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.
cols(..., .default = col_guess()) cols_only(...)
... | Either column objects created by |
---|---|
.default | Any named columns not explicitly overridden in |
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.
Other parsers: col_skip
,
cols_condense
,
parse_datetime
, parse_factor
,
parse_guess
, parse_logical
,
parse_number
, parse_vector
#> cols( #> 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() #> )