Select the nth value of aggregated column after group by in R - r

Question

Given df as follows:
# group value
# 1 A 8
# 2 A 1
# 3 A 7
# 4 B 3
# 5 B 2
# 6 B 6
# 7 C 4
# 8 C 5
df <- structure(list(group = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L), .Label = c("A", "B", "C"), class = "factor"), value = c(8L,
1L, 7L, 3L, 2L, 6L, 4L, 5L)), .Names = c("group", "value"), class = "data.frame", row.names = c(NA,
-8L))
And a vector of indices (possibly with NA):
inds <- c(2,1,NA)
How we can get the nth element of column value per group, preferably in base R?
For example, based on inds, we want the second element of value in group A, first element in group B, NA in group C. So the result would be:
#[1] 1 3 NA

Answer 1

Here is a solution with mapply and split:
mapply("[", with(df, split(value, group)), inds)
which returns a named vector
A B C
1 3 NA
with(df, split(value, group)) splits the data frame by group and returns a list of data frames. mapply takes that list and "inds" and applies the subsetting function "[" to each pairs of arguments.

Answer 2

Using levels and sapply you could do:
DF <- structure(list(group = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L), .Label = c("A", "B", "C"), class = "factor"), value = c(8L,
1L, 7L, 3L, 2L, 6L, 4L, 5L)), .Names = c("group", "value"), class = "data.frame", row.names = c(NA,
-8L))
inds <- c(2,1,NA)
lvls = levels(DF$group)
groupInds = sapply(1:length(lvls),function(x) DF$value[DF$group==lvls[x]][inds[x]] )
groupInds
#[1] 1 3 NA

Answer 3

Using again mapply (but not nearly as elegant as IMO's answer):
mapply(function(x, y) subset(df, group == x, value)[y,] ,levels(df$group), inds)

Answer 4

I know you said preferably in base R, but just for the record, here is a data.table way
setDT(df)[, .SD[inds[.GRP], value], by=group][,V1]
#[1] 1 3 NA

Answer 5

I just did come up with another solution:
diag(aggregate(value~group, df, function(x) x[inds])[,-1])
#[1] 1 3 NA
Benchmarking
library(microbenchmark)
library(data.table)
df <- structure(list(group = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L), .Label = c("A", "B", "C"), class = "factor"), value = c(8L,
1L, 7L, 3L, 2L, 6L, 4L, 5L)), .Names = c("group", "value"), class = "data.frame", row.names = c(NA,
-8L))
inds <- c(2,1,NA)
f_Imo <- function(df) as.vector(mapply("[", with(df, split(value, group)), inds))
f_Osssan <- function(df) {lvls = levels(df$group);sapply(1:length(lvls),function(x) df$value[df$group==lvls[x]][inds[x]])}
f_User2321 <- function(df) unlist(mapply(function(x, y) subset(df, group == x, value)[y,] ,levels(df$group), inds))
f_dww <- function(df) setDT(df)[, .SD[inds[.GRP], value], by=group][,V1]
f_m0h3n <- function(df) diag(aggregate(value~group, df, function(x) x[inds])[,-1])
all.equal(f_Imo(df), f_Osssan(df), f_User2321(df), f_dww(df), f_m0h3n(df))
# [1] TRUE
microbenchmark(f_Imo(df), f_Osssan(df), f_m0h3n(df), f_User2321(df), f_dww(df))
# Unit: microseconds
# expr min lq mean median uq max neval
# f_Imo(df) 71.004 85.1180 91.52996 91.748 96.8810 121.048 100
# f_Osssan(df) 252.788 276.5265 318.70529 287.648 301.5495 2651.492 100
# f_m0h3n(df) 1422.627 1555.4365 1643.47184 1618.740 1670.7095 4729.827 100
# f_User2321(df) 2889.738 3000.3055 3148.44916 3037.945 3118.7860 6013.442 100
# f_dww(df) 2960.740 3086.2790 3206.02147 3143.381 3250.9545 5976.229 100