Introduction to R, RStudio and Quarto

Overview

  1. Getting Started
    • About R
    • The R Studio IDE
    • Import and eyeball data
  2. Anatomy of a data.frame
    • Data structure
    • Classes
    • Vectors
    • Subsetting
  3. Wrap Up
    • Summary and key takeaways
  1. Markdown and universal writing
    • Office Model vs. Engineering Model
    • Excel failures
    • Markdown
  2. Writing reports in Quarto
    • What is Quarto?
    • YAML header
    • Code chunks
    • Text formatting
    • Run and render your code
    • Inline code
    • Tables
    • Preset themes
    • Report parameters

Getting Started

About R

  • R is a programming language and free software environment for statistical computing and graphics.

  • The R language is widely (and increasingly) used in academic and non-academic research in fields like:

    • Economics
    • Statistics
    • Biostatistics

The R Studio IDE

The Console panel

  • This is where you communicate with R
    • You can write instructions after the >, press enter, and R will execute
    • Try with 1+1:
1+1
[1] 2

The Source panel

  • This is where you write and save your code (File > New File > R Script)

    • Separate different commands with a line break
    • The # symbol allows you to comment your code
    • Everything after the # will be ignored by R until the next line break
1+1 # Do not put 2+2 on the same line, press enter to go to next line 
2+2

The Source panel

  • To send the command from the source panel to the console panel:
    1. Click on/Highlight the line(s) you want to execute
    2. Press ctrl + enter

  • If you do not highlight anything the line of code where your cursor stands will be executed

  • Check the console to see the output of your code

The Environment panel

  • Data analysis requires manipulating datasets, vectors, functions, etc.

    • These elements are stored in the environment panel.

  • For instance, we can assign a value to an object using <-:

x <- 1
  • Now that the object x is stored in your environment, you can use it
x + 1 
[1] 2
  • You can also modify that object at any point:
x <- x + 1 
x
[1] 2

The Files/Plots/… panel

  • In this panel, we’ll mainly be interested in the following 4 tabs:

    • Files: Shows your working directory

    • Plots: Where R returns plots

    • Packages: A library of tools that we can load if needed

    • Help: Where to look for documentation on R functions

The Files/Plots/… panel

  • Enter ?getwd() in the console to see what a help file looks like:

    • It describes what the command does
    • It explains the different parameters of the command
    • It gives examples of how to use the command

Practice

03:00
  1. Open a new R script (Ctrl + Shift + N) and write a code to create these objects:
Objects to create
Object name: a b c
Assigned value: 2 4 5
  1. Run this code and create a new object named result that takes the value \(\frac{b\times c}{a} + (b-a)^c\)
Basic operations in R
Operation: Addition Subtraction Multiplication Division Exponentiation Parentheses
Symbol in R: + - * / ^ ()
  1. Print result in your console and save your script somewhere on your computer (Ctrl+S)

Solution

  1. Open a new R script (Ctrl + Shift + N) and write a code to create these objects:
Objects to create
Object name: a b c
Assigned value: 2 4 5 
a <- 2
b <- 4
c <- 5
  1. Run this code and create a new object named result that takes the value \(\frac{b\times c}{a} + (b-a)^c\)
result <- b*c/a + (b-a)^c
  1. Print result in your console and save your script somewhere on your computer (Ctrl+S)
result
[1] 42

Import and Eyeball Data

  • We now know how to use R as a calculator, but our goal is to analyze data!

    • Take for instance the statistics from the last season of Ligue 1 available at fbref.com

Import and Eyeball Data

  • You can download the dataset for the 2021/22 season by clicking here or from the course webpage.
    • Note that the file extension is .csv (for Comma Separated Values).
    • Let’s take a look at the first 5 lines of the raw .csv file:
Wk,Day,Date,Time,Home,xG,Score,xG,Away,Attendance,Venue,Referee,Match Report,Notes
1,Fri,2021-08-06,21:00,Monaco,2.0,1–1,0.3,Nantes,7500,Stade Louis II.,Antony Gautier,Match Report,
1,Sat,2021-08-07,17:00,Lyon,1.4,1–1,0.8,Brest,29018,Groupama Stadium,Mikael Lesage,Match Report,
1,Sat,2021-08-07,21:00,Troyes,0.8,1–2,1.2,Paris S-G,15248,Stade de l'Aube,Amaury Delerue,Match Report,
1,Sun,2021-08-08,13:00,Rennes,0.6,1–1,2.0,Lens,22567,Roazhon Park,Bastien Dechepy,Match Report,

  • The .csv format is very common and follows a specific structure:

    • Each line corresponds to a row (the first row typically contains column names).
    • For each row, values of each column are separated by commas.

  • But how do we get it into our RStudio environment?

Import and Eyeball Data

  • To import stuff in R we use read functions

    • They take the file directory as an input
    • And give the file content as an output

  • The read function dedicated to .csv files is read.csv (later we will mostly use read_csv)

  • Remember we use the arrow (<-) to create objects in R

data <- read.csv("/Users/jan/Downloads/ligue1.csv")

Important

Make sure you have the right path to your data file. Also, make sure you use correct backlashes “/”. Do NOT use “\”.

  • Let’s inspect this new object
  • The first thing we can do is to use head() to print the top rows
head(data, 3)
  Wk Day       Date  Time   Home  xG Score xG.1      Away Attendance
1  1 Fri 2021-08-06 21:00 Monaco 2.0   1-1  0.3    Nantes       7500
2  1 Sat 2021-08-07 17:00   Lyon 1.4   1-1  0.8     Brest      29018
3  1 Sat 2021-08-07 21:00 Troyes 0.8   1-2  1.2 Paris S-G      15248
             Venue        Referee Match.Report Notes
1  Stade Louis II. Antony Gautier Match Report    NA
2 Groupama Stadium  Mikael Lesage Match Report    NA
3  Stade de l'Aube Amaury Delerue Match Report    NA

  • tail() would print the bottom rows

  • We can also run View(data) (a new tab will pop-up in your Source panel)

Seems like it worked!

Or kind of worked…

These kind of weird characters pop up when there is an encoding issue

  • Thankfully, read.csv() has many options that can be set as inputs, including encoding!
  • Usually the UTF-8 encoding is the solution to French characters
data <- read.csv("/Users/jan/Downloads/ligue1.csv", encoding = "UTF-8")

When you will be facing similar issues, check out the arguments of read.csv() by typing ?read.csv

Overview

  1. Getting Started
    • About R
    • The R Studio IDE
    • Import and eyeball data
  1. Anatomy of a data.frame
    • Data structure
    • Classes
    • Vectors
    • Subsetting

Anatomy of a data.frame

Data Structure

  • Now that we imported the data properly, we can check out its str()ucture in more details
str(data)

Data Structure

  • Don’t be scared of the output!
str(data)
'data.frame':   380 obs. of  14 variables:
 $ Wk          : int  1 1 1 1 1 1 1 1 1 1 ...
 $ Day         : chr  "Fri" "Sat" "Sat" "Sun" ...
 $ Date        : chr  "2021-08-06" "2021-08-07" "2021-08-07" "2021-08-08" ...
 $ Time        : chr  "21:00" "17:00" "21:00" "13:00" ...
 $ Home        : chr  "Monaco" "Lyon" "Troyes" "Rennes" ...
 $ xG          : num  2 1.4 0.8 0.6 0.7 0.4 0.8 2.1 0.7 0.5 ...
 $ Score       : chr  "1-1" "1-1" "1-2" "1-1" ...
 $ xG.1        : num  0.3 0.8 1.2 2 3.3 0.9 0.2 1.3 1.4 2 ...
 $ Away        : chr  "Nantes" "Brest" "Paris S-G" "Lens" ...
 $ Attendance  : int  7500 29018 15248 22567 18748 23250 18030 20461 15551 13500 ...
 $ Venue       : chr  "Stade Louis II." "Groupama Stadium" "Stade de l'Aube" "Roazhon Park" ...
 $ Referee     : chr  "Antony Gautier" "Mikael Lesage" "Amaury Delerue" "Bastien Dechepy" ...
 $ Match.Report: chr  "Match Report" "Match Report" "Match Report" "Match Report" ...
 $ Notes       : logi  NA NA NA NA NA NA ...

Data Structure

  • str() says that data is a data.frame, and gives its numbers of observations (rows) and variables (columns)
str(data)
## 'data.frame':    380 obs. of  14 variables:

Data Structure

  • It also gives the variables names
str(data)
## 'data.frame':    380 obs. of  14 variables:
##  $ Wk          
##  $ Day         
##  $ Date        
##  $ Time        
##  $ Home        
##  $ xG          
##  $ Score       
##  $ xG.1        
##  $ Away        
##  $ Attendance  
##  $ Venue       
##  $ Referee     
##  $ Match.Report
##  $ Notes

Data Structure

  • The first values of each variable
str(data)
## 'data.frame':    380 obs. of  14 variables:
##  $ Wk          :      1 1 1 1 1 1 1 1 1 1 ...
##  $ Day         :      "Fri" "Sat" "Sat" "Sun" ...
##  $ Date        :      "2021-08-06" "2021-08-07" "2021-08-07" "2021-08-08" ...
##  $ Time        :      "21:00" "17:00" "21:00" "13:00" ...
##  $ Home        :      "Monaco" "Lyon" "Troyes" "Rennes" ...
##  $ xG          :      2 1.4 0.8 0.6 0.7 0.4 0.8 2.1 0.7 0.5 ...
##  $ Score       :      "1–1" "1–1" "1–2" "1–1" ...
##  $ xG.1        :      0.3 0.8 1.2 2 3.3 0.9 0.2 1.3 1.4 2 ...
##  $ Away        :      "Nantes" "Brest" "Paris S-G" "Lens" ...
##  $ Attendance  :      7500 29018 15248 22567 18748 23250 18030 20461 15551 13500 ...
##  $ Venue       :      "Stade Louis II." "Groupama Stadium" "Stade de l'Aube" "Roazhon Park" ...
##  $ Referee     :      "Antony Gautier" "Mikael Lesage" "Amaury Delerue" "Bastien Dechepy" ...
##  $ Match.Report:      "Match Report" "Match Report" "Match Report" "Match Report" ...
##  $ Notes       :       NA NA NA NA NA NA ...

Data Structure

  • As well as the class of each variable
str(data)
## 'data.frame':    380 obs. of  14 variables:
##  $ Wk          : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Day         : chr  "Fri" "Sat" "Sat" "Sun" ...
##  $ Date        : chr  "2021-08-06" "2021-08-07" "2021-08-07" "2021-08-08" ...
##  $ Time        : chr  "21:00" "17:00" "21:00" "13:00" ...
##  $ Home        : chr  "Monaco" "Lyon" "Troyes" "Rennes" ...
##  $ xG          : num  2 1.4 0.8 0.6 0.7 0.4 0.8 2.1 0.7 0.5 ...
##  $ Score       : chr  "1–1" "1–1" "1–2" "1–1" ...
##  $ xG.1        : num  0.3 0.8 1.2 2 3.3 0.9 0.2 1.3 1.4 2 ...
##  $ Away        : chr  "Nantes" "Brest" "Paris S-G" "Lens" ...
##  $ Attendance  : int  7500 29018 15248 22567 18748 23250 18030 20461 15551 13500 ...
##  $ Venue       : chr  "Stade Louis II." "Groupama Stadium" "Stade de l'Aube" "Roazhon Park" ...
##  $ Referee     : chr  "Antony Gautier" "Mikael Lesage" "Amaury Delerue" "Bastien Dechepy" ...
##  $ Match.Report: chr  "Match Report" "Match Report" "Match Report" "Match Report" ...
##  $ Notes       : logi  NA NA NA NA NA NA ...

Data Structure

  • But what does the class correspond to?
str(data)
## 'data.frame':    380 obs. of  14 variables:
##  $ Wk          : int  ?
##  $ Day         : chr  ?
##  $ Date        : chr  ?
##  $ Time        : chr  ?
##  $ Home        : chr  ?
##  $ xG          : num  ?
##  $ Score       : chr  ?
##  $ xG.1        : num  ?
##  $ Away        : chr  ?
##  $ Attendance  : int  ?
##  $ Venue       : chr  ?
##  $ Referee     : chr  ?
##  $ Match.Report: chr  ?
##  $ Notes       : logi  ?

Classes

Numeric

These are simply numbers:

class(3)
[1] "numeric"
class(-1.89278)
[1] "numeric"
  • Numeric variable classes include:
    • int for round numbers
    • dbl for 2-decimal numbers

Character

They must be surrounded by " or ':

class("Paris Saint-Germain")
[1] "character"
class("35")
[1] "character"
  • We also call these values:
    • Character strings
    • Or just strings

Logical

Something either TRUE of FALSE:

3 >= 4
[1] FALSE
class(3 >= 4)
[1] "logical"
class(TRUE)
[1] "logical"
Operator Meaning
== Equal to
> Greater than
< Less than
>= Greater than or equal to
<= Less than or equal to
& And
| Or
! Opposite

Classes

Guess the output!

as.numeric("2022")
[1] 2022

What about this one?

as.character(2022-2023)
[1] "-1"

And a final one.

as.character(2022>2023)
[1] "FALSE"

Classes

numeric character logical
as.numeric() No effect Converts strings of numbers into numeric values
Returns NA if characters in the string		 Returns 1 if TRUE
Returns 0 if FALSE
as.character() Converts numeric values
into strings of numbers		 No effect Returns "TRUE" if TRUE
Returns "FALSE" if FALSE
as.logical() Returns TRUE if != 0
Returns FALSE if 0		 Returns TRUE if "T" or"TRUE"
Returns FALSE if "F" or "FALSE"
Returns NA otherwise		 No effect

NA stands for ‘Not Available’, it corresponds to a missing value

Classes

One last mystery…

str(data)
'data.frame':   380 obs. of  14 variables:
 $ Wk          : int  1 1 1 1 1 1 1 1 1 1 ...
 $ Day         : chr  "Fri" "Sat" "Sat" "Sun" ...
 $ Date        : chr  "2021-08-06" "2021-08-07" "2021-08-07" "2021-08-08" ...
 $ Time        : chr  "21:00" "17:00" "21:00" "13:00" ...
 $ Home        : chr  "Monaco" "Lyon" "Troyes" "Rennes" ...
 $ xG          : num  2 1.4 0.8 0.6 0.7 0.4 0.8 2.1 0.7 0.5 ...
 $ Score       : chr  "1-1" "1-1" "1-2" "1-1" ...
 $ xG.1        : num  0.3 0.8 1.2 2 3.3 0.9 0.2 1.3 1.4 2 ...
 $ Away        : chr  "Nantes" "Brest" "Paris S-G" "Lens" ...
 $ Attendance  : int  7500 29018 15248 22567 18748 23250 18030 20461 15551 13500 ...
 $ Venue       : chr  "Stade Louis II." "Groupama Stadium" "Stade de l'Aube" "Roazhon Park" ...
 $ Referee     : chr  "Antony Gautier" "Mikael Lesage" "Amaury Delerue" "Bastien Dechepy" ...
 $ Match.Report: chr  "Match Report" "Match Report" "Match Report" "Match Report" ...
 $ Notes       : logi  NA NA NA NA NA NA ...

Classes

Are these dollar signs here for a reason?

str(data)
## 'data.frame':    380 obs. of  14 variables:
##  $ Wk          
##  $ Day         
##  $ Date        
##  $ Time        
##  $ Home        
##  $ xG          
##  $ Score       
##  $ xG.1        
##  $ Away        
##  $ Attendance  
##  $ Venue       
##  $ Referee     
##  $ Match.Report
##  $ Notes

Vectors

It’s actually just a reference to the fact that $ allows to extract a variable from a dataset

data$Home
  [1] "Monaco"        "Lyon"          "Troyes"        "Rennes"       
  [5] "Bordeaux"      "Strasbourg"    "Nice"          "Saint-Étienne"
  [9] "Metz"          "Montpellier"   "Lorient"       "Lille"        
 [13] "Paris S-G"     "Angers"        "Clermont Foot" "Brest"        
 [17] "Nantes"        "Reims"         "Lens"          "Marseille"    
 [21] "Brest"         "Monaco"        "Saint-Étienne" "Lyon"         
 [25] "Strasbourg"    "Metz"          "Montpellier"   "Bordeaux"     
 [29] "Rennes"        "Nantes"        "Nice"          "Marseille"    
 [33] "Troyes"        "Strasbourg"    "Angers"        "Lens"         
 [37] "Clermont Foot" "Lille"         "Reims"         "Lorient"      
 [41] "Paris S-G"     "Monaco"        "Montpellier"   "Rennes"       
 [45] "Bordeaux"      "Brest"         "Metz"          "Nantes"       
 [49] "Lyon"          "Strasbourg"    "Lens"          "Saint-Étienne"
 [53] "Nice"          "Troyes"        "Clermont Foot" "Reims"        
 [57] "Angers"        "Marseille"     "Paris S-G"     "Rennes"       
 [61] "Nantes"        "Lille"         "Montpellier"   "Monaco"       
 [65] "Lyon"          "Lens"          "Lorient"       "Angers"       
 [69] "Metz"          "Saint-Étienne" "Strasbourg"    "Paris S-G"    
 [73] "Lyon"          "Bordeaux"      "Troyes"        "Brest"        
 [77] "Reims"         "Clermont Foot" "Marseille"     "Lens"         
 [81] "Montpellier"   "Nice"          "Rennes"        "Lorient"      
 [85] "Monaco"        "Angers"        "Nantes"        "Lille"        
 [89] "Saint-Étienne" "Paris S-G"     "Clermont Foot" "Lyon"         
 [93] "Troyes"        "Brest"         "Bordeaux"      "Metz"         
 [97] "Strasbourg"    "Montpellier"   "Marseille"     "Saint-Étienne"
[101] "Nantes"        "Lille"         "Nice"          "Rennes"       
[105] "Lens"          "Lorient"       "Reims"         "Monaco"       
[109] "Marseille"     "Nice"          "Paris S-G"     "Metz"         
[113] "Lyon"          "Angers"        "Bordeaux"      "Troyes"       
[117] "Montpellier"   "Strasbourg"    "Brest"         "Clermont Foot"
[121] "Lens"          "Lille"         "Bordeaux"      "Marseille"    
[125] "Saint-Étienne" "Lorient"       "Reims"         "Nantes"       
[129] "Nice"          "Rennes"        "Monaco"        "Paris S-G"    
[133] "Rennes"        "Brest"         "Metz"          "Angers"       
[137] "Troyes"        "Strasbourg"    "Clermont Foot" "Lens"         
[141] "Lille"         "Nice"          "Saint-Étienne" "Reims"        
[145] "Lorient"       "Bordeaux"      "Monaco"        "Montpellier"  
[149] "Marseille"     "Angers"        "Troyes"        "Strasbourg"   
[153] "Brest"         "Metz"          "Rennes"        "Nantes"       
[157] "Paris S-G"     "Lyon"          "Clermont Foot" "Marseille"    
[161] "Lille"         "Lens"          "Saint-Étienne" "Montpellier"  
[165] "Monaco"        "Reims"         "Lorient"       "Nice"         
[169] "Bordeaux"      "Nantes"        "Brest"         "Reims"        
[173] "Lille"         "Angers"        "Rennes"        "Troyes"       
[177] "Metz"          "Strasbourg"    "Paris S-G"     "Lyon"         
[181] "Saint-Étienne" "Lorient"       "Nice"          "Monaco"       
[185] "Marseille"     "Montpellier"   "Troyes"        "Bordeaux"     
[189] "Bordeaux"      "Lens"          "Brest"         "Metz"         
[193] "Clermont Foot" "Nantes"        "Lyon"          "Nice"         
[197] "Saint-Étienne" "Paris S-G"     "Rennes"        "Reims"        
[201] "Monaco"        "Lorient"       "Strasbourg"    "Troyes"       
[205] "Marseille"     "Clermont Foot" "Montpellier"   "Lille"        
[209] "Lyon"          "Brest"         "Lens"          "Metz"         
[213] "Clermont Foot" "Nantes"        "Angers"        "Bordeaux"     
[217] "Montpellier"   "Paris S-G"     "Angers"        "Lyon"         
[221] "Marseille"     "Saint-Étienne" "Monaco"        "Lorient"      
[225] "Reims"         "Nice"          "Troyes"        "Strasbourg"   
[229] "Rennes"        "Lille"         "Paris S-G"     "Montpellier"  
[233] "Lyon"          "Monaco"        "Angers"        "Brest"        
[237] "Nantes"        "Clermont Foot" "Lens"          "Metz"         
[241] "Lille"         "Lens"          "Nantes"        "Nice"         
[245] "Saint-Étienne" "Reims"         "Rennes"        "Lorient"      
[249] "Bordeaux"      "Marseille"     "Montpellier"   "Strasbourg"   
[253] "Paris S-G"     "Monaco"        "Angers"        "Metz"         
[257] "Clermont Foot" "Brest"         "Troyes"        "Lyon"         
[261] "Lorient"       "Lens"          "Nice"          "Saint-Étienne"
[265] "Nantes"        "Reims"         "Rennes"        "Bordeaux"     
[269] "Lille"         "Marseille"     "Lille"         "Montpellier"  
[273] "Troyes"        "Paris S-G"     "Strasbourg"    "Clermont Foot"
[277] "Metz"          "Angers"        "Lyon"          "Brest"        
[281] "Saint-Étienne" "Lens"          "Nantes"        "Monaco"       
[285] "Angers"        "Rennes"        "Bordeaux"      "Lorient"      
[289] "Reims"         "Marseille"     "Nice"          "Lille"        
[293] "Strasbourg"    "Metz"          "Saint-Étienne" "Troyes"       
[297] "Montpellier"   "Clermont Foot" "Lyon"          "Paris S-G"    
[301] "Lorient"       "Reims"         "Clermont Foot" "Bordeaux"     
[305] "Monaco"        "Angers"        "Brest"         "Lens"         
[309] "Strasbourg"    "Marseille"     "Rennes"        "Saint-Étienne"
[313] "Lille"         "Nice"          "Nantes"        "Metz"         
[317] "Montpellier"   "Troyes"        "Lyon"          "Paris S-G"    
[321] "Reims"         "Monaco"        "Bordeaux"      "Lorient"      
[325] "Troyes"        "Angers"        "Strasbourg"    "Lens"         
[329] "Brest"         "Marseille"     "Lyon"          "Saint-Étienne"
[333] "Paris S-G"     "Rennes"        "Metz"          "Nantes"       
[337] "Nice"          "Clermont Foot" "Lille"         "Reims"        
[341] "Strasbourg"    "Lens"          "Rennes"        "Troyes"       
[345] "Brest"         "Monaco"        "Montpellier"   "Lorient"      
[349] "Bordeaux"      "Marseille"     "Lille"         "Brest"        
[353] "Metz"          "Clermont Foot" "Angers"        "Reims"        
[357] "Lorient"       "Paris S-G"     "Nice"          "Nantes"       
[361] "Nice"          "Bordeaux"      "Montpellier"   "Rennes"       
[365] "Monaco"        "Troyes"        "Strasbourg"    "Metz"         
[369] "Lyon"          "Saint-Étienne" "Lille"         "Brest"        
[373] "Nantes"        "Clermont Foot" "Angers"        "Lorient"      
[377] "Paris S-G"     "Reims"         "Marseille"     "Lens"

Vectors

  • Variables are basically objects that we call vectors
    • Vectors are sequences of values that have the same class
    • R won’t let you create a vector containing elements of different classes

We make our own vectors using the c()oncatenate function

some_vector <- c("Hello world", 35, FALSE)
some_vector 
[1] "Hello world" "35"          "FALSE"

Note that R will coerce the different elements into the same class when we create a vector (in this case character)

class(some_vector)
[1] "character"
  • The fact that vectors are homogeneous in class allows that operations apply to all their elements
c(1, 2, 3) / 3
[1] 0.3333333 0.6666667 1.0000000
3 / c(1, 2, 3) 
[1] 3.0 1.5 1.0

Subsetting

  • With $, you can extract a single variable from a dataset
  • You can extract several variables and specific observations from a data frame using []

\[\text{data}[\text{row(s)}, \:\:\text{column(s)}]\]

  • Inside the brackets, indicate what you want to keep using:
    • Indices: e.g., the third column has index 3
    • Logical: A vector of TRUE and FALSE
    • Names: They must be in quotation marks

Example:

data[1, c("Venue", "Attendance")]
            Venue Attendance
1 Stade Louis II.       7500

We can also subset single vectors:

vector <- c(3, 2, 1)
vector[c(TRUE, TRUE, FALSE)]
[1] 3 2

Practice

04:00

  1. Download and import the dataset if you haven’t already

  2. Combine the use of [] and the function nrow() to obtain the last value of the Wk variable

  3. Subset the home team, the score, and the away team for matches that occured during the last week

Tip

Instead of str(), you can use the names() function to display all the variable names of a data frame.

Solution

  1. Download and import the dataset if you haven’t already
data <- read.csv("/Users/jan/Downloads/ligue1.csv")
  1. Combine the use of [] and the function nrow() to obtain the last value of the Wk variable.
last_week <- data[nrow(data), "Wk"]
last_week
[1] 38
  1. Subset the home team, the score, and the away team for matches that occured during the last week
names(data)
 [1] "Wk"           "Day"          "Date"         "Time"         "Home"        
 [6] "xG"           "Score"        "xG.1"         "Away"         "Attendance"  
[11] "Venue"        "Referee"      "Match.Report" "Notes"       
data[Wk == last_week, c("Home", "Score", "Away")]
Error: object 'Wk' not found
  • Oops! Seems like R couldn’t find the Wk variable
    • R was looking for Wk in our environment
    • But there is no Wk there
  • We must refer to the data frame data which is in our environment
    • Then we can access Wk using the $ symbol
data[data$Wk == last_week, c("Home", "Score", "Away")]
             Home Score          Away
371         Lille   2-2        Rennes
372         Brest   2-4      Bordeaux
373        Nantes   1-1 Saint-Étienne
374 Clermont Foot   1-2          Lyon
375        Angers   2-0   Montpellier
376       Lorient   1-1        Troyes
377     Paris S-G   5-0          Metz
378         Reims   2-3          Nice
379     Marseille   4-0    Strasbourg
380          Lens   2-2        Monaco

Overview

  1. Getting Started
    • About R
    • The R Studio IDE
    • Import and eyeball data
  1. Anatomy of a data.frame
    • Data structure
    • Classes
    • Vectors
    • Subsetting
  1. Wrap Up
    • Summary and key take-aways

Wrap Up

Import data

data <- read.csv("/Users/jan/Downloads/ligue1.csv")

Class

is.numeric("1.6180339") # What would be the output?
[1] FALSE

Subsetting

data$Home[3] # What would be the output?
[1] "Troyes"

Overview

  1. Getting Started
    • About R
    • The R Studio IDE
    • Import and eyeball data
  1. Anatomy of a data.frame
    • Data structure
    • Classes
    • Vectors
    • Subsetting
  1. Wrap Up
    • Summary and key take-aways
  1. Markdown and universal writing
    • Office Model vs. Engineering Model
    • Excel failures
    • Markdown

Markdown and universal writing

Office Model vs. Engineering Model

Writing up research is a complicated, messy process!

Office Model vs. Engineering Model

  • Loads of puzzle pieces:
    • Data
    • Statistical results
    • Fieldwork
    • Analysis
    • Figures
    • Tables
    • Citations
    • Text
  • Each of these comes from a different place

Office Model vs. Engineering Model

Two general approaches for this mess:

The Office model

  • Manually put everything in one document (and repeat often)

The Engineering model

  • Work with the raw pieces and compile it all in the end

The Office Model

Everything lives in one .docx file

  • Drag images in
  • Copy/paste stats from R
  • Connect Word to Zotero or Endnote
  • Track versions with filenames:
    • ms.docx, ms2_final.docx, ms2_final_final.docx

Final output = .docx file

The Engineering Model

Everything lives separately and is combined in the end

  • Type text in a plain text document
  • Import images automatically
  • Import stats automatically from R scripts (.R or .qmd) or .do files
  • Store citations in reference manager
  • Track versions with git

Final output = whatever you want (Word, PDF, HTML)

Office Model vs. Engineering Model

There is no one right way!

The Office model

Cons:

  • With changing analyses or data, manually updating your doc is laborous
  • Chaos-prone:
    • You got to remember which script generated what)
  • Error-prone:
    • It is easy to forget to update all figures, tables, results in text, etc.

The Engineering model

Cons:

  • A bit of an entry cost
    • Need to learn a new coding language
  • You’ll always work with people who only use Word

Office Model vs. Engineering Model

The Office model

Pros:

  • No coding, easy environments
  • The whole world runs on Word

The Engineering model

Pros:

  • Less cognitive load
    • While everything seems complex in the beginning, no chaos because all is documented and transparent
  • Less work load (in the long run)
    • No need to copy/paste new results, add updated figures, reformat citation, etc.
  • Transparency
    • There’s a record of everything you do
    • Your findings are reproducible by anyone (and yourself!)

Excel failures

Dept:GDP ratio 90%+ → -0.1% growth

Paul Ryan’s 2013 House budget resolution

Excel failures

Thomas Herndon

From Paul Krugman, “The Excel Depression”

Engineering model in real life

Private companies and governments use the engineering model to write reports on data

Airbnb

The UK’s reproducible analysis pipeline

So, what is Markdown?

  • There are many different typesetting languages in which you can write.
  • The most widely used ones are perhaps LaTeX (mostly scientific work), HTML (web-based stuff) and Word.
  • How to get around learning all of them and having to switch for different outputs?
    • Write in one simplified syntax (Markdown)
    • Convert to whatever output you want

And what is RMarkdown/Quarto?

  • Quarto and RMarkdown are publishing systems which use markdown language.
  • They allow you to render to different outcome formats
# To HTML
quarto render manuscript.qmd --to html

# To Word
quarto render manuscript.qmd --to docx

# To PDF (through LaTeX)
quarto render manuscript.qmd --to pdf
  • They allow to combine code, figures, tables, images, text etc. (more on that now)

Overview

  1. Getting Started
    • About R
    • The R Studio IDE
    • Import and eyeball data
    • Use functions
  1. Anatomy of a data.frame
    • Data structure
    • Classes
    • Vectors
    • Subsetting
  1. Wrap Up
    • Summary and key take-aways
  1. Markdown and universal writing
    • Office Model vs. Engineering Model
    • Excel failures
    • Markdown
  1. Writing reports in Quarto
    • What is Quarto?
    • YAML header
    • Code chunks
    • Text formatting
    • Run and render your code
    • Inline code
    • Tables
    • Preset themes
    • Report parameters

Writing reports in Quarto

What is Quarto?

  • Quarto is an open-source publishing system in which you can both write/run code (R/Python/Julia/Observable) and edit text

  • Quarto is the newer, fancier version of RMarkdown (which only worked with R code)

  • It is structured around 3 types of content:

    • Code chunks to run and render the output
    • Editable text to display
    • YAML metadata for the Quarto build process

What is Quarto?

  • Let’s create our first Quarto document!

  • Click on File > New File > Quarto document

What is Quarto?

It creates a template containing the 3 types of content:


YAML header



Text



Code Chunk

Basic principles

YAML Header

  • The YAML header contains general information related to the file configuration:

    • Title/subtitle (in quotes)
    • Author/date (in quotes)
    • Output type (html/pdf)
    • Editor configuration (use source, not visual)
    • …
  • It should be specified at the very beginning of the document and surrounded by three dashes like this:
---
title: "My first Quarto document"
subtitle: "What a blast"
author: "My Name"
date: "05/01/2024"
format: html
editor: source
---

Basic principles

Code Chunks

  • Code chunks are blocks of R code that can be run when working on and rendering the .qmd file
  • You can insert a code chunk using Ctrl + Alt + i or by typing the backticks chunk delimiters as follows
1 + 1
  • When rendering the document, R will execute the code
    • Both the code and the output will appear in the document like so
1 + 1
[1] 2

Basic principles

Code Chunks

  • The content to be displayed from the code chunk can be specified in chunk options
    • For instance, to display only the output and not the code chunk, you can set echo to FALSE
```{r, echo = F}
1+1
```
```{r}
#| echo: false
1+1 
```
  • And the output will only be
[1] 2
  • Instead of
1 + 1
[1] 2

Basic principles

Code Chunks

Chunk Options to Know
Option Default Effect
eval TRUE Whether to evaluate the code and include its results
echo TRUE Whether to display code along with its results
warning TRUE Whether to display warnings
error TRUE Whether to display errors
message TRUE Whether to display messages
results 'markup' 'hide' to hide the output
fig.width 7 Width in inches for plots created in chunk
fig.height 7 Height in inches for plots created in chunk

Basic principles

Code Chunks

  • For an option to be the default for the whole document, set it up in the YAML header:
---
title: "My first Quarto document"
format: html
execute:
  echo: false
  warning: false
---

Basic principles

Text Formatting

  • Quarto is not only about rendering code but also about writing actual text

  • You can write paragraphs as you would normally do on a typical report

  • And Quarto provides convenient ways to format your text

  • Unlike most text editing software, in source Quarto text formatting isn’t about clicking on dedicated buttonds

  • It relies on symbols that should be written along with the text

Basic principles

Text Formatting

Type… …to get 
Some text in a paragraph.

More text in the next paragraph. Always
use empty lines between paragraphs.

Some text in a paragraph.

More text in the next paragraph. Always use empty lines between paragraphs.
*Italic* or _Italic_ Italic
**Bold** or __Bold__ Bold
# Heading 1

Heading 1
## Heading 2

Heading 2
### Heading 3

Heading 3
(Go up to heading level 6 with ######)
[Link text](https://www.example.com) Link text

Basic principles

Run and render your code

  • You have different options to execute the content of a code chunk in Quarto

    • Check out the buttons at the top right of the chunk

  • To render a Quarto file, click on the render button

Useful features

Inline code

  • Quarto allows to include R output directly in text
  • To do this, use `r r_code_here`
```{r}
#| label: find-avg-mpg
#| echo: false
number_of_days <- 5
```

We are `r number_of_days` days into the week.

… would render to this:

We are 5 days into the week.

That’s it for today :)