Mutate

Create or change columns

SAS: DATA step assignment (new = ...;, overwrite existing)

dplyr::mutate() adds or replaces columns. One call can set several columns. The result is a new data frame; the input is unchanged unless you overwrite it.

Quick reference

library(dplyr)

df |>
  mutate(
    AVAL = VALUE,
    AVALC = as.character(VALUE)
  )

R	SAS
`mutate(NEW = expr)`	DATA step `new = expr;`
`mutate(VAR = expr)`	overwrite existing variable
several columns in one `mutate()`	several assignments in one DATA step

Trap: mutate() returns a new table; assign it or pipe onward. It does not change the input object in place unless you overwrite the name.

Worked examples below.

Add a column

library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

dm <- data.frame(
  USUBJID = c("STD-001-0001", "STD-001-0002"),
  AGE = c(54L, 61L)
)

dm |>
  mutate(AGEU = "YEARS")

       USUBJID AGE  AGEU
1 STD-001-0001  54 YEARS
2 STD-001-0002  61 YEARS

SAS:

ageu = "YEARS";

Overwrite a column

dm |>
  mutate(AGE = AGE + 1L)

       USUBJID AGE
1 STD-001-0001  55
2 STD-001-0002  62

Same name on the left replaces the column. Order matters when later columns use earlier ones in the same mutate().

Several columns

dm |>
  mutate(
    AGEU = "YEARS",
    AGEGR1 = if_else(AGE < 65L, "<65", ">=65")
  )

       USUBJID AGE  AGEU AGEGR1
1 STD-001-0001  54 YEARS    <65
2 STD-001-0002  61 YEARS    <65

One mutate() is one DATA step block of assignments. Prefer that over chained one-column mutate() calls when the columns belong together.

Not in place

mutate(dm, ...) alone does nothing lasting. Use dm <- dm |> mutate(...) or keep piping into the next verb.