type_convert {readr} | R Documentation |
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 type.convert()
.
type_convert(df, col_types = NULL, na = c("", "NA"), trim_ws = TRUE, locale = default_locale())
df |
A data frame. |
col_types |
One of If If a column specification created by Unlike other functions |
na |
Character vector of strings to use for missing values. Set this
option to |
trim_ws |
Should leading and trailing whitespace 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
|
df <- data.frame( x = as.character(runif(10)), y = as.character(sample(10)), stringsAsFactors = FALSE ) str(df) str(type_convert(df)) df <- data.frame(x = c("NA", "10"), stringsAsFactors = FALSE) str(type_convert(df)) # Type convert can be used to infer types from an entire dataset type_convert( read_csv(readr_example("mtcars.csv"), col_types = cols(.default = col_character())))