[R] Applying a user-defined function

[arun]

Hi Pradip,

Another way to get the results would be:
 res<-cbind(test1,do.call(data.frame,lapply(test1[,seq(1,6,2)],CutQuintiles)))
 colnames(res)[7:9]<-paste("newcols_",colnames(res)[7:9],"")
sapply(res,is.factor)
 #              ObtMj_P               ObtMj_SE           ExpPrevMed_P 
  #               FALSE                  FALSE                  FALSE 
   #      ExpPrevMed_SE               ParMon_P              ParMon_SE 
  #               FALSE                  FALSE                  FALSE 
#     newcols_ ObtMj_P  newcols_ ExpPrevMed_P      newcols_ ParMon_P  
   #               TRUE                   TRUE                   TRUE 
Hope it helps.
A.K.




----- Original Message -----
From: "Muhuri, Pradip (SAMHSA/CBHSQ)" <Pradip.Muhuri at samhsa.hhs.gov>
To: R help <r-help at r-project.org>
Cc: 
Sent: Tuesday, January 8, 2013 10:06 PM
Subject: Re: [R] Applying a user-defined function


Hello List,

Last time, Arun's following solution worked to create 3 new columns (1,3,5).  Now how would I tweak this function to create corresponding (additional) columns (7,8,9) of mode factor (levels = 1,2,3,4,5)?

Thanks for your continued support.

Pradip

####### cut and paste from the reproducible example
CutQuintiles <- function( x) {
  cut (x,quantile (x, (0:5/5)),include.lowest=TRUE)
}

#apply the CutQuintile () on every odd-numbered columns of the "test1" data frame
test1$newcols <- sapply(test1 [, seq (1,6,2)], CutQuintiles)

# name 3 new columns based on the odd-numbered columns
names(test1$newcols) <- paste (names(test1 [, seq (1,6,2)]), "_cat")




###### Reproducible Example


test1 <- read.table (text=
"State,ObtMj_P,ObtMj_SE,ExpPrevMed_P,ExpPrevMed_SE,ParMon_P,ParMon_SE
Alabama,49.60,1.37,80.00,0.91,12.10,0.68
Alaska,55.00,1.41,81.80,1.08,12.40,0.90
Arizona,52.50,1.56,79.60,1.20,15.80,1.08
Arkansas,50.50,1.22,78.00,0.78,12.80,0.72
California,51.10,0.65,80.50,0.53,13.00,0.41
Colorado,55.10,1.26,81.70,1.03,12.10,0.72
Connecticut,56.30,1.28,85.00,0.93,14.60,0.77
Delaware,53.60,1.30,79.50,1.04,14.70,0.97
District of Columbia,53.50,1.22,76.20,1.03,14.30,1.13
Florida,52.70,0.67,78.90,0.52,14.10,0.45
Georgia,52.50,1.15,79.30,1.02,15.90,0.98
Hawaii,49.40,1.33,83.80,1.12,16.00,1.06
Idaho,48.30,1.23,82.40,0.99,11.90,0.74
Illinois,52.70,0.63,81.00,0.46,13.60,0.40
Indiana,49.60,1.16,80.90,0.91,12.60,0.82
Iowa,46.30,1.37,82.10,1.01,13.60,0.87
Kansas,44.30,1.43,79.20,0.98,12.90,0.79
Kentucky,52.90,1.37,78.70,1.05,14.60,0.98
Louisiana,49.70,1.23,76.80,1.06,14.50,0.76
Maine,55.60,1.44,82.90,0.93,16.70,0.83
Maryland,53.90,1.46,83.60,0.95,14.00,0.80
Massachusetts,55.40,1.41,81.00,1.15,14.70,0.80
Michigan,52.40,0.62,80.50,0.47,15.00,0.43
Minnesota,51.50,1.20,84.40,0.87,14.40,0.86
Mississippi,43.20,1.14,76.60,0.91,12.30,0.78
Missouri,48.70,1.20,80.30,0.90,13.70,0.12
Montana,56.40,1.16,83.70,0.95,12.10,0.68
Nebraska,45.70,1.51,83.40,0.95,12.40,0.90
Nevada,54.20,1.17,80.60,1.07,15.80,1.08
New Hampshire,56.10,1.30,83.30,0.93,12.80,0.72
New Jersey,53.20,1.45,83.70,0.95,13.00,0.41
New Mexico,57.60,1.34,78.90,1.03,12.10,0.72
New York,53.70,0.67,82.60,0.48,14.60,0.77
North Carolina,52.20,1.26,81.90,0.84,14.70,0.97
North Dakota,48.60,1.34,84.20,0.88,14.30,1.13
Ohio,50.90,0.61,82.70,0.49,14.10,0.45
Oklahoma,47.20,1.42,78.80,1.33,15.90,0.98
Oregon,54.00,1.35,80.60,1.14,16.00,1.06
Pennsylvania,53.00,0.63,79.90,0.47,11.90,0.74
Rhode Island,57.20,1.20,79.50,1.02,13.60,0.40
South Carolina,50.50,1.21,79.50,0.95,12.60,0.82
South Dakota,43.40,1.30,81.70,1.05,13.60,0.87
Tennessee,48.90,1.35,78.40,1.35,12.90,0.79
Texas,48.70,0.62,79.00,0.48,14.60,0.98
Utah,42.00,1.49,85.00,0.93,14.50,0.76
Vermont,58.70,1.24,83.70,0.84,16.70,0.83
Virginia,51.80,1.18,82.00,1.04,14.00,0.80
Washington,53.50,1.39,84.10,0.96,14.70,0.80
West Virginia,52.80,1.07,79.80,0.93,15.00,0.43
Wisconsin,49.90,1.50,83.50,1.02,14.40,0.86
Wyoming,49.20,1.29,82.00,0.85,12.30,0.78
", sep=",", row.names='State',  header=TRUE, as.is=TRUE)

# change names () to lower case

names (test1) <- tolower (names (test1))

#Write a cut/quantile function to apply on different columns of the data frame

CutQuintiles <- function( x) {
  cut (x,quantile (x, (0:5/5)),include.lowest=TRUE)
}

#apply the CutQuintile () on every odd-numbered columns of the "test1" data frame
test1$newcols <- sapply(test1 [, seq (1,6,2)], CutQuintiles)

# name 3 new columns based on the odd-numbered columns
names(test1$newcols) <- paste (names(test1 [, seq (1,6,2)]), "_cat")

dim (test1)
options (width=100)
test1




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