Create new dummy variable columns from categorical variable

Question

I have a several data sets with 75,000 observations and a type variable that can take on a value 0-4. I want to add five new dummy variables to each data set f

Answer 1

Drew, this is much faster and shouldn't cause any crashes.

> binom <- data.frame(data=runif(1e5),type=sample(0:4,1e5,TRUE))
> for(t in unique(binom$type)) {
+   binom[paste("type",t,sep="")] <- ifelse(binom$type==t,1,0)
+ }
> head(binom)
        data type type2 type4 type1 type3 type0
1 0.11787309    2     1     0     0     0     0
2 0.11884046    4     0     1     0     0     0
3 0.92234950    4     0     1     0     0     0
4 0.44759259    1     0     0     1     0     0
5 0.01669651    2     1     0     0     0     0
6 0.33966184    3     0     0     0     1     0

Answer 2

ifelse is vectorized, so if I understand your code correctly, you don't need that sapply. And I wouldn't use merge - I would use SQLite or PostgreSQL.

Some sample data would help too :-)

Answer 3

If you're open to using the data.table package, mltools has a one_hot() method.

library(data.table)
library(mltools)

binom <- data.table(y=runif(1e5), x=runif(1e5), catVar=as.factor(sample(0:4,1e5,TRUE)))
one_hot(binom)

                 y          x catVar_0 catVar_1 catVar_2 catVar_3 catVar_4
     1: 0.90511891 0.83045050        0        0        1        0        0
     2: 0.91375984 0.73273830        0        0        0        1        0
     3: 0.01926608 0.10301409        0        0        1        0        0
     4: 0.48691138 0.24428157        0        1        0        0        0
     5: 0.60660396 0.09132816        0        0        1        0        0
    ---                                                                   
 99996: 0.12908356 0.26157731        0        1        0        0        0
 99997: 0.96397273 0.98959000        0        1        0        0        0
 99998: 0.16818414 0.37460941        1        0        0        0        0
 99999: 0.72610508 0.72055867        1        0        0        0        0
100000: 0.89710998 0.24155507        0        0        0        0        1

Usage

one_hot(dt, cols = "auto", sparsifyNAs = FALSE, 
        naCols = FALSE, dropCols = TRUE,
        dropUnusedLevels = FALSE)

Which column(s) should be one-hot-encoded? cols = "auto" encodes all unordered factor columns. Therefore, the command below is equivalent. This is only important when the data.table contains factors that should not be encoded.

one_hot(binom, cols="catVar")

Answer 4

You can use the package called dummies

binom <- data.frame(y=runif(1e5), x=runif(1e5), catVar=as.factor(sample(0:4,1e5,TRUE)))
head(binom)

          y          x catVar
1 0.4143348 0.09721401      1
2 0.3140782 0.54340539      3
3 0.1262037 0.51820499      2
4 0.7159850 0.13167720      3
5 0.8203528 0.94116026      3
6 0.2169781 0.82020216      1

Solution:

library(dummies)
binom<-dummy.data.frame(binom)
head(binom)

          y          x catVar0 catVar1 catVar2 catVar3 catVar4
1 0.4143348 0.09721401       0       1       0       0       0
2 0.3140782 0.54340539       0       0       0       1       0
3 0.1262037 0.51820499       0       0       1       0       0
4 0.7159850 0.13167720       0       0       0       1       0
5 0.8203528 0.94116026       0       0       0       1       0
6 0.2169781 0.82020216       0       1       0       0       0

Answer 5

The nnet package for single-layer neural networks (which don't understand factors) has a conversion command: class.ind.

Answer 6

R has a "sub-language" to translate formulas into design matrix, and in the spirit of the language you can take advantage of it. It's fast and concise. Example: you have a cardinal predictor x, a categorical predictor catVar, and a response y.

> binom <- data.frame(y=runif(1e5), x=runif(1e5), catVar=as.factor(sample(0:4,1e5,TRUE)))
> head(binom)
          y          x catVar
1 0.5051653 0.34888390      2
2 0.4868774 0.85005067      2
3 0.3324482 0.58467798      2
4 0.2966733 0.05510749      3
5 0.5695851 0.96237936      1
6 0.8358417 0.06367418      2

You just do

> A <- model.matrix(y ~ x + catVar,binom) 
> head(A)
  (Intercept)          x catVar1 catVar2 catVar3 catVar4
1           1 0.34888390       0       1       0       0
2           1 0.85005067       0       1       0       0
3           1 0.58467798       0       1       0       0
4           1 0.05510749       0       0       1       0
5           1 0.96237936       1       0       0       0
6           1 0.06367418       0       1       0       0

Done.