Vfrdtyky

I have looking for but not found how make a simple if for many columns in dplyr.

I have this code (it works):

library(dplyr)

library(magrittr)

data("PlantGrowth")

PlantGrowth %>% mutate (

  a=if_else(group=="ctrl", weight*2, weight*100),

  b=if_else(group=="ctrl", weight*1,5, weight/100),

  c=if_else(group=="ctrl", weight*4, weight*100),

  d=if_else(group=="ctrl", weight*5, weight/1000)

)

And I would like to not repeat the condition. Something like that:

PlantGrowth %>% mutate_if_foo (

  group=="ctrl",{

   a=weight*2,

   b=weight*1,5,

   c=weight*4,

   d=weight*5

  }

)%>% mutate_if_foo (

  group!="ctrl",{

   a=weight*100,

   b=weight/100),

   c=weight*100),

   d=weight/1000)

  }

)

I've found many answers on mutate_if,mutate_all, mutate_at , case_when but they don't answer at my question.

Please with dplyr / tidyverse.

Thanks in advance

EDIT

I've tried, from @Rohit_das idea about functions.

mtcars %>% ( function(df) { 

  if (df$am==1){

    df%>% mutate(

      a=df$mpg*3,

      b=df$cyl*10) 

   }else{ 

     df%>% mutate(

      a=df$disp*300,

      d=df$cyl*1000) 

   }

}) 

but I have Warning message:

In if (df$am == 1) { : 

the condition has length > 1 

and only the first element will be used

Not sure I understand the issue here. If you just want to reduce the verbosity of the code then just create a custom function

customif = function(x,y) { 

   if_else(group=="ctrl", weight*x, weight*y)

}

then you can call this function in your mutate as

PlantGrowth %>% mutate (

  a=customif(2,100),

  b=customif(1,5, 1/100),

  c=customif(4, 100),

  d=customif(5, 1/1000)

)

I think I found a neat solution with purrr. It takes a data frame of inputs and then dynamically names new columns a:d with new inputs for each column. First column will use x = 2, y = 100 and z = "a" and then the next row, and so on. The cool thing with functional programming like this is that it is very easy to scale up.

library(tidyverse)



iterate <- tibble(x = c(2, 1.5, 4, 5),

                  y = c(100, 1/100, 100, 1/1000),

                  z = c("a", "b", "c", "d"))



fun <- function(x, y, z) {

  PlantGrowth %>% 

    mutate(!!z := if_else(group == "ctrl", weight * x, weight * y)) %>% 

    select(3)

}



PlantGrowth %>% 

  bind_cols(

    pmap_dfc(iterate, fun)

    ) %>% 

  as_tibble

Which gives you the same df:

# A tibble: 30 x 6

   weight group     a     b     c     d

    <dbl> <fct> <dbl> <dbl> <dbl> <dbl>

 1   4.17 ctrl   8.34  6.26  16.7  20.8

 2   5.58 ctrl  11.2   8.37  22.3  27.9

 3   5.18 ctrl  10.4   7.77  20.7  25.9

 4   6.11 ctrl  12.2   9.17  24.4  30.6

 5   4.5  ctrl   9     6.75  18    22.5

I think I've found an answer. I tested on mtcars. I didn't test yet on my real code.

Comment please if I you think I am wrong in the concept.

The conditions of the filters have to be exclusives else I will take duplicate lines.

        library(dplyr)

        library(magrittr)

        library(tibble) # only if necessary to preserve rownames

        mtcars %>% ( function(df) { 

                rbind(

                    (df 

                     %>% tibble::rownames_to_column(.) %>%tibble::rowid_to_column(.)  # to preserve rownames

                     %>%dplyr::filter(am==1) 

                     %>%dplyr::mutate(

                        a=mpg*3,

                        b=cyl*10,d=NA)),

                    (df 

                     %>% tibble::rownames_to_column(.) %>%tibble::rowid_to_column(.)  # to preserve rownames

                     %>%dplyr::filter(am!=1) 

                     %>%dplyr::mutate(

                      a=disp*3,

                      d=cyl*100,b=NA))

                )

        })     %>%arrange(rowid)