根据列组将百分位数添加到数据表

Question

I have a datatable with scores and would like to add in the score columns the percentile based on the group and age they are in.

Age  Group  Score1 Score2  
22   A      95     85  
23   B      88     76  
25   B      84     56  
22   A      68     65  
25   B      76     85  
23   B      59     75 

So for example, the 22A 23B and 25B would all be separate in the calculation of percentiles.

Answer 1

Your posted example has very few cases for each group, so I'm using mtcars as an example:

library(dplyr)

# example data
df = mtcars %>% select(am, cyl, disp, wt)

Assume that am, cyl are your grouping variables and disp, wt are your scores:

df %>% 
  group_by(am, cyl) %>%
  mutate_at(vars(disp, wt), funs(prc = cume_dist)) %>%
  ungroup() %>%
  arrange(am, cyl) %>%  # not needed; only for visualisation
  data.frame()          # not needed; only for visualisation

#    am cyl  disp    wt  disp_prc     wt_prc
# 1   0   4 146.7 3.190 1.0000000 1.00000000
# 2   0   4 140.8 3.150 0.6666667 0.66666667
# 3   0   4 120.1 2.465 0.3333333 0.33333333
# 4   0   6 258.0 3.215 1.0000000 0.25000000
# 5   0   6 225.0 3.460 0.7500000 1.00000000
# 6   0   6 167.6 3.440 0.5000000 0.75000000
# 7   0   6 167.6 3.440 0.5000000 0.75000000
# 8   0   8 360.0 3.440 0.6666667 0.16666667
# 9   0   8 360.0 3.570 0.6666667 0.33333333
# 10  0   8 275.8 4.070 0.2500000 0.75000000
# 11  0   8 275.8 3.730 0.2500000 0.41666667
# 12  0   8 275.8 3.780 0.2500000 0.50000000
# 13  0   8 472.0 5.250 1.0000000 0.83333333
# 14  0   8 460.0 5.424 0.9166667 1.00000000
# 15  0   8 440.0 5.345 0.8333333 0.91666667
# 16  0   8 318.0 3.520 0.4166667 0.25000000
# 17  0   8 304.0 3.435 0.3333333 0.08333333
# 18  0   8 350.0 3.840 0.5000000 0.58333333
# 19  0   8 400.0 3.845 0.7500000 0.66666667
# 20  1   4 108.0 2.320 0.7500000 0.87500000
# 21  1   4  78.7 2.200 0.3750000 0.75000000
# 22  1   4  75.7 1.615 0.2500000 0.25000000
# 23  1   4  71.1 1.835 0.1250000 0.37500000
# 24  1   4  79.0 1.935 0.5000000 0.50000000
# 25  1   4 120.3 2.140 0.8750000 0.62500000
# 26  1   4  95.1 1.513 0.6250000 0.12500000
# 27  1   4 121.0 2.780 1.0000000 1.00000000
# 28  1   6 160.0 2.620 1.0000000 0.33333333
# 29  1   6 160.0 2.875 1.0000000 1.00000000
# 30  1   6 145.0 2.770 0.3333333 0.66666667
# 31  1   8 351.0 3.170 1.0000000 0.50000000
# 32  1   8 301.0 3.570 0.5000000 1.00000000

You can then round your percentiles to 2 decimal places, or create a % value and combine them with your actual scores in one column.