This might work for you: cols <- LETTERS # actually this will be cols <- colnames(df) in your example # Create a data frame to select columns choose <- data.frame(cols, select=0, stringsAsFactors=FALSE) # Run the editor and replace 0 with 1 in the select column # for each variable you wish to include fix(choose) # Your list of variables will be the vector mycols mycols <- choose$cols[choose$select==1]
David L. Carlson Department of Anthropology Texas A&M University -----Original Message----- From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of BR_email Sent: Sunday, April 23, 2017 3:47 PM To: Jeff Newmiller <jdnew...@dcn.davis.ca.us>; r-help@r-project.org Subject: Re: [R] "Copy-pastable" output of 1000 plus variables Jeff: Thanks, Please see my reply to David. Bruce Bruce Ratner, Ph.D. The Significant Statistician™ (516) 791-3544 Statistical Predictive Analtyics -- www.DMSTAT1.com Machine-Learning Data Mining and Modeling -- www.GenIQ.net Jeff Newmiller wrote: > Coming from an Excel background, copying and pasting seems attractive, but it > does not create a reproducible record of what you did so it becomes quite > tiring and frustrating after some time has passed and you return to your > analysis. > > Nitpick: you put the setdiff function in the row selection position, an error > I am sure Hadley did not recommend. > > Since R is programmable, there are far more ways to select columns than just > setdiff. Since your description of desired features is vague, you are > unlikely to get the answer you would really like from your email. Some > possibilities to think about: > > a) use regular expressions and grep or grepl to select by similar character > patterns. E.g. all columns including the the substring "value" or "key": > grep( "key|value", names( dta ). Possible to specify very complex selection > patterns, but there are whole books on regular expressions, so you can't > expect to learn all about them on this R-specific mailing list. > > b) use a separate csv file with a column listing each column name, and then > one column for each subset you want to define, using TRUE/FALSE values to > include or not include the column name identified. E.g. > > # typically easier to manage in an external data file, online for example only > colsets <- read.csv( text= > "Colname,set1,set2 > key,TRUE,TRUE > value1,TRUE,FALSE > value2,TRUE,FALSE > factor1,FALSE,TRUE > ",header=TRUE,as.is=TRUE) > dta[ , colsets$set1 ] > > Also your criteria of "clean listing" and "copy-pasteable" are likely > mutually exclusive, depending how you interpret them. You might be able to > use dput to export a set of column names that can be re-imported accurately, > but you might not regard it as "clean" if you are thinking "readable". ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
