CODEWITHSUNDEEP
rfunction
Alex
Alex

Reputation: 1294

Pass a function on multiple Variables

I am learning how to run functions. Hopefully, this would be an easy question to answer.

I have a df and I want to pass the function w to some of the variables in the df.

df <- data.frame(id= c(1,1,1,2,2,2,3,3,3), time=c(1,2,3,1,2,3,1,2,3),y = rnorm(9), x1 = rnorm(9), x2 = c(0,0,0,0,1,0,1,1,1),c2 = rnorm(9))
library(data.table)
library(dplyr)

w<-function(data,var1,var2){
  x <- substitute(var1)
  y <- substitute(var2)
  data<-setDT(data)[,paste("times",(var1), (var2), sep = "_"):=eval(var1)*eval(var2)]
}

df2<- w(df,y,x1)

When I apply the function to a single variable the function works. but I would like to apply it to a series of variables in my data frame, but for some reason, the function fails when I try to apply it to multiple variables at once. does anyone know how I could make it work?

So far I tried the following

vars<-c("x1","x2")
df3<- lapply(vars, function(x) w(df,y, x))

thanks a lot for your help

Upvotes: 0

Views: 46

Answers (1)

linog
linog

Reputation: 6226

data.table works well with variable names. You use get to unquote names and get them evaluated in the scope of the data.table object. I wrote a blog post exactly on that subject, if it can help you.

df <- data.frame(id= c(1,1,1,2,2,2,3,3,3), time=c(1,2,3,1,2,3,1,2,3),y = rnorm(9), x1 = rnorm(9), x2 = c(0,0,0,0,1,0,1,1,1),c2 = rnorm(9))

library(data.table)
setDT(df)

Your function can be simplified to:

w <- function(data, var1, var2){
  if (!inherits(data, "data.table")){
        setDT(data)
  }
  data[,(paste("times",var1,var2, sep = "_")) := get(var1)*get(var2)]
}

And you call it by using variable names

vars<-c("x1","x2")
lapply(vars, function(x) w(df,"y", x))


df
   id time           y         x1 x2          c2 times_y_x1 times_y_x2
1:  1    1 -0.81438357  0.4493933  0 -0.39143328 -0.3659786  0.0000000
2:  1    2  0.36358498 -1.3574671  0  0.06062278 -0.4935547  0.0000000
3:  1    3  0.04049807  0.2860555  0  1.58123937  0.0115847  0.0000000
4:  2    1  0.15490901 -0.8654069  0 -1.09874917 -0.1340593  0.0000000
5:  2    2 -0.87899821  0.2863604  1 -0.73161360 -0.2517103 -0.8789982
6:  2    3  0.37881104  1.6135654  0  1.30268569  0.6112364  0.0000000
7:  3    1 -0.72990680  0.5867623  1  0.41856548 -0.4282818 -0.7299068
8:  3    2 -0.53344035  0.5073415  1  0.64326809 -0.2706364 -0.5334404
9:  3    3 -0.27674109 -0.5226920  1 -2.28723895  0.1446504 -0.2767411

Note that you update your dataframe since you use := so you don't need to reassign the output

Upvotes: 1

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