haven read_sas() in R: Import SAS Data Files

The haven read_sas() function imports a SAS .sas7bdat data file into an R tibble. It needs no SAS installation and can attach a value-label catalog when you supply one.

read_sas("data.sas7bdat")                            # read a SAS data file
read_sas("data.sas7bdat", "formats.sas7bcat")        # attach a value-label catalog
read_sas("data.sas7bdat", col_select = c(id, age))   # read only some columns
read_sas("data.sas7bdat", n_max = 1000)              # read the first 1000 rows
read_sas("data.sas7bdat", skip = 5)                  # skip the first 5 rows
read_sas("data.sas7bdat", encoding = "latin1")       # set the text encoding
read_xpt("data.xpt")                                 # read a SAS transport file

What read_sas() does

read_sas() turns a SAS .sas7bdat file into a tibble. You pass it a file path and it returns a tidy data frame with one column per SAS variable. The function reads the binary SAS format directly, so you do not need a SAS license or any SAS software on the machine.

A SAS export often ships with a second file, a .sas7bcat catalog, that stores value labels such as 1 = "Male". Pass that catalog as the second argument and read_sas() attaches the labels to the matching columns. Without it, coded columns arrive as plain numbers.

Syntax and key arguments

Most calls need only the data_file path; the rest control which rows and columns you read. SAS files can be large, so these arguments let you pull a slice instead of the whole table.

The arguments you reach for most are col_select (keep only the variables you need), n_max (preview a huge file fast), and catalog_file (decode value labels). The encoding argument matters only when a file carries non-ASCII text and the embedded encoding is wrong.

read_sas() examples

haven ships an example iris.sas7bdat file, so every example below runs without a SAS file of your own. Build the path with system.file() and pass it to read_sas().

The result is a tibble. SAS variable names use underscores, so Sepal.Length from base R becomes Sepal_Length here.

Read only the columns you need with col_select. It uses tidyselect syntax, so you name columns without quotes. This is the fastest way to trim a wide SAS export.

Limit how much you read with n_max and skip. Use n_max to preview a large file and skip to jump past rows you do not want.

Confirm the object class once the file is in memory. read_sas() always returns a tibble, which prints cleanly and works with every tidyverse function.

read_sas() vs read_sav(), read_dta() and read.csv

read_sas() is one of three haven readers, each tied to a statistical file format. They share the same return type and most arguments, so picking the right one is just a matter of matching the file extension.

Use read_sas() for native SAS data files. Switch to read_xpt() when the file is a SAS transport file, which regulators such as the FDA often require. For data that already lives in a CSV, read.csv() or readr::read_csv() is simpler and needs no extra package.

Common pitfalls

Pointing read_sas() at a path that does not exist. The function cannot guess where the file is. A typo in the path or a missing working directory triggers an immediate error.

Coded columns arriving as labelled instead of plain values. When a SAS file stores formats, read_sas() returns those columns with class haven_labelled. They look like numbers but carry hidden labels, which breaks some functions. Convert them with as_factor() to get readable factors or zap_labels() to strip the labels and keep raw values.

Forgetting the catalog file. Value labels live in a separate .sas7bcat file. If you read only the .sas7bdat, coded columns stay as bare integers. Pass the catalog as the second argument to decode them.

Try it yourself

Related haven functions

read_sas() works alongside a small set of haven readers and label helpers. Reach for the one that matches your file or your cleanup task.

• read_xpt(): read a SAS transport .xpt file, the format regulators often require.
• read_sav(): read an SPSS .sav file into a tibble.
• read_dta(): read a Stata .dta file into a tibble.
• write_xpt(): write a tibble back out as a SAS transport file.
• as_factor(): turn SAS labelled columns into readable factors.
• zap_labels(): strip value labels and keep the raw underlying values.

For the full argument reference, see the haven read_sas() documentation on tidyverse.org.

FAQ

How do I import a SAS file into R?

Install the haven package with install.packages("haven"), load it with library(haven), then call read_sas("path/to/file.sas7bdat"). The function reads the binary SAS format directly and returns the data as a tibble. You do not need SAS itself, a license, or any SAS software on the machine. The path can be absolute or relative to your working directory.

What package reads SAS files in R?

The haven package is the standard choice. It reads .sas7bdat files with read_sas() and SAS transport .xpt files with read_xpt(), and it is part of the tidyverse. The older sas7bdat package and foreign::read.ssd() still exist, but haven is faster, actively maintained, and preserves value labels and SAS formats that the alternatives drop.

Do I need SAS installed to use read_sas()?

No. read_sas() parses the .sas7bdat binary format itself through the bundled ReadStat C library. No SAS installation, license, or server connection is involved. This is the main reason haven replaced older tools: it lets anyone open SAS data on any operating system without paying for SAS.

What is the difference between read_sas() and read_xpt()?

read_sas() reads native SAS data files with the .sas7bdat extension, the format SAS uses internally. read_xpt() reads SAS transport files with the .xpt extension, an older interchange format used for sharing data, especially in regulated clinical submissions. Both return tibbles. Match the function to the file extension you actually have.

Why are my SAS columns showing as labelled in R?

The SAS file stored formats or value labels, so read_sas() returns those columns with class haven_labelled. They display as numbers with attached labels. Call as_factor() on the data to convert labelled columns into factors, or zap_labels() to remove the labels and keep the raw values. This step is common when importing survey data.