2.2 Installing R Packages
Related Reading: IDS 1.5
When you download
R, you get “base”
R. Base R contains “basic” functions that are commonly used by most R users. To give some examples, base R gives you the ability add, subtract, divide, or multiply numbers. Base R gives you the ability to calculate the mean (the function is called
mean) or standard deviation (the function is called
sd) of a vector of numbers.
Base R is quite powerful and probably the majority of code you will write in R will only involve Base R.
That being said, there are many cases where it is useful to expand the base functionality of
R. This is done through packages. Packages expand the functionality of R. R is open source so these packages are contributed by users.
It also typically wouldn’t make sense for someone to install all available R packages. For example, a geographer might want to install a much different set of packages relative to an economist. Therefore, we will typically install only the additional functionality that we specifically want.
Example 2.1 In this example, we’ll install the
dslabs package (which is from the IDS book) and the
lubridate package (which is a package for working with dates in R).
# install dslabs package install.packages("dslabs") # install lubridate package install.packages("lubridate")
Installing a package is only the first step to using a package. You can think of installing a package like downloading a package. To actually use a package, you need to load it into memory (i.e., “attach” it) or at least be clear about the package where a function that you are trying to call comes from.
Example 2.2 Dates can be tricky to work with in R (and in programming languages generally). For example, they are not exactly numbers, but they also have more structure than just a character string. The
lubridate package contains functions for converting numbers/strings into dates.
<- "07-15-1985" bday class(bday) # R doesn't know this is actually a date yet #>  "character" # load the package library(lubridate) # mdy stands for "month, day, year" # if date were in different format, could use ymd, etc. <- mdy(bday) date_bday date_bday#>  "1985-07-15" # now R knows this is a date class(date_bday) #>  "Date"
Another (and perhaps better) way to call a function from a package is to use the
:: syntax. In this case, you do not need the call to
library from above. Instead, you can try
::mdy(bday) lubridate#>  "1985-07-15"
This does exactly the same thing as the code before. What is somewhat better about this code is that it is easier to tell that the
mdy function came from the
2.2.1 A list of useful R packages
AER— package containing data from Applied Econometrics with R
wooldridge— package containing data from Wooldridge’s text book
ggplot2— package to produce sophisticated looking plots
dplyr— package containing tools to manipulate data
haven— package for loading different types of data files
plm— package for working with panel data
fixest— another package for working with panel data
ivreg— package for IV regressions, diagnostics, etc.
estimatr— package that runs regressions but with standard errors that economists often like more than the default options in
modelsummary— package for producing nice output of more than one regression and summary statistics
As of this writing, there are currently 18,004 R packages available on CRAN (R’s main repository for contributed packages).