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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.
You can install the development version of minty like so:
if (!require("remotes")){
install.packages("remotes")
}::install_github("gesistsa/minty") remotes
A character-only data.frame
<- data.frame(maybe_age = c("17", "18", "019"),
text_only 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)
<- type_convert(text_only)
data
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" ...
parse_datetime("1979-10-14T10:11:12.12345")
#> [1] "1979-10-14 10:11:12 UTC"
<- locale("fr")
fr parse_date("1 janv. 2010", "%d %b %Y", locale = fr)
#> [1] "2010-01-01"
<- locale("de", decimal_mark = ",")
de 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
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
<- parse_guess(c("2019-07-21", "2019-08-31", "2019-10-01", "IDK"), na = "IDK")
res
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
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
.
<- minty::type_convert(text_only)
data
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
<- readr::type_convert(text_only)
data #>
#> ── 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
).
<- minty::type_convert(text_only, verbose = TRUE)
data #> 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.
::parse_logical(c("true", "fake", "IDK"), na = "IDK")
minty#> [1] TRUE NA NA
::parse_logical(c("true", "fake", "IDK"), na = "IDK")
readr#> 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
Some features from vroom
have been ported to
minty
, but not readr
.
## tidyverse/readr#1526
::type_convert(data.frame(a = c("NaN", "Inf", "-INF"))) |> str()
minty#> 'data.frame': 3 obs. of 1 variable:
#> $ a: num NaN Inf -Inf
::type_convert(data.frame(a = c("NaN", "Inf", "-INF"))) |> str()
readr#>
#> ── 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
).
::parse_guess(c("1", "2", "drei"))
minty#> [1] "1" "2" "drei"
::parse_guess(c("1", "2", "drei"), guess_max = 2)
minty#> [1] 1 2 NA
::parse_guess(c("1", "2", "drei"))
readr#> [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()
).
::parse_guess(c(" 1", " 2 ", " 3 "), trim_ws = TRUE)
minty#> [1] 1 2 3
::parse_guess(c(" 1", " 2 ", " 3 "), trim_ws = TRUE)
readr#> [1] "1" "2" "3"
##tidyverse/readr#1536
::type_convert(data.frame(a = "1 ", b = " 2"), trim_ws = TRUE) |> str()
minty#> 'data.frame': 1 obs. of 2 variables:
#> $ a: num 1
#> $ b: num 2
::type_convert(data.frame(a = "1 ", b = " 2"), trim_ws = TRUE) |> str()
readr#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> a = col_character(),
#> b = col_double()
#> )
#> 'data.frame': 1 obs. of 2 variables:
#> $ a: chr "1"
#> $ b: num 2
For parsing ambiguous date(time)
Guess column types of a text file
Thanks to:
readr
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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