The goal of sysuse is to store the Stata example datasets locally so that using them in R will be even easier.
Example datasets are of great help when it comes to learning new concepts or tools in data science. Many R packages provide example code using datasets such as mtcars or iris. For new learners who are not familiar with such data (or for those who dislike cars and flowers), being forced to use them might add unnecessary cognitive load.
Stata users who are new to R might find it instructive to switch back to some old familiar datasets such as uslifeexp or nlsw88. Thanks to the R packages haven and webuse, loading datasets from Stata's online collection is a breeze:
webuse::webuse("nlsw88")
nlsw88
#> # A tibble: 2,246 x 17
#> idcode age race married never_married grade collgrad south
#> <dbl> <dbl> <dbl+lbl> <dbl+lbl> <dbl> <dbl> <dbl+lbl> <dbl>
#> 1 1 37 2 0 0 12 0 0
#> 2 2 37 2 0 0 12 0 0
#> 3 3 42 2 0 1 12 0 0
#> 4 4 43 1 1 0 17 1 0
#> 5 6 42 1 1 0 12 0 0
#> 6 7 39 1 1 0 12 0 0
#> 7 9 37 1 0 0 12 0 0
#> 8 12 40 1 1 0 18 1 0
#> 9 13 40 1 1 0 14 0 0
#> 10 14 40 1 1 0 15 0 0
#> # ... with 2,236 more rows, and 9 more variables: smsa <dbl+lbl>,
#> # c_city <dbl>, industry <dbl+lbl>, occupation <dbl+lbl>,
#> # union <dbl+lbl>, wage <dbl>, hours <dbl>, ttl_exp <dbl>, tenure <dbl>The function webuse::webuse() is like the Stata command webuse. Similarly, the current package sysuse tries to call the datasets from local directories, just like Stata's sysuse command. Once the package is installed, you can load your favorite data without the internet connection and with just one line of code:
sysuse::nlsw88
#> # A tibble: 2,246 x 17
#> idcode age race married never_married grade collgrad south
#> <dbl> <dbl> <dbl+lbl> <dbl+lbl> <dbl> <dbl> <dbl+lbl> <dbl>
#> 1 1 37 2 0 0 12 0 0
#> 2 2 37 2 0 0 12 0 0
#> 3 3 42 2 0 1 12 0 0
#> 4 4 43 1 1 0 17 1 0
#> 5 6 42 1 1 0 12 0 0
#> 6 7 39 1 1 0 12 0 0
#> 7 9 37 1 0 0 12 0 0
#> 8 12 40 1 1 0 18 1 0
#> 9 13 40 1 1 0 14 0 0
#> 10 14 40 1 1 0 15 0 0
#> # ... with 2,236 more rows, and 9 more variables: smsa <dbl+lbl>,
#> # c_city <dbl>, industry <dbl+lbl>, occupation <dbl+lbl>,
#> # union <dbl+lbl>, wage <dbl>, hours <dbl>, ttl_exp <dbl>, tenure <dbl># install.packages("devtools")
devtools::install_github("jjchern/sysuse")
# To uninstall the package, use:
# remove.packages("sysuse")Suppose you try to learn how to do a two-way table of frequency counts in tidyverse and come across an excellent tutorial from the wonderful @jennybc. The code is just what you want to learn, but it might be off-putting to see that mtcars are being used again, and the two columns cyl and vs could be some of the dullest variables you can imagine. It would be nice to use the nlsw88 instead:
library(tidyverse)
sysuse::nlsw88 %>%
haven::as_factor() %>%
count(married, race) %>%
spread(race, n, fill = 0)
#> # A tibble: 2 x 4
#> married white black other
#> * <fctr> <dbl> <dbl> <dbl>
#> 1 single 487 309 8
#> 2 married 1150 274 18