Introduction to Programming

The objective of this course is to provide you with the necessary bases in R programming to be able to carry out your empirical homeworks and research projects. The course is structured in 4 classes of 2 hours. No preliminary knowledge in programming is required, but part of the content of the last lecture is based on what you’re going to see in Econometrics I. Lectures will take place in the computer room R2-07 at Campus Jourdan, but feel free to bring up your own laptop (in that case install R and R Studio before the first class).

Material

		Slides	Data	Group 1	Group 2	Group 3
Lecture 1	Data manipulation	html - pdf	data	01/09 09:00-11:00	01/09 11:15-13:15	01/09 14:30-16:30
Lecture 2	Data visualization	html - pdf	data	02/09 09:00-11:00	02/09 11:15-13:15	02/09 14:30-16:30
Lecture 3	R Markdown & LaTeX	html - pdf	data	07/09 14:00-16:00	05/09 09:00-11:00	05/09 16:00-18:00
Lecture 4	Econometrics in R	html - pdf	data	07/10 09:00-11:00	07/10 11:15-13:15	07/10 14:30-16:30

Contents

I. Data manipulation

1. Getting started

• 1.1. About R

• 1.2. The R Studio IDE

• 1.3. Import and eyeball data

• 1.4. Use functions

2. Anatomy of a data.frame

• 2.1. Data structure

• 2.2. Classes

• 2.3. Vectors

• 2.4. Subsetting

3. The dplyr grammar

• 3.1. Packages

• 3.2. Basic functions

• 3.3. group_by() and summarise()

4. A few words on learning R

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

• 4.2. Where to find help

• 4.3. When it doesn't work at all

• 5. Wrap up!

II. Data visualization

1. The ggplot() function

• 1.1. Basic structure

• 1.2. Axes

• 1.3. Theme()

• 1.4. Annotation

2. Adding dimensions

• 2.1. More axes

• 2.2. More facets

• 2.3. More labels

3. Types of geometry

• 3.1. Points and lines

• 3.2. Barplots and histograms

• 3.3. Densities and boxplots

4. How (not) to lie with graphics

• 4.1. Cumulative representations

• 4.2. Axis manipulations

• 4.3. Interpolation

• 5. Wrap up!

III. R Markdown & LaTeX

1. Basic principles

• 1.1. What is R Markdown?

• 1.2. YAML header

• 1.3. Code chunks

• 1.4. Text formatting

• 1.5. Run and knit your code

2. Useful features

• 2.1. Inline code

• 2.2. Tables

• 2.3. Preset themes

• 2.4. Report parameters

3. LaTeX for equations

• 3.1. What is LaTeX?

• 3.2. LaTeX syntax

• 3.3. Large equations

• 4. Wrap up!

IV. Econometrics in R

1. Regressions

• 1.1. On continuous variables

• 1.2. On binary variables

• 1.3. On categorical variables

2. Case study

• 2.1. Variable transformation

• 2.2. Functional form

• 2.3. Control variables

• 2.4. Interactions

3. Inference

• 3.1. Hypothesis testing

• 3.2. Confidence intervals

4. Report and export results

• 4.1. Regression tables

• 4.2. Plot coefficients

• 5. Wrap up!

Cheatsheets

Functions glossary

References

This course is adapted from the one I teach in CPES-2, inspired by:

• Introduction to R, by H. Bull and P. Charousset. Paris School of Economics (2020)
• Advanced Mircoeconometrics, by D. Margolis and F. Libois. Paris School of Economics (2020)
• How to Lie with Graphics, by C. Bontemps. Toulouse School of Economics (2020)
• Introduction to Econometrics with R, by F. Oswald, V. Viers, P. Villedieu, and G. Kenedi. SciencesPo Dep. of Economics (2020)
• Introduction to Causal Inference, by L. Zabrocki. Paris Sciences et Lettres - CPES (2020)
• Geolocalized Datasets and Applications for Economics, by F. Libois and E. Madinier. Paris School of Economics (2020)
• Countless posts on stackoverflow and R-bloggers