Data manipulation

# Data manipulation
## Lecture 1
### Louis SIRUGUE
### M1 APE - Fall 2022

---

### Welcome to the course!

<center>About me</center>

<ul>
 <li>PhD student at the Paris School of Economics</li>
</ul>

<ul>
 <li>I work primarily on:</li>
 <ul>
 <li>Intergenerational (income) mobility</li>
 <li>Residential segregation</li>
 <li>Discrimination</li>
 </ul>
</ul>

<ul>
 <li>I do empirical research, so I use Econometrics and (R) programming on a daily basis</li>
</ul>

<ul>
 <li>You can reach me at <a href="mailto:louis.sirugue@psemail.eu">louis.sirugue@psemail.eu</a> for any question or comment about the course</li>
</ul>
]

<center>About the course</center>

<ul>
 <li>Objective: Learning R programming to carry out your empirical homeworks and research projects</li>
</ul>

<ul>
 <li>4 $\times$ 2 hours:</li>
 <ol>
 <li>Data manipulation</li>
 <li>Data visualization</li>
 <li>R Markdown & LaTeX</li>
 <li>Econometrics in R</li>
 </ol>
</ul>

<ul>
 <li>Lectures 1 to 3 are about learning R</li>
</ul>

<center>1-month break for you to follow the first lectures/tutorials in Econometrics I</center>

<ul>
 <li>Lectures 4 is about Econometrics using R</li>
</ul>

]

---

<h3>Let's delve into it!</h3>

<ul style = "margin-left:1.5cm;list-style: none">
 <li>1. Getting started</li>
 <ul style = "list-style: none">
 <li>1.1. About R</li>
 <li>1.2. The R Studio IDE</li>
 <li>1.3. Import and eyeball data</li>
 <li>1.4. Use functions</li>
 </ul>
</ul>

<ul style = "margin-left:1.5cm;list-style: none">
 <li>2. Anatomy of a data.frame</li>
 <ul style = "list-style: none">
 <li>2.1. Data structure</li>
 <li>2.2. Classes</li>
 <li>2.3. Vectors</li>
 <li>2.4. Subsetting</li>
 </ul>
</ul>

]

<ul style = "margin-left:-1cm;list-style: none">
 <li>3. The dplyr grammar</li>
 <ul style = "list-style: none">
 <li>3.1. Packages</li>
 <li>3.2. Basic functions</li>
 <li>3.3. group_by() and summarise()</li>
 </ul>
</ul>

<ul style = "margin-left:-1cm;list-style: none">
 <li>4. A few words on learning R</li>
 <ul style = "list-style: none">
 <li>4.1. When it doesn't work the way you want</li>
 <li>4.2. Where to find help</li>
 <li>4.3. When it doesn't work at all</li>
 </ul>
</ul>

<ul style = "margin-left:-1cm;list-style: none"><li>5. Wrap up!</li></ul>
]

---

<h3>Let's delve into it!</h3>

---

### 1. Getting started

#### 1.1. About R

* R is a **programming language** and free software environment for **statistical computing and graphics**
 
<img style = "margin-left:16cm;margin-top:2.5cm;" src = "rstudio_logo.png" width = "300"/>

<ul>
 <li style = "margin-bottom:.25cm;">The R language is widely (and increasingly) used in academic and non-academic research in fields like:</li>
 <ul>
 <li>Economics</li>
 <li>Statistics</li>
 <li>Biostats</li>
 </ul>
</ul>

<ul>
 <li style = "margin-bottom:.25cm;">Things you can do with R:</li>
 <ul>
 <li><a href="https://louissirugue.github.io/data-analysis-course/project/example.html">Reports</a></li>
 <li><a href="https://www.r-graph-gallery.com/bubble_chart_interactive_ggplotly.html">Nice plots</a></li>
 <li><a href="https://louissirugue.github.io/intro_to_R/home.html">All the material of this course</a></li>
 <li><a href="https://pubs.aeaweb.org/doi/pdfplus/10.1257/app.20200447">Academic research</a></li>
 <li><a href="https://www.kaggle.com/xavierconort">Win kaggle competitions</a></li>
 <li><a href="https://vac-lshtm.shinyapps.io/ncov_tracker/?_ga=2.29922175.1739025613.1656421238-871875772.1628005923">Interactive data visualization</a></li>
 <li><a href="https://www.data-to-art.com/">Art</a></li>
 </ul>
</ul>
 
---

### 1. Getting started

#### 1.2. The R Studio IDE

<center>
<img src = "rstudio.png" width = "770"/>
</center>
 
---

### 1. Getting started

#### 1.2. The R Studio IDE

&#10140; The Console panel

<ul>
 <li>This is where you communicate with R
 <ul>
 <li>You can write instructions after the >, press enter and R will execute</li>
 <li>Try with 1+1:</li>
 </ul>
</ul>

```r
1+1
```

```
## [1] 2
```
 
---

### 1. Getting started

#### 1.2. The R Studio IDE

&#10140; The Source panel

<ul>
 <li>This is where you write and save your code (File > New File > R Script)</li>
 <ul>
 <li>Separate different commands with a line break</li>
 <li>The # symbol allows to comment your code</li>
 <li>Everything after # will be ignored by R until the next line break</li>
 </ul>
</ul>

```r
1+1 # Do not put 2+2 on the same line, press enter to go to next line 
2+2
```
 
---

### 1. Getting started

#### 1.2. The R Studio IDE

&#10140; The Source panel

<ul>
 <li>To send the command from the source panel to the console panel:</li>
 <ol>
 <li>Highlight the lines you want to execute</li>
 <li>Press ctrl + enter</li>
 </ol>
</ul>

<ul><li>If you do not highlight anything the line of code where your cursor stands will be executed</li></ul>

<ul><li>Check the console to see the output of your code</li></ul>
 
---

### 1. Getting started

#### 1.2. The R Studio IDE

&#10140; The Environment panel

<ul>
 <li>Data analysis requires manipulating datasets, vectors, functions, etc.</li>
 <ul>
 <li>These elements are stored in the environment panel</li>
 </ul>
</ul>

* For instance we can assign a value to an object using `<-`

```r
x <- 1
```

<center> &#10140; You now have an object called 'x' in your environment, which takes the value 1</center>

---

### 1. Getting started

#### 1.2. The R Studio IDE

&#10140; The Environment panel

* Now that the object `x` is stored in your environment, you can use it:

```r
x + 1
```

```
## [1] 2
```

]

* You can also modify that object at any point:

```r
x <- x + 1
x
```

```
## [1] 2
```

]

---

### 1. Getting started

#### 1.2. The R Studio IDE

&#10140; The Files/Plots/... panel

* In this panel we'll mainly be interested in the following 4 tabs

--
<ul style = "margin-top:1em;"><li>Files: Shows your working directory</li></ul>
--
<ul style = "margin-top:1em;"><li>Plots: Where R returns plots</li></ul>
--
<ul style = "margin-top:1em;"><li>Packages: A library of tools that we can load if needed</li></ul>
--
<ul style = "margin-top:1em;"><li>Help: Where to look for documentation on R functions</li></ul>

---

### 1. Getting started

#### 1.2. The R Studio IDE

&#10140; The Files/Plots/... panel

*  Enter `?getwd()` in the console to see what a **help file** looks like
 
---

### 1. Getting started

#### 1.2. The R Studio IDE

&#10140; The Files/Plots/... panel

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

<ul><ul><li>It describes what the command does</li></ul></ul>
<ul><ul><li>It explains the different parameters of the command</li></ul></ul>
<ul><ul><li>It gives examples of how to use the command</li></ul></ul>

---

### Practice

#### 1) Open a new R script (`Ctrl + Shift + N`) and write a code to create these objects:

<table>
<caption>Objects to create</caption>
<tbody>
 <tr>
 <td style="text-align:left;"> Object name: </td>
 <td style="text-align:center;"> a </td>
 <td style="text-align:center;"> b </td>
 <td style="text-align:center;"> c </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #014D64 !important;"> Assigned value: </td>
 <td style="text-align:center;background-color: #014D64 !important;"> 2 </td>
 <td style="text-align:center;background-color: #014D64 !important;"> 4 </td>
 <td style="text-align:center;background-color: #014D64 !important;"> 5 </td>
 </tr>
</tbody>
</table>

#### 2) Run this code and create a new object named `result` that takes the value `$\frac{b\times c}{a} + (b-a)^c$`

<table class="table table-hover table-condensed" style="width: auto !important; margin-left: auto; margin-right: auto;">
<caption>Basic operations in R</caption>
<tbody>
 <tr>
 <td style="text-align:left;"> Operation: </td>
 <td style="text-align:center;"> Addition </td>
 <td style="text-align:center;"> Subtraction </td>
 <td style="text-align:center;"> Multiplication </td>
 <td style="text-align:center;"> Division </td>
 <td style="text-align:center;"> Exponentiation </td>
 <td style="text-align:center;"> Parentheses </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #014D64 !important;"> Symbol in R: </td>
 <td style="text-align:center;background-color: #014D64 !important;"> + </td>
 <td style="text-align:center;background-color: #014D64 !important;"> - </td>
 <td style="text-align:center;background-color: #014D64 !important;"> * </td>
 <td style="text-align:center;background-color: #014D64 !important;"> / </td>
 <td style="text-align:center;background-color: #014D64 !important;"> ^ </td>
 <td style="text-align:center;background-color: #014D64 !important;"> () </td>
 </tr>
</tbody>
</table>

#### 3) Print `result` in your console and save your script somewhere in your computer (`Ctrl+S`)

<center><h3>You've got 3 minutes!</h3></center>

---

### Solution

#### 1) Open a new R script (`Ctrl + Shift + N`) and write a code to create these objects:

.pull-left[
<table class="table table-hover table-condensed" style="width: auto !important; margin-left: auto; margin-right: auto;">
<caption>Objects to create</caption>
<tbody>
 <tr>
 <td style="text-align:left;"> Object name: </td>
 <td style="text-align:center;"> a </td>
 <td style="text-align:center;"> b </td>
 <td style="text-align:center;"> c </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #014D64 !important;"> Assigned value: </td>
 <td style="text-align:center;background-color: #014D64 !important;"> 2 </td>
 <td style="text-align:center;background-color: #014D64 !important;"> 4 </td>
 <td style="text-align:center;background-color: #014D64 !important;"> 5 </td>
 </tr>
</tbody>
</table>
]

```r
a <- 2
b <- 4
c <- 5
```
]

#### 2) Run this code and create a new object named `result` that takes the value `$\frac{b\times c}{a} + (b-a)^c$`

```r
result <- b*c/a + (b-a)^c
```

#### 3) Print `result` in your console and save your script somewhere in your computer (`Ctrl + S`)

```r
result
```

```
## [1] 42
```

---

### 1. Getting started

#### 1.3. Import and eyeball data

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

<center>&#10140; Take for instance the statistics from the last season of Ligue 1 available at <a href = "https://fbref.com/en/comps/13/schedule/Ligue-1-Scores-and-Fixtures">fbref.com</a></center>

---

### 1. Getting started

#### 1.3. Import and eyeball data

<ul>
 <li>You can download this dataset <a href = "https://louissirugue.github.io/intro_to_R/lecture1/data.zip">here</a> or from the course webpage</li>
 <ul>
 <li>Note that the extension of the file is .csv (for Comma Separated Values)</li>
 <li>Let's have a look at first 5 lines of the raw csv file</li>
 </ul>
</ul>

--

```text
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,
```

<ul>
 <li>The .csv format is very common and has a very codified structure</li>
 <ul>
 <li>We can see that each line corresponds to a row (the first row generally contains column names)</li>
 <li>And for each row the values of each column are separated by commas</li>
 </ul>
</ul>

<center>&#10140; But how to get it in our R studio environment?</center>

---

### 1. Getting started

#### 1.3. Import and eyeball data

.pull-left[
<ul>
 <li>To import stuff in R we use read functions</li>
 <ul>
 <li>They take the file directory as an input</li>
 <li>And give the file content as an output</li>
 </ul>
</ul>
]

```r
function(input)
```

```
## [1] "output"
```
]

<ul>
 <li>The read function dedicated to .csv files is read.csv()</li>
</ul>

```r
fb <- read.csv("C:\User\Documents\ligue1.csv")
```

```
## Error: '\U' used without hex digits in character string starting ""C:\U"
```

<center>Oops, slashes must be the other way around!</center>

```r
fb <- read.csv("C:/User/Documents/ligue1.csv")
```

<center>&#10140; Let's inspect this new object to check that it worked</center>

---

### 1. Getting started

#### 1.3. Import and eyeball data

* The first thing we can do is to use `head()` to print the **top rows**

```r
head(fb, 4)
```
--

```
##   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
## 4  1 Sun 2021-08-08 13:00 Rennes 0.6   1-1  2.0      Lens      22567
##              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
## 4     Roazhon Park Bastien Dechepy Match Report    NA
```

* `tail()` would print the **bottom rows**

* We can also run **`View(`**`fb`**`)`** *(a new tab will pop-up in your Source panel)*
 
---

### 1. Getting started

#### 1.3. Import and eyeball data

Seems like it worked!
</center>
 
---

### 1. Getting started

#### 1.3. Import and eyeball data

... or kind of worked?
</center>
 
---

### 1. Getting started

#### 1.4. Use functions

<ul>
 <li>That kind of weird characters kicks in when there is an encoding issue</li>
 <ul>
 <li>Thankfully, read.csv() can take many other inputs, including encoding!</li>
 <li>Usually the UTF-8 encoding is the solution to French characters</li>
 </ul>
</ul>

```r
fb <- read.csv("C:/User/Documents/ligue1.csv", encoding = "UTF-8")
```

<ul>
 <li>When you will be facing similar issues, check out the arguments of read.csv() using ?read.csv</li>
</ul>

---

### 1. Getting started

#### 1.4. Use functions

<ul>
 <li>From the documentation you can see that functions have many arguments</li>
 <ul>
 <li>Some without default values: You need to specify the argument for the function to work</li>
 <li>Some with default values: If you don't specify these arguments, defaults will be used</li>
 </ul>
</ul>

```r
read.csv(file, header = TRUE, sep = ",", quote = "\"",
         dec = ".", fill = TRUE, comment.char = "", ...)
```

* You don't need to write the argument names only for those written in the correct order

```r
read.csv(file = "dt.csv")
```

```r
read.csv("dt.csv")
```

]
.pull-right[
`$$\Longleftrightarrow$$`

`$$\underset{\Longleftrightarrow}{?}$$`

]
]
.right-column[

```r
read.csv("dt.csv")
```

```r
read.csv("dt.csv", sep = ",")
```
]
 
---

### 1. Getting started

#### 1.4. Use functions

```r
read.csv(file, header = TRUE, sep = ",", quote = "\"",
         dec = ".", fill = TRUE, comment.char = "", ...)
```

* You don't need to write the argument names only for those written in the correct order

```r
read.csv(file = "dt.csv")
```

```r
read.csv("dt.csv")
```

```r
read.csv("dt.csv", sep = ",")
```

]
.pull-right[
`$$\Longleftrightarrow$$`

`$$\Longleftrightarrow$$`

`$$\underset{\Longleftrightarrow}{?}$$`

]
]
.right-column[

```r
read.csv("dt.csv")
```

```r
read.csv("dt.csv", sep = ",")
```

```r
read.csv("dt.csv", ",")
```
]
 
---

### 1. Getting started

#### 1.4. Use functions

```r
read.csv(file, header = TRUE, sep = ",", quote = "\"",
         dec = ".", fill = TRUE, comment.char = "", ...)
```

* You don't need to write the argument names only for those written in the correct order

```r
read.csv(file = "dt.csv")
```

```r
read.csv("dt.csv")
```

```r
read.csv("dt.csv", sep = ",")
```

]
.pull-right[
`$$\Longleftrightarrow$$`

`$$\Longleftrightarrow$$`

`$$\not\not\Longleftrightarrow$$`

`$$\underset{\Longleftrightarrow}{?}$$`

]
]
.right-column[

```r
read.csv("dt.csv")
```

```r
read.csv("dt.csv", sep = ",")
```

```r
read.csv("dt.csv", ",")
```

```r
read.csv("dt.csv", TRUE, ",")
```
]
 
---

### 1. Getting started

#### 1.4. Use functions

```r
read.csv(file, header = TRUE, sep = ",", quote = "\"",
         dec = ".", fill = TRUE, comment.char = "", ...)
```

* You don't need to write the argument names only for those written in the correct order

```r
read.csv(file = "dt.csv")
```

```r
read.csv("dt.csv")
```

```r
read.csv("dt.csv", sep = ",")
```

]
.pull-right[
`$$\Longleftrightarrow$$`

`$$\Longleftrightarrow$$`

`$$\not\not\Longleftrightarrow$$`

`$$\Longleftrightarrow$$`

]
]
.right-column[

```r
read.csv("dt.csv")
```

```r
read.csv("dt.csv", sep = ",")
```

```r
read.csv("dt.csv", ",")
```

```r
read.csv("dt.csv", TRUE, ",")
```
]

---

<h3>Overview</h3>

<ul style = "margin-left:1.5cm;list-style: none">
 <li>1. Getting started &#10004;</li>
 <ul style = "list-style: none">
 <li>1.1. About R</li>
 <li>1.2. The R Studio IDE</li>
 <li>1.3. Import and eyeball data</li>
 <li>1.4. Use functions</li>
 </ul>
</ul>

]

<ul style = "margin-left:-1cm;list-style: none"><li>5. Wrap up!</li></ul>
]

---

<h3>Overview</h3>

<ul style = "margin-left:1.5cm;list-style: none">
 <li>2. Anatomy of a data.frame </li>
 <ul style = "list-style: none">
 <li>2.1. Data structure</li>
 <li>2.2. Classes</li>
 <li>2.3. Vectors</li>
 <li>2.4. Subsetting</li>
 </ul>
</ul>

]

---

### 2. Anatomy of a `data.frame`

#### 2.1. Data structure

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

```r
str(fb)
```

---

### 2. Anatomy of a `data.frame`

#### 2.1. Data structure

* *Don't be scared of the output!*

```r
str(fb)
```

```
## '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 ...
```

---

### 2. Anatomy of a `data.frame`

#### 2.1. Data structure

* `str()` says that `fb` is a `data.frame`, and gives its numbers of **observations** (rows) and **variables** (columns)

```r
str(fb)
```

```text
## 'data.frame':    380 obs. of  14 variables:
```

---

### 2. Anatomy of a `data.frame`

#### 2.1. Data structure

* It also gives the **variables names**

```r
str(fb)
```

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

---

### 2. Anatomy of a `data.frame`

#### 2.1. Data structure

* The **first values** of each variable

```r
str(fb)
```

```text
## '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 ...
```

---

### 2. Anatomy of a `data.frame`

#### 2.1. Data structure

* As well as the **class** of each variable

```r
str(fb)
```

```text
## '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 ...
```

---

### 2. Anatomy of a `data.frame`

#### 2.1. Data structure

* But what does the **class** correspond to?

```r
str(fb)
```

```text
## '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  ?
```

---

### 2. Anatomy of a `data.frame`

#### 2.2. Classes

.left-column[
.pull-left[
<center>Numeric</center>
]
.pull-right[
<center>Character</center>
]
]
.right-column[

<center>Logical</center>
]

---

### 2. Anatomy of a `data.frame`

#### 2.2. Classes

These are simply numbers:

```r
class(3)
```

```
## [1] "numeric"
```

```r
class(-1.6180339)
```

```
## [1] "numeric"
```

Numeric variable classes include:

<ul>
 <li>int for round numbers</li>
 <li>dbl for 2-decimal numbers</li>
</ul>
]
.pull-right[
<center>Character</center>
]
]
.right-column[

<center>Logical</center>
]

---

### 2. Anatomy of a `data.frame`

#### 2.2. Classes

These are simply numbers:

```r
class(3)
```

```
## [1] "numeric"
```

```r
class(-1.6180339)
```

```
## [1] "numeric"
```

Numeric variable classes include:

<ul>
 <li>int for round numbers</li>
 <li>dbl for 2-decimal numbers</li>
</ul>
]
.pull-right[
<center>Character</center>

They must be surrounded by `"`:

```r
class("Roazhon Park")
```

```
## [1] "character"
```

```r
class("35")
```

```
## [1] "character"
```

We also call these values:

<ul>
 <li>Character strings</li>
 <li>Or just strings</li>
</ul>

]
]
.right-column[

<center>Logical</center>
]

---

### 2. Anatomy of a `data.frame`

#### 2.2. Classes

These are simply numbers:

```r
class(3)
```

```
## [1] "numeric"
```

```r
class(-1.6180339)
```

```
## [1] "numeric"
```

Numeric variable classes include:

<ul>
 <li>int for round numbers</li>
 <li>dbl for 2-decimal numbers</li>
</ul>
]
.pull-right[
<center>Character</center>

They must be surrounded by `"`:

```r
class("Roazhon Park")
```

```
## [1] "character"
```

```r
class("35")
```

```
## [1] "character"
```

We also call these values:

<ul>
 <li>Character strings</li>
 <li>Or just strings</li>
</ul>

]
]
.right-column[

<center>Logical</center>

Something either `TRUE` of `FALSE`:

```r
3 >= 4
```

```
## [1] FALSE
```

```r
class(T)
```

```
## [1] "logical"
```

Most common logical operators:

<ul>
 <li>== > < <= >=</li>
 <li>& (and) | (or) ! (opposite)</li>
</ul>
]

---

### 2. Anatomy of a `data.frame`

#### 2.2. Classes

<center>Guess the output!</center>

```r
as.numeric("2022")
```
--

```
## [1] 2022
```
--

<center>What about this one?</center>

```r
as.character(2022-2023)
```
--

```
## [1] "-1"
```
--

<center>A final one:</center>

```r
as.character(2022>2023)
```
--

```
## [1] "FALSE"
```

---

### 2. Anatomy of a `data.frame`

#### 2.2. Classes

* To know everything:

<center>Class conversion table:</center>

<table class="table table-hover table-condensed" style="width: auto !important; margin-left: auto; margin-right: auto;">
<caption></caption>
 <thead>
 <tr>
 <th style="text-align:left;"> </th>
 <th style="text-align:center;"> numeric </th>
 <th style="text-align:center;"> character </th>
 <th style="text-align:center;"> logical </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> as.numeric() </td>
 <td style="text-align:center;"> No effect </td>
 <td style="text-align:center;"> Converts strings of numbers into numeric values Returns NA if characters in the string </td>
 <td style="text-align:center;"> Returns 1 if TRUE Returns 0 if FALSE </td>
 </tr>
 <tr>
 <td style="text-align:left;"> as.character() </td>
 <td style="text-align:center;"> Converts numeric values into strings of numbers </td>
 <td style="text-align:center;"> No effect </td>
 <td style="text-align:center;"> Returns "TRUE" if TRUE Returns "FALSE" if FALSE </td>
 </tr>
 <tr>
 <td style="text-align:left;"> as.logical() </td>
 <td style="text-align:center;"> Returns TRUE if != 0 Returns FALSE if 0 </td>
 <td style="text-align:center;"> Returns TRUE if "T" or"TRUE" Returns FALSE if "F" or "FALSE" Returns NA otherwise </td>
 <td style="text-align:center;"> No effect </td>
 </tr>
</tbody>
</table>

<center>NA stands for 'Not Available', it corresponds to a missing value</center>

---

### 2. Anatomy of a `data.frame`

#### 2.2. Classes

* Great! But there is one last mystery...

```r
str(fb)
```

---

### 2. Anatomy of a `data.frame`

#### 2.2. Classes

* Are these dollar signs here for a reason?

```r
str(fb)
```

---

### 2. Anatomy of a `data.frame`

#### 2.3. Vectors

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

```r
fb$Wk
```

```
##   [1]  1  1  1  1  1  1  1  1  1  1  2  2  2  2  2  2  2  2  2  2  3  3  3  3  3  3  3  3  3
##  [30]  4  4  4  4  4  4  4  4  4  4  5  5  5  5  5  5  5  5  5  5  6  6  6  6  6  6  6  6  6
##  [59]  6  7  7  7  7  7  7  7  7  7  7  8  8  8  8  8  8  8  8  8  8  9  9  9  9  9  9  9  9
##  [88]  9  9 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11  3 12 12 12 12 12 12
## [117] 12 12 12 12 13 13 13 13 13 13 13 13 13 13 14 14 14 14 14 14 14 14 14 15 15 15 15 15 15
## [146] 15 15 15 15 16 16 16 16 16 16 16 16 16 16 17 17 17 17 17 17 17 17 17 17 18 18 18 18 18
## [175] 18 18 18 18 18 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 21 21 21 21 21 21 21 21
## [204] 21 21 19 20 20 22 22 22 22 22 22 22 22 22 22 20 14 23 23 23 23 23 23 23 23 23 23 24 24
## [233] 24 24 24 24 24 24 24 24 25 25 25 25 25 25 25 25 25 25 26 26 26 26 26 26 26 26 26 26 27
## [262] 27 27 27 27 27 27 27 27 27 28 28 28 28 28 28 28 28 28 28 29 29 29 29 29 29 29 29 29 29
## [291] 30 30 30 30 30 30 30 30 30 30 31 31 31 31 31 31 31 31 31 31 32 32 32 32 32 32 32 32 32
## [320] 32 33 33 33 33 33 33 33 33 33 33 34 34 34 34 34 34 34 34 34 34 35 35 35 35 35 35 35 35
## [349] 35 35 36 36 36 36 36 36 36 36 36 36 37 37 37 37 37 37 37 37 37 37 38 38 38 38 38 38 38
## [378] 38 38 38
```

---

### 2. Anatomy of a `data.frame`

#### 2.3. Vectors

<ul>
 <li>We call these objects vectors</li>
 <ul>
 <li>Vectors are basically sequences of values that have the same class</li>
 <li>R won't let you create a vector containing elements of different classes</li>
 </ul>
</ul>

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

```r
c("Hello world", 35, FALSE)
```

```
## [1] "Hello world" "35"          "FALSE"
```

<ul>
 <li>The fact that vectors are homogeneous in class allows that operations apply to all their elements</li>
</ul>

```r
c(1, 2, 3) / 3
```

```
## [1] 0.3333333 0.6666667 1.0000000
```
]

```r
3 / c(1, 2, 3)
```

```
## [1] 3.0 1.5 1.0
```
]

---

### 2. Anatomy of a `data.frame`

#### 2.4. Subsetting

<ul>
 <li>But $ is not the only way to extract a variable from a dataset</li>
 <ul>
 <li>You can also make use of the [ ] subsetting operator</li>
 </ul>
</ul>

$$\text{data}[\text{row}, \:\:\text{columns}] $$

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

* Example:

```r
fb[1, c("Venue", "Attendance")]
```

```
##             Venue Attendance
## 1 Stade Louis II.       7500
```
]

* Brackets also work for vectors:

```r
vec <- c(3, 2, 1)
vec[c(T, F, T)]
```

```
## [1] 3 1
```
]

---

### Practice

#### 1) Download and import the dataset if you haven't already

#### 2) Combine the use of `[ ]` and `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

<center><h3>You've got 6 minutes!</h3></center>

---

### Solution

#### 1) Download and import the dataset if you haven't already

```r
fb <- read.csv("C:/User/Documents/ligue1.csv", encoding = "UTF-8")
```

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

```r
last_week <- fb[nrow(fb), "Wk"]
last_week
```

```
## [1] 38
```

---

### Solution

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

```r
fb[Wk == last_week, c("Home", "Score", "Away")]
```

```
## Error in `[.data.frame`(fb, Wk == last_week, c("Home", "Score", "Away")): object 'Wk' not found
```

<ul>
 <li>Oops! Seems like R couldn't find the Wk variable</li>
 <ul>
 <li>R was looking for Wk in our environment</li>
 <li>But there is no Wk there</li>
 </ul>
</ul>

<ul>
 <li>We must refer to fb which is in our environment</li>
 <ul>
 <li>Then we can access Wk using the $ symbol</li>
 </ul>
</ul>

```r
fb[fb$Wk == 38, 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
```
]

---

<h3>Overview</h3>

<ul style = "margin-left:1.5cm;list-style: none">
 <li>2. Anatomy of a data.frame &#10004;</li>
 <ul style = "list-style: none">
 <li>2.1. Data structure</li>
 <li>2.2. Classes</li>
 <li>2.3. Vectors</li>
 <li>2.4. Subsetting</li>
 </ul>
</ul>

]

<ul style = "margin-left:-1cm;list-style: none"><li>5. Wrap up!</li></ul>
]

---

<h3>Overview</h3>

]

---

### 3. The `dplyr` grammar

#### 3.1. Packages

<ul>
 <li>So far we only used functions that are directly available in R</li>
 <ul>
 <li>But there are tons of user-created functions out there that can make your life so much easier</li>
 <li>These functions are shared in what we call packages</li>
 </ul>
</ul>

<ul>
 <li>Packages are bundles of functions that R users put at the disposal of other R users</li>
 <ul>
 <li>Packages are centralized on the <a href = "https://cran.r-project.org/">Comprehensive R Archive Network (CRAN)</a></li>
 <li>To download and install a CRAN package you can simply use install.packages()</li>
 </ul>
</ul>

<ul>
 <li>All the functions of the dplyr grammar are gathered in the dplyr package</li>
 <ul>
 <li>We can download these functions and make them ready to use with the install.packages() function</li>
 </ul>
</ul>

```r
install.packages("dplyr") # Requires an internet connection
```

<ul>
 <li>The tidyverse package is now installed on your computer</li>
 <ul>
 <li>You won't have to do it again</li>
 </ul>
</ul>
 
---

### 3. The `dplyr` grammar

#### 3.1. Packages

* The `dplyr` package is now on your computer, but it is not loaded in R

```r
ls("package:dplyr")
```

```
## Error in as.environment(pos): no item called "package:dplyr" on the search list
```

<ul>
 <li>You need to use the library() command to load it</li>
</ul>

```r
library(dplyr)
ls("package:dplyr")[1:5]
```

```
## [1] "%>%"        "across"     "add_count"  "add_count_" "add_row"
```

<ul>
 <li>But even though the package is permanently installed, it is loaded only for your current session</li>
 <ul>
 <li>Each time you start a new R session, you'll have to load the packages you need with library()</li>
 </ul>
</ul>

---

### 3. The `dplyr` grammar

#### 3.2. Basic functions

`dplyr` is a **grammar** of data manipulation providing very **user-friendly functions** to handle the most common **data manipulation** tasks:

* `mutate()`: add/modify variables
 * `select()`: keep/drop variables (columns)
 * `filter()`: keep/drop observations (rows)
 * `arrange()`: sort rows according to the values of given variable(s)
 * `summarise()`: aggregate the data into descriptive statistics

<ul style = "margin-left:6cm;">
 <li>A very handy operator to use with the dplyr grammar is the pipe %>%</li>
</ul>

<ul style = "margin-left:6cm;">
 <ul>
 <li>You can basically read a %>% b() as "apply function b() to object a"</li>
 </ul>
</ul>

<ul style = "margin-left:6cm;">
 <ul>
 <li>With this operator you can easily chain the operations you apply to an object</li>
 </ul>
</ul>

---

### 3. The `dplyr` grammar

#### 3.2. Basic functions

```r
fb 
                                                    #
                                                    #
                                                    #
                                                    #
                                                    #
                                                    #
```

```text
##     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  ...
## 4    1 Sun 2021-08-08 13:00        Rennes 0.6   1–1  2.0          Lens      22567  ...
## 5    1 Sun 2021-08-08 15:00      Bordeaux 0.7   0–2  3.3 Clermont Foot      18748  ...
## 6    1 Sun 2021-08-08 15:00    Strasbourg 0.4   0–2  0.9        Angers      23250  ...
## 7    1 Sun 2021-08-08 15:00          Nice 0.8   0–0  0.2         Reims      18030  ...
## 8    1 Sun 2021-08-08 15:00 Saint-Étienne 2.1   1–1  1.3       Lorient      20461  ...
## 9    1 Sun 2021-08-08 17:00          Metz 0.7   3–3  1.4         Lille      15551  ...
 ...  ... ...        ...   ...           ... ...   ...  ...           ...        ...  ...                      
```

---

### 3. The `dplyr` grammar

#### 3.2. Basic functions

```r
fb %>%
  select(Home, xG, Score, xG.1, Away)               # Keep/drop certain columns
                                                    #
                                                    #
                                                    #
                                                    #
                                                    #
```

```text
##              Home  xG Score xG.1          Away
## 1          Monaco 2.0   1–1  0.3        Nantes
## 2            Lyon 1.4   1–1  0.8         Brest
## 3          Troyes 0.8   1–2  1.2     Paris S-G
## 4          Rennes 0.6   1–1  2.0          Lens
## 5        Bordeaux 0.7   0–2  3.3 Clermont Foot
## 6      Strasbourg 0.4   0–2  0.9        Angers
## 7            Nice 0.8   0–0  0.2         Reims
## 8   Saint-Étienne 2.1   1–1  1.3       Lorient
## 9            Metz 0.7   3–3  1.4         Lille
 ...             ... ...   ...  ...           ...
```

---

### 3. The `dplyr` grammar

#### 3.2. Basic functions

```r
fb %>%
  select(Home, xG, Score, xG.1, Away) %>%           # Keep/drop certain columns
  mutate(home_winner = xG > xG.1)                   # Create a new variable
                                                    #
                                                    #
                                                    #
                                                    #
```

```text
##              Home  xG Score xG.1          Away home_winner
## 1          Monaco 2.0   1–1  0.3        Nantes        TRUE
## 2            Lyon 1.4   1–1  0.8         Brest        TRUE
## 3          Troyes 0.8   1–2  1.2     Paris S-G       FALSE
## 4          Rennes 0.6   1–1  2.0          Lens       FALSE
## 5        Bordeaux 0.7   0–2  3.3 Clermont Foot       FALSE
## 6      Strasbourg 0.4   0–2  0.9        Angers       FALSE
## 7            Nice 0.8   0–0  0.2         Reims        TRUE
## 8   Saint-Étienne 2.1   1–1  1.3       Lorient        TRUE
## 9            Metz 0.7   3–3  1.4         Lille       FALSE
 ...             ... ...   ...  ...           ...         ...
```

---

### 3. The `dplyr` grammar

#### 3.2. Basic functions

```r
fb %>%
  select(Home, xG, Score, xG.1, Away) %>%           # Keep/drop certain columns
  mutate(home_winner = xG > xG.1) %>%               # Create a new variable
  filter(Home == "Rennes")                          # Keep/drop certain rows 
                                                    #
                                                    #
                                                    #
```

```text
##      Home  xG Score xG.1          Away home_winner
## 1  Rennes 0.6   1–1  2.0          Lens       FALSE
## 2  Rennes 0.9   1–0  0.5        Nantes        TRUE
## 3  Rennes 1.0   0–2  0.5         Reims        TRUE
## 4  Rennes 2.4   6–0  0.3 Clermont Foot        TRUE
## 5  Rennes 0.8   2–0  1.4     Paris S-G       FALSE
## 6  Rennes 1.5   1–0  0.6    Strasbourg        TRUE
## 7  Rennes 3.8   4–1  1.1          Lyon        TRUE
## 8  Rennes 3.1   2–0  0.7   Montpellier        TRUE
## 9  Rennes 0.8   1–2  0.6         Lille        TRUE
         ... ...   ...  ...           ...         ...
```

---

### 3. The `dplyr` grammar

#### 3.2. Basic functions

```r
fb %>%
  select(Home, xG, Score, xG.1, Away) %>%           # Keep/drop certain columns
  mutate(home_winner = xG > xG.1) %>%               # Create a new variable
  filter(Home == "Rennes") %>%                      # Keep/drop certain rows
  arrange(-xG)                                      # Sort rows
                                                    #
                                                    #
```

```text
##      Home  xG Score xG.1          Away home_winner
## 1  Rennes 3.8   4–1  1.1          Lyon        TRUE
## 2  Rennes 3.3   6–0  0.4      Bordeaux        TRUE
## 3  Rennes 3.3   6–1  0.9          Metz        TRUE
## 4  Rennes 3.1   2–0  0.7   Montpellier        TRUE
## 5  Rennes 2.7   2–0  0.3         Brest        TRUE
## 6  Rennes 2.6   4–1  0.4        Troyes        TRUE
## 7  Rennes 2.4   6–0  0.3 Clermont Foot        TRUE
## 8  Rennes 1.9   2–3  2.9        Monaco       FALSE
## 9  Rennes 1.7   2–0  0.3        Angers        TRUE
         ... ...   ...  ...           ...         ...
```

---

### 3. The `dplyr` grammar

#### 3.2. Basic functions

```r
fb %>%
  select(Home, xG, Score, xG.1, Away) %>%           # Keep/drop certain columns
  mutate(home_winner = xG > xG.1) %>%               # Create a new variable
  filter(Home == "Rennes") %>%                      # Keep/drop certain rows
  arrange(-xG) %>%                                  # Sort rows
  summarise(expected_wins = mean(home_winner),      # Aggregate into statistics
            expected_goals = sum(xG))               #
```

```
##   expected_wins expected_goals
## 1     0.8421053           36.6
```

---

### 3. The `dplyr` grammar

#### 3.2. Basic functions

* Here are two very **handy functions** to use within `mutate()`

```r
fb %>%
  select(Home, Attendance) %>%  
  mutate(att_bin = ifelse(Attendance > 10000, 
                          "Large", 
                          "Low")
         ) %>% head()
```

```
##         Home Attendance att_bin
## 1     Monaco       7500     Low
## 2       Lyon      29018   Large
## 3     Troyes      15248   Large
## 4     Rennes      22567   Large
## 5   Bordeaux      18748   Large
## 6 Strasbourg      23250   Large
```
]

```r
fb %>%
 select(Home, xG, xG.1, Away) %>% 
 mutate(xWin = case_when(xG > xG.1 ~ "Home",
 xG == xG.1 ~ "Draw",
 xG < xG.1 ~ "Away")
 ) %>% head()
```

```
##         Home  xG xG.1          Away xWin
## 1     Monaco 2.0  0.3        Nantes Home
## 2       Lyon 1.4  0.8         Brest Home
## 3     Troyes 0.8  1.2     Paris S-G Away
## 4     Rennes 0.6  2.0          Lens Away
## 5   Bordeaux 0.7  3.3 Clermont Foot Away
## 6 Strasbourg 0.4  0.9        Angers Away
```
]

---

### 3. The `dplyr` grammar

#### 3.3. group_by() and summarise()

* With `group_by()` you can perform **computations separately** for the different **categories of a variable**

```r
fb %>%
  select(Wk, Home, xG) %>%
  mutate(all.xG = mean(xG)) %>%
  head(10)
```

```
##    Wk          Home  xG   all.xG
## 1   1        Monaco 2.0 1.473421
## 2   1          Lyon 1.4 1.473421
## 3   1        Troyes 0.8 1.473421
## 4   1        Rennes 0.6 1.473421
## 5   1      Bordeaux 0.7 1.473421
## 6   1    Strasbourg 0.4 1.473421
## 7   1          Nice 0.8 1.473421
## 8   1 Saint-Étienne 2.1 1.473421
## 9   1          Metz 0.7 1.473421
## 10  1   Montpellier 0.5 1.473421
```
]

```r
fb %>%
  select(Wk, Home, xG) %>%
* group_by(Home) %>%
  mutate(home.xG = mean(xG)) %>%
  head(6)
```

```
## # A tibble: 6 x 4
## # Groups: Home [6]
## Wk Home xG home.xG
## <int> <chr> <dbl> <dbl>
## 1 1 Monaco 2 1.69
## 2 1 Lyon 1.4 2.07
## 3 1 Troyes 0.8 1.21
## 4 1 Rennes 0.6 1.93
## 5 1 Bordeaux 0.7 1.23
## 6 1 Strasbourg 0.4 1.73
```
]

---

### 3. The `dplyr` grammar

#### 3.3. group_by() and summarise()

<ul>
 <li>It is particularly useful with summarise()</li>
 <ul>
 <li>summarise keeps the grouping variable</li>
 <li>and computes statistics for each category</li>
 </ul>
</ul>

```r
fb %>%
  group_by(Wk) %>%
  summarise(n = n(),
            tot_xG = sum(xG)+sum(xG.1),
            avg_WG = tot_xG/n) %>%
  head(4)
```

```
## # A tibble: 4 x 4
## Wk n tot_xG avg_WG
## <int> <int> <dbl> <dbl>
## 1 1 10 23.4 2.34
## 2 2 10 26.6 2.66
## 3 3 10 25.7 2.57
## 4 4 10 30.4 3.04
```

]

<center>mutate() $\neq$ summarise()</center>

<ul>
 <li>mutate() takes an operation that converts:</li>
 <ul>
 <li>A vector into another vector</li>
 </ul>
 <li>summarise() takes an operation that converts:</li>
 <ul>
 <li>A vector into a value</li>
 </ul>
</ul>
]

<center>Ungrouping</center>

<ul>
 <li>group_by() applies to all subsequent operations</li>
 <li>To cancel its effect you must ungroup() the data</li>
</ul>

```r
fb %>% 
  group_by(Wk) %>% 
  mutate(test = mean(xG)) %>% 
* ungroup() %>%
  ...
```
]

---

### Practice

#### 1) Start from the `fb` dataset and keep only the variables `Home`, `Score` and `Away`

#### 2) Use the `separate()` function from `tidyr` to split the `Score` variable into `home_score` and `away_score`

```r
data.frame(x = "a_b") %>% 
  separate(x, c("x", "y"), "_")
```

```
##   x y
## 1 a b
```

#### 3) Convert these two variables into numeric vectors

#### 4) Create a variable named `winner` that takes the values `Home`, `Draw` and `Away` depending on the score

#### 5) Use `group_by()` and `summarise() `to compute the percentage of draws, home wins and away wins

<center><h3>You've got 10 minutes!</h3></center>

---

### Solution

#### 1) Start from the `fb` dataset and keep only the variables `Home`, `Score` and `Away`

```r
fb %>% 
* select(Home, Score, Away) %>%
  head(2)
```

```
##     Home Score   Away
## 1 Monaco   1-1 Nantes
## 2   Lyon   1-1  Brest
```

#### 2) Use the `separate()` function from `tidyr` to split the `Score` variable into `home_score` and `away_score`

```r
fb %>% 
  select(Home, Score, Away) %>% 
* separate(Score, c("home_score", "away_score"), "-") %>%
  head(2)
```

```
##     Home home_score away_score   Away
## 1 Monaco          1          1 Nantes
## 2   Lyon          1          1  Brest
```

---

### Solution

#### 3) Convert these two variables into numeric vectors

#### 4) Create a variable named `winner` that takes the values `Home`, `Draw` and `Away` depending on the score

```r
fb %>% 
 select(Home, Score, Away) %>% 
 separate(Score, c("home_score", "away_score"), "-") %>%
* mutate(home_score = as.numeric(home_score),
* away_score = as.numeric(away_score),
* winner = case_when(home_score < away_score ~ "Away",
* home_score == away_score ~ "Draw",
* home_score > away_score ~ "Home")) %>%
 head()
```

```
##         Home home_score away_score          Away winner
## 1     Monaco          1          1        Nantes   Draw
## 2       Lyon          1          1         Brest   Draw
## 3     Troyes          1          2     Paris S-G   Away
## 4     Rennes          1          1          Lens   Draw
## 5   Bordeaux          0          2 Clermont Foot   Away
## 6 Strasbourg          0          2        Angers   Away
```

---

### Solution

#### 5) Use `group_by()` and `summarise() `to compute the percentage of draws, home wins and away wins

```r
fb %>% 
 select(Home, Score, Away) %>% 
 separate(Score, c("home_score", "away_score"), "-") %>%
 mutate(home_score = as.numeric(home_score),
 away_score = as.numeric(away_score),
 winner = case_when(home_score < away_score ~ "Away",
 home_score == away_score ~ "Draw",
 home_score > away_score ~ "Home")) %>%
* group_by(winner) %>%
* summarise(pct = 100 * (n() / nrow(fb)))
```

```
## # A tibble: 3 x 2
## winner pct
## <chr> <dbl>
## 1 Away 30.5
## 2 Draw 26.8
## 3 Home 42.6
```

---

<h3>Overview</h3>

]

<ul style = "margin-left:-1cm;list-style: none">
 <li>3. The dplyr grammar &#10004;</li>
 <ul style = "list-style: none">
 <li>3.1. Packages</li>
 <li>3.2. Basic functions</li>
 <li>3.3. group_by() and summarise()</li>
 </ul>
</ul>

<ul style = "margin-left:-1cm;list-style: none"><li>5. Wrap up!</li></ul>
]

---

<h3>Overview</h3>

]

---

### 4. A few words on learning R

#### 4.1. When it doesn't work the way you want

<ul>
 <li>When things do not work the way you want, NAs are the usual suspects</li>
 <ul>
 <li>For instance, this is how the mean function reacts to NAs:</li>
 </ul>
</ul>
 
--

```r
mean(c(1, 2, NA))
```

```
## [1] NA
```
--

```r
mean(c(1, 2, NA), na.rm = T)
```

```
## [1] 1.5
```

<ul>
 <li>You should systematically check for NAs!</li>
</ul>

```r
is.na(c(1, 2, NA))
```

```
## [1] FALSE FALSE  TRUE
```

---

### 4. A few words on learning R

#### 4.1. When it doesn't work the way you want

<ul>
 <li>Don't pipe blindfolded!</li>
 <ul>
 <li>Check that each command does what it's expected to do</li>
 <li>View or print your data at each step</li>
 </ul>
</ul>

```r
fb %>% 
  select(Home, Score, Away) %>%
  head(1)
```

```
##     Home Score   Away
## 1 Monaco   1-1 Nantes
```

```r
fb %>% 
  select(Home, Score, Away) %>% 
  separate(Score, c("home_score", "away_score"), "-") %>%
  head(1)
```

```
##     Home home_score away_score   Away
## 1 Monaco          1          1 Nantes
```

---

### 4. A few words on learning R

#### 4.2. Where to find help

<ul>
 <li>Oftentimes things don't work either because:</li>
 <ul>
 <li>You don't understand a function's argument</li>
 <li>Or you don't know that there exists an argument that you should use</li>
 </ul>
</ul>

<ul>
 <li>This is precisely what help files are made for</li>
 <ul>
 <li>Every function has a help file, just enter ? and the name of your function in the console</li>
 <li>The help file will pop up in the Help tab of R studio</li>
 </ul>
</ul>

```r
?paste
```

---

### 4. A few words on learning R

#### 4.2. Where to find help

<ul>
 <li>Search on the internet!</li>
 <ul>
 <li>Your question is for sure already asked and answered on <a href = "https://stackoverflow.com/">stackoverflow</a></li>
 </ul>
</ul>
 
--

---

### 4. A few words on learning R

#### 4.3. When it doesn't work at all

* Sometimes R breaks and returns an error (usually kind of cryptic)

```r
read.csv("C:\Users\Documents\R")
```

```
## Error: '\U' used without hex digits in character string starting ""C:\U"
```

<ol>
 <li>Look for keywords that might help you understand where it comes from</li>
 <li>Paste in on Google with the name of your command</li>
</ol>

---

<h3>Overview</h3>

]

<ul style = "margin-left:-1cm;list-style: none">
 <li>4. A few words on learning R &#10004;</li>
 <ul style = "list-style: none">
 <li>4.1. When it doesn't work the way you want</li>
 <li>4.2. Where to find help</li>
 <li>4.3. When it doesn't work at all</li>
 </ul>
</ul>

<ul style = "margin-left:-1cm;list-style: none"><li>5. Wrap up!</li></ul>
]

---

### 5. Wrap up!

#### 1. Import data

```r
fb <- read.csv("C:/User/Documents/ligue1.csv", encoding = "UTF-8")
```

#### 2. Class

```r
is.numeric("1.6180339") # What would be the output?
```

```
## [1] FALSE
```

#### 3. Subsetting

```r
fb$Home[3]
```

```
## [1] "Troyes"
```

---

### 5. Wrap up!

#### 4. Packages

```r
library(dplyr)
```

#### 5. The dplyr grammar

.left-column[
<table class="table table-hover table-condensed" style="width: auto !important; margin-left: auto; margin-right: auto;">
<caption></caption>
 <thead>
 <tr>
 <th style="text-align:left;"> Function </th>
 <th style="text-align:left;"> Meaning </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> mutate() </td>
 <td style="text-align:left;"> Modify or create a variable </td>
 </tr>
 <tr>
 <td style="text-align:left;"> select() </td>
 <td style="text-align:left;"> Keep a subset of variables </td>
 </tr>
 <tr>
 <td style="text-align:left;"> filter() </td>
 <td style="text-align:left;"> Keep a subset of observations </td>
 </tr>
 <tr>
 <td style="text-align:left;"> arrange() </td>
 <td style="text-align:left;"> Sort the data </td>
 </tr>
 <tr>
 <td style="text-align:left;"> group_by() </td>
 <td style="text-align:left;"> Group the data </td>
 </tr>
 <tr>
 <td style="text-align:left;"> summarise() </td>
 <td style="text-align:left;"> Summarizes variables into 1 observation per group </td>
 </tr>
</tbody>
</table>
]