Read Rectangular Text Data
The goal of 'readr' is to provide a fast and friendly way to read
rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly
parse many types of data found in the wild, while still cleanly failing when
data unexpectedly changes.
The goal of readr is to provide a fast and friendly way to read rectangular data (like csv, tsv, and fwf). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes. If you are new to readr, the best place to start is the data import chapter in R for data science.
# The easiest way to get readr is to install the whole tidyverse: install.packages("tidyverse") # Alternatively, install just readr: install.packages("readr") # Or the the development version from GitHub: # install.packages("devtools") devtools::install_github("tidyverse/readr")
readr is part of the core tidyverse, so load it with:
To accurately read a rectangular dataset with readr you combine two pieces: a function that parses the overall file, and a column specification. The column specification describes how each column should be converted from a character vector to the most appropriate data type, and in most cases it's not necessary because readr will guess it for you automatically.
readr supports seven file formats with seven
read_csv(): comma separated (CSV) files
read_tsv(): tab separated files
read_delim(): general delimited files
read_fwf(): fixed width files
read_table(): tabular files where colums are separated by white-space.
read_log(): web log files
In many cases, these functions will just work: you supply the path to a file and you get a tibble back. The following example loads a sample file bundled with readr:
mtcars <- read_csv(readr_example("mtcars.csv")) #> Parsed with column specification: #> cols( #> mpg = col_double(), #> cyl = col_integer(), #> disp = col_double(), #> hp = col_integer(), #> drat = col_double(), #> wt = col_double(), #> qsec = col_double(), #> vs = col_integer(), #> am = col_integer(), #> gear = col_integer(), #> carb = col_integer() #> )
Note that readr prints the column specification. This is useful because it allows you to check that the columns have been read in as you expect, and if they haven't, you can easily copy and paste into a new call:
mtcars <- read_csv(readr_example("mtcars.csv"), col_types = cols( mpg = col_double(), cyl = col_integer(), disp = col_double(), hp = col_integer(), drat = col_double(), vs = col_integer(), wt = col_double(), qsec = col_double(), am = col_integer(), gear = col_integer(), carb = col_integer() ) )
vignette("column-types") gives more detail on how readr guess the column types, how you can override the defaults, and provides some useful tools for debugging parsing problems.
There are two main alternatives to readr: base R and data.table's
fread(). The most important differences are discussed below.
Compared to the corresponding base functions, readr functions:
Use a consistent naming scheme for the parameters (e.g.
Are much faster (up to 10x).
Leave strings as is by default, and automatically parse common date/time formats.
Have a helpful progress bar if loading is going to take a while.
All functions work exactly the same way regardless of the current locale. To override the US-centric defaults, use
data.table has a function similar to
read_csv() called fread. Compared to fread, readr functions:
Are slower (currently ~1.2-2x slower. If you want absolutely the best performance, use
Use a slightly more sophisticated parser, recognising both doubled (
"""") and backslash escapes (
"\""), and can produce factors and date/times directly.
Forces you to supply all parameters, where
fread()saves you work by automatically guessing the delimiter, whether or not the file has a header, and how many lines to skip.
Are built on a different underlying infrastructure. Readr functions are designed to be quite general, which makes it easier to add support for new rectangular data formats.
fread()is designed to be as fast as possible.
Functions in readr
|count_fields||Count the number of fields in each line of a file|
|datasource||Create a source object.|
|format_delim||Convert a data frame to a delimited string|
|col_skip||Skip a column|
|cols||Create column specification|
|date_names||Create or retrieve date names|
|guess_encoding||Guess encoding of file|
|read_delim||Read a delimited file (including csv & tsv) into a tibble|
|read_delim_chunked||Read a delimited file by chunks|
|output_column||Preprocess column for output|
|parse_atomic||Parse logicals, integers, and reals|
|readr_example||Get path to readr example|
|cols_condense||Examine the column specifications for a data frame|
|parse_guess||Parse using the "best" type|
|parse_number||Parse numbers, flexibly|
|read_table||Read whitespace-separated columns into a tibble|
|readr-package||readr: Read Rectangular Text Data|
|spec_delim||Generate a column specification|
|tokenize||Tokenize a file/string.|
|type_convert||Re-convert character columns in existing data frame|
|write_delim||Write a data frame to a delimited file|
|read_lines||Read/write lines to/from a file|
|read_lines_chunked||Read lines from a file or string by chunk.|
|read_log||Read common/combined log file into a tibble|
|read_rds||Read/write RDS files.|
|problems||Retrieve parsing problems|
|read_file||Read/write a complete file|
|read_fwf||Read a fixed width file into a tibble|
|parse_vector||Parse a character vector.|
Last month downloads
|License||GPL (>= 2) | file LICENSE|
|Packaged||2017-05-16 16:03:56 UTC; jhester|
|Date/Publication||2017-05-16 19:03:57 UTC|
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