Group a data.frame by percentile ranges in variable

percentify() is the main function in percentify. It takes a data.frame or tibble, and creates groups based on the quantiles lower and upper bounds specified. This become handy once you start working with multiple overlapping bounds.

autoplot.percentiled_df(object)

percentify(data, var, lower = 0, upper = 1, key = ".percentile")

Arguments

object	The percentiled_df data frame returned from any percentify functions.
data	A data.frame or tibble,
var	Variable to do grouping by as string or symbol.
lower	Numerical values for lower bound of ranges. Must be between 0 and 1. Length of lower and upper must be equal.
upper	Numerical values for upper bound of ranges. Must be between 0 and 1. Length of lower and upper must be equal.
key	A single character specifying the name of the virtual group that is added. Defaults to ".percentile".

Value

percentile grouped tibble

Details

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

See also

Other percentile samplers: percentify_cut, percentify_max, percentify_min, percentify_random

Examples

library(dplyr)
library(broom)
percent_mtcars <- percentify(mtcars, mpg,
                             lower = c(0.2, 0.4),
                             upper = c(0.6, 0.8)
                             )

percent_mtcars
#> # A tibble: 32 x 11
#> # Groups:   .percentile_mpg [2]
#>      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: 2 x 4
#>   .percentile_mpg mean_hp mean_wt n_obs
#>   <chr>             <dbl>   <dbl> <int>
#> 1 20%-60%            157.    3.40    14
#> 2 40%-80%            123.    3.03    12

percent_mtcars %>%
  group_modify(~tidy(lm(disp ~ wt + cyl, data = .x)))
#> # A tibble: 6 x 6
#> # Groups:   .percentile_mpg [2]
#>   .percentile_mpg term        estimate std.error statistic  p.value
#>   <chr>           <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 20%-60%         (Intercept)  -279.        97.9   -2.85   0.0159  
#> 2 20%-60%         wt              1.25      36.1    0.0346 0.973   
#> 3 20%-60%         cyl            74.3       14.7    5.07   0.000361
#> 4 40%-80%         (Intercept)  -266.        86.8   -3.06   0.0135  
#> 5 40%-80%         wt             76.3       40.1    1.90   0.0898  
#> 6 40%-80%         cyl            40.9       13.0    3.15   0.0118  

library(ggplot2)
autoplot(percent_mtcars)