POLISCI 9590 - Lab0: Introduction to R and RMD
William Poirier
2025-09-10
0. Bonjour/Hi!
- Office: SSC 7336
- Email: wpoirier@uwo.ca
- Contact policy:
- Open door = please come in, I’ll be happy to help!
- Closed door = I’m working or in a meeting! Knock if important and urgent!
1. R and RStudio
1.2 Coding languages VS IDE
- R is a language, like Spanish, Japanese, or French.
- RStudio is an integrated development environments (IDE), like paper or a word document.
- Hence, you write a language (R) on paper (RStudio).
1.3 RStudio – A Tour
When you open the app.
Once you open the editor
Running your first line of code
- To run a line of code:
- Put cursor anywhere on line and press
cmd + enter/ctrl + enter. - Select line or multiple lines and press
cmd + enter/ctrl + enter. - Select line or multiple lines and click on
Runbutton at top of screen.
- Put cursor anywhere on line and press
1.4 Managing your directories
- Directory: a file system.
- Working directory: the folder your computer is currently pointing to.
You need to tell your computer where to look at in order for it to find the documents, datasets, or files you want to use.
So PLEASE, make sure to organize your files in some way. Otherwise you are going to get lost.
- Shortcut, use when folder/file selected via the Finder (MAC) or File
Explorer (PC):
- Mac:
option + command + c - PC:
shift + right click > Copy as Path
- Mac:
# Option 1: where you want
setwd("/Users/williampoirier/Dropbox/Website/files/uwo/R_Workshop_2025/rcode") # Mac
setwd("C:\Users\williampoirier\Dropbox\Website\files\uwo\R_Workshop_2025\rcode") # PC
# Option 2: where the R file is saved
setwd(dirname(rstudioapi::getSourceEditorContext()$path))2. RMarkdown
2.1 What?
- Markup language: a text-encoding system tailored
towards the format and structure of a document. Some popular ones:
- HTML for websites,
- LaTeX for pdf,
- Markdown for websites, pdf, and Word.
2.3 Why R-Markdown?
- RMarkdown is an extension of Markdown.
- Allow the integration of
code chunks. - Very useful for short technical reports, like the homework you’ll be asked to produce.
- This tutorial was done via an RMarkdown file!
2.4 How?
First, make sure it is installed by running the following line in your console. You won’t have to do this again.
install.packages("rmarkdown")- As with R files, Files > New File > R Markdown…
- Basic formatting:
- Another bullet point.
- Headers from largest to smallest: #, ##, ###, etc.
- Bullet points and numbered list: indentation and either - or a number.
- Italic: *italic stuff*.
- Bold: **bold stuff**.
- Comments: <!-- a comment -->
- Write math: \(\frac{\sum_{i=1}^{n}x_i}{n}\)
3. Writing code in RMD files
Code must be place within code chuncks.
Initiate them by writing delimiters
```{r}and```.Or use a shortcut to initiate code chuncks:
- PC:
Ctrl + Alt + i - MAC:
Cmd + Option + i
- PC:
3.1 Installing and Loading Packages
RShips with many functions. But sometimes we need other functions.- We
installandloadpackages to add functions to our library. - Packages are boxes filled with functions.
install.packages("a package")installs packages.library(a package)loads (or makes available) the functions from a specific package.
# Installing packages (you do this only once)
install.packages("tidyverse")
install.packages("rio")
install.packages("devtools")
devtools::install_github("https://github.com/davidaarmstrong/damisc.git")
install.packages("knitr")
# Loading packages
library(tidyverse)
library(rio)
library(devtools)
library(DAMisc)
library(knitr)3.2 Basic Operations
Try running those operations yourself!
# Assignment (putting what's on the right in the box that's on the left.)
A <- 1
B <- "1"
C <- TRUE
D <- 3L
# Add
A+A
# [1] 2 (This is me writing what the console is going to output.)
# Substract
A-B
# Error in A - B : non-numeric argument to binary operator
# Why is this not working???
# Multiply
A*C
# [1] 1
# Why does this one work?
# Divide
A/A
# [1] 1
# Power
D^D
# [1] 27
# Square root
sqrt(D^D)
# [1] 5.196152
# Modulo
12%%5
# [1] 23.3 Data types
There are 4 main data types in R:
- Numeric: The real numbers (-1,-.5,0,.5,1,3.141593…)
- Character: Text
- Logical:
TRUE(1) orFALSE(0) – abbreviate usingTandF - Integer: …, -3, -2, -1, 0, 1, 2, 3, …
# Take our original objects, class() will tell you what type of object they are.
class(A)
# [1] "numeric"
class(B)
# [1] "character"
# Note here that a number like 1 can be made to be a character with ""
# That's why A+B doesn't work, you can't add numbers and letters together.
class(C)
# [1] "logical"
class(D)
# [1] "integer"3.4 Comparison and Logic
# New objects
A <- 3
B <- 8
C <- 2
# Comparisons
A == B
# [1] FALSE
A != B
# [1] TRUE
A > B
# [1] FALSE
A < B
# [1] TRUE
A >= B
# [1] FALSE
A <= B
# [1] FALSE
# Logic
# The AND operator &
A > C & A < B
# [1] TRUE
# The OR operator |
A < C | A < B
# [1] TRUE
# Is A in the following?
A %in% 1:10
# [1] TRUE
# Not!
A > B
# [1] FALSE
!A > B
# [1] TRUE3.5 Data structures
# Vector (One dimension, one data type)
myVector <- c(1,2,3)
myVector## [1] 1 2 3# Matrix (Two dimensions, one data type)
myMatrix <- matrix(seq(1:16),nrow=4,ncol=4)
myMatrix## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
## [3,] 3 7 11 15
## [4,] 4 8 12 16# Dataframe (Two dimensions, one data type per column)
myDataFrame <- data.frame(Fruits=c("Apple","Banana","Cherry","Kiwi"),
Scores=c(7,7,9,3),
MakesAGoodPie=c(T,T,T,F))
myDataFrame## Fruits Scores MakesAGoodPie
## 1 Apple 7 TRUE
## 2 Banana 7 TRUE
## 3 Cherry 9 TRUE
## 4 Kiwi 3 FALSE# Lists (A bunch of stuff)
# (Parentheses arround assigment prints it uppon execution)
(Stuff <- list(myVector,myMatrix,myDataFrame)) ## [[1]]
## [1] 1 2 3
##
## [[2]]
## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
## [3,] 3 7 11 15
## [4,] 4 8 12 16
##
## [[3]]
## Fruits Scores MakesAGoodPie
## 1 Apple 7 TRUE
## 2 Banana 7 TRUE
## 3 Cherry 9 TRUE
## 4 Kiwi 3 FALSE3.6 Indexing
# Use Square Brakets []
# For a vector
myVector <- c("A","B","C")
# Get the first element of the vector
myVector[1]## [1] "A"# Get the first element from the end of the vector
myVector[-1]## [1] "B" "C"# Get elements 1 and 3
myVector[c(1,3)]## [1] "A" "C"# Get elements 1 through 3 (1,2,3)
myVector[1:3]## [1] "A" "B" "C"# Same idea for matrices and dataframes, but they have two dimensions!
# So you need two things, the row and the column number [row,column]
# Take the following dataframe
(myDataFrame <- data.frame(Fruits=c("Apple","Banana","Cherry","Kiwi"),
Scores=c(7,7,9,3),
MakesAGoodPie=c(T,T,T,F)))## Fruits Scores MakesAGoodPie
## 1 Apple 7 TRUE
## 2 Banana 7 TRUE
## 3 Cherry 9 TRUE
## 4 Kiwi 3 FALSE# Extract the value in the cell on the first row and third column
myDataFrame[1,3]## [1] TRUE# Extract the first row, but with only columns from the second to last.
myDataFrame[1,2:ncol(myDataFrame)]## Scores MakesAGoodPie
## 1 7 TRUE# Extract rows from the first till the third, and the first and second column.
myDataFrame[1:3,1:2]## Fruits Scores
## 1 Apple 7
## 2 Banana 7
## 3 Cherry 9# $ used to extract a column
myDataFrame$Scores## [1] 7 7 9 3# Indexing can be used to create subsets
myDataFrame[myDataFrame$MakesAGoodPie==T,]## Fruits Scores MakesAGoodPie
## 1 Apple 7 TRUE
## 2 Banana 7 TRUE
## 3 Cherry 9 TRUE3.7 Usefull functions
# Dimensions of objects
# How many elements in the vector?
length(myVector)## [1] 3# How many columns in a dataframe?
length(myDataFrame)## [1] 3ncol(myDataFrame)## [1] 3# How many rows in a dataframe?
nrow(myDataFrame)## [1] 4# How many rows and columns?
dim(myDataFrame)## [1] 4 3# What's going on in the data
# First 6 rows
head(myDataFrame)## Fruits Scores MakesAGoodPie
## 1 Apple 7 TRUE
## 2 Banana 7 TRUE
## 3 Cherry 9 TRUE
## 4 Kiwi 3 FALSE# Last 6 rows
tail(myDataFrame)## Fruits Scores MakesAGoodPie
## 1 Apple 7 TRUE
## 2 Banana 7 TRUE
## 3 Cherry 9 TRUE
## 4 Kiwi 3 FALSE# Can use the n argument to set the number of rows you want, here it's 2
tail(myDataFrame,n = 2)## Fruits Scores MakesAGoodPie
## 3 Cherry 9 TRUE
## 4 Kiwi 3 FALSE# Gives you statistics for each variable in your dataframe.
summary(myDataFrame)## Fruits Scores MakesAGoodPie
## Length:4 Min. :3.0 Mode :logical
## Class :character 1st Qu.:6.0 FALSE:1
## Mode :character Median :7.0 TRUE :3
## Mean :6.5
## 3rd Qu.:7.5
## Max. :9.0# Frequency of each value contained in a variable
table(myDataFrame$MakesAGoodPie)##
## FALSE TRUE
## 1 3# A basic histogram
hist(myDataFrame$Scores)
3.8 Why do all this? (Showing off)
## Generating data
### X from a normal distribution
X <- rnorm(1000,mean=0,sd=1)
### W some group
W <- sample(c("Red","Blue","Orange"),size=1000,replace = T,prob=c(0.4,0.4,0.1))
### W_num the groups as numbers
W_num <- ifelse(W %in% c("Red","Blue"),0.3,-0.5)
### Y depends on X, W and some normally distributed error.
Y <- 0.6*X+W_num+X*W_num+rnorm(1000,mean=0,sd=1)
### Putting it all together
dat <- data.frame(id=1:100,X,Y,W)
## Vizualization with ggplot
ggplot(dat, aes(x=X,y=Y,color=W,fill=W))+
geom_point(alpha=0.3)+
geom_smooth(method="lm")+
scale_color_manual(values=c("blue","orange","red"))+
scale_fill_manual(values=c("blue","orange","red"))+
scale_x_continuous("X title",breaks = seq(-5,5,1))+
scale_y_continuous("Y title",breaks = seq(-5,5,1))+
theme_minimal()+
theme(legend.position = "top")## `geom_smooth()` using formula = 'y ~ x'
4. Exercises
Install and Load the
carDatapackage.How many columns and rows does the
TitanicSurvivaldataset fromcarDatahave?What is the mean age of known passenger of the titanic? (Hint: use the
mean()function and one of its arguments to ignore the NAs.)Create a graph of the distribution of age of the titanic passengers. (Hint: use the
hist()function.)
5. What/Who can help me?
- What:
- Who:
- Me
- Any upper-year PhD student.
5.1 An algorithm on how to solve your problems
- You’ve notices that you have been stuck on a problem for a good 10-20 min.
- STOP! Get up, take a walk.
- Take a piece of paper.
- Write down the end goal, what the thing you’re looking at should look like.
- Draw pictures/write with words/write math/whatever -> you have to know what you want!
- Write down the state the data/analysis is in.
- Draw pictures/write with words/write math/whatever -> you have to know where you are!
- At this point you may notice you are not actually where you are
supposed to be.
- Go back a step in the code, and start at 3 again. Do this until you are at a point where everything is in order.
- This might take you further up the code than you thought, don’t give up!
- Now you have a point A and a point B. You just need to figure out how to get to B. Easy!
- Sometimes doing 3 and 4 will make the solution readily apparent! Lucky you!
- If it is not the case, you now have A and B clear enough that searching for a solution on Google or GPT-4 should give good results.
- If after all these steps, you still can’t find a solution, come see me!
5.2 An algorithm on how to not have problems in the first place
- You want to do something in R.
- STOP! Take a piece of paper!!!
- Write down the end goal, what you are trying to achieve.
- Draw pictures/write with words/write math/whatever -> you have to know what you want!
- Write down the step you’ll have to take to get there.
- From opening the data to the end graph.
- You now have a guide to what needs doing, you now just have to worry about R syntax.
- Write R code for each step, making sure to comment along the way and check that every line works before moving to the next step.
- Future you is gonna be thankful that present you (or future past you) wrote down what was going on in their head at the time.