[R] "Copy-pastable" output of 1000 plus variables
br at dmstat1.com
Mon Apr 24 12:51:56 CEST 2017
Your code worked beautifully.
This little ditty should be high-profile for those who work big data,
which are virtually never accompanied with a data dictionary.
This code is the first step to grab the data at large to bring it down
David L Carlson wrote:
> 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
> # 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-bounces at r-project.org] On Behalf Of BR_email
> Sent: Sunday, April 23, 2017 3:47 PM
> To: Jeff Newmiller <jdnewmil at dcn.davis.ca.us>; r-help at r-project.org
> Subject: Re: [R] "Copy-pastable" output of 1000 plus variables
> Thanks, Please see my reply to David.
> 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=
>> 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".
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