Independent and Paired T-test using R Worksheet

In this worksheet you will learn how to use R to perform an independent or Paired T-test. This test takes 2 groups which are unpaired or paired (see suggested video below) and calculates if there is a statistically significant difference between them.

Important notes

You cannot do repeated T-tests if you have multiple groups. T-test is only for comparing two groups. If you have 3 or more groups you should do One Way ANOVA.

You must have R installed on your computer and ideally also RStudio. See https://rstudio-education.github.io/hopr/starting.html for a guide.
You have confirmed your data is suitable for parametric tests by testing normality and homogeny of variance, following the Normality and homogeny of variance testing using R worksheet.
- NOTE: If your data requires a non-parametric test, the equivalent for an independent T-test is the wilcox rank sum test. Simply replace every mention of t.test below with wilcox

It is recommended that you have followed the Concepts in Computer Programming and Introduction to R tutorials before starting.
An understanding of tidy data. See https://www.youtube.com/watch?v=KW1laBLEiw0
An understanding of paired vs unpaired samples. See https://www.youtube.com/watch?v=-6vDjGR41YM
An understanding of parametric tests vs non-parametric tests: https://www.youtube.com/watch?v=biXY84hDX5M
An understanding of the melt format in R: https://www.statology.org/melt-in-r/

Open RStudio
Read in the UnpairedDataset1.tsv file. The read.delim will automatically assign row 1 as a header so no extra flags need to be passed to it
```
unpaired <- read.delim(“UnpairedDataset1.tsv")
```
T-tests are only performed on two groups, but our dataset has 3 groups. We will select just Group 1 and Group 2 for the test
```
unpairedGroups <- unpaired[,c("Group.1","Group.2")]
```
To perform a t-test the data must in a ‘melted’ long format. Install the reshape 2 package (if needed) and then load the library
```
install.packages("reshape2")
library("reshape2")
```
Melt the data frame so it is in the right format for the T-test
```
meltedUnpairedGroups=melt(unpairedGroups)
```

Perform the t-test and store in a variable

up_ttest = t.test(value ~ variable, data=meltedUnpairedGroups)

View the p-value of the test
```
up_ttest$p.value
```
Paired Steps
Open RStudio
Read in the Paired.Dataset1.tsv file. This file has sample names in column 1 so you must pass that flag to read.delim
```
paired <- read.delim("PairedDataset1.tsv", row.names=1)
```
T-tests are only performed on two groups, but our dataset has 3 groups. We will select just Condition 1 and Condition 2 for the test
```
pairedGroups <- paired[,c("Condition.1","Condition.2")]
```
To perform a t-test the data must in a ‘melted’ long format. Install the reshape 2 package (if needed) and then load the library
```
install.packages("reshape2")
library("reshape2")
```
Melt the data frame so it is in the right format for the T-test
```
meltedPairedGroups=melt(pairedGroups)
```
Perform the t-test and store in a variable. Note the use of the paired flag for paired data
```
p_ttest = t.test(value ~ variable, data=meltedPairedGroups , paired= TRUE)
```
View the p-value of the test
```
p_ttest$p.value
```