readsas

R package using Rcpp to parse a SAS file into a data.frame(). Currently read.sas is the main function and feature of this package.

The package allows (experimental) reading of sas7bdat files that are

• (un)compressed

As with other releases of the read series, focus is again on being as accurate as possible. Speed is welcome, but a secondary goal.

Installation

With remotes:

remotes::install_github("JanMarvin/readsas")

With r-universe:

options(repos = c(
  janmarvin = 'https://janmarvin.r-universe.dev',
  CRAN = 'https://cloud.r-project.org'))
install.packages('readsas')

Basic usage

fl <- system.file("extdata", "cars.sas7bdat", package = "readsas")

dd <- read.sas(fl)

head(dd)
#>   speed dist
#> 1     4    2
#> 2     4   10
#> 3     7    4
#> 4     7   22
#> 5     8   16
#> 6     9   10

Select columns or rows

This should be much faster, since unselected cells of the entire data frame are skipped when reading, and it is memory efficient to load only specific columns or rows. However, the file header is always read in its entirety. If the file header is large enough, it will still take some time to read.

fl <- system.file("extdata", "mtcars.sas7bdat", package = "readsas")

dd <- read.sas(fl, select.cols = c("VAR1", "mpg", "hp"),
               select.rows = c(2:5), rownames = TRUE)

head(dd)
#>                    mpg  hp
#> Mazda RX4 Wag     21.0 110
#> Datsun 710        22.8  93
#> Hornet 4 Drive    21.4 110
#> Hornet Sportabout 18.7 175

Thanks

The documentation of the sas7bdat package by Matt Shotwell and Clint Cummins in their R package sas7bdat, by Jared Hobbs for the python library sas7bdat, and by EPAM in the Java library parso was crucial. Without their decryption of the SAS format, this package would not have been possible.

Further testing was done using the R package haven by Hadley Wickam and Evan Miller.