How do I calculate the column that depends on the function that uses the value of the variable of each row?
This is a layout based on mtcars
what I would like to do:
 calculate a column that counts the number of cars that have less offset (
disp
) of the current row within the same transmission type
category (am
)  the expected column are the values ββI would like to get

try1
 one try with a functionfindInterval
, the problem is that I can't count it across the category dependent subsets (am
)
I've tried solutions with *apply
, but somehow I could never get the function called to only work on a subset that depends on the value of the variable of the string being processed (hopefully this makes sense).
x = mtcars[1:6,c("disp","am")]
# expected values are the number of cars that have less disp while having the same am
x$expected = c(1,1,0,1,2,0)
#this ordered table is for findInterval
a = x[order(x$disp),]
a
# I use the findInterval function to get the number of values and I try subsetting the call
# 0.1 is to deal with the closed intervalq
x$try1 = findInterval(x$disp0.1, a$disp[a$am==x$am])
x
# try1 values are not computed depending on the subsetting of a
Any decision will be followed; using the function findInterval
is optional.
I would prefer to have a more general solution to calculate the value of a column by calling a function that takes values ββfrom the current row to calculate the expected value.
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As @dimitris_ps pointed out, the previous solution neglects duplicate values. As a consequence, a remedy is provided.
library(dplyr)
x %>%
group_by(am) %>%
mutate(expected=findInterval(disp, sort(disp) + 0.0001))
or
library(data.table)
setDT(x)[, expected:=findInterval(disp, sort(disp) + 0.0001), by=am]
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