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.
cols(..., .default = col_guess())cols_only(...)
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.
Any named columns not explicitly overridden in ...
will be read with this column type.
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()
# NOT RUN {
cols(a = col_integer())
cols_only(a = col_integer())
# You can also use the standard abbreviations
cols(a = "i")
cols(a = "i", b = "d", c = "_")
# 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
# }
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