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_min(data, var, q = numeric(), lower = 0)

Arguments

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

Examples

library(dplyr)
library(broom)
percent_mtcars <- percentify_min(mtcars, mpg, c(0.25, 0.5, 0.75, 1))

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 0%-50%             193.    3.90    17
#> 3 0%-75%             167.    3.54    25
#> 4 0%-100%            147.    3.22    32

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%-25%          (Intercept)     28.0     60.1      0.466 0.658      
#>  2 0%-25%          wt              80.0     13.8      5.80  0.00115    
#>  3 0%-50%          (Intercept)   -433.     113.      -3.85  0.00178    
#>  4 0%-50%          wt              67.5     16.3      4.15  0.000977   
#>  5 0%-50%          cyl             64.5     14.9      4.34  0.000674   
#>  6 0%-75%          (Intercept)   -240.      43.6     -5.50  0.0000158  
#>  7 0%-75%          wt              69.3     14.5      4.77  0.0000924  
#>  8 0%-75%          cyl             38.7      7.73     5.01  0.0000516  
#>  9 0%-100%         (Intercept)   -190.      27.2     -7.00  0.000000107
#> 10 0%-100%         wt              59.5     12.0      4.96  0.0000281  
#> 11 0%-100%         cyl             37.1      6.57     5.65  0.00000423 

library(ggplot2)
autoplot(percent_mtcars)