Subtract even columns from odd ones in a data frame

R has vectorised operations to deal with this kind of task in a single call:

data[c(1,3)] - data[c(2,4)]
## or for every column until the end of the dataset
data[seq(1,ncol(data),2)] - data[seq(2,ncol(data),2)]
#  A C
#1 4 7
#2 3 6
#3 2 5
#4 1 4

See this previous discussion for lots of useful advice - Selecting multiple odd or even columns/rows for dataframe

You can extend this so the naming is done automatically:

s <- seq(1,ncol(data),2)
data[paste0(names(data[s]), "minus", names(data)[-s])] <- data[s] - data[-s]
data

#  A B C D AminusB CminusD
#1 6 2 9 2       4       7
#2 5 2 8 2       3       6
#3 4 2 7 2       2       5
#4 3 2 6 2       1       4

You can choose every other column with c(TRUE,FALSE) or its negation. The binary-minus has a dataframe method:

data[c(TRUE,FALSE)] - data[c(FALSE,TRUE)]
  A C
1 4 7
2 3 6
3 2 5
4 1 4

If you wanted to name then meaningfull you could use paste on the names:

 paste( names(data[c(TRUE,FALSE)]) , "_minus_", names( data[c(FALSE,TRUE)]) )

Many basic operations on data.frames are vectorized meaning that addition, subtraction, multiplication, etc, are element wise. I.e. the following works:

data <- data.frame(A = c(6,5,4,3), B = c(2,2,2,2), C = c(9,8,7,6), D = c(2,2,2,2))

data$AminusB <- data$A - data$B
data$CminusD <- data$C - data$D

print(data)
#  A B C D AminusB CminusD
#1 6 2 9 2       4       7
#2 5 2 8 2       3       6
#3 4 2 7 2       2       5
#4 3 2 6 2       1       4

You can also access column 4. say, by data[4] or data[,4] or data[,"D"] and more. See help("["). Depending on how you want your output, there are many options how to do it. With a simple for-loop you can loop over your columns and make all differences.

Having 170 columns, specifying every column name would be daunting. If all of your columns are numeric, you can do this:

#Sample data
set.seed(123)
df <- data.frame(x = floor(rnorm(5, 10, 2)),
                 y = floor(rnorm(5, 30, 2)),
                 z = floor(rnorm(5, 50, 2)))
   x  y  z
1  8 33 52
2  9 30 50
3 13 27 50
4 10 28 50
5 10 29 48    

Subtracting columns:

df[-1] - df[-ncol(df)]

  y  z
1 25 19
2 21 20
3 14 23
4 18 22
5 19 19

Just another approach using apply:

-t(apply(data, 1, diff))[ , seq(1, ncol(data)-1, by=2)]

#      B D
# [1,] 4 7
# [2,] 3 6
# [3,] 2 5
# [4,] 1 4