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Create column specification

Source: R/parser.R
cols.Rd

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. In general you can substitute list() for cols() without changing the behavior.

Usage

col_skip()

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

cols_only(...)

Arguments

...

Either column objects created by col_*(), or their abbreviated character names (as described in the col_types argument of read_delim). 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.

.default

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

Value

a list / S3 object representing column specification

Details

The available specifications are: (with string abbreviations in brackets)

  • col_logical() [l], containing only T, F, TRUE or FALSE.

  • col_integer() [i], integers.

  • col_double() [d], doubles.

  • col_character() [c], everything else.

  • col_factor(levels, ordered) [f], a fixed set of values.

  • col_date(format = "") [D]: with the locale's date_format.

  • col_time(format = "") [t]: with the locale's time_format.

  • col_datetime(format = "") [T]: ISO8601 date times

  • col_number() [n], numbers containing the grouping_mark

  • col_skip() [_, -], don't import this column.

  • col_guess() [?], parse using the "best" type based on the input.

See also

Other parsers: parse_datetime(), parse_factor(), parse_guess(), parse_logical(), parse_number(), parse_vector()

Other parsers: parse_datetime(), parse_factor(), parse_guess(), parse_logical(), parse_number(), parse_vector()

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()
#> )

# 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()
#> )