Data organisation with spreadsheets

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Saving an Excel file to CSV.
Saving an Excel file to CSV.

Figure 9

The risks of having commas inside comma-separated data.
The risks of having commas inside comma-separated data.

Figure 10

A typical data analysis workflow.
A typical data analysis workflow.

R and RStudio

Figure 1

Exponential increase of the number of packages available on [CRAN](https://cran.r-project.org/), the Comprehensive R Archive Network. From the R Journal, Volume 10/2, December 2018.
Exponential increase of the number of packages available on CRAN, the Comprehensive R Archive Network. From the R Journal, Volume 10/2, December 2018.

Figure 2

RStudio interface screenshot. Clockwise from top left: Source, Environment/History, Files/Plots/Packages/Help/Viewer, Console.
RStudio interface screenshot. Clockwise from top left: Source, Environment/History, Files/Plots/Packages/Help/Viewer, Console.

Figure 3

Set 'Save workspace to .RData on exit' to 'Never'
Set ‘Save workspace to .RData on exit’ to ‘Never’

Figure 4

Set the default text encoding to UTF-8 to save us headache in the coming future. (Figure from the link above).
Set the default text encoding to UTF-8 to save us headache in the coming future. (Figure from the link above).

Figure 5

Example of a working directory structure.
Example of a working directory structure.

Figure 6

How it should look like at the beginning of this lesson
How it should look like at the beginning of this lesson

Figure 7

Directory structure for a sample bioinformatics project.
Directory structure for a sample bioinformatics project.

Figure 8

Figure 9

RStudio help interface.
RStudio help interface.

Introduction to R

Figure 1

Two normal distributions: *N(0, 1)* on the left and *N(100, 5)* on the right.
Two normal distributions: N(0, 1) on the left and N(100, 5) on the right.

Starting with data

Figure 1

Figure 2

Bar plot of the number of females and males.
Bar plot of the number of females and males.

Figure 3

Manipulating and analysing data with dplyr

Figure 1

Wide pivot of the `rna` data.
Wide pivot of the rna data.

Figure 2

Long pivot of the `rna` data.
Long pivot of the rna data.

Figure 3

Data visualization

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Figure 12

Figure 13

Figure 14

Figure 15

Figure 16

Figure 17

Figure 18

Figure 19

Figure 20

Figure 21

Figure 22

Figure 23

Figure 24

Figure 25

Figure 26

Figure 27

Figure 28

Figure 29

Figure 30

Figure 31

Figure 32

Figure 33

Figure 34

Figure 35

Figure 36

Figure 37

Figure 38

Figure 39

Figure 40

Figure 41

Figure 42

Figure 43

Figure 44

Figure 45

Figure 46

Figure 47

Figure 48

Figure 49

Figure 50

Successive layers added on top of each other.
Successive layers added on top of each other.

Figure 51

Plotting boxplots (top) and histograms (bottom) vectors (left) or a matrices (right).
Plotting boxplots (top) and histograms (bottom) vectors (left) or a matrices (right).

Next steps

Figure 1

Figure 2