Function reference • readr

Read rectangular files

These functions parse rectangular files (like csv or fixed-width format) into tibbles. They specify the overall structure of the file, and how each line is divided up into fields.

read_delim() read_csv() read_csv2() read_tsv(): Read a delimited file (including CSV and TSV) into a tibble

read_fwf() fwf_empty() fwf_widths() fwf_positions() fwf_cols(): Read a fixed width file into a tibble

read_log(): Read common/combined log file into a tibble

read_table(): Read whitespace-separated columns into a tibble

Column specification

The column specification describes how each column is parsed from a character vector in to a more specific data type. readr does make an educated guess about the type of each column, but you’ll need override those guesses when it gets them wrong.

problems() stop_for_problems(): Retrieve parsing problems

cols() cols_only(): Create column specification

cols_condense() spec(): Examine the column specifications for a data frame

spec_delim() spec_csv() spec_csv2() spec_tsv() spec_table(): Generate a column specification

Column parsers

Column parsers define how a single column is parsed, or how to parse a single vector. Each parser comes in two forms: parse_xxx() which is used to parse vectors that already exist in R and col_xxx() which is used to parse vectors as they are loaded by a read_xxx() function.

parse_logical() parse_integer() parse_double() parse_character() col_logical() col_integer() col_double() col_character(): Parse logicals, integers, and reals

parse_datetime() parse_date() parse_time() col_datetime() col_date() col_time(): Parse date/times

parse_factor() col_factor(): Parse factors

parse_guess() col_guess() guess_parser(): Parse using the "best" type

parse_number() col_number(): Parse numbers, flexibly

col_skip(): Skip a column

Locale controls

The “locale” controls all options that vary from country-to-country or language-to-language. This includes things like the character used as the decimal mark, the names of days of the week, and the encoding. See vignette("locales") for more details.

locale() default_locale(): Create locales

date_names() date_names_lang() date_names_langs(): Create or retrieve date names

Write rectangular files

Despite its name, readr also provides a number of functions to write data frames to disk, or to convert them to in-memory strings.

format_delim() format_csv() format_csv2() format_tsv(): Convert a data frame to a delimited string

write_delim() write_csv() write_csv2() write_excel_csv() write_excel_csv2() write_tsv(): Write a data frame to a delimited file

Readr editions

readr supports two editions of parser. Version one is a single threaded eager parser that readr used by default from its first release to version 1.4.0. Version two is a multi-threaded lazy parser used by default from readr 2.0.0 onwards.

with_edition() local_edition(): Temporarily change the active readr edition

edition_get(): Retrieve the currently active edition

Read non-rectangular files

These functions parse non-rectangular files (like csv or fixed-width format) into long (so-called melted) format. They specify the overall structure of the file, and how each line is divided up into fields.

melt_delim() melt_csv() melt_csv2() melt_tsv(): Return melted data for each token in a delimited file (including csv & tsv)

melt_fwf(): Return melted data for each token in a fixed width file

melt_table() melt_table2(): Return melted data for each token in a whitespace-separated file

Low-level IO and debugging tools

These functions can be used with non-rectangular files, binary data, and to help debug rectangular files that fail to parse.

read_file() read_file_raw() write_file(): Read/write a complete file

read_lines() read_lines_raw() write_lines(): Read/write lines to/from a file

read_rds() write_rds(): Read/write RDS files.

read_builtin(): Read built-in object from package

count_fields(): Count the number of fields in each line of a file

guess_encoding(): Guess encoding of file

type_convert(): Re-convert character columns in existing data frame

readr_example(): Get path to readr example

clipboard(): Returns values from the clipboard

show_progress(): Determine whether progress bars should be shown

readr_threads(): Determine how many threads readr should use when processing

should_show_types(): Determine whether column types should be shown

should_read_lazy(): Determine whether to read a file lazily