Mapping SAS Formats to R – Part 1

  • SAS
  • R

By Michael Walshe


A Comprehensive Guide for the SAS Programmer

In this blog, we explore the process of mapping SAS formats to R for efficient data manipulation. While there is no single function or package in R that replicates SAS formats comprehensively, we can leverage different methods in R to achieve similar results.


1    Introduction to SAS Formats

SAS formats and informats are a swiss-army knife of data manipulation, providing in one package methods to:

  • Perform a look-up from one value to another
  • Group data into bins, based on custom conditions and logic
  • Apply a ‘mask’ to data. This means that you can change the way that data is displayed, without changing the underlying data (e.g. a value of -0.6534 could print as (65.3%))
  • Read formatted data as a different type, for example to read a value of £1,000.00 as a numeric value

Because of the sheer number of different use-cases for SAS formats, there is no single function or package in R which can provide the same results. However, this can be seen as a benefit, logically separating the different functionalities and making R easier to understand and use. In this article we’ll assess a few different methods for the first two use-cases.


2    Look-ups

A SAS format, typically a user-defined one, can be used as a look-up table – for example from a region code to the full name of that region. In R this could be achieved with:

  • A join or merge
  • A named vector
  • Using a function such as dplyr::case_when to encode the look-up
2.1    Method 1: Using A Join

Joins are a widespread method for performing some form of look-up, we will demonstrate using the base R merge function.

my_data <- data.frame(
  region_code = c("SA", "SA", "E", "AS", "AN", "NA"),
  measure = runif(6)

lookup_df <- data.frame(
  region_code = c("E", "NA", "SA", "AS", "AF", "AU", "AN"),
  region_full = c(
    "North America",
    "South America",

merge(my_data, lookup_df)

#>   region_code   measure   region_full
#> 1          AN 0.9404673     Antartica
#> 2          AS 0.8830174          Asia
#> 3           E 0.4089769        Europe
#> 4          NA 0.0455565 North America
#> 5          SA 0.2875775 South America
#> 6          SA 0.7883051 South America
2.2    Method 2: Using A Named Vector

Where you have a simple mapping of keys to values, a named vector can be a good method in R to move from one to the other. Note here that the keys should be the names of the vector, and the values are the elements. Then indexing using a key produces the value.

lookup_vector <- c(
"E" = "Europe",
"NA" = "North America",
"SA" = "South America",
"AS" = "Asia",
"AF" = "Africa",
"AU" = "Australia",
"AN" = "Antartica"

my_data$region_full <- lookup_vector[my_data$region_code]

Note that if the keys were numbers, we would have to create the named vector in two steps, first creating a vector of values then assigning to the names function. However this is not recommended, as it can be unclear what e.g. lookup_vector[3] should return.

2.3    Method 3: Using {dplyr}

dplyr::case_when() is a very useful way to create a new vector based on several conditions. It is similar to a case when expression in SQL. For a simple look-up, the conditions will be equality with our input vector. This also lets us map values not in our look-up table to some default.

my_data$region_full <- dplyr::case_when(
my_data$region_code == "E" ~ "Europe",
my_data$region_code == "NA" ~ "North America",
my_data$region_code == "SA" ~ "South America",
my_data$region_code == "AS" ~ "Asia",
my_data$region_code == "AF" ~ "Africa",
my_data$region_code == "AU" ~ "Australia",
my_data$region_code == "AN" ~ "Antartica",
.default = "Unknown"

This is a little clunky and includes some repetition, but we can use the recently added case_match function to improve it, and at the same time move this into a function to let us easily re-use this mapping.

region_lookup <- function(x) {
 "E" ~ "Europe",
 "NA" ~ "North America",
 "SA" ~ "South America",
 "AS" ~ "Asia",
 "AF" ~ "Africa",
 "AU" ~ "Australia",
 "AN" ~ "Antartica",
 .default = "Unknown"


#> [1] "South America" "South America" "Europe"        "Asia"         
#> [5] "Antartica"     "North America"


3    Binning

Binning is the process of grouping data in ranges of values. SAS user defined formats can be created to group numeric values into these bins. It’s used in a variety of situations, from analysis and visualisation to statistics. As such, there is a base R function cut that can provide most of the functionality we need.

The simplest behaviour is to provide the number of bins, R will create a factor with labels indicating the intervals.

ages <- c(59, 22, 20, 62, 30)

cut(ages, breaks=3)

#> [1] (48,62] (20,34] (20,34] (48,62] (20,34]
#> Levels: (20,34] (34,48] (48,62]

We can also define custom intervals and labels, note that labels needs to be one element shorter than breaks.

  breaks=c(0, 40, 60, 100),
  labels=c("Young", "Middle-Aged", "Old")

#> [1] Middle-Aged Young       Young       Old         Young      
#> Levels: Young Middle-Aged Old

There are many packages in R that provide a function to bin data for different use-cases, as one example we can again use dplyr::case_when(), this time to group data. This produces a character vector rather than a factor.

  ages <= 40 ~ "Young",
  ages <= 60 ~ "Middle-Aged",
  .default = "Old"

#> [1] "Middle-Aged" "Young"       "Young"       "Old"         "Young"


 4    Honourable Mentions

A useful package to mention is {fmtr}, which is designed to try and replicate the experience of SAS user-defined formats in R, including conditions and format catalogues. However, it also has limited popularity, and it is best practice to try to write idiomatic R rather than to replicate SAS exactly.



In this blog, we explored various methods for mapping SAS formats to R, enabling effective data manipulation. By leveraging techniques such as look-ups and binning, you can achieve similar functionality in R.

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