Currently, readr automatically recognises the following types of columns:
col_logical() [l], containing only T, F, TRUE or FALSE.
col_integer() [i], integers.
col_double() [d], doubles.
col_character() [c], everything else.
col_date(format = "") [D]: Y-m-d dates.
col_datetime(format = "") [T]: ISO8601 date times
col_number() [n], finds the first number in the field. A number is defined as a sequence of -, “0-9”, decimal_mark and grouping_mark. This is useful for currencies and percentages.
To recognise these columns, readr inspects the first 1000 rows of your dataset. This is not guaranteed to be perfect, but it’s fast and a reasonable heuristic. If you get a lot of parsing failures, you’ll need to re-read the file, overriding the default choices as described below.
You can also manually specify other column types:
col_skip() [_, -], don’t import this column.
col_date(format), dates with given format.
col_datetime(format, tz), date times with given format. If the timezone is UTC (the default), this is >20x faster than loading then parsing with strptime().
col_time(format), times. Returned as number of seconds past midnight.
col_factor(levels, ordered), parse a fixed set of known values into a factor
Use the col_types argument to override the default choices. There are two ways to use it:
With a string: "dc__d": read first column as double, second as character, skip the next two and read the last column as a double. (There’s no way to use this form with types that take additional parameters.)
With a (named) list of col objects:
read_csv("iris.csv", col_types = cols(
Sepal.Length = col_double(),
Sepal.Width = col_double(),
Petal.Length = col_double(),
Petal.Width = col_double(),
Species = col_factor(c("setosa", "versicolor", "virginica"))
))Or, with their abbreviations:
read_csv("iris.csv", col_types = cols(
Sepal.Length = "d",
Sepal.Width = "d",
Petal.Length = "d",
Petal.Width = "d",
Species = col_factor(c("setosa", "versicolor", "virginica"))
))Any omitted columns will be parsed automatically, so the previous call is equivalent to:
read_csv("iris.csv", col_types = cols(
Species = col_factor(c("setosa", "versicolor", "virginica"))
)If you only want to read specified columns, use cols_only():
read_csv("iris.csv", col_types = cols_only(
Species = col_factor(c("setosa", "versicolor", "virginica"))
)As well as specifying how to parse a column from a file on disk, each of the col_xyz() functions has an equivalent parse_xyz() that parsers a character vector. These are useful for testing and examples, and for rapidly experimenting to figure out how to parse a vector given a few examples.
parse_logical(), parse_integer(), parse_double(), and parse_character() are straightforward parsers that produce the corresponding atomic vector.
Make sure to read vignette("locales") to learn how to deal with doubles.
parse_integer() and parse_double() are strict: the input string must be a single number with no leading or trailing characters. parse_number() is more flexible: it ignores non-numeric prefixes and suffixes, and knows how to deal with grouping marks. This makes it suitable for reading currencies and percentages:
parse_number(c("0%", "10%", "150%"))
#> [1] 0 10 150
parse_number(c("$1,234.5", "$12.45"))
#> [1] 1234.50 12.45Note that parse_guess() will only guess that a string is a number if it has no leading or trailing characters, otherwise it’s too prone to false positives. That means you’ll typically needed to explicitly supply the column type for number columns.
str(parse_guess("$1,234"))
#> chr "$1,234"
str(parse_guess("1,234"))
#> num 1234readr supports three types of date/time data:
readr will guess date and date time fields if they’re in ISO8601 format:
parse_datetime("2010-10-01 21:45")
#> [1] "2010-10-01 21:45:00 UTC"
parse_date("2010-10-01")
#> [1] "2010-10-01"Otherwise, you’ll need to specify the format yourself:
parse_datetime("1 January, 2010", "%d %B, %Y")
#> [1] "2010-01-01 UTC"
parse_datetime("02/02/15", "%m/%d/%y")
#> [1] "2015-02-02 UTC"When reading a column that has a known set of values, you can read directly into a factor.
parse_factor(c("a", "b", "a"), levels = c("a", "b", "c"))
#> [1] a b a
#> Levels: a b creadr will never turn a character vector into a factor unless you explicitly ask for it.