Transforming Binary data - r

Question

I have a dataframe that only consists of 0 and 1. So for each individual instead of having one column with a factoral value (ex. low price, 4 rooms) I have
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21
1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 1 0
2 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 1
3 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0
4 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0
How can I transform the dataset in R, so that I create new columns (#number of rooms) and give the position of the 1 (in the 4th column) a vhigh value?
I have multiple expenatory varibales I need to do this for. the 21 columns are representing 6 variables for 1000+ observations. should be something like this
PurchaseP. NumberofRooms ...
1. vhigh. 4
2. low. 4
3. vhigh. 1
4. vhigh. 2
Just did it for the first 2 epxlenatory varibales here, but essentially it repeats like this with each explenatory variable has 3-4 possible factoral values.
V1:V4 = purchase price, V5:V8 = number of rooms,V9:V11 = floors, and so on
In my head something like this could work
create a if statemt to give each 1 a value depending on column position, ex. if value in V4=1 then name "vhigh". and do this for each Vx
Then combine each column V1:V4, V5:V8, V9:V11 (depending on if it has 3-4 possible factoral/integer values) while ignoring 0 values.
Would this work, or is there a simpler approach? How would one code this in R?

Answer 1

Here is an approach that should work for you. I wrote a function, which will take as arguments your data.frame, the columns representing one of your variables of interest (e.g. purchase price is stored in columns 1 to 4), and the names of the levels you would like as a result. The function will then return the result you requested. You'll need to write this out for the 6 variables you are interested in.
I'll simulate some data and illustrate the approach.
df <- data.frame(matrix(rep(c(0,0,0,1, 1,0,0,0, 1,0,0,0,0,0,0,1), 2),
nrow = 4, byrow = T))
df
#> X1 X2 X3 X4 X5 X6 X7 X8
#> 1 0 0 0 1 1 0 0 0
#> 2 1 0 0 0 0 0 0 1
#> 3 0 0 0 1 1 0 0 0
#> 4 1 0 0 0 0 0 0 1
We'll say that the first four columns are the purchase price in v.low to v.high, and the second four are the number of rooms (1:4). We'll write a function that takes this information as arguments and returns the result:
rangeToCol <- function(df, # Your data.frame
range, # the columns that incode the category of interest
lev.names # The names of the category levels
) {
tdf <- df[range]
lev.names[unlist(apply(tdf, 1, function(rw){which(rw==1)}))]
}
new.df <- data.frame(PurchaseP = rangeToCol(df, 1:4,
c('vlow','low','high','vhigh')),
NumberofRooms = rangeToCol(df, 5:8, c(1:4)))
new.df
#> PurchaseP NumberofRooms
#> 1 vhigh 1
#> 2 vlow 4
#> 3 vhigh 1
#> 4 vlow 4