I have several dataframes in my dataset that contain numeric values, but are type chr. They look like this:
IDs FCC_Faces FCNC_Faces FCC_Objects FCNC_Objects FCC_Scenes FCNC_Scenes FCC_Words FCNC_Words Age Group
<chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 CON_L01 1 0.58330000000000004 0.83330000000000004 0.5833000000~ 0.6 0.6 0.9165999~ 0.66659999~ 63 0
2 CON_L03 0.92 0.83 1 0.92 0.9 0.9 0.83 1 37 0
3 CON_L04 0.75 0.83 0.92 0.92 0.9 0.8 0.67 0.92 48 0
4 CON_L05 0.75 1 0.92 1 0.9 1 1 1 49 0
5 CON_L07 0.58 0.17 0.57999999999999996 0.75 0.8 0.7 0.83 0.67 69 0
6 CON_L10 0.92 0.67 0.83 0.5799999999~ 0.8 0.9 0.33 0.57999999~ 58 0
7 CON_L14 0.83 0.83 0.83 0.75 0.8 0.9 0.92 0.67 62 0
8 CON_L16 1 0.92 NA NA 0.9 0.9 1 1 40 0
9 CON_L17 0.83 0.57999999999999996 0.75 0.83 0.9 0.6 1 0.83 48 0
10 CON_L18 0.75 0.75 0.75 0.75 0.9 0.5 0.75 0.67 55 0
# ... with 70 more rows
I wanted to write a function to which I could pass the dataframe, the column names, and have it convert all of them to numeric. My first attempt was just using lapply():
cols_to_numeric <- function(dframe, columns) {
dframe[ , columns] <- lapply(dframe[ , columns], numeric)
return(dframe)
}
However, I get this error:
Error in FUN(X[[i]], ...) : invalid 'length' argument
3.
FUN(X[[i]], ...)
2.
lapply(dframe[, columns], numeric)
So, looking for another solution, this thread suggested a nice approach using Tidyverse:
cols_to_numeric <- function(dframe, columns) {
require(magrittr)
require(tidyverse)
dframe %<>% mutate_at(columns, numeric)
return(dframe)
}
But I get a very similar error:
Error: Problem with `mutate()` input `FCC_Faces`.
x invalid 'length' argument
i Input `FCC_Faces` is `(function (length = 0L) ...`.
But the length of this variable is very clearly not zero:
> length(CON_data$FCC_Faces)
[1] 80
Further, if I just do the coercion for each column manually, it works without complaints:
> CON_data$FCC_Faces <- as.numeric(CON_data$FCC_Faces)
> str(CON_data)
'data.frame': 80 obs. of 11 variables:
$ IDs : chr "CON_L01" "CON_L03" "CON_L04" "CON_L05" ...
$ FCC_Faces : num 1 0.92 0.75 0.75 0.58 0.92 0.83 1 0.83 0.75 ...
$ FCNC_Faces : chr "0.58330000000000004" "0.83" "0.83" "1" ...
$ FCC_Objects : chr "0.83330000000000004" "1" "0.92" "0.92" ...
$ FCNC_Objects: chr "0.58330000000000004" "0.92" "0.92" "1" ...
$ FCC_Scenes : chr "0.6" "0.9" "0.9" "0.9" ...
$ FCNC_Scenes : chr "0.6" "0.9" "0.8" "1" ...
$ FCC_Words : chr "0.91659999999999997" "0.83" "0.67" "1" ...
$ FCNC_Words : chr "0.66659999999999997" "1" "0.92" "1" ...
$ Age : num 63 37 48 49 69 58 62 40 48 55 ...
$ Group : num 0 0 0 0 0 0 0 0 0 0 ...
>
What on earth am I doing wrong here? I don’t want to have to manually coerce every single column for each dataframe every time I make any changes.
>Solution :
A possible solution:
library(dplyr)
df <- tibble(
x = c("0.2", "0.4", "0.9"),
y = c("1.2", "2.4", "3.9"))
df %>%
mutate(across(x:y, as.numeric))
#> # A tibble: 3 × 2
#> x y
#> <dbl> <dbl>
#> 1 0.2 1.2
#> 2 0.4 2.4
#> 3 0.9 3.9