[R] List of Levels for all Factor variables

[arun]

HI,
You can also try this:
set.seed(1)
dat1<-data.frame(col1=factor(sample(1:25,10,replace=TRUE)),col2=sample(letters[1:10],10,replace=TRUE),col3=factor(rep(1:5,each=2)))

sapply(lapply(mapply(c,lapply(names(sapply(dat1,levels)),function(x) x),sapply(dat1,levels)),function(x) paste(x[1],":",paste(x[-1],collapse=" "))),print)
#[1] "col1 : 2 6 7 10 15 16 17 23 24"
#[1] "col2 : b c d e g h j"
#[1] "col3 : 1 2 3 4 5"
#[1] "col1 : 2 6 7 10 15 16 17 23 24" "col2 : b c d e g h j"          
#[3] "col3 : 1 2 3 4 5"   

A.K.    




----- Original Message -----
From: "Lopez, Dan" <lopez235 at llnl.gov>
To: "R help (r-help at r-project.org)" <r-help at r-project.org>
Cc: 
Sent: Tuesday, October 16, 2012 11:19 AM
Subject: [R] List of Levels for all Factor variables

Hi,

I want to get a clean succinct list of all levels for all my factor variables.

I have a dataframe that's something like #1 below. This is just an example subset of my data and my actual dataset has 70 variables. I know how to narrow down my list of variables to just my factor variables by using #2 below (thanks to Bert Gunter). I can also get list of all levels for all my factor variables using #3 below. But I what I want to find out is if there is a way to get this list in a similar fashion to what the str function returns: without all the extra spacing and carriage returns. That's what I mean by "clean succinct list".

BTW I also tried playing around with several of the parameters for the str function itself but could not find a way to accomplish what I want to accomplish.



1.       DATAFRAME

> str(mydata)
'data.frame':  11868 obs. of  26 variables:
$ EMPLID          : int  431108 32709 19730 10850 48786 2004 237628 558 3423 743175 ...
$ NAME            : Factor w/ 6402 levels "Aaron Cathy E",..: 2777 242 161 104 336 4254 1595 1244 3669 4760 ...
$ TRAIN           : int  1 1 1 1 1 1 1 1 1 1 ...
$ TARGET          : int  0 0 0 0 0 0 0 0 0 0 ...
$ APPT_TYP_CD_LL  : Factor w/ 3 levels "FX","IN","IP": 2 2 2 2 2 2 2 2 2 2 ...
$ ORG_NAM_LL      : Factor w/ 18 levels "Business","Chief Financial Officer",..: 11 7 7 9 4 4 18 18 8 4 ...
$ NEW_DISCIPLINE  : Factor w/ 15 levels "100s","300s",..: 14 6 4 1 11 11 14 2 1 1 ...
$ SERIES          : Factor w/ 10 levels "100s","300s",..: 9 6 4 1 9 9 9 2 1 1 ...
$ AGE             : int  62 53 46 62 55 59 50 36 34 53 ...
$ SERVICE         : int  13 29 16 26 18 9 19 11 8 26 ...
$ AGE_SERVICE     : int  75 82 62 87 73 69 69 47 42 79 ...
$ HIEDUCLV        : Factor w/ 6 levels "Associate","Bachelor",..: 5 6 6 6 5 2 3 2 2 1 ...
$ GENDER          : Factor w/ 2 levels "F","M": 2 2 2 1 2 2 2 2 2 1 ...
$ RETCD           : Factor w/ 2 levels "TCP1","TCP2": 2 1 2 2 2 1 1 2 1 2 ...
$ FLSASTATUS      : Factor w/ 2 levels "E","N": 1 2 2 1 1 1 1 1 1 1 ...
$ MONTHLY_RT      : int  17640 6932 5845 9809 11473 8719 19190 8986 7231 6758 ...
$ RETSTATUSDERIVED: Factor w/ 4 levels "401K","DOUBLE DIPPERS",..: 2 4 3 2 3 4 4 3 4 3 ...
$ ETHNIC_GRP_CD   : Factor w/ 8 levels "AMIND","ASIAN",..: 8 8 8 8 8 8 8 8 8 8 ...
$ COMMUTE_BIN     : Factor w/ 7 levels "","<15","15 - 24",..: 5 7 2 2 4 3 3 6 3 2 ...
$ EEO_CLASS       : Factor w/ 4 levels "M","S1","S2",..: 1 2 4 4 4 4 1 2 4 2 ...
$ WRK_SCHED       : Factor w/ 6 levels "12HR","4/10s",..: 3 3 3 3 3 3 3 3 4 4 ...
$ FWT_MAR_STATUS  : Factor w/ 2 levels "M","S": 1 1 1 1 2 1 1 1 1 2 ...
$ COVERED_DP      : int  2 2 4 0 1 3 1 2 0 0 ...
$ YRS_IN_SERIES   : int  13 29 16 26 18 9 19 3 7 26 ...
$ SAVINGS_PCT     : int  10 0 6 19 8 0 10 15 15 18 ...
$ Generation      : Factor w/ 4 levels "Baby Boomers",..: 1 1 2 1 1 1 1 2 2 1 ...

2. Create mydataF to only include factor variables (and exclude NAME which I am not interested in)

> mydataF<-mydata[,sapply(mydata,function(x)is.factor(x))][,-1]

3. Get a list of all levels

> sapply(mydataF,function(x)levels(x))

$APPT_TYP_CD_LL

[1] "FX" "IN" "IP"



$ORG_NAM_LL

[1] "Business"                        "Chief Financial Officer"         "Chief Information Office"        "Computation"                     "Engineering"                     "ESH and Quality"

[7] "Facilities and Infrastructure"   "Global Security"                 "NIF"          "NO"              "Office of the Director"          "Operations and Business Office"

[13] "Physical and Life Sciences"      "Planning and Financial Services" "ST"   "Security Organization"           "Strategic Human Resources Mgmt"  "WCI"



$NEW_DISCIPLINE

[1] "100s"                       "300s"                       "400s"                       "500s"                       "600s"                       "800s"                       "900s"

[8] "Chem  Science"              "Engineering"                "Life Sciences"              "Math  Computer Science  IT" "Physics"                    "pre100s"                    "PSTS Other"

[15] "Re"



$SERIES   ......

Daniel Lopez
Workforce Analyst
HRIM - Workforce Analytics & Metrics


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