriptomics course that is also given by NBIS. We will use some of the counts table that was generated in the course after different transformations like manual filtering for low counts, VST and DESeq2. You don’t have to know what these exactly mean to do these exercises :) These are basically the same data at different stages of the transcriptomics analysis.

## 4.2 ggplot part 1

Below is the link for the basic plotting exercise. there will be some basic plotting exercises first using R base graphics and comparing them to the grid graphics using ggplot2. Followed by practicing the first basics in ggplot: geoms, colors and aesthetics.

Geoms, colors and aesthetics

## 4.3 ggplot part 2

Below is the link for the second exercise where you will look into facets, barplots and errorbars.

Facets, barplots and errorbars

## 4.4 ggplot part 3

Below is the link for the third exercise where you will look into facets, barplots and errorbars.

Axes, labels, legends and themes

## 4.5 Combining plots

In this part of the lab, we will look into: how one can combine different plots that we have made using different tools. Also to look into some of the advantages of cowplot and ggpubr.

Combining plots

## 4.6 PCA and Heatmaps

Here, we look into building PCA plots with ggplot. We will also look into making heatmaps with both pheatmap and geom_tile in ggplot.

PCA and Heatmap

## 4.7 Rshiny Part I

Here, we get introduced to how to start writing a Rshiny code. So, naturally we start to play around with the UI mainly to see what kinds of different UIs that one can use.

Rshiny Part I

## 4.8 Rshiny Part II

Here, we look into some of the important Rshiny functions that one can use in the server part of the shiny code like reactivity, Error validation and Download options in the server.

Rshiny Part II

## 4.9 Rshiny Covid App

In this part, we learn how to write an app from scratch! So, we take the visualization of the covid data as an example from few countries that we would like to plot!

Rshiny Covid App

# 5 Optional exercises

Below are specific exercises for people who are interested in specific topics.

## 5.1 Phylogenetic trees

Below are some exercises in working with phylogenetic trees in R using mainly a package called ggtree.

Phylogenetic trees

## 5.2 Map-data using ggmap

Below is if you want publication-grade phylogenetic trees.

Map-data using ggmap

# 6 Solutions to exercises:

Here I have compiled all the solutions for the different exercises in each of the sections:

Solutions

# 7 Rshiny

This part contains all the labs and details to learn to make a simple Rshiny app.

## 7.1 Rshiny Intro

This section contains the very simple terms of what an Rshiny app is and it’s main components.

Rshiny Intro

## 7.2 Rshiny modules

This section contains explanations and tutorials of some of the functions to modularize the Rshiny app’s function in general.

Modularizing Rshiny

## 7.3 A simple app

This section basically contains how to go about starting and making a simple Rshiny app.

A simple app

# 9 Extra Tutorial for ggplot

In the following link, you can find several different ways to use ggplot and obtain incredible visualizations of the data. Depending on the kind of data you have and the kind of visualization you would like to see, you can follow it on the table contents in this following page:

Top 50 ggplot2 Visualizations