ICO Workshop R & RStudio

Part 1

Introduction to R and RStudio

Sven De Maeyer & Tine van Daal

2nd - 4th July, 2024

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Overzicht

Some practical stuff --- (click here)
What is R and why should you use R? --- (click here)
Install R & RStudio --- (click here)
RStudio interface --- (click here)
The basics in R (click here)
Installing and using packages --- (click here)
Importing data --- (click here)
Working with R-projects --- (click here)

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About us

University of Antwerp; Faculty of Social Sciences; dept. Training- en Education Sciences
diehaRd useRs of R

About Sven?

Research interests? Psychometry, writing research, learning strategies, education for sustainable development, linguistics, ...
Main focus? Comparative Judgement + Learning from comparisons

Blog: https://svendemaeyer.netlify.app/
Twitter: @svawa
github: https://github.com/Sdemaeyer2

About Tine?

Research interests? Comparative judgement, learning from complementary texts, assessment with technology, replication studies, data visualisation, ...
Main focus? (Peer) assessment, learning (processes), educational technology, learning analytics

Twitter: @TinevanDaal
github: https://github.com/tvdaal

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Getting to now each other...

Your position?

Research?

Expectations?

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1. Some practical stuff5 / 43

Materials

Various types of files:

Rmd-files (for slides) (.Rmd)
Qmd-files (for exercises) (.Qmd)
R-scripts (as example) (.R)
data sets (.sav, .csv, ...)
html-version of slides and exercises (.html)

All these files can be found at the dedicated web-page: https://icoworkshop-rstudio-2024.netlify.app/#quick-overview

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At wo`R`k

We will provide you with the necessary information to start coding yourself.

🙏 Make it interactive! Feel free to ask questions!

Of course, we will also put you to woRk! Do you get stuck? Is R not working along?

🙏 Do not hesitate to ask our help!

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2. What is R and why should you use R?8 / 43

What is `R`?

Powerful tool for:

Data Science
Statistical analysis
Statistical programming
Data visualisation ...

A 'language'

$\to$ We will have to learn some vocabulary and grammar ...

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Why `R`?

Free AND open source!
In constant development
Thousands of "add-ins" (packages)
Almost every algorithm and technique is implemented in R
R is ahead of commercial statistical software
Can facilitate reproducibility and open science practices
Large community (Help will always be given ...)

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3. Install R and RStudio11 / 43

Install `R`

https://cran.r-project.org/

In case you haven't already installed R. Do it now! Are you stuck? Let us know.

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A quick tour in `R` ...

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Install `RStudio`

Go to https://posit.co/

Click on Download RStudio

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Install `RStudio`

Click on Download RStudio

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Install `RStudio`

Scroll down

Click on Download RStudio

Choose the right operating and download RStudio

In case you haven't already installed RStudio. Do it now! Are you stuck? Let us know.

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4. RStudio interface17 / 43

`RStudio` environment

Let us discover RStudio together!

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5. The basics in R19 / 43

Type of objects

vectors
matrices
arrays
data frames
lists
functions

...

Figure from https://www.edureka.co/blog/r-programming-language

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Vectors

Figure from https://www.edureka.co/blog/r-programming-language

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First steps in `R` ; let's make a script ...

Time to get started with the real work and code together

You can find the code on the next slides.

There is also a script Fruit.R that contains the same code (see course material for Part 1 online)

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Code-Blocks: character vector

We create a vector and call it Fruit

Fruit <- c("Apples", "Bananas", "Lemons", "Berries", "Peaches", NA)

c() represents concatenate, all elements between the brackets ( separated by ,) are 'merged' into one element
<- means that we want to store the result in an object (a vector) that we call Fruit
the " " that surround the elements indicate that these are of the type character

Let's look at the object Fruit by printing it to the console

Fruit

## [1] "Apples"  "Bananas" "Lemons"  "Berries" "Peaches" NA

We can also check the structure of the object _Fruit_ by using the **str()** function

str(Fruit)

##  chr [1:6] "Apples" "Bananas" "Lemons" "Berries" "Peaches" NA

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Code-Blocks: numeric vector

We create a vector called Weight

Weight <- c(230, 191, 93, 100, 48, 244)

In this case, the elements are 'recorded' as numeric elements

str(Weight)

##  num [1:6] 230 191 93 100 48 244

Now, we can start calculating... For example, we can apply the function mean( )

mean(Weight)

## [1] 151

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Code-Blocks: logical vector

We create a vector called Yellow

Yellow <- c(F, T, T, F, F, F)

The element of the vector Yellow are 'logical operators' (TRUE or FALSE)

str(Yellow)

##  logi [1:6] FALSE TRUE TRUE FALSE FALSE FALSE

Let us have a closer look at the vector Yellow

Yellow

## [1] FALSE  TRUE  TRUE FALSE FALSE FALSE

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Code-Blocks: a data frame

We create a data.frame called Fruit_data using the function data.frame( )

Fruit_data <- data.frame(Fruit, Weight, Yellow)

Let's have a look at *Fruit_data*

Fruit_data

##     Fruit Weight Yellow
## 1  Apples    230  FALSE
## 2 Bananas    191   TRUE
## 3  Lemons     93   TRUE
## 4 Berries    100  FALSE
## 5 Peaches     48  FALSE
## 6    <NA>    244  FALSE

We can check the structure of the object Fruit_data

str(Fruit_data)

## 'data.frame':    6 obs. of  3 variables:
##  $ Fruit : chr  "Apples" "Bananas" "Lemons" "Berries" ...
##  $ Weight: num  230 191 93 100 48 244
##  $ Yellow: logi  FALSE TRUE TRUE FALSE FALSE FALSE

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Code-Blocks: subsetting a data frame

The $ operator is used to refer to a vector

Fruit_data$Weight

## [1] 230 191  93 100  48 244

By indexing specific columns- (and/or) row-numbers can be selected [row, column]

Examples of indexing:

Retrieve element that is located at row 1 and column 1
```
Fruit_data[1,1]
```
```
## [1] "Apples"
```

Retrieve all elements in column 3

Fruit_data[,3]

## [1] FALSE  TRUE  TRUE FALSE FALSE FALSE

Retrieve all elements in row 3

Fruit_data[3,]

##    Fruit Weight Yellow
## 3 Lemons     93   TRUE

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6. Install and use packages28 / 43

Packages?

The univeRsum is in constant development
Package = extensions of the Base-functions
Range from specialised to generic/universal packages
Overview on https://cran.r-project.org/web/packages/available_packages_by_name.html
Which package(s) to use?

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Welcome to the `tidyverse`

A MUST-HAVE for everyone!

More information at https://tidyverse.org

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`install.packages( )` and `library( )`

Packages can be downloaded and installed

by 'clicking' in RStudio
by using a function in the console or script

What Sven usually does: install.packages()
What Tine usually does: click and go!

For example,

install.packages("tidyverse", 
                 dependencies = T)

Packages should be activated at the start of the session

by 'clicking' in RStudio
by using a function in de console or script

What we both usually do: library()

For example,

library("tidyverse")

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7. Working with R-projects32 / 43

Handling all these files

During analyses, we will regularly handle different files:

data sets
scripts
output (html-files; figures; ...)

Consequently, we should refer to these files in our code.

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Taking the right path ...

There are two ways to refer to these files.

USING ABSOLUTE PATHS

We can do this using an ABSOLUTE path. Below is an example of an absolute path:

'c:/Users/Sven/Mijn Documenten/UAntwerpen/Analyses/ProjectX/R_Script/Analysescript1.R'

Absolute paths make it difficult to share your work; you get into trouble when you change laptops; ...

USING RELATIVE PATHS

To avoid these problems, RStudio introduced the concept of an R-Project.

Within a project you can use RELATIVE paths (that starts from the folder in which you save the project).

Below is an example of a relative path:

'~R_Script/Analysescript1.R'

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If I put a project of Sven on my laptop, the RELATIVE paths might get me into trouble.
For example, the Rmd-file 'Slides_part1.Rmd' is located at

'c:/Users/Sven/Dropbox/ICO_R_2022/Presentations/Part 1/Slides_part1.Rmd'

Of course, I do not have the same structure on my laptop...

To facilitate sharing of projects, the package here has been created.

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Creating relative paths with function `here()`

The function here() creates a paths relative to the top-level directory of my laptop.

library(here)
here()

## [1] "/Users/demaeyer/Documents/Praatjes/Workshops/R Course ICO 2024"

here("Presentations", "Part 1", "Slides_part1_Rmd")

## [1] "/Users/demaeyer/Documents/Praatjes/Workshops/R Course ICO 2024/Presentations/Part 1/Slides_part1_Rmd"

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Exercises

Creating a project in RStudio

You can find the folder Exercises at the Github: https://github.com/Sdemaeyer2/R_workshop_ICO2024/. Download this folder and put it somewhere on your laptop.

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Creating a project in `RStudio`

Click on File/New Project...

Next, you can choose:

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8. Importing data39 / 43

Data comes in different formats

Data exist in various formats, but the most common ones are:

MS Excel
SPSS / Stata / SAS
text (csv, tab-delimited)

Various methods and packages have been developed to import these types of data

We will show some possibilities

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Importing MS Excel data with the `readxl` pakket

Make sure that the package is installed on your laptop Then, you can use the code below to import the data

library(readxl)
Data <- read_excel(
  path = "<path to map and file>"
)

Imagine: you have an excel-file Datacollection1 with multiple sheets
Goal: you only want to import the sheet with name Group2 and save it as a data frame Data_Group2

Data_Groep2 <- read_excel(
  path = "<path to map and file>/Datacollection1.xlsx",
  sheet = "Group1"
)

You shouldn't use the argument sheet if:

the excel-file only includes 1 sheet
there are multiple sheets but you only want to import the first one

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Importing SPSS data with package `foreign`

Make sure that the package is installed on your laptop Then, you can use the code below to import the data

library(foreign)
Data <- read.spss(
  file = "<path to map and file>",
  use.value.labels = FALSE,
  to.data.frame = TRUE
)

use.value.labels argument:

In SPSS, data is labelled. Do you want these labels to appear in the R data-frame?

For example. Variable with 3 categories: 1 = "Low", 2 = "Average", 3 = "High". These labels are included in the SPSS-file. You can choose to import these labels in the R data frame (use.value.labels = TRUE) or only the numbers 1, 2 and 3 that represent these categories (use.value.labels = FALSE)

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Importing csv-files with function `read.table( )`

csv-files usually look like this (separated by , or ;)

Column1, Column2, Column3
1, 3, 5
2, 4, 6
8, 10, 99

To import these data, the function read_table( ) is most convenient...

Data <- read.table(
  file = "<path to map and file>",
  header = TRUE,
  sep = ",", 
  dec = "."
)

header argument:

The first row includes column (variable) names or not?
sep argument:

Which character is used to separate the values in the different columns?
dec argument:

Which character is used to indicate a decimal point? (Can be a point or a comma)

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Overzicht

Some practical stuff --- (click here)
What is R and why should you use R? --- (click here)
Install R & RStudio --- (click here)
RStudio interface --- (click here)
The basics in R (click here)
Installing and using packages --- (click here)
Importing data --- (click here)
Working with R-projects --- (click here)

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ICO Workshop R & RStudio

Overzicht

About us

Getting to now each other...

1. Some practical stuff

Materials

At woRk

2. What is R and why should you use R?

What is R?

Why R?

3. Install R and RStudio

Install R

A quick tour in R ...

Install RStudio

Install RStudio

Install RStudio

4. RStudio interface

RStudio environment

5. The basics in R

Type of objects

Vectors

First steps in R ; let's make a script ...

Code-Blocks: character vector

Code-Blocks: numeric vector

Code-Blocks: logical vector

Code-Blocks: a data frame

Code-Blocks: subsetting a data frame

6. Install and use packages

Packages?

Welcome to the tidyverse

install.packages( ) and library( )

7. Working with R-projects

Handling all these files

Taking the right path ...

Sharing projects?

Creating relative paths with function here()

Exercises

Creating a project in RStudio

8. Importing data

Data comes in different formats

Importing MS Excel data with the readxl pakket

Importing SPSS data with package foreign

Importing csv-files with function read.table( )

Overzicht

Help

At wo`R`k

2. What is `R` and why should you use `R`?

What is `R`?

Why `R`?

3. Install `R` and `RStudio`

Install `R`

A quick tour in `R` ...

Install `RStudio`

Install `RStudio`

Install `RStudio`

4. `RStudio` interface

`RStudio` environment

5. The basics in `R`

First steps in `R` ; let's make a script ...

Welcome to the `tidyverse`

`install.packages( )` and `library( )`

7. Working with `R`-projects

Creating relative paths with function `here()`

Creating a project in `RStudio`

Importing MS Excel data with the `readxl` pakket

Importing SPSS data with package `foreign`

Importing csv-files with function `read.table( )`