Convert comma separated string to numeric columns

Question

I have a dataset with several columns, one of which is a column for reaction times. These reaction times are comma separated to denote the reaction times (of the same participant) for the different trials.

For example: row 1 (i.e.: the data from participant 1) has the following under the column "reaction times"

reaction_times
2000,1450,1800,2200

Hence these are the reaction times of participant 1 for trials 1,2,3,4.

I now want to create a new data set in which the reaction times for these trials all form individual columns. This way I can calculate the mean reaction time for each trial.

              trial 1  trial 2  trial 3  trial 4 
participant 1:   2000     1450     1800     2200

I tried the colsplit from the reshape2 package but that doesn't seem to split my data into new columns (perhaps because my data is all in 1 cell).

Any suggestions?

Answer 1

I think you are looking for the strsplit() function;

a = "2000,1450,1800,2200"
strsplit(a, ",")
[[1]]                                                                                                                                                       
[1] "2000" "1450" "1800" "2200"   

Notice that strsplit returns a list, in this case with only one element. This is because strsplit takes vectors as input. Therefore, you can also put a long vector of your single cell characters into the function and get back a splitted list of that vector. In a more relevant example this look like:

# Create some example data
dat = data.frame(reaction_time = 
       apply(matrix(round(runif(100, 1, 2000)), 
                     25, 4), 1, paste, collapse = ","),
                     stringsAsFactors=FALSE)
splitdat = do.call("rbind", strsplit(dat$reaction_time, ","))
splitdat = data.frame(apply(splitdat, 2, as.numeric))
names(splitdat) = paste("trial", 1:4, sep = "")
head(splitdat)
  trial1 trial2 trial3 trial4
1    597   1071   1430    997
2    614    322   1242   1140
3   1522   1679     51   1120
4    225   1988   1938   1068
5    621    623   1174     55
6   1918   1828    136   1816

and finally, to calculate the mean per person:

apply(splitdat, 1, mean)
[1] 1187.50  361.25  963.75 1017.00  916.25 1409.50  730.00 1310.75 1133.75
[10]  851.25  914.75  881.25  889.00 1014.75  676.75  850.50  805.00 1460.00
[19]  901.00 1443.50  507.25  691.50 1090.00  833.25  669.25

Answer 2

A nifty, if rather heavy-handed, way is to use read.csv in conjunction with textConnection. Assuming your data is in a data frame, df:

x <- read.csv(textConnection(df[["reaction times"]]))