Select columns of specific types and manipulate their content

here it is my problem in the form of a reproducible example and my partial attempt of solution

# input
mydf_in<-data.frame(a=letters[6:10], 
                 b=c("<0.5","2","<0.5", "9", "10"), 
                 c=1:5, 
                 d=6:10, 
                 e=c("<0.8","12","<0.8", "<0.8", "<0.8"))

mydf_in

# output 
# the desired final result

mydf_out<-data.frame(a=letters[6:10],
                     b=c(0.5,2,0.5,9,10),
                     b_flag=c(1,0,1,0,0),
                     c=1:5, 
                     d=6:10, 
                     e=c(0.8,12,0.8,0.8,0.8),
                     e_flag=c(1,0,1,1,1)
                     )

mydf_out

library(tidyverse)

mydf_in %>%
  select(where(~ is.character(.x) & 
                 any(str_detect(.x, "<")
                     )
               )
         ) %>%
  
  # in between here is missing the creation and
  # the population of the flagging columns, i.e. "b_flag" and "e_flag"
  
  mutate(across(everything(), ~ as.numeric(str_replace(.x, "<", ""))))

in short, what is missing in the between of the above code snippet, for each selected column:

• create a corresponding flagging column
• populate the rows of the flagging column with 1 or 0 depending on the presence of the sign "<" (see desired output)

>Solution :

If we want to use the conditions explicitly, instead of select use mutate with the where the condition – to create the ‘flag’ columns loop over the columns with across and to change the column types use across

library(dplyr)
library(stringr)
mydf_in %>%
    mutate(across(where(~ is.character(.x) & 
        any(str_detect(.x, fixed("<")))), ~
     +(str_detect(.x, fixed("<"))), .names = "{.col}_flag"), 
   across(where(~ is.character(.x) & 
    any(str_detect(.x, fixed("<")))), ~ readr::parse_number(.)))

-output

  a    b c  d    e b_flag e_flag
1 f  0.5 1  6  0.8      1      1
2 g  2.0 2  7 12.0      0      0
3 h  0.5 3  8  0.8      1      1
4 i  9.0 4  9  0.8      0      1
5 j 10.0 5 10  0.8      0      1