How do I count the occurrences of a factor in several columns, grouping by one column?

Question

I have a seemingly simple question, but I cannot figure out how to get exactly what I want.

My data looks like this:

      Job     C/C++     Java     Python
  Student     FALSE     TRUE      FALSE
Developer      TRUE     TRUE       TRUE
Developer      TRUE     TRUE      FALSE
 Sysadmin      TRUE    FALSE      FALSE
  Student     FALSE     TRUE       TRUE

I would like to group by the "Job" column and count the number of TRUEs in each column. My desired output would look like this:

      Job     C/C++     Java     Python
  Student         0        2          1
Developer         2        2          1 
 Sysadmin         1        0          0

Any help would be greatly appreciated.

Answer 1

Assuming your data.frame is called "temp", just use aggregate:

aggregate(. ~ Job, temp, sum)
#         Job C.C.. Java Python
# 1 Developer     2    2      1
# 2   Student     0    2      1
# 3  Sysadmin     1    0      0

---

The logic is that TRUE and FALSE equate to numeric values of "1" and "0", so you can simply use sum when aggregating.

---

And, to add the "tidyverse" solution for completeness:

library(tidyverse)
temp %>% 
  group_by(Job) %>% 
  summarise_all(sum)
# # A tibble: 3 x 4
#   Job       C.C..  Java Python
#   <chr>     <int> <int>  <int>
# 1 Developer     2     2      1
# 2 Student       0     2      1
# 3 Sysadmin      1     0      0

---

Here's your data in a format that is easy to copy-and-paste. This was obtained by using dput(your-actual-data-frame-name) and is what you should use in the future when posting R questions to Stack Overflow.

temp <- structure(list(Job = c("Student", "Developer", "Developer", "Sysadmin", 
          "Student"), C.C.. = c(FALSE, TRUE, TRUE, TRUE, FALSE), Java = c(TRUE, 
          TRUE, TRUE, FALSE, TRUE), Python = c(FALSE, TRUE, FALSE, FALSE, TRUE)),
          .Names = c("Job", "C.C..", "Java", "Python"), class = "data.frame", 
          row.names = c(NA, -5L))

Answer 2

Alternative plyr and data.table solutions:

data.table:

require(data.table)
tmp.dt <- data.table(temp, key="Job")
tmp.dt[, lapply(.SD, sum), by=Job]

#         Job C.C.. Java Python
# 1: Developer     2    2      1
# 2:   Student     0    2      1
# 3:  Sysadmin     1    0      0

plyr:

require(plyr)
ddply(temp, .(Job), function(x) colSums(x[-1]))

#         Job C.C.. Java Python
# 1 Developer     2    2      1
# 2   Student     0    2      1
# 3  Sysadmin     1    0      0

Edit: If instead of TRUE/FALSE, you've to count the number of Newbie's, then:

With data.table:

require(data.table)
tmp.dt <- data.table(temp, key="Job")
tmp.dt[, lapply(.SD, function(x) sum(x == "Newbie")), by=Job]

With plyr:

require(plyr)
ddply(temp, .(Job), function(x) colSums(x[-1] == "Newbie"))