Summarise columns

Enhanced summarise().

Usage

summ(
  .data,
  ...,
  .by = NULL,
  .keep_all = FALSE,
  .detail = FALSE,
  .stat = character(0)
)

tabstat(
  .data,
  ...,
  .by = NULL,
  .keep_all = FALSE,
  .detail = FALSE,
  .stat = "mean"
)

Arguments

.data

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).

...

<tidy-select> or <data-masking> Variables to tabulate.

Both tidyselect (e.g., starts_with()) and data masking (e.g., x_sq = x^2) are supported. See examples below.

.by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

.keep_all

A logical. If TRUE, all variables are kept.

.detail

A logical. If TRUE, the detailed summary is returned.

.stat

A character vector. If specified, only the listed statistics are returned.

Value

A tibble

Examples

summ(lifeexp)
#> Warning: country is non-numeric and thus removed.
#> Warning: region is a labelled variable (*).
#> # A tibble: 5 × 8
#>   name      type        n unique   min     mean        sd   max
#>   <chr>     <chr>   <dbl>  <dbl> <dbl>    <dbl>     <dbl> <dbl>
#> 1 *region   dbl+lbl    68      3   1      1.5       0.743     3
#> 2 popgrowth dbl        68     30  -0.5    0.972     0.931     3
#> 3 lexp      dbl        68     18  54     72.3       4.72     79
#> 4 gnppc     dbl        63     62 370   8675.    10635.    39980
#> 5 safewater dbl        40     29  28     76.1      17.9     100

summ(lifeexp, .by = region)
#> Warning: country is non-numeric and thus removed.
#> # A tibble: 12 × 9
#>        region name      type      n unique      min      mean        sd      max
#>    <hvn_lbll> <chr>     <chr> <dbl>  <dbl>    <dbl>     <dbl>     <dbl>    <dbl>
#>  1          1 popgrowth dbl      44     20   -0.5       0.525     0.718     2.80
#>  2          1 lexp      dbl      44     15   65        73.1       4.15     79   
#>  3          1 gnppc     dbl      41     40  370     10738.    11794.    39980   
#>  4          1 safewater dbl      17     12   55        79.8      18.6     100   
#>  5          2 popgrowth dbl      14     10    0.700     1.69      0.749     3   
#>  6          2 lexp      dbl      14     12   54        71.2       6.41     79   
#>  7          2 gnppc     dbl      12     12  410      5817.     8929.    29240   
#>  8          2 safewater dbl      13     13   28        75        19.2      99   
#>  9          3 popgrowth dbl      10      8    0.700     1.93      0.617     2.90
#> 10          3 lexp      dbl      10      7   62        70.3       3.83     75   
#> 11          3 gnppc     dbl      10     10 1010      3645      2255.     8030   
#> 12          3 safewater dbl      10     10   39        71.2      15.0      89   

tabstat(lifeexp, .by = region)
#> Warning: country is non-numeric and thus removed.
#> # A tibble: 12 × 4
#>        region name      type       mean
#>    <hvn_lbll> <chr>     <chr>     <dbl>
#>  1          1 popgrowth dbl       0.525
#>  2          1 lexp      dbl      73.1  
#>  3          1 gnppc     dbl   10738.   
#>  4          1 safewater dbl      79.8  
#>  5          2 popgrowth dbl       1.69 
#>  6          2 lexp      dbl      71.2  
#>  7          2 gnppc     dbl    5817.   
#>  8          2 safewater dbl      75    
#>  9          3 popgrowth dbl       1.93 
#> 10          3 lexp      dbl      70.3  
#> 11          3 gnppc     dbl    3645    
#> 12          3 safewater dbl      71.2  

lifeexp %>%
  summ(
    dplyr::where(is.numeric),
    .by = region,
    .stat = c("mean", "sd")
  )
#> # A tibble: 12 × 5
#>        region name      type       mean        sd
#>    <hvn_lbll> <chr>     <chr>     <dbl>     <dbl>
#>  1          1 popgrowth dbl       0.525     0.718
#>  2          1 lexp      dbl      73.1       4.15 
#>  3          1 gnppc     dbl   10738.    11794.   
#>  4          1 safewater dbl      79.8      18.6  
#>  5          2 popgrowth dbl       1.69      0.749
#>  6          2 lexp      dbl      71.2       6.41 
#>  7          2 gnppc     dbl    5817.     8929.   
#>  8          2 safewater dbl      75        19.2  
#>  9          3 popgrowth dbl       1.93      0.617
#> 10          3 lexp      dbl      70.3       3.83 
#> 11          3 gnppc     dbl    3645      2255.   
#> 12          3 safewater dbl      71.2      15.0