This repository contains the material for the PhD Workshop R for Data Science in the tidyverse by Matteo Sostero at Sant'Anna School of Advanced Studies (Pisa) in May 2018. The workshop is intended mainly for first-year PhDs in Economics, but all members of the Computational Modellers Society are welcome to attend!

Image credit: Kevin Ku

The workshop covers topics on data science throughout a typical research project using the tidyverse. No prior knowledge of R is required! Hopefully even those of you familiar with “base R” (but not the tidyverse) will find something new.

The material is based on R for Data Science by Garrett Grolemund and Hadley Wickham. See also their book R for Data Science: Import, Tidy, Transform, Visualize, and Model Data published by O'Reilly Media, 2017.

Please bring your laptop 💻 and charger 🔌!

Preparation (10 minutes ⌛):

1. Install an up-to-date version of R;
2. Install an up-to-date version of RStudio;
3. (Windows only) install Rtools;
4. Install git on your system;
5. Get a copy of the material by creating a new project in RStudio:
   1. File > New Project > Version Control > Git
   2. The repository URL is https://github.com/CoMoS-SA/workshop-R-tidyverse.git
   3. Create project as a subdirectory of your choice;
   4. ☑️ “Open in new session”
6. In RStudio, install tidyverse and nycflights13:

install.packages("tidyverse")
install.packages("nycflights13")

By popular demand, more sessions will be scheduled in the next few weeks.

(In no particular order):

• 📃 Data input: importing data from “messy” files, reading common and exotic formats and directory structures; preserving metadata; web scraping.
• 📐 Data transformation: “tidying” strategies for fixing common issues with data; text processing with regular expressions; reshaping (wide-long) tabular data; merging and appending data.
• ♻️ Automation: using the pipe %>%; automate tasks with functional programming with purrr.
• ✨ Tidy statistical modeling: automated approaches to estimation and diagnostics.
• 📊 Visualization: plot data with ggplot2; principles and recipes for visualization.

Course slides.

Session notebooks:

• 1-Data_transformation.md
• 2-Data_types_lists_map.md
• 3-Data_input_output.md
• 4-Tidy_modelling.md