Count number of occurances of a string in R under different conditions - r

Question

I have a dataframe, with multiple columns called "data" which looks like this:
Preferences Status Gender
8a 8b 9a Employed Female
10b 11c 9b Unemployed Male
11a 11c 8e Student Female
That is, each customer selected 3 preferences and specified other information such as Status and Gender. Each preference is given by a [number][letter] combination, and there are c. 30 possible preferences. The possible preferences are:
8[a - c]
9[a - k]
10[a - d]
11[a - c]
12[a - i]
I want to count the number of occurrences of each preference, under certain conditions for the other columns - eg. for all women.
The output will ideally be a dataframe that looks like this:
Preference Female Male Employed Unemployed Student
8a 1034 934 234 495 203
8b 539 239 609 394 235
8c 124 395 684 94 283
9a 120 999 895 945 345
9b 978 385 596 923 986
etc.
What's the most efficient way to achieve this?
Thanks.

Answer 1

I am assuming you are starting with something that looks like this:
mydf <- structure(list(
Preferences = c("8a 8b 9a", "10b 11c 9b", "11a 11c 8e"),
Status = c("Employed", "Unemployed", "Student"),
Gender = c("Female", "Male", "Female")),
.Names = c("Preferences", "Status", "Gender"),
class = c("data.frame"), row.names = c(NA, -3L))
mydf
# Preferences Status Gender
# 1 8a 8b 9a Employed Female
# 2 10b 11c 9b Unemployed Male
# 3 11a 11c 8e Student Female
If that's the case, you need to "split" the "Preferences" column (by spaces), transform the data into a "long" form, and then reshape it to a wide form, tabulating while you do so.
With the right tools, this is pretty straightforward.
library(devtools)
library(data.table)
library(reshape2)
source_gist(11380733) # for `cSplit`
dcast.data.table( # Step 3--aggregate to wide form
melt( # Step 2--convert to long form
cSplit(mydf, "Preferences", " ", "long"), # Step 1--split "Preferences"
id.vars = "Preferences"),
Preferences ~ value, fun.aggregate = length)
# Preferences Employed Female Male Student Unemployed
# 1: 10b 0 0 1 0 1
# 2: 11a 0 1 0 1 0
# 3: 11c 0 1 1 1 1
# 4: 8a 1 1 0 0 0
# 5: 8b 1 1 0 0 0
# 6: 8e 0 1 0 1 0
# 7: 9a 1 1 0 0 0
# 8: 9b 0 0 1 0 1
I also tried a dplyr + tidyr approach, which looks like the following:
library(dplyr)
library(tidyr)
mydf %>%
separate(Preferences, c("P_1", "P_2", "P_3")) %>% ## splitting things
gather(Pref, Pvals, P_1:P_3) %>% # stack the preference columns
gather(Var, Val, Status:Gender) %>% # stack the status/gender columns
group_by(Pvals, Val) %>% # group by these new columns
summarise(count = n()) %>% # aggregate the numbers of each
spread(Val, count) # spread the values out
# Source: local data table [8 x 6]
# Groups:
#
# Pvals Employed Female Male Student Unemployed
# 1 10b NA NA 1 NA 1
# 2 11a NA 1 NA 1 NA
# 3 11c NA 1 1 1 1
# 4 8a 1 1 NA NA NA
# 5 8b 1 1 NA NA NA
# 6 8e NA 1 NA 1 NA
# 7 9a 1 1 NA NA NA
# 8 9b NA NA 1 NA 1
Both approaches are actually pretty quick. Test it with some better sample data than what you shared, like this:
preferences <- c(paste0(8, letters[1:3]),
paste0(9, letters[1:11]),
paste0(10, letters[1:4]),
paste0(11, letters[1:3]),
paste0(12, letters[1:9]))
set.seed(1)
nrow <- 10000
mydf <- data.frame(
Preferences = vapply(replicate(nrow,
sample(preferences, 3, FALSE),
FALSE),
function(x) paste(x, collapse = " "),
character(1L)),
Status = sample(c("Employed", "Unemployed", "Student"), nrow, TRUE),
Gender = sample(c("Male", "Female"), nrow, TRUE)
)