如何将条目长度≤1的所有列转换为数字？

Question

I have a data frame with ~80 columns, and ~20-40 of those columns have single-digit integers that were stored as characters. Other character columns are complete sentences (so, length >>> 1 ), and so get coerced to NA if I try mutate_if(is.character, as.numeric) .

I would like to transform those efficiently, and based on this question , I was hoping for something like this:

df %>% map_if(is.character & length(.) <= 1, as.numeric)

However, that doesn't work. I'm hoping for a tidy solution, maybe using purrr ?

Answer 1

The best function for these situations is type_convert() , from readr :

"[ type_convert() re-converts character columns in a data frame], which is useful if you need to do some manual munging - you can read the columns in as character, clean it up with (eg) regular expressions and other transformations, and then let readr take another stab at parsing it."

So, all you need to do is add it at the end of your pipe:

df %>% ... %>% type_convert() 

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Alternatively, we can use type.convert from base R , which would automatically detect the column type based on the value and change it

df[] <- type.convert(df, as.is = TRUE)

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If the constraint is to look for columns that have only one character

i1 <- !colSums(nchar(as.matrix(df)) > 1)
df[i1] <- type.convert(df[i1])

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If we want to use tidyverse , there is parse_guess from readr

library(tidyverse)
library(readr)
df %>%
     mutate_if(all(nchar(.) == 1), parse_guess)

Answer 2

You could check for nchar of the column in mutate_if

library(dplyr)
df %>%  mutate_if(~all(nchar(.) == 1) & is.character(.), as.numeric) 

Using with an example data

df <- data.frame(a = c("ab", "bc", "de", "de", "ef"), 
                 b = as.character(1:5), stringsAsFactors = FALSE)

df1 <- df %>% mutate_if(~all(nchar(.) == 1) & is.character(.), as.numeric) 

str(df1)
#'data.frame':  5 obs. of  2 variables:
# $ a: chr  "ab" "bc" "de" "de" ...
# $ b: num  1 2 3 4 5

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You could do the same with map_if as well however, it returns a list back and you need to convert it back to dataframe

library(purrr)

df %>% 
   map_if(~all(nchar(.) == 1) & is.character(.), as.numeric) %>% 
   as.data.frame(., stringsAsFactors = FALSE)