Find variables that occur only in ONE row in R - r

Question

Using BASE R, I wonder how to answer the following question:
Are there any value on X or Y that occurs only in one row but not others? If yes, produce my desired output below.
f <- data.frame(id = c(rep("AA",4), rep("BB",2), rep("CC",2)), X = c(1,2,2,3,1,4,3,3),
Y = c(99,7,8,7,6,7,7,7))
Desired output:
list(BB = c(X = 4, Y = 6), AA = c(Y = c(99, 8)))
# $BB
# X Y
# 4 6
# $AA
# Y1 Y2 # Would be a plus if shows `Y Y` instead of `Y1 Y2`
# 99 8

Answer 1

There are two big ideas with this base approach:
Since we need to compare all the values, we should just recombine everything into one data.frame.
Making the unsplit data.frame long will save us some extra steps.
#https://stackoverflow.com/questions/58786052/find-variables-that-occur-only-once-across-a-split-data-frame-in-r/58788854#58788854
f <- data.frame(id = c(rep("AA",4), rep("BB",2), rep("CC",2)), X = c(1,2,2,3,1,4,3,3),
Y = c(99,7,8,7,6,7,7,7))
m <- split(f, f$id) # Here is `m`
unsplit <- do.call(rbind, c(m, make.row.names = F))
molten <- data.frame(unsplit[, 1, drop = F], stack(unsplit[, -1]))
# res <- subset(molten, !duplicated(values) & !duplicated(values, fromLast = T))
res <- molten[as.logical(ave(molten[['values']], molten[['ind']], FUN = function(x) !duplicated(x) & !duplicated(x, fromLast = T))), ]
#I would stop here
res
#> id values ind
#> 6 BB 4 X
#> 9 AA 99 Y
#> 11 AA 8 Y
#> 13 BB 6 Y
#to get exact output
res_vector <- res$values
names(res_vector) <- res$ind
split(res_vector, as.character(res$id))
#> $AA
#> Y Y
#> 99 8
#>
#> $BB
#> X Y
#> 4 6
Created on 2019-11-10 by the reprex package (v0.3.0)
Here's another base approach that may be less complicated:
####Way 1 with rapply
vec <- rapply(lapply(m, '[', mods), I)
unique_vec <- vec[!duplicated(vec) & !duplicated(vec, fromLast = T)]
vec_names <- do.call(rbind, strsplit(names(unique_vec), '.', fixed = T))
names(unique_vec) <- substr(vec_names[, 2], 1, 1) #turns Y1 into Y
split(unique_vec, vec_names[, 1])
###Way 2 with data.frame already do.call(rbind, m)
vec <- unlist(
lapply(f[, -1],
function(x){
ind <- !duplicated(x) & !duplicated(x, fromLast = T)
ret <- x[ind]
names(ret) <- f[ind, 1]
ret
}
)
)
#this is likely overly simplified:
split(vec, sub('.*\\.', '', names(vec)))
#this leads to exact result
vec_names <- do.call(rbind, strsplit(names(vec), '.', fixed = T))
names(vec) <- vec_names[, 1]
split(vec, vec_names[, 2])
$AA
Y Y
99 8
$BB
X Y
4 6
OP brings up using table() in a hint. duplicated() is very performant:
unlist(lapply(f[mods], function(y) names(which(table(y) == 1))))
# X Y1 Y2 Y3
# "4" "6" "8" "99"
vec
#X.BB Y.AA Y.AA Y.BB
# 4 99 8 6
# A tibble: 2 x 13
expression min median `itr/sec` mem_alloc
<bch:expr> <bch> <bch:> <dbl> <bch:byt>
1 table_meth 321us 336us 2794. 10.3KB
2 dup_meth 132us 136us 7105. 31.7KB
bench::mark(
table_meth = {unlist(lapply(f[mods], function(y) names(which(table(y) == 1))))},
dup_meth = {
#could get slight performance boost with
#f_id <- f[['id']]
unlist(
lapply(f[, -1],
function(x){
ind <- !duplicated(x) & !duplicated(x, fromLast = T)
ret <- x[ind]
names(ret) <- f[ind, 1]
#names(ret) <- f_id[ind]
ret
}
)
)}
, check = F
)
And similar idea in data.table:
library(data.table)
molten_dt <- melt(rbindlist(m), id.vars = 'id')
molten_dt[!duplicated(value, by = variable) &
!duplicated(value, by = variable, fromLast = T)]
And similar idea in dplyr:
library(dplyr)
library(tidyr)
m%>%
bind_rows()%>%
pivot_longer(cols = -id)%>%
group_by(name)%>%
filter(!duplicated(value) & !duplicated(value, fromLast = T))%>%
group_by(id)%>%
group_split()

Answer 2

It's not pure functional programming but it is base R:
lapply(split(df, df$id), function(z){
X <- z$X[which(!(z$X %in% df$X[duplicated(df$X)]))]
Y <- z$Y[which(!(z$Y %in% df$Y[duplicated(df$Y)]))]
cbind(X, Y)
}
)
Data:
df <-
structure(list(
id = structure(
c(1L, 1L, 1L, 1L, 2L, 2L, 3L, 3L),
.Label = c("AA", "BB", "CC"),
class = "factor"
),
X = c(1,
2, 2, 3, 1, 4, 3, 3),
Y = c(99, 7, 8, 7, 6, 7, 7, 7)
),
class = "data.frame",
row.names = c(NA,-8L))