The key problem that readr solves is parsing a flat file into a tibble. Parsing is the process of taking a text file and turning it into a rectangular tibble where each column is the appropriate part. Parsing takes place in three basic stages:

1. The flat file is parsed into a rectangular matrix of strings.

2. The type of each column is determined.

3. Each column of strings is parsed into a vector of a more specific type.

It’s easiest to learn how this works in the opposite order Below, you’ll learn how the:

1. Vector parsers turn a character vector in to a more specific type.

2. Column specification describes the type of each column and the strategy readr uses to guess types so you don’t need to supply them all.

3. Rectangular parsers turn a flat file into a matrix of rows and columns.

Each parse_*() is coupled with a col_*() function, which will be used in the process of parsing a complete tibble.

## Vector parsers

It’s easiest to learn the vector parses using parse_ functions. These all take a character vector and some options. They return a new vector the same length as the old, along with an attribute describing any problems.

### Atomic vectors

parse_logical(), parse_integer(), parse_double(), and parse_character() are straightforward parsers that produce the corresponding atomic vector.

parse_integer(c("1", "2", "3"))
#> [1] 1 2 3
parse_double(c("1.56", "2.34", "3.56"))
#> [1] 1.56 2.34 3.56
parse_logical(c("true", "false"))
#> [1]  TRUE FALSE

By default, readr expects . as the decimal mark and , as the grouping mark. You can override this default using locale(), as described in vignette("locales").

### Flexible numeric parser

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   12.45

### Date/times

readr supports three types of date/time data:

• dates: number of days since 1970-01-01.
• times: number of seconds since midnight.
• datetimes: number of seconds since midnight 1970-01-01.
parse_datetime("2010-10-01 21:45")
#> [1] "2010-10-01 21:45:00 UTC"
parse_date("2010-10-01")
#> [1] "2010-10-01"
parse_time("1:00pm")
#> 13:00:00

Each function takes a format argument which describes the format of the string. If not specified, it uses a default value:

• parse_datetime() recognises ISO8601 datetimes.

• parse_date() uses the date_format specified by the locale(). The default value is %AD which uses an automatic date parser that recognises dates of the format Y-m-d or Y/m/d.

• parse_time() uses the time_format specified by the locale(). The default value is %At which uses an automatic time parser that recognises times of the form H:M optionally followed by seconds and am/pm.

In most cases, you will need to supply a format, as documented in parse_datetime():

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"

### Factors

When reading a column that has a known set of values, you can read directly into a factor. parse_factor() will generate a warning if a value is not in the supplied levels.

parse_factor(c("a", "b", "a"), levels = c("a", "b", "c"))
#> [1] a b a
#> Levels: a b c
parse_factor(c("a", "b", "d"), levels = c("a", "b", "c"))
#> Warning: 1 parsing failure.
#> row col           expected actual
#>   3  -- value in level set      d
#> [1] a    b    <NA>
#> attr(,"problems")
#> # A tibble: 1 × 4
#>     row   col expected           actual
#>   <int> <int> <chr>              <chr>
#> 1     3    NA value in level set d
#> Levels: a b c

## Column specification

It would be tedious if you had to specify the type of every column when reading a file. Instead readr, uses some heuristics to guess the type of each column. You can access these results yourself using guess_parser():

guess_parser(c("a", "b", "c"))
#> [1] "character"
guess_parser(c("1", "2", "3"))
#> [1] "double"
guess_parser(c("1,000", "2,000", "3,000"))
#> [1] "number"
guess_parser(c("2001/10/10"))
#> [1] "date"

The guessing policies are described in the documentation for the individual functions. Guesses are fairly strict. For example, we don’t guess that currencies are numbers, even though we can parse them:

guess_parser("$1,234") #> [1] "character" parse_number("$1,234")
#> [1] 1234

There are two parsers that will never be guessed: col_skip() and col_factor(). You will always need to supply these explicitly.

You can see the specification that readr would generate for a column file by using spec_csv(), spec_tsv() and so on:

x <- spec_csv(readr_example("challenge.csv"))

For bigger files, you can often make the specification simpler by changing the default column type using cols_condense()

mtcars_spec <- spec_csv(readr_example("mtcars.csv"))
mtcars_spec
#> cols(
#>   mpg = col_double(),
#>   cyl = col_double(),
#>   disp = col_double(),
#>   hp = col_double(),
#>   drat = col_double(),
#>   wt = col_double(),
#>   qsec = col_double(),
#>   vs = col_double(),
#>   am = col_double(),
#>   gear = col_double(),
#>   carb = col_double()
#> )

cols_condense(mtcars_spec)
#> cols(
#>   .default = col_double()
#> )

By default readr only looks at the first 1000 rows. This keeps file parsing speedy, but can generate incorrect guesses. For example, in challenge.csv the column types change in row 1001, so readr guesses the wrong types. One way to resolve the problem is to increase the number of rows:

x <- spec_csv(readr_example("challenge.csv"), guess_max = 1001)

Another way is to manually specify the col_type, as described below.

## Rectangular parsers

readr comes with five parsers for rectangular file formats:

• read_csv() and read_csv2() for csv files
• read_tsv() for tabs separated files
• read_fwf() for fixed-width files
• read_log() for web log files

Each of these functions firsts calls spec_xxx() (as described above), and then parses the file according to that column specification:

df1 <- read_csv(readr_example("challenge.csv"))
#> Rows: 2000 Columns: 2
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> dbl  (1): x
#> date (1): y
#>
#> ℹ Use spec() to retrieve the full column specification for this data.
#> ℹ Specify the column types or set show_col_types = FALSE to quiet this message.

The rectangular parsing functions almost always succeed; they’ll only fail if the format is severely messed up. Instead, readr will generate a data frame of problems. The first few will be printed out, and you can access them all with problems():

problems(df1)
#> # A tibble: 0 × 5
#> # … with 5 variables: row <int>, col <int>, expected <chr>, actual <chr>,
#> #   file <chr>

You’ve already seen one way of handling bad guesses: increasing the number of rows used to guess the type of each column.

df2 <- read_csv(readr_example("challenge.csv"), guess_max = 1001)
#> Rows: 2000 Columns: 2
#> ── Column specification ──────────────────────────────────────────────────
#> Delimiter: ","
#> dbl  (1): x
#> date (1): y
#>
#> ℹ Use spec() to retrieve the full column specification for this data.
#> ℹ Specify the column types or set show_col_types = FALSE to quiet this message.

Another approach is to manually supply the column specification.

### Overriding the defaults

In the previous examples, you may have noticed that readr printed the column specification that it used to parse the file:

#> Parsed with column specification:
#> cols(
#>   x = col_integer(),
#>   y = col_character()
#> )

You can also access it after the fact using spec():

spec(df1)
#> cols(
#>   x = col_double(),
#>   y = col_date(format = "")
#> )
spec(df2)
#> cols(
#>   x = col_double(),
#>   y = col_date(format = "")
#> )

(This also allows you to access the full column specification if you’re reading a very wide file. By default, readr will only print the specification of the first 20 columns.)

If you want to manually specify the column types, you can start by copying and pasting this code, and then tweaking it fix the parsing problems.

df3 <- read_csv(
col_types = list(
x = col_double(),
y = col_date(format = "")
)
)

In general, it’s good practice to supply an explicit column specification. It is more work, but it ensures that you get warnings if the data changes in unexpected ways. To be really strict, you can use stop_for_problems(df3). This will throw an error if there are any parsing problems, forcing you to fix those problems before proceeding with the analysis.

### Available column specifications

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.

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 = list(
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 = list(
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 will lead to the same result as:

read_csv("iris.csv", col_types = list(
Species = col_factor(c("setosa", "versicolor", "virginica")))
)

You can also set a default type that will be used instead of relying on the automatic detection for columns you don’t specify:

read_csv("iris.csv", col_types = list(
Species = col_factor(c("setosa", "versicolor", "virginica")),
.default = col_double())
)

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")))
)

### Output

The output of all these functions is a tibble. Note that characters are never automatically converted to factors (i.e. no more stringsAsFactors = FALSE) and column names are left as is, not munged into valid R identifiers (i.e. there is no check.names = TRUE). Row names are never set.

Attributes store the column specification (spec()) and any parsing problems (problems()).