pivot_wider() "widens" data, increasing the number of columns and
decreasing the number of rows. The inverse transformation is
pivot_longer().
Learn more in vignette("pivot").
pivot_wider(data, id_cols = NULL, names_from = name, names_prefix = "", names_sep = "_", names_repair = "check_unique", values_from = value, values_fill = NULL, values_fn = NULL)
Arguments
| data | A data frame to pivot. |
|---|---|
| id_cols | A set of columns that uniquely identifies each observation.
Defaults to all columns in |
| names_from, values_from | A pair of arguments describing which column
(or columns) to get the name of the output column ( If |
| names_prefix | String added to the start of every variable name. This is
particularly useful if |
| names_sep | If |
| names_repair | What happen if the output has invalid column names?
The default, |
| values_fill | Optionally, a named list specifying what each |
| values_fn | Optionally, a named list providing a function that will be
applied to the |
Details
pivot_wider() is an updated approach to spread(), designed to be both
simpler to use and to handle more use cases. We recomend you use
pivot_wider() for new code; spread() isn't going away but is no longer
under active development.
See also
pivot_wider_spec() to pivot "by hand" with a data frame that
defines a pivotting specification.
Examples
# See vignette("pivot") for examples and explanation fish_encounters#> # A tibble: 114 x 3 #> fish station seen #> <fct> <fct> <int> #> 1 4842 Release 1 #> 2 4842 I80_1 1 #> 3 4842 Lisbon 1 #> 4 4842 Rstr 1 #> 5 4842 Base_TD 1 #> 6 4842 BCE 1 #> 7 4842 BCW 1 #> 8 4842 BCE2 1 #> 9 4842 BCW2 1 #> 10 4842 MAE 1 #> # … with 104 more rowsfish_encounters %>% pivot_wider(names_from = station, values_from = seen)#> # A tibble: 19 x 12 #> fish Release I80_1 Lisbon Rstr Base_TD BCE BCW BCE2 BCW2 MAE MAW #> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> #> 1 4842 1 1 1 1 1 1 1 1 1 1 1 #> 2 4843 1 1 1 1 1 1 1 1 1 1 1 #> 3 4844 1 1 1 1 1 1 1 1 1 1 1 #> 4 4845 1 1 1 1 1 NA NA NA NA NA NA #> 5 4847 1 1 1 NA NA NA NA NA NA NA NA #> 6 4848 1 1 1 1 NA NA NA NA NA NA NA #> 7 4849 1 1 NA NA NA NA NA NA NA NA NA #> 8 4850 1 1 NA 1 1 1 1 NA NA NA NA #> 9 4851 1 1 NA NA NA NA NA NA NA NA NA #> 10 4854 1 1 NA NA NA NA NA NA NA NA NA #> 11 4855 1 1 1 1 1 NA NA NA NA NA NA #> 12 4857 1 1 1 1 1 1 1 1 1 NA NA #> 13 4858 1 1 1 1 1 1 1 1 1 1 1 #> 14 4859 1 1 1 1 1 NA NA NA NA NA NA #> 15 4861 1 1 1 1 1 1 1 1 1 1 1 #> 16 4862 1 1 1 1 1 1 1 1 1 NA NA #> 17 4863 1 1 NA NA NA NA NA NA NA NA NA #> 18 4864 1 1 NA NA NA NA NA NA NA NA NA #> 19 4865 1 1 1 NA NA NA NA NA NA NA NA# Fill in missing values fish_encounters %>% pivot_wider( names_from = station, values_from = seen, values_fill = list(seen = 0) )#> # A tibble: 19 x 12 #> fish Release I80_1 Lisbon Rstr Base_TD BCE BCW BCE2 BCW2 MAE MAW #> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> #> 1 4842 1 1 1 1 1 1 1 1 1 1 1 #> 2 4843 1 1 1 1 1 1 1 1 1 1 1 #> 3 4844 1 1 1 1 1 1 1 1 1 1 1 #> 4 4845 1 1 1 1 1 0 0 0 0 0 0 #> 5 4847 1 1 1 0 0 0 0 0 0 0 0 #> 6 4848 1 1 1 1 0 0 0 0 0 0 0 #> 7 4849 1 1 0 0 0 0 0 0 0 0 0 #> 8 4850 1 1 0 1 1 1 1 0 0 0 0 #> 9 4851 1 1 0 0 0 0 0 0 0 0 0 #> 10 4854 1 1 0 0 0 0 0 0 0 0 0 #> 11 4855 1 1 1 1 1 0 0 0 0 0 0 #> 12 4857 1 1 1 1 1 1 1 1 1 0 0 #> 13 4858 1 1 1 1 1 1 1 1 1 1 1 #> 14 4859 1 1 1 1 1 0 0 0 0 0 0 #> 15 4861 1 1 1 1 1 1 1 1 1 1 1 #> 16 4862 1 1 1 1 1 1 1 1 1 0 0 #> 17 4863 1 1 0 0 0 0 0 0 0 0 0 #> 18 4864 1 1 0 0 0 0 0 0 0 0 0 #> 19 4865 1 1 1 0 0 0 0 0 0 0 0# Generate column names from multiple variables us_rent_income %>% pivot_wider(names_from = variable, values_from = c(estimate, moe))#> # A tibble: 52 x 6 #> GEOID NAME estimate_income estimate_rent moe_income moe_rent #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 01 Alabama 24476 747 136 3 #> 2 02 Alaska 32940 1200 508 13 #> 3 04 Arizona 27517 972 148 4 #> 4 05 Arkansas 23789 709 165 5 #> 5 06 California 29454 1358 109 3 #> 6 08 Colorado 32401 1125 109 5 #> 7 09 Connecticut 35326 1123 195 5 #> 8 10 Delaware 31560 1076 247 10 #> 9 11 District of Columbia 43198 1424 681 17 #> 10 12 Florida 25952 1077 70 3 #> # … with 42 more rows# Can perform aggregation with values_fn warpbreaks <- as_tibble(warpbreaks[c("wool", "tension", "breaks")]) warpbreaks#> # A tibble: 54 x 3 #> wool tension breaks #> <fct> <fct> <dbl> #> 1 A L 26 #> 2 A L 30 #> 3 A L 54 #> 4 A L 25 #> 5 A L 70 #> 6 A L 52 #> 7 A L 51 #> 8 A L 26 #> 9 A L 67 #> 10 A M 18 #> # … with 44 more rowswarpbreaks %>% pivot_wider( names_from = wool, values_from = breaks, values_fn = list(breaks = mean) )#> # A tibble: 3 x 3 #> tension A B #> <fct> <dbl> <dbl> #> 1 L 44.6 28.2 #> 2 M 24 28.8 #> 3 H 24.6 18.8