type_convert: Re-convert character columns in existing data frame

Description

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 utils::type.convert().

Usage

type_convert(df, col_types = NULL, na = c("", "NA"), trim_ws = TRUE,
  locale = default_locale())

Arguments

A data frame.

col_types

One of NULL, a cols() specification, or a string. See vignette("readr") for more details.

If NULL, column types will be imputed using all rows.

Character vector of strings to interpret as missing values. Set this option to character() to indicate no missing values.

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 locale() to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/month names.

Examples

Run this code

# NOT RUN {
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

# first read the data as character
data <- read_csv(readr_example("mtcars.csv"),
                 col_types = cols(.default = col_character()))
str(data)
# Then convert it with type_convert
type_convert(data)
# }

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