Hi R'ers:
I need a jump start to obtain my objective.
Assistance is greatly appreciated.
Bruce
*******
#Given Gender Dataset
r1 <- c( 1, 2, 3)
c1 <- c( "male", "female", "NA")
GENDER <- data.frame(r1,c1)
names(d1_3) <- c("ID","Gender")
GENDER
--------------
_OBJECTIVE_: To dummify GENDER,
i.e., to generate two new numeric columns,
Gender_male and Gender_female,
such that:
when Gender="male" then Gender_male=1 and Gender_female=0
when Gender="female" then Gender_male=0 and Gender_female=1
when Gender="NA" then Gender_male=0 and Gender_female=0
So, with the given dataset, the resultant dataset would be as follows:
Desired Extended Gender Dataset
ID Gender Gender_male Gender_female
1 male 1 0
2 female 0 1
3 NA 0 0
--
Bruce Ratner, Ph.D.
> On Apr 2, 2017, at 11:48 AM, BR_email <br at dmstat1.com> wrote: > > Hi R'ers: > I need a jump start to obtain my objective. > Assistance is greatly appreciated. > Bruce > > ******* > #Given Gender Dataset > r1 <- c( 1, 2, 3) > c1 <- c( "male", "female", "NA") > GENDER <- data.frame(r1,c1) > names(d1_3) <- c("ID","Gender")#ITYM: names(GENDER) <- c("ID","Gender")> GENDER > -------------- > _OBJECTIVE_: To dummify GENDER, > i.e., to generate two new numeric columns, > Gender_male and Gender_female, > such that: > when Gender="male" then Gender_male=1 and Gender_female=0 > when Gender="female" then Gender_male=0 and Gender_female=1 > when Gender="NA" then Gender_male=0 and Gender_female=0 > > So, with the given dataset, the resultant dataset would be as follows: > Desired Extended Gender Dataset > ID Gender Gender_male Gender_female > 1 male 1 0 > 2 female 0 1 > 3 NA 0 0With that correction I think you might want:> model.matrix( ID ~ Gender+0, data=GENDER )Genderfemale Gendermale GenderNA 1 0 1 0 2 1 0 0 3 0 0 1 attr(,"assign") [1] 1 1 1 attr(,"contrasts") attr(,"contrasts")$Gender [1] "contr.treatment" If you assigned that to an object name, say "obj" you could get your desired result with:> obj <- model.matrix( ID ~ Gender+0, data=GENDER ) > cbind(GENDER[ , 1, drop=FALSE], obj[,-3] )ID Genderfemale Gendermale 1 1 0 1 2 2 1 0 3 3 0 0 I get the sense that you are trying to replicate a workflow that you developed in some other language and I think it would be more efficient for you to actually learn R rather than trying to write SAS or SPSS in R. If you like getting "into the weeds" of the language then I suggest trying to read the code in the `lm` function. It might help to refer back to Venables and Ripley's "S Programming" or reading Wickham's "Advanced R" pages on the web. --> Bruce Ratner, Ph.D. > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.David Winsemius Alameda, CA, USA
Hello, Try the following. GENDER$Gender_male <- as.integer(GENDER$Gender == "male") GENDER$Gender_female <- as.integer(GENDER$Gender == "female") Hope this helps, Rui Barradas Em 02-04-2017 19:48, BR_email escreveu:> Hi R'ers: > I need a jump start to obtain my objective. > Assistance is greatly appreciated. > Bruce > > ******* > #Given Gender Dataset > r1 <- c( 1, 2, 3) > c1 <- c( "male", "female", "NA") > GENDER <- data.frame(r1,c1) > names(d1_3) <- c("ID","Gender") > GENDER > -------------- > _OBJECTIVE_: To dummify GENDER, > i.e., to generate two new numeric columns, > Gender_male and Gender_female, > such that: > when Gender="male" then Gender_male=1 and Gender_female=0 > when Gender="female" then Gender_male=0 and Gender_female=1 > when Gender="NA" then Gender_male=0 and Gender_female=0 > > So, with the given dataset, the resultant dataset would be as follows: > Desired Extended Gender Dataset > ID Gender Gender_male Gender_female > 1 male 1 0 > 2 female 0 1 > 3 NA 0 0 >
> On Apr 2, 2017, at 12:19 PM, David Winsemius <dwinsemius at comcast.net> wrote: > >> >> On Apr 2, 2017, at 11:48 AM, BR_email <br at dmstat1.com> wrote: >> >> Hi R'ers: >> I need a jump start to obtain my objective. >> Assistance is greatly appreciated. >> Bruce >> >> ******* >> #Given Gender Dataset >> r1 <- c( 1, 2, 3) >> c1 <- c( "male", "female", "NA")It's also important to realize that this "NA" is not actually a missing value but was rather a character string. If it had not been quoted at the time of data input , it would have been a missing value. -- David>> GENDER <- data.frame(r1,c1) >> names(d1_3) <- c("ID","Gender") > > #ITYM: > names(GENDER) <- c("ID","Gender") > >> GENDER >> -------------- >> _OBJECTIVE_: To dummify GENDER, >> i.e., to generate two new numeric columns, >> Gender_male and Gender_female, >> such that: >> when Gender="male" then Gender_male=1 and Gender_female=0 >> when Gender="female" then Gender_male=0 and Gender_female=1 >> when Gender="NA" then Gender_male=0 and Gender_female=0 >> >> So, with the given dataset, the resultant dataset would be as follows: >> Desired Extended Gender Dataset >> ID Gender Gender_male Gender_female >> 1 male 1 0 >> 2 female 0 1 >> 3 NA 0 0 > > With that correction I think you might want: > >> model.matrix( ID ~ Gender+0, data=GENDER ) > Genderfemale Gendermale GenderNA > 1 0 1 0 > 2 1 0 0 > 3 0 0 1 > attr(,"assign") > [1] 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$Gender > [1] "contr.treatment" > > If you assigned that to an object name, say "obj" you could get your desired result with: > >> obj <- model.matrix( ID ~ Gender+0, data=GENDER ) >> cbind(GENDER[ , 1, drop=FALSE], obj[,-3] ) > ID Genderfemale Gendermale > 1 1 0 1 > 2 2 1 0 > 3 3 0 0 > > > I get the sense that you are trying to replicate a workflow that you developed in some other language and I think it would be more efficient for you to actually learn R rather than trying to write SAS or SPSS in R. If you like getting "into the weeds" of the language then I suggest trying to read the code in the `lm` function. It might help to refer back to Venables and Ripley's "S Programming" or reading Wickham's "Advanced R" pages on the web. > > -- >> Bruce Ratner, Ph.D. >> >> ______________________________________________ >> R-help at 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. > > David Winsemius > Alameda, CA, USADavid Winsemius Alameda, CA, USA
David: Thank you. It's perfect. FYI: regarding your comment about "NA," yes, I filled it in just for the example. Again, thanks for your professional and polite reply. 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 David Winsemius wrote:>> On Apr 2, 2017, at 11:48 AM, BR_email <br at dmstat1.com> wrote: >> >> Hi R'ers: >> I need a jump start to obtain my objective. >> Assistance is greatly appreciated. >> Bruce >> >> ******* >> #Given Gender Dataset >> r1 <- c( 1, 2, 3) >> c1 <- c( "male", "female", "NA") >> GENDER <- data.frame(r1,c1) >> names(d1_3) <- c("ID","Gender") > #ITYM: > names(GENDER) <- c("ID","Gender") > >> GENDER >> -------------- >> _OBJECTIVE_: To dummify GENDER, >> i.e., to generate two new numeric columns, >> Gender_male and Gender_female, >> such that: >> when Gender="male" then Gender_male=1 and Gender_female=0 >> when Gender="female" then Gender_male=0 and Gender_female=1 >> when Gender="NA" then Gender_male=0 and Gender_female=0 >> >> So, with the given dataset, the resultant dataset would be as follows: >> Desired Extended Gender Dataset >> ID Gender Gender_male Gender_female >> 1 male 1 0 >> 2 female 0 1 >> 3 NA 0 0 > With that correction I think you might want: > >> model.matrix( ID ~ Gender+0, data=GENDER ) > Genderfemale Gendermale GenderNA > 1 0 1 0 > 2 1 0 0 > 3 0 0 1 > attr(,"assign") > [1] 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$Gender > [1] "contr.treatment" > > If you assigned that to an object name, say "obj" you could get your desired result with: > >> obj <- model.matrix( ID ~ Gender+0, data=GENDER ) >> cbind(GENDER[ , 1, drop=FALSE], obj[,-3] ) > ID Genderfemale Gendermale > 1 1 0 1 > 2 2 1 0 > 3 3 0 0 > > > I get the sense that you are trying to replicate a workflow that you developed in some other language and I think it would be more efficient for you to actually learn R rather than trying to write SAS or SPSS in R. If you like getting "into the weeds" of the language then I suggest trying to read the code in the `lm` function. It might help to refer back to Venables and Ripley's "S Programming" or reading Wickham's "Advanced R" pages on the web. >
Rui: I tried your suggestion, which was not fruitful. Another R-helper suggested the code below, which worked perfectly. Thanks for your suggestion and time spent. Regards, Bruce obj <- model.matrix( ID ~ Gender+0, data=GENDER ) cbind(GENDER[ , 1, drop=FALSE], obj[,-3] ) Bruce Ratner, Ph.D. The Significant Statistician? (516) 791-3544 Statistical Predictive Analtyics -- www.DMSTAT1.com Machine-Learning Data Mining and Modeling -- www.GenIQ.net Rui Barradas wrote:> Hello, > > Try the following. > > GENDER$Gender_male <- as.integer(GENDER$Gender == "male") > GENDER$Gender_female <- as.integer(GENDER$Gender == "female") > > Hope this helps, > > Rui Barradas > > Em 02-04-2017 19:48, BR_email escreveu: >> Hi R'ers: >> I need a jump start to obtain my objective. >> Assistance is greatly appreciated. >> Bruce >> >> ******* >> #Given Gender Dataset >> r1 <- c( 1, 2, 3) >> c1 <- c( "male", "female", "NA") >> GENDER <- data.frame(r1,c1) >> names(d1_3) <- c("ID","Gender") >> GENDER >> -------------- >> _OBJECTIVE_: To dummify GENDER, >> i.e., to generate two new numeric columns, >> Gender_male and Gender_female, >> such that: >> when Gender="male" then Gender_male=1 and Gender_female=0 >> when Gender="female" then Gender_male=0 and Gender_female=1 >> when Gender="NA" then Gender_male=0 and Gender_female=0 >> >> So, with the given dataset, the resultant dataset would be as follows: >> Desired Extended Gender Dataset >> ID Gender Gender_male Gender_female >> 1 male 1 0 >> 2 female 0 1 >> 3 NA 0 0 >> > > >