Additional R Packages
We will need some additional libraries to conduct our statistical analysis.
All Users
Proceed as follows:
- Open RStudio
- In the console, copy and paste the following:
to_install <-c( "reshape", "rmarkdown",
"plm", "Hmisc", "sandwich",
"Ecdat", "knitr",
"httr", "xml2",
"xtable","tidyverse", "AER",
"rdd", "car", "aod", "lmtest",
"fixest", "nlme", "lme4",
"multiwayvcov",
"lubridate", "haven",
"ivpack", "readxl",
"ggrepel",
"dbplyr", "remotes",
"rticles", "here",
"optparse", "rlist"
)
install.packages(to_install)
- If you are asked if you want to install packages that need compilation, type
y
followed byReturn
to confirm this. - Wait until all the packages have been installed and the you are done.
- It may take a while, so be patient
Note that many dependencies get installed along the way.
We also want some packages to be installed from Github - these typically still under development:
from_gh <- c("ddsjoberg/gtsummary",
"vincentarelbundock/modelsummary",
"rstudio/fontawesome",
"rstudio/gt",
"rstudio/renv"
)
remotes::install_github(from_gh)
Package List May Update
This list of packages may be updated during the course. There has been a raft of new, easier to use packages for some tools that we will introduce. Before we list them here, we want to test them out a little more rigorously.
Check back here before Week 3 of the course to see if there are changes.