Summary of values across rows and columns in R - r

Question

I have a dataset that looks like:
Group A B C D
XYZ 4 Na 1 3
XYZ Na 2 2 1
DEF 4 3 2 1
DEF 3 3 1 1
PQR 1 Na Na 1
PQR 3 2 2 4
I want the summary of this dataset across rows and columns for the count of each value as below:
Group 4 3 2 1
XYZ 1 1 2 2
DEF 1 3 1 3
PQR 1 1 2 2
The count of 4 in the dataset for group XYZ across all rows and columns is 1, for 2 and 1 its 2, for 3 its 1. I can do this by creating 4 new columns 4,3,2,1 and getting the count row wise and then column wise, but this is not efficient and scalable. I am sure there is a better way to get this done.

Answer 1

Using reshape2 package we can melt and dcast as follows,
library(reshape2)
dcast(na.omit(melt(df, id.vars = 'Group')), Group ~ value, fun.aggregate = length)
# Group 1 2 3 4
#1 DEF 3 1 3 1
#2 PQR 2 2 1 1
#3 XYZ 2 2 1 1

Answer 2

This uses no packages and is just one line. Here DF$Group[row(DF[-1])] is a Group labels vector such that each element corresponds to the unravelled numeric vector unlist(DF[-1]).
table(DF$Group[row(DF[-1])], unlist(DF[-1]))
giving:
1 2 3 4
DEF 3 1 3 1
PQR 2 2 1 1
XYZ 2 2 1 1
If the order of rows and columns shown in the question is important then to we can create factors from each of the two table arguments with the factor levels being defined in the orders desired. In this case we use the following line instead of the line of code above:
table(Group = factor(DF$Group[row(DF[-1])], unique(DF$Group)), factor(unlist(DF[-1]), 4:1))
giving:
Group 4 3 2 1
XYZ 1 1 2 2
DEF 1 3 1 3
PQR 1 1 2 2
The above produces an object of class "table". This is a particularly suitable class for tabulated frequencies. For example, once in this form ftable can be used to easily rearrange it further as in ftable(tab, row.vars = 2) or ftable(tab, row.vars = 1:2) where tab is the above computed table.
If a data.frame were preferred then convert it like this:
cbind(Group = rownames(tab), as.data.frame.matrix(tab))
The input data.frame DF is defined reproducibly in Note 2 at the end.
Alternatives
Although the above seems the most direct here are some other alternatives that also use no packages:
1) by For each set of rows having the same Group value the anonymous function creates a data.frame identifying the Group, converting the columns other than the first to a factor with the indicated levels and running table to get the counts. The "by" list that is returned is sorted back to the original order and we rbind everything back together.
do.call("rbind",
by(DF, DF$Group, function(x) {
data.frame(Group = x[1,1],
as.list(table(factor(unlist(x[, -1]), levels = 4:1))),
check.names = FALSE)
})[unique(DF$Group)])
giving:
Group 4 3 2 1
XYZ XYZ 1 1 2 2
DEF DEF 1 3 1 3
PQR PQR 1 1 2 2
1a) This slightly shorter variation would also work. It returns a matrix identifying the groups using row names.
kount <- function(x) table(factor(unlist(x), levels = 4:1))
m <- do.call("rbind", by(DF[, -1], DF$Group, kount)[unique(DF$Group)])
giving:
> m
4 3 2 1
XYZ 1 1 2 2
DEF 1 3 1 3
PQR 1 1 2 2
2) outer
gps <- unique(DF$Group)
levs <- 4:1
kount2 <- function(g, lv) sum(subset(DF, Group == g)[-1] == lv, na.rm = TRUE)
m <- outer(gps, levs, Vectorize(kount2))
dimnames(m) <- list(gps, levs))
giving this matrix:
> m
4 3 2 1
XYZ 1 1 2 2
DEF 1 3 1 3
PQR 1 1 2 2
3) sapply
kount3 <- function(g) table(factor(unlist(DF[DF$Group == g, -1]), levels = 4:1))
gps <- as.character(unique(DF$Group))
do.call("rbind", sapply(gps, kount3, simplify = FALSE))
giving:
4 3 2 1
XYZ 1 1 2 2
DEF 1 3 1 3
PQR 1 1 2 2
4) aggregate
aggregate(1:nrow(DF), DF["Group"], function(ix)
table(factor(unlist(DF[ix, -1]), levels = 4:1)))[unique(DF$Group), ]
giving:
Group x.4 x.3 x.2 x.1
3 XYZ 1 1 2 2
1 DEF 1 3 1 3
2 PQR 1 1 2 2
5) tapply
do.call("rbind", tapply(1:nrow(DF), DF$Group, function(ix)
table(factor(unlist(DF[ix, -1]), levels = 4:1))))[unique(DF$Group), ]
6) reshape
with(reshape(DF, dir = "long", varying = list(2:5)),
table(factor(Group, unique(DF$Group)), factor(A, 4:1)))
giving:
4 3 2 1
XYZ 1 1 2 2
DEF 1 3 1 3
PQR 1 1 2 2
Note 1: (1a), (2), (3), (5) and (6) produce a matrix or table result with groups as row names. If you prefer a data frame with Groups as a column then supposing that m is the matrix, add this:
data.frame(Group = rownames(m), m, check.names = FALSE)
Note 2: The input DF in reproducible form is:
Lines <- "Group A B C D
XYZ 4 Na 1 3
XYZ Na 2 2 1
DEF 4 3 2 1
DEF 3 3 1 1
PQR 1 Na Na 1
PQR 3 2 2 4"
DF <- read.table(text = Lines, header = TRUE, na.strings = "Na")

Answer 3

We can use dplyr/tidyr
library(dplyr)
library(tidyr)
df1 %>%
mutate_each(funs(replace(., .=="Na", NA))) %>%
gather(Var, Val, A:D, na.rm=TRUE) %>%
group_by(Group, Val) %>%
tally() %>%
spread(Val, n)
# Group `1` `2` `3` `4`
#* <chr> <int> <int> <int> <int>
#1 DEF 3 1 3 1
#2 PQR 2 2 1 1
#3 XYZ 2 2 1 1