问题
I would like to count all combinations in a data.frame.
The data look like this
9 10 11 12
1 1 1 1 1
2 0 0 0 0
3 0 0 0 0
4 1 1 1 1
5 1 1 1 1
6 0 0 0 0
7 1 0 0 1
8 1 0 0 1
9 1 1 1 1
10 1 1 1 1
The output I want is simply
comb n
1 1 1 1 5
0 0 0 0 3
1 0 0 1 2
Do you know any simple function to do that ?
Thanks
dt = structure(list(`9` = c(1, 0, 0, 1, 1, 0, 1, 1, 1, 1), `10` = c(1,
0, 0, 1, 1, 0, 0, 0, 1, 1), `11` = c(1, 0, 0, 1, 1, 0, 0, 0,
1, 1), `12` = c(1, 0, 0, 1, 1, 0, 1, 1, 1, 1)), .Names = c("9",
"10", "11", "12"), class = "data.frame", row.names = c(NA, -10L
))
回答1:
We can either use data.table
or dplyr
. These are very efficient. We convert the 'data.frame' to 'data.table' (setDT(dt)
), grouped by all the columns of 'dt' (names(dt)
), we get the nrow (.N
) as the 'Count'
library(data.table)
setDT(dt)[,list(Count=.N) ,names(dt)]
Or we can use a similar methodology using dplyr
.
library(dplyr)
names(dt) <- make.names(names(dt))
dt %>%
group_by_(.dots=names(dt)) %>%
summarise(count= n())
Benchmarks
In case somebody wants to look at some metrics (and also to backup my claim earlier (efficient!
)),
set.seed(24)
df1 <- as.data.frame(matrix(sample(0:1, 1e6*6, replace=TRUE), ncol=6))
akrunDT <- function() {
as.data.table(df1)[,list(Count=.N) ,names(df1)]
}
akrunDplyr <- function() {
df1 %>%
group_by_(.dots=names(df1)) %>%
summarise(count= n())
}
cathG <- function() {
aggregate(cbind(n = 1:nrow(df1))~., df1, length)
}
docendoD <- function() {
as.data.frame(table(comb = do.call(paste, df1)))
}
deena <- function() {
table(apply(df1, 1, paste, collapse = ","))
}
Here are the microbenchmark
results
library(microbenchmark)
microbenchmark(akrunDT(), akrunDplyr(), cathG(), docendoD(), deena(),
unit='relative', times=20L)
# Unit: relative
# expr min lq mean median uq max neval cld
# akrunDT() 1.000000 1.000000 1.000000 1.00000 1.000000 1.0000000 20 a
# akrunDplyr() 1.512354 1.523357 1.307724 1.45907 1.365928 0.7539773 20 a
# cathG() 43.893946 43.592062 37.008677 42.10787 38.556726 17.9834245 20 c
# docendoD() 18.778534 19.843255 16.560827 18.85707 17.296812 8.2688541 20 b
# deena() 90.391417 89.449547 74.607662 85.16295 77.316143 34.6962954 20 d
回答2:
A base R solution with aggregate
:
aggregate(seq(nrow(dt))~., data=dt, FUN=length)
# 9 10 11 12 seq(nrow(dt))
#1 0 0 0 0 3
#2 1 0 0 1 2
#3 1 1 1 1 5
edit
To get colnames more conformed to your output, you can do:
`colnames<-`(aggregate(seq(nrow(dt))~., data=dt, FUN=length), c("c", "o", "m", "b", "n"))
# c o m b n
#1 0 0 0 0 3
#2 1 0 0 1 2
#3 1 1 1 1 5
Or, shorter:
aggregate(cbind(n = 1:nrow(dt))~., dt, length)
# 9 10 11 12 n
#1 0 0 0 0 3
#2 1 0 0 1 2
#3 1 1 1 1 5
回答3:
You could try the following approach using only base R:
as.data.frame(table(comb = do.call(paste, dt)))
# comb Freq
#1 0 0 0 0 3
#2 1 0 0 1 2
#3 1 1 1 1 5
回答4:
Maybe that too : table(apply(dt, 1, paste, collapse = ","))
回答5:
Also in base R:
Use unique.matrix
to get the list of unique combinations.
uncs <- unique.matrix(as.matrix(df), MARGIN = 1)
Then make comparisons and count:
cnts <- colSums(apply(uncs, 1, function(r) apply(dt, 1, function(r2) all(r == r2))))
cbind(comb = apply(uncs, 1, paste), n = cnts)
来源:https://stackoverflow.com/questions/34312324/r-count-all-combinations