Group a data.frame by percentile ranges in variable by cutting points

This function is a small wrapper around percentify. This function takes a vector of points and creates ranges between those points, resulting in non-overlapping groups.

percentify_cut(data, var, q = numeric())

Arguments

data	A data.frame or tibble,
var	Variable to do grouping by as string or symbol.
q	Numerical values for cutting points. Must be between 0 and 1.

Value

percentile grouped tibble

Details

There is a ggplot2::autoplot() to visualize the the percentile ranges.

See also

cut_evenly

Other percentile samplers: autoplot.percentiled_df, percentify_max, percentify_min, percentify_random

Examples

library(dplyr)
library(broom)
percent_mtcars <- percentify_cut(mtcars, mpg, c(0.25, 0.5, 0.75))

percent_mtcars
#> # A tibble: 32 x 11
#> # Groups:   .percentile_mpg [4]
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # … with 22 more rows

summarize(percent_mtcars,
          mean_hp = mean(hp),
          mean_wt = mean(wt),
          n_obs = n()
          )
#> # A tibble: 4 x 4
#>   .percentile_mpg mean_hp mean_wt n_obs
#>   <chr>             <dbl>   <dbl> <int>
#> 1 0%-25%            226.     4.28     8
#> 2 25%-50%           164.     3.57     9
#> 3 50%-75%           120.     2.95    10
#> 4 75%-100%           78.1    2.21     9

percent_mtcars %>%
  group_modify(~tidy(lm(disp ~ wt + cyl, data = .x)))
#> # A tibble: 10 x 6
#> # Groups:   .percentile_mpg [4]
#>    .percentile_mpg term        estimate std.error statistic p.value
#>    <chr>           <chr>          <dbl>     <dbl>     <dbl>   <dbl>
#>  1 0%-25%          (Intercept)     28.0     60.1      0.466 0.658  
#>  2 0%-25%          wt              80.0     13.8      5.80  0.00115
#>  3 25%-50%         (Intercept)    -87.0    225.      -0.388 0.712  
#>  4 25%-50%         wt             -53.9     65.1     -0.828 0.439  
#>  5 25%-50%         cyl             76.6     17.5      4.38  0.00466
#>  6 50%-75%         (Intercept)   -259.      97.3     -2.66  0.0323 
#>  7 50%-75%         wt              87.8     45.3      1.94  0.0939 
#>  8 50%-75%         cyl             33.0     15.7      2.11  0.0728 
#>  9 75%-100%        (Intercept)     12.1     21.7      0.557 0.595  
#> 10 75%-100%        wt              40.5      9.51     4.26  0.00373

# cut_evenly() can be used to create cuts
percentify_cut(mtcars, mpg, cut_evenly(8))
#> # A tibble: 32 x 11
#> # Groups:   .percentile_mpg [8]
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # … with 22 more rows

library(ggplot2)
autoplot(percent_mtcars)