Coerce variables in data frame to appropriate format

Question

I\'m working a data frame which consists of multiple different data types (numerics, characters, timestamps), but unfortunately all of them are received as characters. Hence I n

Answer 1

The recent file readers like data.table::fread or the readr package do a pretty decent job in identifying and converting columns to the appropriate type.

So my first reaction was to suggest to write the data to file and read it in again, e.g.,

library(data.table)
fwrite(df, "dummy.csv")
df_new <- fread("dummy.csv")
str(df_new)

Classes ‘data.table’ and 'data.frame':    4 obs. of  2 variables:
 $ val1: int  1 2 3 4
 $ val2: chr  "A" "B" "C" "D"
 - attr(*, ".internal.selfref")=<externalptr>

or without actually writing to disk:

df_new <- fread(paste(capture.output(fwrite(df, "")), collapse = "\n"))

However, d.b's suggestions are much smarter but need some polishing to avoid coercion to factor:

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

'data.frame': 4 obs. of  2 variables:
 $ val1: int  1 2 3 4
 $ val2: chr  "A" "B" "C" "D"

or

df[] <- lapply(df, readr::parse_guess)

Answer 2

You should check dataPreparation package. You will find function findAndTransformNumerics function that will do exactly what you want.

require(dataPreparation)
data("messy_adult")
sapply(messy_adult[, .(num1, num2, mail)], class)
   num1        num2        mail 
"character" "character"    "factor" 

messy_adult is an ugly data set to illustrate functions from this package. Here num1 and num2 are strings :/

messy_adult <- findAndTransformNumerics(messy_adult)
[1] "findAndTransformNumerics: It took me 0.18s to identify 3 numerics column(s), i will set them as numerics"
[1] "setColAsNumeric: I will set some columns as numeric"
[1] "setColAsNumeric: I am doing the columnnum1"
[1] "setColAsNumeric: 0 NA have been created due to transformation to numeric."
[1] "setColAsNumeric: I will set some columns as numeric"
[1] "setColAsNumeric: I am doing the columnnum2"
[1] "setColAsNumeric: 0 NA have been created due to transformation to numeric."
[1] "setColAsNumeric: I am doing the columnnum3"
[1] "setColAsNumeric: 0 NA have been created due to transformation to numeric."
[1] "findAndTransformNumerics: It took me 0.09s to transform 3 column(s) to a numeric format."

Here we performed the search and it logged what it found

And know:

sapply(messy_adult[, .(num1, num2, mail)], class)
     num1      num2      mail 
"numeric" "numeric"  "factor" 

Hope it helps!

Disclamer: I'm the author of this package.