fetch out the frequency of numeric values

[str_guru]

now i want to create a function which can detect the frequency of numeric values in all columns excepting columns have "Text".

i was thing about the logic like to convert all the char values into numeric and then divide by total. if the total is 100% then its numeric or the column is character column.

is there any other way to do this ..??

library(tidyverse)
    df <-  tibble(Region = c("AU","USA",65,"USA","!UK",88,"USA","CA","!UK"),
                  lock = c(26,18,NA,1,"Test",15,NA,"21%",13),
                  type= c("sale",NA,NA,"target","target",NA,"sale",NA,"target"),
                  state_Text=c("TX","CA","NY","OT","DE","WN","WA","PH","NJ"))
    

[FJCC]

I'm not sure I understand your goal. Here are two methods for finding the fraction of each column that can be coerced to be a number. The as.numeric() function does not coerce 21% to a number but parse_number() does. I get 0.22 for the Region column and either 0.55 or 0.66 for the lock column.

library(tidyverse)
df <-  tibble(Region = c("AU","USA",65,"USA","!UK",88,"USA","CA","!UK"),
              lock = c(26,18,NA,1,"Test",15,NA,"21%",13),
              type= c("sale",NA,NA,"target","target",NA,"sale",NA,"target"),
              state_Text=c("TX","CA","NY","OT","DE","WN","WA","PH","NJ"))
str(df)
#> tibble [9 × 4] (S3: tbl_df/tbl/data.frame)
#>  $ Region    : chr [1:9] "AU" "USA" "65" "USA" ...
#>  $ lock      : chr [1:9] "26" "18" NA "1" ...
#>  $ type      : chr [1:9] "sale" NA NA "target" ...
#>  $ state_Text: chr [1:9] "TX" "CA" "NY" "OT" ...
map_dbl(df, ~ 1 - sum(is.na(as.numeric(.x)))/nrow(df))
#> Warning in .f(.x[[i]], ...): NAs introduced by coercion

#> Warning in .f(.x[[i]], ...): NAs introduced by coercion

#> Warning in .f(.x[[i]], ...): NAs introduced by coercion

#> Warning in .f(.x[[i]], ...): NAs introduced by coercion
#>     Region       lock       type state_Text 
#>  0.2222222  0.5555556  0.0000000  0.0000000

map_dbl(df, ~ 1 - sum(is.na(parse_number(.x)))/nrow(df))
#> Warning: 7 parsing failures.
#> row col expected actual
#>   1  -- a number    AU 
#>   2  -- a number    USA
#>   4  -- a number    USA
#>   5  -- a number    !UK
#>   7  -- a number    USA
#> ... ... ........ ......
#> See problems(...) for more details.
#> Warning: 1 parsing failure.
#> row col expected actual
#>   5  -- a number   Test
#> Warning: 5 parsing failures.
#> row col expected actual
#>   1  -- a number sale  
#>   4  -- a number target
#>   5  -- a number target
#>   7  -- a number sale  
#>   9  -- a number target
#> Warning: 9 parsing failures.
#> row col expected actual
#>   1  -- a number     TX
#>   2  -- a number     CA
#>   3  -- a number     NY
#>   4  -- a number     OT
#>   5  -- a number     DE
#> ... ... ........ ......
#> See problems(...) for more details.
#>     Region       lock       type state_Text 
#>  0.2222222  0.6666667  0.0000000  0.0000000

^{Created on 2023-10-03 with reprex v2.0.2}

[str_guru]

so in my original data frame column definition already provided as character to all column now i wanted to check the frequency of numeric values columns have .

[FJCC]

Sorry, I do not understand the difference between what I did and what you want.

My result can be summarized as this: None of the values in your original data are numbers. All of the columns are characters. In the Region column, 2 of the 9 values can be converted to numbers and in the lock column, 5 or 6 of the values can be converted to numbers. If you do the conversion, the values that cannot be converted are changed to NA.

What other information do you want to get?