minty

R-CMD-check CRAN status

minty (Minimal type guesser) is a package with the type inferencing and parsing tools (the so-called 1e parsing engine) extracted from readr (with permission, see this issue tidyverse/readr#1517). Since July 2021, these tools are not used internally by readr for parsing text files. Now vroom is used by default, unless explicitly call the first edition parsing engine (see the explanation on editions).

readr’s 1e type inferencing and parsing tools are used by various R packages, e.g. readODS and surveytoolbox for parsing in-memory objects, but those packages do not use the main functions (e.g. readr::read_delim()) of readr. As explained in the README of readr, those 1e code will be eventually removed from readr. minty aims at providing a set of minimal, long-term, and compatible type inferencing and parsing tools for those packages.

Installation

You can install the development version of minty like so:

if (!require("remotes")){
    install.packages("remotes")
}
remotes::install_github("gesistsa/minty")

Example

A character-only data.frame

text_only <- data.frame(maybe_age = c("17", "18", "019"),
                        maybe_male = c("true", "false", "true"),
                        maybe_name = c("AA", "BB", "CC"),
                        some_na = c("NA", "Not good", "Bad"),
                        dob = c("2019/07/21", "2019/08/31", "2019/10/01"))
str(text_only)
#> 'data.frame':    3 obs. of  5 variables:
#>  $ maybe_age : chr  "17" "18" "019"
#>  $ maybe_male: chr  "true" "false" "true"
#>  $ maybe_name: chr  "AA" "BB" "CC"
#>  $ some_na   : chr  "NA" "Not good" "Bad"
#>  $ dob       : chr  "2019/07/21" "2019/08/31" "2019/10/01"
## built-in function type.convert:
## except numeric, no type inferencing
str(type.convert(text_only, as.is = TRUE))
#> 'data.frame':    3 obs. of  5 variables:
#>  $ maybe_age : int  17 18 19
#>  $ maybe_male: chr  "true" "false" "true"
#>  $ maybe_name: chr  "AA" "BB" "CC"
#>  $ some_na   : chr  NA "Not good" "Bad"
#>  $ dob       : chr  "2019/07/21" "2019/08/31" "2019/10/01"

Inferencing the column types

library(minty)
data <- type_convert(text_only)
data
#>   maybe_age maybe_male maybe_name  some_na        dob
#> 1        17       TRUE         AA     <NA> 2019-07-21
#> 2        18      FALSE         BB Not good 2019-08-31
#> 3       019       TRUE         CC      Bad 2019-10-01
str(data)
#> 'data.frame':    3 obs. of  5 variables:
#>  $ maybe_age : chr  "17" "18" "019"
#>  $ maybe_male: logi  TRUE FALSE TRUE
#>  $ maybe_name: chr  "AA" "BB" "CC"
#>  $ some_na   : chr  NA "Not good" "Bad"
#>  $ dob       : Date, format: "2019-07-21" "2019-08-31" ...

Type-based parsing tools

parse_datetime("1979-10-14T10:11:12.12345")
#> [1] "1979-10-14 10:11:12 UTC"
fr <- locale("fr")
parse_date("1 janv. 2010", "%d %b %Y", locale = fr)
#> [1] "2010-01-01"
de <- locale("de", decimal_mark = ",")
parse_number("1.697,31", local = de)
#> [1] 1697.31
parse_number("$1,123,456.00")
#> [1] 1123456
## This is perhaps Python
parse_logical(c("True", "False"))
#> [1]  TRUE FALSE

Type guesser

parse_guess(c("True", "TRUE", "false", "F"))
#> [1]  TRUE  TRUE FALSE FALSE
parse_guess(c("123.45", "1990", "7619.0"))
#> [1]  123.45 1990.00 7619.00
res <- parse_guess(c("2019-07-21", "2019-08-31", "2019-10-01", "IDK"), na = "IDK")
res
#> [1] "2019-07-21" "2019-08-31" "2019-10-01" NA
str(res)
#>  Date[1:4], format: "2019-07-21" "2019-08-31" "2019-10-01" NA

Differences: readr vs minty

Unlike readr and vroom, please note that minty is mainly for non-interactive usage. Therefore, minty emits fewer messages and warnings than readr and vroom.

data <- minty::type_convert(text_only)
data
#>   maybe_age maybe_male maybe_name  some_na        dob
#> 1        17       TRUE         AA     <NA> 2019-07-21
#> 2        18      FALSE         BB Not good 2019-08-31
#> 3       019       TRUE         CC      Bad 2019-10-01
data <- readr::type_convert(text_only)
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   maybe_age = col_character(),
#>   maybe_male = col_logical(),
#>   maybe_name = col_character(),
#>   some_na = col_character(),
#>   dob = col_date(format = "")
#> )
data
#>   maybe_age maybe_male maybe_name  some_na        dob
#> 1        17       TRUE         AA     <NA> 2019-07-21
#> 2        18      FALSE         BB Not good 2019-08-31
#> 3       019       TRUE         CC      Bad 2019-10-01

verbose option is added if you like those messages, default to FALSE. To keep this package as minimal as possible, these optional messages are printed with base R (not cli).

data <- minty::type_convert(text_only, verbose = TRUE)
#> Column specification:
#> cols(  maybe_age = col_character(),  maybe_male = col_logical(),  maybe_name = col_character(),  some_na = col_character(),  dob = col_date(format = ""))

At the moment, minty does not use the problems mechanism by default.

minty::parse_logical(c("true", "fake", "IDK"), na = "IDK")
#> [1] TRUE   NA   NA
readr::parse_logical(c("true", "fake", "IDK"), na = "IDK")
#> Warning: 1 parsing failure.
#> row col           expected actual
#>   2  -- 1/0/T/F/TRUE/FALSE   fake
#> [1] TRUE   NA   NA
#> attr(,"problems")
#> # A tibble: 1 × 4
#>     row   col expected           actual
#>   <int> <int> <chr>              <chr> 
#> 1     2    NA 1/0/T/F/TRUE/FALSE fake

Similar packages

For parsing ambiguous date(time)

Guess column types of a text file

Acknowledgements

Thanks to: