Substituting the results of a calculation

Question

I'm munging data, specifically, I've opened this pdf http://pubs.acs.org/doi/suppl/10.1021/ja105035r/suppl_file/ja105035r_si_001.pdf and scraped the data from table s4,

    1a 1b 1a 1b
1 5.27 4.76 5.09 4.75
2 2.47 2.74 2.77 2.80
4 1.14 1.38 1.12 1.02
6 7.43 7.35 7.22-7.35a 7.25-7.36a
7 7.38 7.34 7.22-7.35a 7.25-7.36a
8 7.23 7.20 7.22-7.35a 7.25-7.36a
9(R) 4.16 3.89 4.12b 4.18b
9(S) 4.16 3.92 4.12b 4.18b
10 1.19 0.91 1.21 1.25

pasted it into notepad and saved it as a txt file.

s4 <- read.table("s4.txt", header=TRUE, stringsAsFactors=FALSE)

gives,

   X1a  X1b      X1a.1      X1b.1
1 5.27 4.76       5.09       4.75
2 2.47 2.74       2.77       2.80
4 1.14 1.38       1.12       1.02
6 7.43 7.35 7.22-7.35a 7.25-7.36a
7 7.38 7.34 7.22-7.35a 7.25-7.36a
8 7.23 7.20 7.22-7.35a 7.25-7.36a

in order to use the data I need to change it all to numeric and remove the letters, thanks to this link R regex gsub separate letters and numbers I can use the following code,

gsub("([[:alpha:]])","",s4[,3])

I can get rid of the extraneous letters.

What I want to do now, and the point of the question, is to change the ranges,

"7.22-7.35" "7.22-7.35" "7.22-7.35"

with their means,

"7.29"

Could I use gsub for this? (or would I need to strsplit across the hyphen, combine into a vector and return the mean?).

Answer 1

You need a single regex in strsplit for this task (removing letters and splitting):

s4[] <- lapply(s4, function(x) {
  if (is.numeric(x)) x
  else sapply(strsplit(as.character(x), "-|[[:alpha:]]"), 
              function(y) mean(as.numeric(y)))
})

The result:

> s4
   X1a  X1b X1a.1 X1b.1
1 5.27 4.76 5.090 4.750
2 2.47 2.74 2.770 2.800
4 1.14 1.38 1.120 1.020
6 7.43 7.35 7.285 7.305
7 7.38 7.34 7.285 7.305
8 7.23 7.20 7.285 7.305

Answer 2

Here's an approach that seems to work right on the sample data:

df[] <- lapply(df, function(col){
  col <- gsub("([[:alpha:]])","", col)
  col <- ifelse(grepl("-", col), mean(as.numeric(unlist(strsplit(col[grepl("-", col)], "-")))), col)
  as.numeric(col)
})

> df
#   X1a  X1b X1a.1 X1b.1
#1 5.27 4.76 5.090 4.750
#2 2.47 2.74 2.770 2.800
#4 1.14 1.38 1.120 1.020
#6 7.43 7.35 7.285 7.305
#7 7.38 7.34 7.285 7.305
#8 7.23 7.20 7.285 7.305

Disclaimer: It only works right if the ranges in each column are all the same (as in the sample data)