Description Usage Arguments Value Useful functions Scoped mutation and transmuation Tidy data See Also Examples
mutate()
adds new variables and preserves existing;
transmute()
drops existing variables.
1 2 3 |
.data |
A tbl. All main verbs are S3 generics and provide methods
for |
... |
Name-value pairs of expressions. Use These arguments are automatically quoted and
evaluated in the context of the data
frame. They support unquoting and
splicing. See |
An object of the same class as .data
.
+
, -
etc
log()
lead()
, lag()
dense_rank()
, min_rank()
, percent_rank()
, row_number()
,
cume_dist()
, ntile()
cumsum()
, cummean()
, cummin()
, cummax()
, cumany()
, cumall()
na_if()
, coalesce()
if_else()
, recode()
, case_when()
The three scoped variants of mutate()
(mutate_all()
,
mutate_if()
and mutate_at()
) and the three variants of
transmute()
(transmute_all()
, transmute_if()
,
transmute_at()
) make it easy to apply a transformation to a
selection of variables.
When applied to a data frame, row names are silently dropped. To preserve,
convert to an explicit variable with tibble::rownames_to_column()
.
Other single table verbs: arrange
,
filter
, select
,
slice
, summarise
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # Newly created variables are available immediately
mtcars %>% as_tibble() %>% mutate(
cyl2 = cyl * 2,
cyl4 = cyl2 * 2
)
# You can also use mutate() to remove variables and
# modify existing variables
mtcars %>% as_tibble() %>% mutate(
mpg = NULL,
disp = disp * 0.0163871 # convert to litres
)
# window functions are useful for grouped mutates
mtcars %>%
group_by(cyl) %>%
mutate(rank = min_rank(desc(mpg)))
# see `vignette("window-functions")` for more details
# You can drop variables by setting them to NULL
mtcars %>% mutate(cyl = NULL)
# mutate() vs transmute --------------------------
# mutate() keeps all existing variables
mtcars %>%
mutate(displ_l = disp / 61.0237)
# transmute keeps only the variables you create
mtcars %>%
transmute(displ_l = disp / 61.0237)
|
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