Title

Title

pivot_count(data, cols = NULL, rows = NULL, pivot = T, wt = NULL)

Arguments

data	A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
cols	a character vector of items
rows	a character vector of items
pivot	logical: should wide table be returned - col categories as columns (TRUE), or left long and tidy (FALSE)?

Value

Examples

tidy_titanic %>% pivot_count(rows = sex)
#> # A tibble: 2 × 2
#>   sex    count
#>   <fct>  <dbl>
#> 1 Male    1731
#> 2 Female   470
tidy_titanic %>% pivot_count(cols = sex)
#> # A tibble: 1 × 2
#>    Male Female
#>   <dbl>  <dbl>
#> 1  1731    470
tidy_titanic %>% pivot_count(rows = survived, cols = sex)
#> # A tibble: 2 × 3
#>   survived  Male Female
#>   <fct>    <dbl>  <dbl>
#> 1 No        1364    126
#> 2 Yes        367    344
tidy_titanic %>% pivot_count(rows = c(survived, sex), cols = age)
#> # A tibble: 4 × 4
#>   survived sex    Child Adult
#>   <fct>    <fct>  <dbl> <dbl>
#> 1 No       Male      35  1329
#> 2 No       Female    17   109
#> 3 Yes      Male      29   338
#> 4 Yes      Female    28   316
tidy_titanic %>% pivot_count(rows = c(survived), cols = c(age, sex))
#> # A tibble: 2 × 5
#>   survived Child_Male Child_Female Adult_Male Adult_Female
#>   <fct>         <dbl>        <dbl>      <dbl>        <dbl>
#> 1 No               35           17       1329          109
#> 2 Yes              29           28        338          316
tidy_titanic %>% pivot_count(cols = c(survived), rows = c(age, sex, class))
#> # A tibble: 16 × 5
#>    age   sex    class    No   Yes
#>    <fct> <fct>  <fct> <dbl> <dbl>
#>  1 Child Male   1st       0     5
#>  2 Child Male   2nd       0    11
#>  3 Child Male   3rd      35    13
#>  4 Child Male   Crew      0     0
#>  5 Child Female 1st       0     1
#>  6 Child Female 2nd       0    13
#>  7 Child Female 3rd      17    14
#>  8 Child Female Crew      0     0
#>  9 Adult Male   1st     118    57
#> 10 Adult Male   2nd     154    14
#> 11 Adult Male   3rd     387    75
#> 12 Adult Male   Crew    670   192
#> 13 Adult Female 1st       4   140
#> 14 Adult Female 2nd      13    80
#> 15 Adult Female 3rd      89    76
#> 16 Adult Female Crew      3    20
tidy_titanic %>% pivot_count(rows = c(survived, sex), cols = age, pivot = F)
#> # A tibble: 8 × 4
#>   age   survived sex    count
#>   <fct> <fct>    <fct>  <dbl>
#> 1 Child No       Male      35
#> 2 Child No       Female    17
#> 3 Child Yes      Male      29
#> 4 Child Yes      Female    28
#> 5 Adult No       Male    1329
#> 6 Adult No       Female   109
#> 7 Adult Yes      Male     338
#> 8 Adult Yes      Female   316
flat_titanic %>% pivot_count(rows = sex, wt = freq)
#> # A tibble: 2 × 2
#>   sex    count
#>   <fct>  <dbl>
#> 1 Male    1731
#> 2 Female   470