根据两列改变百分位排名

Question

I've previously asked the following question: Mutate new column over a large list of tibbles & the solutions giving were perfect. I now have a follow-up question to this.

I now have the following dataset:

df1:

name	group	competition	metric	value
A	A	comp A	distance	10569
B	A	comp B	distance	12939
C	A	comp C	distance	11532
A	A	comp B	psv-99	29.30
B	A	comp A	psv-99	30.89
C	A	comp C	psv-99	32.00

I now want to find out the percentile rank of all the values in df1, but only based on the group & one of the competitions - competition A.

Answer 1

We could slice the rows where the 'comp A' is found %in% competition , then do a grouping by 'group' column and create a new column percentile with percent_rank

library(dplyr)
df <- df %>%
   slice(which(competition %in% "comp A")) %>%
   group_by(group) %>%
   mutate(percentile = percent_rank(value))

Answer 2

Maybe just change metric to competition in the previous code? It would give you the percentile rank for all competitions, including A.

df1 %>% 
  group_nest(group, competition) %>% 
  mutate(percentile = map(data, ~percent_rank(.$value))) %>% 
  unnest(c(data, percentile))

Answer 3

You can filter the competition and group_by group .

library(dplyr)

df %>%
  filter(competition == "comp A") %>%
  group_by(group) %>%
  mutate(percentile = percent_rank(value))