Introduction to R

.title[
# Introduction to R
]
.subtitle[
## Lecture 1
]
.author[
### Louis SIRUGUE
]
.date[
### CPES 2 - Fall 2023
]

---

### 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:</li>
 <ul>
 <li style = "margin-bottom:.3cm;">Give you the necessary statistical and data visualization tools to perform data analyses</li>
 </ul>
</ul>

<ul>
 <li>Prerequisites:</li>
 <ul>
 <li style = "margin-bottom:.3cm;">None</li>
 </ul>
</ul>

<ul>
 <li>What you'll learn:</li>
 <ul>
 <li>Find and manipulate data</li>
 <li>Summarize data with relevant statistics and compelling graphics</li>
 <li style = "margin-bottom:.3cm;">Carry out an empirical research project</li>
 </ul>
</ul>

<ul>
 <li>Thus, this course is a mix of:</li>
 <ul>
 <li>Programming on R</li>
 <li>Basic Statistics</li>
 <li>Introductory Econometrics</li>
 </ul>
</ul>
]

---

### First semester

#### Course format

<ul>
 <li>The course is divided into 2 parts that are structured the same way:</li>
 <ul>
 <li></li>
 <li></li>
 <li></li>
 <li></li>
 </ul>
</ul>
 
.pull-left[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part I: Introduction to R programming</caption>
<tbody>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 1 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Introduction to R </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 2 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Descriptive statistics </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 3 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Basic data manipulation </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 4 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Data visualization </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 5 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> R Markdown &amp; LaTeX </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 6 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Text data &amp; sentiment analysis </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 7 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Homework correction </td>
 </tr>
</tbody>
</table>
]

.pull-right[

<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part II: Introduction to Econometrics</caption>
<tbody>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 8 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Univariate regressions </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 9 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Multivariate regressions </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 10 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Inference </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 11 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Causality </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 12 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Interpretation </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 13 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Applications in academic research </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 14 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Maps and geolocalized data </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 15 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Final Exam </td>
 </tr>
</tbody>
</table>
]

---

### First semester

#### Course format

<ul>
 <li>The course is divided into 2 parts that are structured the same way:</li>
 <ul>
 <li>3 core lectures with weekly online quizzes</li>
 <li></li>
 <li></li>
 <li></li>
 </ul>
</ul>
 
.pull-left[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part I: Introduction to R programming</caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 1 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Introduction to R* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 2 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Descriptive statistics* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 3 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Basic data manipulation* </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 4 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Data visualization </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 5 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> R Markdown &amp; LaTeX </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 6 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Text data &amp; sentiment analysis </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 7 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Homework correction </td>
 </tr>
</tbody>
</table>
]

.pull-right[

<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part II: Introduction to Econometrics</caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 8 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Univariate regressions* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 9 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Multivariate regressions* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 10 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Inference* </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 11 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Causality </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 12 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Interpretation </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 13 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Applications in academic research </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 14 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Maps and geolocalized data </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 15 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Final Exam </td>
 </tr>
</tbody>
</table>
]

---

### First semester

#### Course format

<ul>
 <li>The course is divided into 2 parts that are structured the same way:</li>
 <ul>
 <li>3 core lectures with weekly online quizzes</li>
 <li>2-3 core lectures without quizzes</li>
 <li></li>
 <li></li>
 </ul>
</ul>
 
.pull-left[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part I: Introduction to R programming</caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 1 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Introduction to R* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 2 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Descriptive statistics* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 3 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Basic data manipulation* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 4 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Data visualization </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 5 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> R Markdown &amp; LaTeX </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 6 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Text data &amp; sentiment analysis </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 7 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Homework correction </td>
 </tr>
</tbody>
</table>
]

.pull-right[

<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part II: Introduction to Econometrics</caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 8 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Univariate regressions* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 9 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Multivariate regressions* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 10 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Inference* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 11 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Causality </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 12 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Interpretation </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 13 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Applications in academic research </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 14 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Maps and geolocalized data </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 15 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Final Exam </td>
 </tr>
</tbody>
</table>
]

---

### First semester

#### Course format

<ul>
 <li>The course is divided into 2 parts that are structured the same way:</li>
 <ul>
 <li>3 core lectures with weekly online quizzes</li>
 <li>2-3 core lectures without quizzes</li>
 <li>1 buffer lecture that will not be in the exams</li>
 <li></li>
 </ul>
</ul>
 
.pull-left[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part I: Introduction to R programming</caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 1 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Introduction to R* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 2 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Descriptive statistics* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 3 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Basic data manipulation* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 4 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Data visualization </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 5 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> R Markdown &amp; LaTeX </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 6 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Text data &amp; sentiment analysis </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 7 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Homework correction </td>
 </tr>
</tbody>
</table>
]

.pull-right[

<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part II: Introduction to Econometrics</caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 8 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Univariate regressions* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 9 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Multivariate regressions* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 10 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Inference* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 11 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Causality </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 12 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Interpretation </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 13 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Applications in academic research </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 14 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Maps and geolocalized data </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Lecture 15 </td>
 <td style="text-align:left;color: #DFE6EB !important;background-color: #DFE6EB !important;"> Final Exam </td>
 </tr>
</tbody>
</table>
]

---

### First semester

#### Course format

<ul>
 <li>The course is divided into 2 parts that are structured the same way:</li>
 <ul>
 <li>3 core lectures with weekly online quizzes</li>
 <li>2-3 core lectures without quizzes</li>
 <li>1 buffer lecture that will not be in the exams</li>
 <li>1 exam (homework/final exam)</li>
 </ul>
</ul>
 
.pull-left[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part I: Introduction to R programming</caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 1 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Introduction to R* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 2 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Descriptive statistics* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 3 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Basic data manipulation* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 4 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Data visualization </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 5 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> R Markdown &amp; LaTeX </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 6 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Text data &amp; sentiment analysis </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Lecture 7 </td>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Homework correction </td>
 </tr>
</tbody>
</table>
]

.pull-right[

<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Part II: Introduction to Econometrics</caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 8 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Univariate regressions* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 9 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Multivariate regressions* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 10 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Inference* </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 11 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Causality </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 12 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Interpretation </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 13 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Applications in academic research </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 14 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Maps and geolocalized data </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Lecture 15 </td>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Final Exam </td>
 </tr>
</tbody>
</table>
]

---

### First semester

#### Grading:

<ul style = "margin-left:3cm;">
 <li>The exam coefficients are:</li>
 <ul>
 <li></li>
 <li></li>
 <li></li>
 </ul>
</ul>
]

.pull-right[

<img src="slides_files/figure-html/unnamed-chunk-12-1.png" width="70%" style="display: block; margin: auto auto auto 0;" />
<ul style = "margin-bottom:-.75cm;">
]

---

### First semester

#### Grading:

<ul style = "margin-left:3cm;">
 <li>The exam coefficients are:</li>
 <ul>
 <li>Online quizzes: 25%</li>
 <li></li>
 <li></li>
 </ul>
</ul>
]

.pull-right[

<img src="slides_files/figure-html/unnamed-chunk-13-1.png" width="70%" style="display: block; margin: auto auto auto 0;" />
<ul style = "margin-bottom:-.75cm;">
]

---

### First semester

#### Grading:

<ul style = "margin-left:3cm;">
 <li>The exam coefficients are:</li>
 <ul>
 <li>Online quizzes: 25%</li>
 <li>Homework: 30%</li>
 <li></li>
 </ul>
</ul>
]

.pull-right[

<img src="slides_files/figure-html/unnamed-chunk-14-1.png" width="70%" style="display: block; margin: auto auto auto 0;" />
<ul style = "margin-bottom:-.75cm;">
]

---

### First semester

#### Grading:

<ul style = "margin-left:3cm;">
 <li>The exam coefficients are:</li>
 <ul>
 <li>Online quizzes: 25%</li>
 <li>Homework: 30%</li>
 <li>Final exam: 45%</li>
 </ul>
</ul>
]

.pull-right[

<img src="slides_files/figure-html/unnamed-chunk-15-1.png" width="70%" style="display: block; margin: auto auto auto 0;" />
<ul style = "margin-bottom:-.75cm;">
]

--
<ul>
 <li>Online quizzes:</li>
 <ul>
 <li>Available in the Material section of the course webpage</li>
 <li>Log in using the verification received by email</li>
 <li>3-5 short questions on to the content of the previous class </li>
 <li>Quizzes open at the end of the lecture and close at the beginning of the next one</li>
 <li>You can retry the quiz as many times as you want before submitting</li>
 <li>To submit click on download results and send the downloaded file to me by email before the next lecture</li>
 </ul>
</ul>

---

### First semester

#### Grading:

<ul>
 <li>Homework:</li>
 <ul>
 <li>Set of exercises related to the first five lectures of the course</li>
 <li>It can be done alone or by pairs</li>
 <li>Must be handed over via email by the end of the sixth week (15th of October 18:00)</li>
 <li>Late submissions will be penalized by half a point for each 30 min. beyond the deadline</li>
 <li>You can help each other but write your answers yourselves (no copy-pasting)</li>
 </ul>
</ul>

<ul>
 <li>Final exam:</li>
 <ul>
 <li>Paper exam in classroom</li>
 <li>You can bring a cheatsheet with you as long as:</li>
 <ul>
 <li>It is handwritten on paper</li>
 <li>It stands on a single A4 (21cm x 29.7cm) page, i.e., recto only</li>
 </ul>
 <li>Cheatsheets that do not comply with these rules will be confiscated</li>
 <li>Beyond that, standard examination rules apply</li>
 </ul>
</ul>

---

### Second semester

#### Course format

<ul>
 <li>The second semester will be all about carrying out your own research project:</li>
 <ul>
 <li></li>
 <li></li>
 <li></li>
 </ul>
</ul>

---

### Second semester

#### Course format

<ul>
 <li>The second semester will be all about carrying out your own research project:</li>
 <ul>
 <li>Guidelines and refreshers will give you time find data and a research question</li>
 <li></li>
 <li></li>
 </ul>
</ul>
 
.pull-left[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption></caption>
<tbody>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 1 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> How to conduct a research project </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 2 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Refresher: R Programming </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 3 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Refresher: Econometrics </td>
 </tr>
</tbody>
</table>

]

]

---

### Second semester

#### Course format

<ul>
 <li>The second semester will be all about carrying out your own research project:</li>
 <ul>
 <li>Guidelines and refreshers will give you time find data and a research question</li>
 <li>The rest of the semester will be made of follow-up sessions</li>
 <li></li>
 </ul>
</ul>
 
.pull-left[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption></caption>
<tbody>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 1 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> How to conduct a research project </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 2 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Refresher: R Programming </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 3 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Refresher: Econometrics </td>
 </tr>
</tbody>
</table>

<table class="table" style="margin-left: auto; margin-right: auto;">
<caption></caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 4 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Presentation of question and data </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 5 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Data cleaning I </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 6 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Data cleaning II </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 7 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Descriptive statistics </td>
 </tr>
</tbody>
</table>
]

.pull-right[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption></caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 8 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Visualizing the data </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 9 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Regression analysis </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 10 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Midterm report feedback </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 11 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Causality assessment </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 12 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Robustness </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 13 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Heterogeneity </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 14 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Last tips </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 15 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Final presentation </td>
 </tr>
</tbody>
</table>
]

---

### Second semester

#### Course format

<ul>
 <li>The second semester will be all about carrying out your own research project:</li>
 <ul>
 <li>Guidelines and refreshers will give you time find data and a research question</li>
 <li>The rest of the semester will be made of follow-up sessions</li>
 <li>Exams will take place at 3 stages of the research process</li>
 </ul>
</ul>
 
.pull-left[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption></caption>
<tbody>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 1 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> How to conduct a research project </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 2 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Refresher: R Programming </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Lecture 3 </td>
 <td style="text-align:left;color: #6794A7 !important;background-color: #DFE6EB !important;"> Refresher: Econometrics </td>
 </tr>
</tbody>
</table>

<table class="table" style="margin-left: auto; margin-right: auto;">
<caption></caption>
<tbody>
 <tr>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Lecture 4 </td>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Presentation of question and data </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 5 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Data cleaning I </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 6 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Data cleaning II </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 7 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Descriptive statistics </td>
 </tr>
</tbody>
</table>
]

.pull-right[
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption></caption>
<tbody>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 8 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Visualizing the data </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 9 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Regression analysis </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Lecture 10 </td>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Follow-up: Midterm report feedback </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 11 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Causality assessment </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 12 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Robustness </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 13 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Heterogeneity </td>
 </tr>
 <tr>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Lecture 14 </td>
 <td style="text-align:left;background-color: #DFE6EB !important;"> Follow-up: Last tips </td>
 </tr>
 <tr>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Lecture 15 </td>
 <td style="text-align:left;color: #00A2D9 !important;background-color: #DFE6EB !important;"> Final presentation </td>
 </tr>
</tbody>
</table>
]

---

### Second semester

#### Grading:

<ul style = "margin-left:1.5cm;">
 <li>The exam coefficients are:</li>
 <ul>
 <li></li>
 <li></li>
 <li></li>
 </ul>
</ul>
]

.pull-right[

<img src="slides_files/figure-html/unnamed-chunk-23-1.png" width="70%" style="display: block; margin: auto auto auto 0;" />
]

---

### Second semester

#### Grading:

<ul style = "margin-left:1.5cm;">
 <li>The exam coefficients are:</li>
 <ul>
 <li>Presentation of research idea: 25%</li>
 <li></li>
 <li></li>
 </ul>
</ul>
]

.pull-right[

<img src="slides_files/figure-html/unnamed-chunk-24-1.png" width="70%" style="display: block; margin: auto auto auto 0;" />
]

---

### Second semester

#### Grading:

<ul style = "margin-left:1.5cm;">
 <li>The exam coefficients are:</li>
 <ul>
 <li>Presentation of research idea: 25%</li>
 <li>Midterm report: 30%</li>
 <li></li>
 </ul>
</ul>
]

.pull-right[

<img src="slides_files/figure-html/unnamed-chunk-25-1.png" width="70%" style="display: block; margin: auto auto auto 0;" />
]

---

### Second semester

#### Grading:

<ul style = "margin-left:1.5cm;">
 <li>The exam coefficients are:</li>
 <ul>
 <li>Presentation of research idea: 25%</li>
 <li>Midterm report: 30%</li>
 <li>Final project/presentation: 45%</li>
 </ul>
</ul>
]

.pull-right[

<img src="slides_files/figure-html/unnamed-chunk-26-1.png" width="70%" style="display: block; margin: auto auto auto 0;" />
<ul style = "margin-bottom:-.75cm;">
]

.pull-left[
<ul>
 <li>Additional information:</li>
 <ul>
 <li>Research projects must be done by pairs</li>
 <li>It can be written in French or in English</li>
 <li>Follow-ups are short meetings with each group individually</li>
 <li>Find an example of what is expected <a href = "https://louissirugue.github.io/metrics_on_R/project/example.html">here</a></li>
 <li>We'll come back to that in due time (January)</li>
 </ul>
</ul> 
]

.pull-right[

<center>&#10140; Check out the <a href = "https://louissirugue.github.io/metrics_on_R/home.html">course webpage</a> for more details, all the information is there!</center>
]
 
---

<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:1.5cm;list-style: none"><li>3. 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/metrics_on_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/metrics_on_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 (<text>:1:20)
```

<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() has many options that can be set as 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:1.5cm;list-style: none"><li>3. Wrap up!</li></ul>
]

---

<h3>Overview</h3>

### 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 eval(expr, envir, enclos): 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:1.5cm;list-style: none"><li>3. Wrap up!</li></ul>
]

---

### 3. 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"
```