extracting unique combinations from a long list of binary variables - r

Question

I have a dataframe containing a long list of binary variables. Each row represents a participant, and columns represent whether a participant made a certain choice (1) or not (0). For the sakes of simplicity, let's say there's only four binary variables and 6 participants.
df <- data.frame(a = c(0,1,0,1,0,1),
b = c(1,1,1,1,0,1),
c = c(0,0,0,1,1,1),
d = c(1,1,0,0,0,0))
>df
# a b c d
# 1 0 1 0 1
# 2 1 1 0 1
# 3 0 1 0 0
# 4 1 1 1 0
# 5 0 0 1 0
# 6 1 1 1 0
In the dataframe, I want to create a list of columns that reflect each unique combination of variables in df (i.e., abc, abd, bcd, cda). Then, for each row, I want to add value "1" if the row contains the particular combination corresponding to the column. So, if the participant scored 1 on "a", "b", and "c", and 0 on "d" he would have a score 1 in the newly created column "abc", but 0 in the other columns. Ideally, it would look something like this.
>df_updated
# a b c d abc abd bcd cda
# 1 0 1 0 1 0 0 0 0
# 2 1 1 0 1 0 1 0 0
# 3 0 1 0 0 0 0 0 0
# 4 1 1 1 0 1 0 0 0
# 5 0 0 1 0 0 0 0 0
# 6 1 1 1 0 0 0 0 0
The ultimate goal is to have an idea of the frequency of each of the combinations, so I can order them from the most frequently chosen to the least frequently chosen. I've been thinking about this issue for days now, but couldn't find an appropriate answer. I would very much appreciate the help.

Answer 1

Something like this?
funCombn <- function(data){
f <- function(x, data){
data <- data[x]
list(
name = paste(x, collapse = ""),
vec = apply(data, 1, function(x) +all(as.logical(x)))
)
}
res <- combn(names(df), 3, f, simplify = FALSE, data = df)
out <- do.call(cbind.data.frame, lapply(res, '[[', 'vec'))
names(out) <- sapply(res, '[[', 'name')
cbind(data, out)
}
funCombn(df)
# a b c d abc abd acd bcd
#1 0 1 0 1 0 0 0 0
#2 1 1 0 1 0 1 0 0
#3 0 1 0 0 0 0 0 0
#4 1 1 1 0 1 0 0 0
#5 0 0 1 0 0 0 0 0
#6 1 1 1 0 1 0 0 0

Answer 2

Base R option using combn :
n <- 3
cbind(df, do.call(cbind, combn(names(df), n, function(x) {
setNames(data.frame(as.integer(rowSums(df[x] == 1) == n)),
paste0(x, collapse = ''))
}, simplify = FALSE))) -> result
result
# a b c d abc abd acd bcd
#1 0 1 0 1 0 0 0 0
#2 1 1 0 1 0 1 0 0
#3 0 1 0 0 0 0 0 0
#4 1 1 1 0 1 0 0 0
#5 0 0 1 0 0 0 0 0
#6 1 1 1 0 1 0 0 0
Using combn create all combinations of column names taking n columns at a time. For each of those combinations assign 1 to those rows where all the 3 combinations are 1 or 0 otherwise.

Answer 3

If you are just looking for a frequency of the combinations (and they don't need to be back in the original data), then you could use something like this:
df <- data.frame(a = c(0,1,0,1,0,1),
b = c(1,1,1,1,0,1),
c = c(0,0,0,1,1,1),
d = c(1,1,0,0,0,0))
n <- names(df)
out <- sapply(n, function(x)ifelse(df[[x]] == 1, x, ""))
combs <- apply(out, 1, paste, collapse="")
sort(table(combs))
# combs
# abd b bd c abc
# 1 1 1 1 2

Answer 4

Ok, so let's use your data, including one row without any 1's:
df <- data.frame(
a = c(0,1,0,1,0,1,0),
b = c(1,1,1,1,0,1,0),
c = c(0,0,0,1,1,1,0),
d = c(1,1,0,0,0,0,0)
)
Now I want to paste all column names together if they have a 1, and then make that a wide table (so that all have a column for a combination). Of course, I fill all resulting NAs with 0's.
df2 <- df %>%
dplyr::mutate(
combination = paste0(
ifelse(a == 1, "a", ""), # There is possibly a way to automate this as well using across()
ifelse(b == 1, "b", ""),
ifelse(c == 1, "c", ""),
ifelse(d == 1, "d", "")
),
combination = ifelse(
combination == "",
"nothing",
paste0("comb_", combination)
),
value = ifelse(
is.na(combination),
0,
1
),
i = dplyr::row_number()
) %>%
tidyr::pivot_wider(
names_from = combination,
values_from = value,
names_repair = "unique"
) %>%
replace(., is.na(.), 0) %>%
dplyr::select(-i)
Since you want to order the original df by frequency, you can create a summary of all combinations (excluding those without anything filled in). Then you just make it a long table and pull the column for every combination (arranged by frequency) from the table.
comb_in_order <- df2 %>%
dplyr::select(
-tidyselect::any_of(
c(
names(df),
"nothing" # I think you want these last.
)
)
) %>%
dplyr::summarise(
dplyr::across(
.cols = tidyselect::everything(),
.fns = sum
)
) %>%
tidyr::pivot_longer(
cols = tidyselect::everything(),
names_to = "combination",
values_to = "frequency"
) %>%
dplyr::arrange(
dplyr::desc(frequency)
) %>%
dplyr::pull(combination)
The only thing to do then is to reconstruct the original df by these after arranging by the columns.
df2 %>%
dplyr::arrange(
across(
tidyselect::any_of(comb_in_order),
desc
)
) %>%
dplyr::select(
tidyselect::any_of(names(df))
)
This should work for all possible combinations.