Split Column and then aggregate count of unique values - r

Question

I have the following dataset:
color type
1 black chair
2 black chair
3 black sofa
4 green table
5 green sofa
I want to split this to form the following dataset:
arg value
1 color black
2 color black
3 color black
4 color green
5 color green
6 type chair
7 type chair
8 type sofa
9 type table
10 type sofa
I would then like to calculate unique values of all arg-value combination:
arg value count
1 color black 3
2 color green 2
3 type chair 2
4 type sofa 2
5 type table 1
It does not need to be sorted by count. This would then be printed in the following output form:
arg unique_count_values
1 color black(3) green(2)
2 type chair(2) sofa(2) table(1)
I tried the following:
AttrList<-colnames(DataSet)
aggregate(.~ AttrList, DataSet, FUN=function(x) length(unique(x)) )
I also tried summary(DataSet) but then I am not sure how to manipulate the result to get it in the desired Output form.
I am relatively new to R. If you find something that would reduce the effort then please let me know. Thanks!
Update
So, I tried the following:
x <- matrix(c(101:104,101:104,105:106,1,2,3,3,4,5,4,5,7,5), nrow=10, ncol=2)
V1 V2
1 101 1
2 102 2
3 103 3
4 104 3
5 101 4
6 102 5
7 103 4
8 104 5
9 105 7
10 106 5
Converting to table:
as.data.frame(table(x))
Which gives me:
x Freq
1 1 1
2 2 1
3 3 2
4 4 2
5 5 3
6 7 1
7 101 2
8 102 2
9 103 2
10 104 2
11 105 1
12 106 1
What should I do so I get this:
V Val Freq
1 V2 1 1
2 V2 2 1
3 V2 3 2
4 V2 4 2
5 V2 5 3
6 V2 7 1
7 V1 101 2
8 V1 102 2
9 V1 103 2
10 V1 104 2
11 V1 105 1
12 V1 106 1

Answer 1

Try
library(tidyr)
library(dplyr)
df %>%
gather(arg, value) %>%
count(arg, value) %>%
summarise(unique_count_values = toString(paste0(value, "(", n, ")")))
Which gives:
#Source: local data frame [2 x 2]
#
# arg unique_count_values
# (fctr) (chr)
#1 color black(3), green(2)
#2 type chair(2), sofa(2), table(1)

Answer 2

Here's a base R approach. I've expanded it out a bit mostly so that I can add comments as to what is happening.
The basic idea is to just use sapply to loop through the columns, tabulate the data in each column, and then use sprintf to extract the relevant parts of the tabulation to achieve your desired output (the names, followed by the values in brackets).
The stack function takes the final named vector and converts it to a data.frame.
stack( ## convert the final output to a data.frame
sapply( ## cycle through each column
mydf, function(x) {
temp <- table(x) ## calculate counts and paste together values
paste(sprintf("%s (%d)", names(temp), temp), collapse = " ")
}))
# values ind
# 1 black (3) green (2) color
# 2 chair (2) sofa (2) table (1) type
If the data are factors, you could also try something like the following, which matches the data you expect, but not the desired output.
stack(apply(summary(mydf), 2, function(x) paste(na.omit(x), collapse = " ")))
# values ind
# 1 black:3 green:2 color
# 2 chair:2 sofa :2 table:1 type