The following is a compendium of all R packages used in this course (in order of appearance), their main uses, and what we use them for1. Each package name below is a link to the package’s website and/or documentation.
You can install all of these packages at once with the following command:
install.packages(c("tidyverse", "ggrepel", "broom", "car", "estimatr", "lmtest",
"huxtable", "infer", "dagitty", "ggdag", "modelsummary", "fixest"))† Indicates package is part of the tidyverse
| Name | Type | Description/Reason(s) for Use | Classes Used |
|---|---|---|---|
ggplot2† |
Plotting | For nice plots | [1.3] |
gganimate |
Plotting | For animating plots | [1.3] |
haven† |
Data Wrangling | For importing nonstandard data files | [1.4] |
dplyr† |
Data Wrangling | For manipulating data (part of tidyverse) | [1.4] |
readr† |
Data Wrangling | For importing most data files | [1.4] |
tidyr† |
Data Wrangling | For reshaping data (wide and long) | [1.4] |
magrittr† |
Data Wrangling | For the pipe | [1.4] |
tibble† |
Data Wrangling | For a friendlier data.frame | [1.4] |
ggrepel |
Plotting | For annotating text that doesn’t cover observations | [1.4] |
broom |
Models | For tidying regression output | [2.3] |
car |
Models | For testing for outliers | [2.5] |
estimatr |
Models | For calculating heteroskedasticity-robust standard errors | [2.5] |
lmtest |
Models | For testing for heteroskedasticity | [2.5] |
huxtable |
Output | For making nice regression tables | [2.5] |
infer |
Models | For simulation and statistical inference | [2.6] |
dagitty |
Models | For working with DAGs in R | [3.2] |
ggdag |
Plotting | For plotting DAGs in ggplot | [3.2] |
modelsummary |
Output | For making nice regression tables | [3.5] |
fixest |
Models | For working with panel data | [4.1] |
Note, many of these packages have multiple uses beyond our purposes!↩︎