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. You might consider minty to be 1.5e parsing engine.
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 FALSEType 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" NADifferences: 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 2019-10-01verbose 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 fakeSome features from vroom have been ported to minty, but not readr.
## tidyverse/readr#1526
minty::type_convert(data.frame(a = c("NaN", "Inf", "-INF"))) |> str()
#> 'data.frame': 3 obs. of 1 variable:
#> $ a: num NaN Inf -Inf
readr::type_convert(data.frame(a = c("NaN", "Inf", "-INF"))) |> str()
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> a = col_character()
#> )
#> 'data.frame': 3 obs. of 1 variable:
#> $ a: chr "NaN" "Inf" "-INF"guess_max is available for parse_guess() and type_convert(), default to NA (same as readr).
minty::parse_guess(c("1", "2", "drei"))
#> [1] "1" "2" "drei"
minty::parse_guess(c("1", "2", "drei"), guess_max = 2)
#> [1] 1 2 NA
readr::parse_guess(c("1", "2", "drei"))
#> [1] "1" "2" "drei"For parse_guess() and type_convert(), trim_ws is considered before type guessing (the expected behavior of vroom::vroom() / readr::read_delim()).
minty::parse_guess(c(" 1", " 2 ", " 3 "), trim_ws = TRUE)
#> [1] 1 2 3
readr::parse_guess(c(" 1", " 2 ", " 3 "), trim_ws = TRUE)
#> [1] "1" "2" "3"
##tidyverse/readr#1536
minty::type_convert(data.frame(a = "1 ", b = " 2"), trim_ws = TRUE) |> str()
#> 'data.frame': 1 obs. of 2 variables:
#> $ a: num 1
#> $ b: num 2
readr::type_convert(data.frame(a = "1 ", b = " 2"), trim_ws = TRUE) |> str()
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> a = col_character(),
#> b = col_double()
#> )
#> 'data.frame': 1 obs. of 2 variables:
#> $ a: chr "1"
#> $ b: num 2