pivot_longer(
data,
cols,
...,
cols_vary = "fastest",
names_to = "name",
names_prefix = NULL,
names_sep = NULL,
names_pattern = NULL,
names_ptypes = NULL,
names_transform = NULL,
names_repair = "check_unique",
values_to = "value",
values_drop_na = FALSE,
values_ptypes = NULL,
values_transform = NULL
A data frame to pivot.
<tidy-select> Columns to pivot into
longer format.
Additional arguments passed on to methods.
cols_vary
When pivoting cols into longer format, how should the
output rows be arranged relative to their original row number?
"fastest", the default, keeps individual rows from cols close
together in the output. This often produces intuitively ordered output
when you have at least one key column from data that is not involved in
the pivoting process.
"slowest" keeps individual columns from cols close together in the
output. This often produces intuitively ordered output when you utilize
all of the columns from data in the pivoting process.
- names_to
A character vector specifying the new column or columns to
create from the information stored in the column names of data specified
by cols.
If length 0, or if NULL is supplied, no columns will be created.
If length 1, a single column will be created which will contain the
column names specified by cols.
If length >1, multiple columns will be created. In this case, one of
names_sep or names_pattern must be supplied to specify how the
column names should be split. There are also two additional character
values you can take advantage of:
NA will discard the corresponding component of the column name.
".value" indicates that the corresponding component of the column
name defines the name of the output column containing the cell values,
overriding values_to entirely.
- names_prefix
A regular expression used to remove matching text
from the start of each variable name.
- names_sep, names_pattern
If names_to contains multiple values,
these arguments control how the column name is broken up.
names_sep takes the same specification as separate(), and can either
be a numeric vector (specifying positions to break on), or a single string
(specifying a regular expression to split on).
names_pattern takes the same specification as extract(), a regular
expression containing matching groups (()).
If these arguments do not give you enough control, use
pivot_longer_spec() to create a spec object and process manually as
needed.
- names_ptypes, values_ptypes
Optionally, a list of column name-prototype
pairs. Alternatively, a single empty prototype can be supplied, which will
be applied to all columns. A prototype (or ptype for short) is a
zero-length vector (like integer() or numeric()) that defines the type,
class, and attributes of a vector. Use these arguments if you want to
confirm that the created columns are the types that you expect. Note that
if you want to change (instead of confirm) the types of specific columns,
you should use names_transform or values_transform instead.
- names_transform, values_transform
Optionally, a list of column
name-function pairs. Alternatively, a single function can be supplied,
which will be applied to all columns. Use these arguments if you need to
change the types of specific columns. For example, names_transform = list(week = as.integer) would convert a character variable called week
to an integer.
If not specified, the type of the columns generated from names_to will
be character, and the type of the variables generated from values_to
will be the common type of the input columns used to generate them.
- names_repair
What happens if the output has invalid column names?
The default, "check_unique" is to error if the columns are duplicated.
Use "minimal" to allow duplicates in the output, or "unique" to
de-duplicated by adding numeric suffixes. See vctrs::vec_as_names()
for more options.
- values_to
A string specifying the name of the column to create
from the data stored in cell values. If names_to is a character
containing the special .value sentinel, this value will be ignored,
and the name of the value column will be derived from part of the
existing column names.
- values_drop_na
If TRUE, will drop rows that contain only NAs
in the value_to column. This effectively converts explicit missing values
to implicit missing values, and should generally be used only when missing
values in data were created by its structure.
Details
pivot_longer() is an updated approach to gather(), designed to be both
simpler to use and to handle more use cases. We recommend you use
pivot_longer() for new code; gather() isn't going away but is no longer
under active development.
Examples
# See vignette("pivot") for examples and explanation
# Simplest case where column names are character data
relig_income
#> # A tibble: 18 × 11
#> religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k`
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Agnostic 27 34 60 81 76 137
#> 2 Atheist 12 27 37 52 35 70
#> 3 Buddhist 27 21 30 34 33 58
#> 4 Catholic 418 617 732 670 638 1116
#> 5 Don’t know/r… 15 14 15 11 10 35
#> 6 Evangelical … 575 869 1064 982 881 1486
#> 7 Hindu 1 9 7 9 11 34
#> 8 Historically… 228 244 236 238 197 223
#> 9 Jehovah's Wi… 20 27 24 24 21 30
#> 10 Jewish 19 19 25 25 30 95
#> 11 Mainline Prot 289 495 619 655 651 1107
#> 12 Mormon 29 40 48 51 56 112
#> 13 Muslim 6 7 9 10 9 23
#> 14 Orthodox 13 17 23 32 32 47
#> 15 Other Christ… 9 7 11 13 13 14
#> 16 Other Faiths 20 33 40 46 49 63
#> 17 Other World … 5 2 3 4 2 7
#> 18 Unaffiliated 217 299 374 365 341 528
#> # ℹ 4 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>, `>150k` <dbl>,
#> # `Don't know/refused` <dbl>
relig_income %>%
pivot_longer(!religion, names_to = "income", values_to = "count")
#> # A tibble: 180 × 3
#> religion income count
#> <chr> <chr> <dbl>
#> 1 Agnostic <$10k 27
#> 2 Agnostic $10-20k 34
#> 3 Agnostic $20-30k 60
#> 4 Agnostic $30-40k 81
#> 5 Agnostic $40-50k 76
#> 6 Agnostic $50-75k 137
#> 7 Agnostic $75-100k 122
#> 8 Agnostic $100-150k 109
#> 9 Agnostic >150k 84
#> 10 Agnostic Don't know/refused 96
#> # ℹ 170 more rows
# Slightly more complex case where columns have common prefix,
# and missing missings are structural so should be dropped.
billboard
#> # A tibble: 317 × 79
#> artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7
#> <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99
#> 2 2Ge+her The … 2000-09-02 91 87 92 NA NA NA NA
#> 3 3 Doors D… Kryp… 2000-04-08 81 70 68 67 66 57 54
#> 4 3 Doors D… Loser 2000-10-21 76 76 72 69 67 65 55
#> 5 504 Boyz Wobb… 2000-04-15 57 34 25 17 17 31 36
#> 6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2
#> 7 A*Teens Danc… 2000-07-08 97 97 96 95 100 NA NA
#> 8 Aaliyah I Do… 2000-01-29 84 62 51 41 38 35 35
#> 9 Aaliyah Try … 2000-03-18 59 53 38 28 21 18 16
#> 10 Adams, Yo… Open… 2000-08-26 76 76 74 69 68 67 61
#> # ℹ 307 more rows
#> # ℹ 69 more variables: wk8 <dbl>, wk9 <dbl>, wk10 <dbl>, wk11 <dbl>,
#> # wk12 <dbl>, wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>,
#> # wk17 <dbl>, wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>,
#> # wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>,
#> # wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, wk31 <dbl>,
#> # wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, …
billboard %>%
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
names_prefix = "wk",
values_to = "rank",
values_drop_na = TRUE
#> # A tibble: 5,307 × 5
#> artist track date.entered week rank
#> <chr> <chr> <date> <chr> <dbl>
#> 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
#> 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
#> 3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
#> 4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
#> 5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87
#> 6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94
#> 7 2 Pac Baby Don't Cry (Keep... 2000-02-26 7 99
#> 8 2Ge+her The Hardest Part Of ... 2000-09-02 1 91
#> 9 2Ge+her The Hardest Part Of ... 2000-09-02 2 87
#> 10 2Ge+her The Hardest Part Of ... 2000-09-02 3 92
#> # ℹ 5,297 more rows
# Multiple variables stored in column names
who %>% pivot_longer(
cols = new_sp_m014:newrel_f65,
names_to = c("diagnosis", "gender", "age"),
names_pattern = "new_?(.*)_(.)(.*)",
values_to = "count"
#> # A tibble: 405,440 × 8
#> country iso2 iso3 year diagnosis gender age count
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl>
#> 1 Afghanistan AF AFG 1980 sp m 014 NA
#> 2 Afghanistan AF AFG 1980 sp m 1524 NA
#> 3 Afghanistan AF AFG 1980 sp m 2534 NA
#> 4 Afghanistan AF AFG 1980 sp m 3544 NA
#> 5 Afghanistan AF AFG 1980 sp m 4554 NA
#> 6 Afghanistan AF AFG 1980 sp m 5564 NA
#> 7 Afghanistan AF AFG 1980 sp m 65 NA
#> 8 Afghanistan AF AFG 1980 sp f 014 NA
#> 9 Afghanistan AF AFG 1980 sp f 1524 NA
#> 10 Afghanistan AF AFG 1980 sp f 2534 NA
#> # ℹ 405,430 more rows
# Multiple observations per row. Since all columns are used in the pivoting
# process, we'll use `cols_vary` to keep values from the original columns
# close together in the output.
anscombe
#> x1 x2 x3 x4 y1 y2 y3 y4
#> 1 10 10 10 8 8.04 9.14 7.46 6.58
#> 2 8 8 8 8 6.95 8.14 6.77 5.76
#> 3 13 13 13 8 7.58 8.74 12.74 7.71
#> 4 9 9 9 8 8.81 8.77 7.11 8.84
#> 5 11 11 11 8 8.33 9.26 7.81 8.47
#> 6 14 14 14 8 9.96 8.10 8.84 7.04
#> 7 6 6 6 8 7.24 6.13 6.08 5.25
#> 8 4 4 4 19 4.26 3.10 5.39 12.50
#> 9 12 12 12 8 10.84 9.13 8.15 5.56
#> 10 7 7 7 8 4.82 7.26 6.42 7.91
#> 11 5 5 5 8 5.68 4.74 5.73 6.89
anscombe %>%
pivot_longer(
everything(),
cols_vary = "slowest",
names_to = c(".value", "set"),
names_pattern = "(.)(.)"
#> # A tibble: 44 × 3
#> set x y
#> <chr> <dbl> <dbl>
#> 1 1 10 8.04
#> 2 1 8 6.95
#> 3 1 13 7.58
#> 4 1 9 8.81
#> 5 1 11 8.33
#> 6 1 14 9.96
#> 7 1 6 7.24
#> 8 1 4 4.26
#> 9 1 12 10.8
#> 10 1 7 4.82
#> # ℹ 34 more rows
