Indexing subgroups by sorted positions in R dataframe - r

Question

I have a dataframe which contains information about several categories, and some associated variables. It is of the form:
ID category sales score
227 A 109 21
131 A 410 24
131 A 509 1
123 B 2 61
545 B 19 5
234 C 439 328
654 C 765 41
What I would like to do is be able to introduce two new columns, salesRank and scoreRank, where I find the item index per category, had they been ordered by sales and score, respectively. I can solve the general case like this:
dF <- dF[order(-dF$sales),]
dF$salesRank<-seq.int(nrow(dF))
but this doesn't account for the categories and so far I've only solved this by breaking up the dataframe. What I want would result in the following:
ID category sales score salesRank scoreRank
227 A 109 21 3 2
131 A 410 24 2 1
131 A 509 1 1 3
123 B 2 61 2 1
545 B 19 5 1 2
234 C 439 328 2 1
654 C 765 41 1 2
Many thanks!

Answer 1

Try:
library(dplyr)
df %>%
group_by(category) %>%
mutate(salesRank = row_number(desc(sales)),
scoreRank = row_number(desc(score)))
Which gives:
#Source: local data frame [7 x 6]
#Groups: category
#
# ID category sales score salesRank scoreRank
#1 227 A 109 21 3 2
#2 131 A 410 24 2 1
#3 131 A 509 1 1 3
#4 123 B 2 61 2 1
#5 545 B 19 5 1 2
#6 234 C 439 328 2 1
#7 654 C 765 41 1 2
From the help:
row_number(): equivalent to rank(ties.method = "first")
min_rank(): equivalent to rank(ties.method = "min")
desc(): transform a vector into a format that will be sorted in descending
order.
As #thelatemail pointed out, for this particular dataset you might want to use min_rank() instead of row_number() which will account for ties in sales/score more appropriately:
> row_number(c(1,2,2,4))
#[1] 1 2 3 4
> min_rank(c(1,2,2,4))
#[1] 1 2 2 4

Answer 2

Use ave in base R with rank (the - is to reverse the rankings from low-to-high to high-to-low):
dF$salesRank <- with(dF, ave(-sales, category, FUN=rank) )
#[1] 3 2 1 2 1 2 1
dF$scoreRank <- with(dF, ave(-score, category, FUN=rank) )
#[1] 2 1 3 1 2 1 2

Answer 3

I have just a base R solution with tapply.
salesRank <- tapply(dat$sales, dat$category, order, decreasing = T)
scoreRank <- tapply(dat$score, dat$category, order, decreasing = T)
cbind(dat, salesRank = unlist(salesRank), scoreRank= unlist(scoreRank))
ID category sales score salesRank scoreRank
A1 227 A 109 21 3 2
A2 131 A 410 24 2 1
A3 131 A 509 1 1 3
B1 123 B 2 61 2 1
B2 545 B 19 5 1 2
C1 234 C 439 328 2 1
C2 654 C 765 41 1 2