Create new numeric columns from 1 string column - r

Question

I'm a beginner. I have a dataset taken from here which consists of people profiles with different attributes, while profession is of them. There are 12 professions: admin., blue-collar, entrepreneur, housemaid, management, retired, self-employed, services, student, technician, unemployed, unknown.
I'd like to apply K-NN to that dataset, so I'd like to distribute the profession column into 12 new columns, and attribute 1 to the corresponding profession, and 0 to all the other 11 professions that don't belong to that person.
I tried foreach package and for loops, unsuccessfully. I'm not being able to work with foreach, and I don't know what to do next, from the following code:
jobs <- data[,2]
jobs
for (job in jobs) {
print(job)
#No idea how to create the new columns here, based on if conditionals
}
How would be the best way to do this?
Thanks.

Answer 1

You can certainly solve the problem using a for loop, but may I suggest a solution that is more efficient in the long run: reshape2 package (https://cran.r-project.org/web/packages/reshape2/).
I have the data from bank-full.csv read into R in object bank. Next reshape2 package needs to be downloaded, installed, and loaded:
install.packages("reshape2")
library(reshape2)
The data can then be shaped into a format where observations are on rows and jobs on columns. An accessory id column is first added to the data:
bank$id<-1:nrow(bank)
Then, taking the columns 2 and 18 (job and id) from the data frame bank and casting them into the aforementioned form can be done as:
tmp<-dcast(bank[,c(2, 18)], id~job, length)
That should give a new data frame tmp, where each job has it's own column. Since every id is present in the data only once, the length function used in the dcast function to aggregate the data puts just zeros and ones in every column.
Last, these new columns can be added to the original data set:
bank<-cbind(bank[,-18], tmp[,-1])
Negative subscripts inside the square brackets delete the columns from the dataset, so this simultaneously let's you get rid off the id column.
Another, even more efficient way to do this is to use the function model.matrix:
bank2<-cbind(bank, model.matrix( ~ 0 + job, bank))
This should give you a data frame with each job as a new column. Note however that it changes the column names a bit (adds job to the beginning of the job columns).