Looking for an apply, tidyr or dplyr solution to a nested for loop situation in R

Question

Weirdly for this one, I think its easier to start by viewing the df.

#reproducible data
quantiles<-c("50","90")
var=c("w","d")
df=data.frame(a=runif(20,0.01,.5),b=runif(20,0.02,.5),c=runif(20,0.03,.5),e=runif(20,0.04,.5),
           q50=runif(20,1,5),q90=runif(20,10,50))
head(df)

I want to automate a function that I've created (below) to calculate vars using different combinations of values from my df. For example, the calculation of w needs to use a and b, and d needs to use c and e such that w = a *q ^ b and d = c * q ^ e. Further, q is a quantile, so I actually want w50, w90, etc., which will correspond to q50, q90 etc. from the df.

The tricky part as i see it is setting the condition to use a & b vs. c & d without using nested loops. I have a function to calculate vars using the appropriate columns, however I can't get all the pieces together efficiently.

#function to calculate the w, d
calc_wd <- function(df,col_name,col1,col2,col3){
  #Calculate and create new column col_name for each combo of var and quantile, e.g. "w_50", "d_50", etc.
  df[[col_name]] <- df[[col1]] * (df[[col2]] ^ (df[[col3]]))
  df
}

I can get this to work for a single case, but not by automating the coefficient swap... you'll see I specify "a" and "b" below.

wd<-c("w_","d_")
make_wd_list<-apply(expand.grid(wd, quantiles), 1, paste,collapse="")
calc_wdv(df,make_wd_list[1],"a",paste0("q",sapply(strsplit(make_wd_list[1],"_"),tail,1)),"b")

Alternatively, I have tried to make this work using nested for loops, but can't seem to append the data correctly. And its ugly.

var=c("w","d")

dataf<-data.frame()
for(j in unique(var)){
    if(j=="w"){
      coeff1="a"
      coeff2="b"
    }else if(j=="d"){
      coeff1="c"
      coeff1="e"
    }
  print(coeff1)
  print(coeff2)
  for(k in unique(quantiles)){
    dataf<-calc_wd(df,paste0(j,k),coeff1,paste0("q",k),coeff2)
    dataf[k,j]=rbind(df,dataf) #this aint right.  tried to do.call outside, etc.
  }

}

In the end, I'm looking to have new columns with w_50, w_90, etc., which use q50, q90 and the corresponding coefficients as defined originally.

Ian Campbell · Accepted Answer

One approach I find easy to type is using purrr::pmap. I like this because when you use with(list(...),), you can access the column names of your data.frame by name. Additionally, you can supply additional arguments.

library(purrr)
pmap_df(df, quant = "q90", ~with(list(...),{
  list(w = a * get(quant) ^ b, d = c * get(quant) ^ e)
  }))
## A tibble: 20 x 2
#        w     d
#     
# 1 0.239  0.295
# 2 0.152  0.392
# 3 0.476  0.828
# 4 0.344  0.236
# 5 0.439  1.00

You could combine this with for example a second map call to iterate over quantiles.

library(dplyr)
map(setNames(quantiles,quantiles),
    ~ pmap_df(df, quant = paste0("q",.x), 
              ~ with(list(...),{list(w = a * get(quant) ^ b, d = c * get(quant) ^ e)}))
    ) %>% do.call(cbind,.)
#         50.w       50.d      90.w      90.d
#1  0.63585897 0.11045837 1.7276019 0.1784987
#2  0.17286184 0.22033649 0.2333682 0.5200265
#3  0.32437528 0.72502654 0.5722203 1.4490065
#4  0.68020897 0.33797621 0.8749206 0.6179557
#5  0.73516886 0.38481785 1.2782923 0.4870877

Then assigning a custom function is trivial.

calcwd <- function(df,quantiles){
  map(setNames(quantiles,quantiles),
    ~ pmap_df(df, quant = paste0("q",.x), 
              ~ with(list(...),{list(w = a * get(quant) ^ b, d = c * get(quant) ^ e)}))
    ) %>% do.call(cbind,.)
}

Looking for an apply, tidyr or dplyr solution to a nested for loop situation in R

Answers (2)

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