Group a data.frame by percentile ranges in variable with constant upper bound

This function is a small wrapper around percentify, where the upper bound is a fixed value.

percentify_max(data, var, q = numeric(), upper = 1)

Arguments

data	A data.frame or tibble,
var	Variable to do grouping by as string or symbol.
q	Numerical values for lower bound of ranges. Must be between 0 and 1.
upper	Numerical values for upper bound of ranges. Must be between 0 and 1. Length of lower and upper must be equal.

Value

percentile grouped tibble

Details

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

See also

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

Examples

library(dplyr)
library(broom)
percent_mtcars <- percentify_max(mtcars, mpg, c(0, 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%-100%           147.     3.22    32
#> 2 25%-100%          120.     2.86    24
#> 3 50%-100%          101.     2.58    17
#> 4 75%-100%           78.1    2.21     9

percent_mtcars %>%
  group_modify(~tidy(lm(disp ~ wt + cyl, data = .x)))
#> # A tibble: 11 x 6
#> # Groups:   .percentile_mpg [4]
#>    .percentile_mpg term        estimate std.error statistic     p.value
#>    <chr>           <chr>          <dbl>     <dbl>     <dbl>       <dbl>
#>  1 0%-100%         (Intercept)   -190.      27.2     -7.00  0.000000107
#>  2 0%-100%         wt              59.5     12.0      4.96  0.0000281  
#>  3 0%-100%         cyl             37.1      6.57     5.65  0.00000423 
#>  4 25%-100%        (Intercept)   -145.      33.5     -4.34  0.000288   
#>  5 25%-100%        wt              25.5     18.3      1.40  0.177      
#>  6 25%-100%        cyl             45.9      7.97     5.76  0.0000102  
#>  7 50%-100%        (Intercept)   -163.      40.1     -4.05  0.00120    
#>  8 50%-100%        wt              46.7     19.2      2.43  0.0289     
#>  9 50%-100%        cyl             38.5     10.3      3.74  0.00220    
#> 10 75%-100%        (Intercept)     12.1     21.7      0.557 0.595      
#> 11 75%-100%        wt              40.5      9.51     4.26  0.00373    

library(ggplot2)
autoplot(percent_mtcars)