R

• Free, open source software and programming language

• Commonly used in psychology research for data analysis and visualization

• We will be using R in this class to do most of our labs and homeworks.

• If you have not done so already, please download R from https://cran.rstudio.com/

RStudio

• Download link: https://posit.co/download/rstudio-desktop/

• Integrated Development Environment (IDE) for R

  • Although R is powerful on its own, it may be a bit clunky to use without RStudio.

• Also download the lab1_template.R and lab1.csv files from the course canvas

Rstudio

• You should now see a screen that looks something like this. For now, let’s only focus on the console on the left side of the screen.

Console + Expressions

• You can type code one line of code and the console will evaluate it for you. For example:

1 + 1

[1] 2

(You can ignore the [1] for now)

• You can do any almost any mathematical operation you want using symbols such as +, -, *, /, etc. Remember that order of operations apply, and you can parentheses () as well.

(1 + 5) / 2 * 3 - 4

[1] 5

Variables

• Oftentimes you want to store some sort of data in a place so that you can use this later. In R, we do this using variables and the assignment operator <-.

x <- 1

• You’ll notice that this time there is no output but something else changed on your screen (what is it?)

• Now, whenever we write x it will take on the value of whatever it was assigned to.

x

[1] 1

x + 1

[1] 2

• You can also assign x back to itself in order to update its value.

x <- x + 100
x

[1] 101

Vectors

• Oftentimes you want to store more than a single number in a variable. You can do so using c() around numbers surrounded by commas.

v <- c(1, 2, 3)

• You can do operations on every number in the vector much like you can with variables.

v * 2

[1] 2 4 6

v + 1

[1] 2 3 4

• You can also obtain a specific number from a vector by specifying its order using square brackets []. This number is known as its index.

v[1]

[1] 1

Data Frames

• Most data that we will be working on in this class come in a tabular format. Excel Spreadsheets are a good example of this.

• We can represent the above data in R using a data frame.

x <- c(1, 2, 3)
y <- c(2, 3, 4)
z <- c(3, 4, 5)
df <- data.frame(x, y, z)
df

  x y z
1 1 2 3
2 2 3 4
3 3 4 5

• As you can see, we first created three vectors x, y, and z. Then, we made a data frame out of those three vectors.

• Data frames can also have operations applied to every number

df * 3

  x  y  z
1 3  6  9
2 6  9 12
3 9 12 15

• You can obtain a specific column by using the $ symbol and the name of the column

df$x

[1] 1 2 3

Opening a .R file

• On the top right of the RStudio, click File –> Open File, and select lab1_template.R.

• You should see something that looks like this on the upper left panel:

  This is your script

Running a file

• You may run the file one line at a time just like the console by pressing the Run button.

• You can also run the entire file at once by clicking on the Source button.

  • Unlike with the console, this will not display the output automatically. However, variables are still updated as usual.

  • To display the output of a variable, use the print() function.

• # denotes lines which will not be run with the rest of the file.

  • Is used to make notes or test out code.

  • Try deleting the # on the last line of code and clicking Source to see what happens.

Files

• Oftentimes you need to use data from files.

• One common file type you will use are .csv files, which stands for comma separated values.

• Try opening the lab1.csv file to see what it looks like.

Set Working Directory

• To make the next part of this presentation work, you must change your current working directory.

  • It’s ok if you don’t know what this means just yet! Basically this is the location from which your code will run.

• Go to Session –> Set Working Directory –> To Source File Location

  • You will have to repeat this step every time you open up a new session in RStudio

read.csv

• You can use the data inside the lab1.csv file by using read.csv() :

df = read.csv("lab1.csv")
df

  x  y
1 1  1
2 2  3
3 3  5
4 4  7
5 5  9
6 6 11
7 7 13
8 8 15
9 9 17

• This will automatically read your .csv data into a data frame, with the first row being the names of each column.

Filenames and Directories

• To be usable by your program, your .csv file must be in the same folder as your .R file.

  • There are exceptions to this.

• Try moving your lab1.csv file to a different location and run read.csv() again to see what happens.

• You can navigate around your files by using the bottom right panel, which we have been ignoring up until now.

Installing Libraries

• Sometimes the base R doesn’t have all of the tools that we need.

• Let’s install psych, which provides functions for psychological research.

# install.packages("psych")

• You will only need to do this step once after which the library will be permanently installed on your machine.

Importing Libraries

• After you install the library, you can have all of its functions become available to you by using library() .

library(psych)

• Now you can use functions such as describe()

describe(df)

  vars n mean   sd median trimmed  mad min max range skew kurtosis   se
x    1 9    5 2.74      5       5 2.97   1   9     8    0     -1.6 0.91
y    2 9    9 5.48      9       9 5.93   1  17    16    0     -1.6 1.83

• If you were to try to use describe() without either installing or importing the library, you will get an error.