Create column specification

cols() includes all columns in the input data, guessing the column types as the default. cols_only() includes only the columns you explicitly specify, skipping the rest.

Usage

cols(..., .default = col_guess(), .delim = NULL)

cols_only(...)

col_logical(...)

col_integer(...)

col_big_integer(...)

col_double(...)

col_character(...)

col_skip(...)

col_number(...)

col_guess(...)

col_factor(levels = NULL, ordered = FALSE, include_na = FALSE, ...)

col_datetime(format = "", ...)

col_date(format = "", ...)

col_time(format = "", ...)

Arguments

...

Either column objects created by col_*(), or their abbreviated character names (as described in the col_types argument of vroom()). If you're only overriding a few columns, it's best to refer to columns by name. If not named, the column types must match the column names exactly. In col_*() functions these are stored in the object.

.default

Any named columns not explicitly overridden in ... will be read with this column type.

.delim

The delimiter to use when parsing. If the delim argument used in the call to vroom() it takes precedence over the one specified in col_types.

levels

Character vector of the allowed levels. When levels = NULL (the default), levels are discovered from the unique values of x, in the order in which they appear in x.

ordered

Is it an ordered factor?

include_na

If TRUE and x contains at least one NA, then NA is included in the levels of the constructed factor.

format

A format specification, as described below. If set to "", date times are parsed as ISO8601, dates and times used the date and time formats specified in the locale().

Unlike strptime(), the format specification must match the complete string.

Details

The available specifications are: (long names in quotes and string abbreviations in brackets)

function	long name	short name	description
`col_logical()`	"logical"	"l"	Logical values containing only `T`, `F`, `TRUE` or `FALSE`.
`col_integer()`	"integer"	"i"	Integer numbers.
`col_big_integer()`	"big_integer"	"I"	Big Integers (64bit), requires the `bit64` package.
`col_double()`	"double", "numeric"	"d"	64-bit double floating point numbers.
`col_character()`	"character"	"c"	Character string data.
`col_factor(levels, ordered)`	"factor"	"f"	A fixed set of values.
`col_date(format = "")`	"date"	"D"	Calendar dates formatted with the locale's `date_format`.
`col_time(format = "")`	"time"	"t"	Times formatted with the locale's `time_format`.
`col_datetime(format = "")`	"datetime", "POSIXct"	"T"	ISO8601 date times.
`col_number()`	"number"	"n"	Human readable numbers containing the `grouping_mark`
`col_skip()`	"skip", "NULL"	"_", "-"	Skip and don't import this column.
`col_guess()`	"guess", "NA"	"?"	Parse using the "best" guessed type based on the input.

Examples

cols(a = col_integer())
#> cols(
#>   a = col_integer()
#> )
cols_only(a = col_integer())
#> cols_only(
#>   a = col_integer()
#> )

# You can also use the standard abbreviations
cols(a = "i")
#> cols(
#>   a = col_integer()
#> )
cols(a = "i", b = "d", c = "_")
#> cols(
#>   a = col_integer(),
#>   b = col_double(),
#>   c = col_skip()
#> )

# Or long names (like utils::read.csv)
cols(a = "integer", b = "double", c = "skip")
#> cols(
#>   a = col_integer(),
#>   b = col_double(),
#>   c = col_skip()
#> )

# You can also use multiple sets of column definitions by combining
# them like so:

t1 <- cols(
  column_one = col_integer(),
  column_two = col_number())

t2 <- cols(
 column_three = col_character())

t3 <- t1
t3$cols <- c(t1$cols, t2$cols)
t3
#> cols(
#>   column_one = col_integer(),
#>   column_two = col_number(),
#>   column_three = col_character()
#> )