About

Lecture Materials for an intensive introduction to programming with R (one-day block-course).

Prerequisites

Compilation depends on bookdown and knitr

install.packages("bookdown")
install.packages("knitr")

In order to get the intended RStudio/Merbivore-style syntax highlighting in the ioslides output, copy style/prettify.js and style/r-lang.j to the respective ioslides folder in the rmarkdown library of your R installation, overwriting the default files with the same names (in OSX with R 3.4, this is: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rmarkdown/rmd/ioslides/ioslides-13.5.1/js/prettify/).

Install the packages used in the code examples:

install.packages("qdapRegex")
install.packages("pacman")
# get a list of all rmd files (slides and notes)
notes_files <- list.files("materials/notes", pattern = "\\.Rmd", full.names =TRUE)
slides_files <- list.files("materials/slides", pattern = "\\.Rmd", full.names = TRUE)
all_files <- c(notes_files, slides_files)

# parse the rmds, extract a list of package dependencies
rmds <- lapply(all_files, readLines)
to_install <- lapply(rmds, qdapRegex::rm_between, 
                     left = c("library(", "require("),
                     right = c(")", ")"),
                     extract = TRUE)
to_install <-  unique(na.omit(unlist(to_install)))
to_install <- to_install[! to_install %in% c("PACKAGE-NAME",  "<PACKAGE NAME>")]

# install all missing packages
pacman::p_load(char = to_install)

Compile all material

Run the this in the terminal to compile all materials (also tests all the R code in the examples):

sh makeall_rintro.sh

Content and Course Structure

The course is designed for first year graduate students in economics (MEcon Program at SEPS-HSG). No former knowledge of R or programming is expected. However, the exercises presuppose basic knowledge of statistics and econometrics. The course is structured in three parts. For each part there are lecture notes (in materials/notes; rmd-files and compiled versions in html and pdf), lecture slides (in materials/slides; rmd-files and compiled version in html), and the source code of all R-examples (in materials/notes).

Part I: Background/Tools

• Why R? Why programming?
• The tools: R, RStudio.

Part II: First steps in R, core concepts.

• First steps in R: R as a calculator, variables.
• Basic programming concepts in R.
• R objects and data structures.
• R functions for basic statistics.

Part III: Working with Data in R.

• Loading/importing data.
• Visualizing data with R/ggplot.
• Basic data analysis with R.

Contribute

Report issues, suggest enhancements, add exercises.

Considerations for future lectures

• spend more time with data types/structures (less function exercises, but more with data type and structures).
• Add visualization and analysis exercise: import data, visualize, run a regression. (take home exercise)