Count values of the whole dataframe - r

Question

I have this dataframe:
> df
X1 X2 X3 X4 X5 X6 X7
1 2 7 2 3 5 6 7
2 4 2 3 6 1 NA 3
3 3 6 4 4 4 7 7
4 6 5 6 NA 3 1 7
5 1 1 2 3 3 3 7
6 4 7 2 4 5 4 2
7 5 NA 4 5 2 2 3
8 3 7 2 4 4 1 5
9 4 5 6 2 5 6 3
10 2 4 6 4 5 6 3
And I want to count the numbers 1,2,3,4 and assign it to x, 6,7 and assign it to y, and all the numbers (1,2,3,4,5,6,7) to z. After this, I will compute y/z - x/z.
I've done it with table(unlist(df)) and after assigning the value individually. However, I'm looking for a solution without a loop or apply(), as I can't see a way to escalate them as I have near 100 columns and 10.000 rows (I know that all of them are integers from 1 to 7 and NA values).
I'm looking for a solution like this:
x <- count(df, c(1,2,3,4), na.rm = TRUE)
y <- count(df, c(6,7), na.rm = TRUE)
z <- count(df, c(1,2,3,4,5,6,7), na.rm = TRUE)
However, it seems that count() doesn't work like that neither exist a function that does that.
Any suggestions?

Answer 1

A base R solution.
vec <- unlist(df)
vec_c <- table(vec)
x <- sum(vec_c[names(vec_c) %in% as.character(1:4)])
y <- sum(vec_c[names(vec_c) %in% as.character(6:7)])
z <- sum(vec_c)
y/z - x/z
# [1] -0.358209
Another idea.
vec <- unlist(df)
x <- sum(vec %in% 1:4)
y <- sum(vec %in% 6:7)
z <- length(vec[!is.na(vec)])
y/z - x/z
# [1] -0.358209
Another idea.
m <- as.matrix(df)
x <- sum(m %in% 1:4)
y <- sum(m %in% 6:7)
z <- sum(!is.na(df))
y/z - x/z
# [1] -0.358209
DATA
df <- read.table(text = " X1 X2 X3 X4 X5 X6 X7
1 2 7 2 3 5 6 7
2 4 2 3 6 1 NA 3
3 3 6 4 4 4 7 7
4 6 5 6 NA 3 1 7
5 1 1 2 3 3 3 7
6 4 7 2 4 5 4 2
7 5 NA 4 5 2 2 3
8 3 7 2 4 4 1 5
9 4 5 6 2 5 6 3
10 2 4 6 4 5 6 3",
header = TRUE)

Answer 2

Here is an option using tidyverse
library(tidyverse)
gather(df, na.rm = TRUE) %>%
count(value) %>%
mutate(n1 = sum(n)) %>%
filter(value %in% c(1:4, 6:7)) %>%
group_by(grp = value %in% 1:4) %>%
summarise(perc = sum(n)/first(n1)) %>%
summarise(z = diff(perc))
# A tibble: 1 x 1
# z
# <dbl>
# 1 0.358

Answer 3

Another approach sticking on table(), putting your counting structure into a list.
count <- setNames(lapply(list(1:4, 6:7, 1:7), function(x){
tab <- table(unlist(d))
return(sum(tab[x]))
}), tail(letters, 3))
> with(count, y/z - x/z)
[1] -0.358209
Data
d <- structure(list(X1 = c(2L, 4L, 3L, 6L, 1L, 4L, 5L, 3L, 4L, 2L),
X2 = c(7L, 2L, 6L, 5L, 1L, 7L, NA, 7L, 5L, 4L), X3 = c(2L,
3L, 4L, 6L, 2L, 2L, 4L, 2L, 6L, 6L), X4 = c(3L, 6L, 4L, NA,
3L, 4L, 5L, 4L, 2L, 4L), X5 = c(5L, 1L, 4L, 3L, 3L, 5L, 2L,
4L, 5L, 5L), X6 = c(6L, NA, 7L, 1L, 3L, 4L, 2L, 1L, 6L, 6L
), X7 = c(7L, 3L, 7L, 7L, 7L, 2L, 3L, 5L, 3L, 3L)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10"))