Currently I have a CSV with mixed input types that I am trying to read in and reformat without having to list off all the column names. Below is an example of the data:
HouseColor, HouseSize, HouseCost Blue, 1600, 160e3 Blue, 1600, 160e3 Actually I have about 60 columns like this, so imagine the above repeated about 30 times column-wise. Luckily the ones in scientific notation are grouped together, i.e. columns 11-56. Using read.csv or as.numeric, is there a way to convert all those in scientific format over to general numeric syntax? Right now I have something like the following input_df<-read.csv(InputFile, skip=0, header=TRUE, strip.white = TRUE) I tried: as.numeric(input_df[, 11:56]) but this returns an error Error: (list) object cannot be coerced to type 'double' Oddly it does appear to work successfully row-wiseas.numeric(input_df[1, 11:56]) as.numeric(input_df[2, 11:56]) etc. However, trying it on multiple rows produces the same error as above: as.numeric(input_df[1:2, 11:56]) After a bit, I became a bit frustrated that this was not working so I tried just deleting the columns: input_df[1, 11:56]<-NULL This also failed, so are there any suggestions about how to convert the values in scientific notation over to standard numeric syntaix? Thank you again again for all your insights and feedback. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
