CODEWITHSUNDEEP
rloopsmetadatadplyr
TomFromWales
TomFromWales

Reputation: 85

Create an arbitrary number of new columns using dplyr in R

I'm not sure if the title is worded well, but here is the situation:

I have a meta data dataset, which can have any number of rows in it, e.g.:

Control_DF <- cbind.data.frame(
  Scenario = c("A","B","C")
  ,Variable = c("V1","V2","V3")
  ,Weight = c("w1","w2","w3")
)

Using the data contained in Control_DF, I want to create a new version of each Variable on my main dataset, where I multiply the variable by the weight. So if my main dataset looks like this:

Main_Data <- cbind.data.frame(
  V1 = c(1,2,3,4)
  ,V2 = c(2,3,4,5)
  ,V2 = c(3,4,5,6)
  ,w1 = c(0.1,0.5,1,0.8)
  ,w2 = c(0.2,1,0.3,0.6)
  ,w2 = c(0.3,0.7,0.1,0.2)   
)

Then, in open code, what I want to do looks like this:

New_Data <- Main_Data %>%
  mutate(
    weighted_V1 = V1 * w1
    ,weighted_V2 = V2 * w2
    ,weighted_V3 = V3 * w3
  )

However, I need a way of not hard coding this, and such that the number of variables being referenced is arbitrary.

Can anyone help me?

Upvotes: 1

Views: 275

Answers (1)

Silence Dogood
Silence Dogood

Reputation: 3597

In base R with lapply, Map and cbind you could do as follows:

# with Control_DF create a list with pairs of <varName,wgt>

controlVarList = lapply(Control_DF$Scenario,function(x) 

as.vector(as.matrix(Control_DF[Control_DF$Scenario==x,c("Variable","Weight")] )) 

)

controlVarList
#[[1]]
#[1] "V1" "w1"
#
#[[2]]
#[1] "V2" "w2"
#
#[[3]]
#[1] "V3" "w3"


# A custom function for multiplication of both columns

fn_weightedVars = function(x) {

# x  = c("V1","w1"); hence x[1] = "V1",x[2] = "w2"
# reference these columns in Main_Data and do scaling
wgtedCol = matrix(Main_Data[,x[1]] * Main_Data[,x[2]],ncol=1)

#rename as required
colnames(wgtedCol)= paste0("weighted_",x[1]) 

#return var
wgtedCol


}


#call function on each each list element

scaledList = Map(fn_weightedVars ,controlVarList)

Output:

scaledDF = do.call(cbind,scaledList)

#combine datasets
New_Data  = data.frame(Main_Data,scaledDF)
New_Data
#  V1 V2 V3  w1  w2  w3 weighted_V1 weighted_V2 weighted_V3
#1  1  2  3 0.1 0.2 0.3         0.1         0.4         0.9
#2  2  3  4 0.5 1.0 0.7         1.0         3.0         2.8
#3  3  4  5 1.0 0.3 0.1         3.0         1.2         0.5
#4  4  5  6 0.8 0.6 0.2         3.2         3.0         1.2

Upvotes: 1

Related Questions