This is useful if you need to do some manual munging - you can read the
columns in as character, clean it up with (e.g.) regular expressions and
then let readr take another stab at parsing it. The name is a homage to
the base utils::type.convert().
Usage
type_convert(
df,
col_types = NULL,
na = c("", "NA"),
trim_ws = TRUE,
locale = default_locale(),
guess_integer = FALSE
)Arguments
- df
A data frame.
- col_types
One of
NULL, acols()specification, or a string. Seevignette("readr")for more details.If
NULL, column types will be imputed using all rows.- na
Character vector of strings to interpret as missing values. Set this option to
character()to indicate no missing values.- trim_ws
Should leading and trailing whitespace (ASCII spaces and tabs) be trimmed from each field before parsing it?
- locale
The locale controls defaults that vary from place to place. The default locale is US-centric (like R), but you can use
locale()to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/month names.- guess_integer
If
TRUE, guess integer types for whole numbers, ifFALSEguess numeric type for all numbers.
Note
type_convert() removes a 'spec' attribute,
because it likely modifies the column data types.
(see spec() for more information about column specifications).
Examples
df <- data.frame(
x = as.character(runif(10)),
y = as.character(sample(10)),
stringsAsFactors = FALSE
)
str(df)
#> 'data.frame': 10 obs. of 2 variables:
#> $ x: chr "0.522271953523159" "0.575344954850152" "0.950669132871553" "0.184336383128539" ...
#> $ y: chr "8" "2" "5" "10" ...
str(type_convert(df))
#>
#> ── Column specification ──────────────────────────────────────────────────
#> cols(
#> x = col_double(),
#> y = col_double()
#> )
#> 'data.frame': 10 obs. of 2 variables:
#> $ x: num 0.522 0.575 0.951 0.184 0.78 ...
#> $ y: num 8 2 5 10 3 6 1 4 7 9
df <- data.frame(x = c("NA", "10"), stringsAsFactors = FALSE)
str(type_convert(df))
#>
#> ── Column specification ──────────────────────────────────────────────────
#> cols(
#> x = col_double()
#> )
#> 'data.frame': 2 obs. of 1 variable:
#> $ x: num NA 10
# Type convert can be used to infer types from an entire dataset
# first read the data as character
data <- read_csv(readr_example("mtcars.csv"),
col_types = list(.default = col_character())
)
str(data)
#> spc_tbl_ [32 × 11] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#> $ mpg : chr [1:32] "21" "21" "22.8" "21.4" ...
#> $ cyl : chr [1:32] "6" "6" "4" "6" ...
#> $ disp: chr [1:32] "160" "160" "108" "258" ...
#> $ hp : chr [1:32] "110" "110" "93" "110" ...
#> $ drat: chr [1:32] "3.9" "3.9" "3.85" "3.08" ...
#> $ wt : chr [1:32] "2.62" "2.875" "2.32" "3.215" ...
#> $ qsec: chr [1:32] "16.46" "17.02" "18.61" "19.44" ...
#> $ vs : chr [1:32] "0" "0" "1" "1" ...
#> $ am : chr [1:32] "1" "1" "1" "0" ...
#> $ gear: chr [1:32] "4" "4" "4" "3" ...
#> $ carb: chr [1:32] "4" "4" "1" "1" ...
#> - attr(*, "spec")=
#> .. cols(
#> .. .default = col_character(),
#> .. mpg = col_character(),
#> .. cyl = col_character(),
#> .. disp = col_character(),
#> .. hp = col_character(),
#> .. drat = col_character(),
#> .. wt = col_character(),
#> .. qsec = col_character(),
#> .. vs = col_character(),
#> .. am = col_character(),
#> .. gear = col_character(),
#> .. carb = col_character()
#> .. )
#> - attr(*, "problems")=<externalptr>
# Then convert it with type_convert
type_convert(data)
#>
#> ── Column specification ──────────────────────────────────────────────────
#> cols(
#> mpg = col_double(),
#> cyl = col_double(),
#> disp = col_double(),
#> hp = col_double(),
#> drat = col_double(),
#> wt = col_double(),
#> qsec = col_double(),
#> vs = col_double(),
#> am = col_double(),
#> gear = col_double(),
#> carb = col_double()
#> )
#> # A tibble: 32 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows