1
votes

I have a column in my dataframe where in every cell there are one or more numbers. If there are many numbers, they are seperated with a space. Furthermore, R considers them as a character vector. I'd really like to convert them to numeric (and if possible sum them up right away). E.g. one of my cells might look like

6 310 21 20 64

I've tried

Reduce(sum,L)

and

as.numeric(L)

but I always get Warning message:

NAs introduced by coercion

Here, L is just a sample object I created to put one of my cells into.

3
Try sum(as.numeric(strsplit("6 310 21 20 64",' ')[[1]])). You might have to modify the code a bit to apply it to all of your data. Post dput of your data if you need further help.etienne
stackoverflow.com/questions/5963269/… Please give some data for Ljogo
Ok, in my example L<-c("6 310 21 20 64"). But this already works fine for me, thanks!GaiusBaltar
@etienne you could probably generalize this to sapply(strsplit(str1,' '), function(x) sum(type.convert(x)))David Arenburg
@DavidArenburg: thanks, I didn't know about type.convertetienne

3 Answers

3
votes

Following my comment, here is a solution using sapply :

sum(as.numeric(strsplit("6 310 21 20 64",' ')[[1]]))

which for the column of the dataframe will give something like this:

sapply(1:nrow(df),function(x){sum(as.numeric(strsplit(str1,' ')[[x]]))})
# 421 421 421

which could be improved in sapply(strsplit(str1,' '), function(x) sum(type.convert(x))), thanks to David Arenburg's suggestion.

5
votes

Here are two more options which work correctly on a vector (it seems)

str1 <- c("6 310 21 20 64", "6 310 21 20 64","6 310 21 20 64")
rowSums(read.table(text = str1))
## [1] 421 421 421

Or using data.table::fread

rowSums(data.table::fread(paste(str1, collapse = "\n")))
# [1] 421 421 421        

Or as mentioned in comments by @akrun, you can use Reduce(`+`,...) instead of rowSums(...) in both cases in order to avoid to marix conversion

4
votes

We can use scan

sum(scan(text=str1, what=numeric(), quiet=TRUE))
#[1] 421

data

str1 <- "6 310 21 20 64"