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readr

Overview

The goal of readr is to provide a fast and friendly way to read rectangular data from delimited files, such as comma-separated values (CSV) and tab-separated values (TSV). It is designed to parse many types of data found in the wild, while providing an informative problem report when parsing leads to unexpected results. If you are new to readr, the best place to start is the data import chapter in R for Data Science.

Installation 

# The easiest way to get readr is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just readr:
install.packages("readr")

Cheatsheet

thumbnail of tidyverse data import cheatsheet

Usage

readr is part of the core tidyverse, so you can load it with:

library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4          ✔ readr     2.1.4.9000
#> ✔ forcats   1.0.0          ✔ stringr   1.5.1     
#> ✔ ggplot2   3.4.3          ✔ tibble    3.2.1     
#> ✔ lubridate 1.9.3          ✔ tidyr     1.3.0     
#> ✔ purrr     1.0.2          
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Of course, you can also load readr as an individual package:

To read a rectangular dataset with readr, you combine two pieces: a function that parses the lines of the file into individual fields and a column specification.

readr supports the following file formats with these read_*() functions:

A column specification describes how each column should be converted from a character vector to a specific data type (e.g. character, numeric, datetime, etc.). In the absence of a column specification, readr will guess column types from the data. vignette("column-types") gives more detail on how readr guesses the column types. Column type guessing is very handy, especially during data exploration, but it’s important to remember these are just guesses. As any data analysis project matures past the exploratory phase, the best strategy is to provide explicit column types.

The following example loads a sample file bundled with readr and guesses the column types:

(chickens <- read_csv(readr_example("chickens.csv")))
#> Rows: 5 Columns: 4
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): chicken, sex, motto
#> dbl (1): eggs_laid
#> 
#> 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.
#> # A tibble: 5 × 4
#>   chicken                 sex     eggs_laid motto                               
#>   <chr>                   <chr>       <dbl> <chr>                               
#> 1 Foghorn Leghorn         rooster         0 That's a joke, ah say, that's a jok…
#> 2 Chicken Little          hen             3 The sky is falling!                 
#> 3 Ginger                  hen            12 Listen. We'll either die free chick…
#> 4 Camilla the Chicken     hen             7 Bawk, buck, ba-gawk.                
#> 5 Ernie The Giant Chicken rooster         0 Put Captain Solo in the cargo hold.

Note that readr prints the column types – the guessed column types, in this case. This is useful because it allows you to check that the columns have been read in as you expect. If they haven’t, that means you need to provide the column specification. This sounds like a lot of trouble, but luckily readr affords a nice workflow for this. Use spec() to retrieve the (guessed) column specification from your initial effort.

spec(chickens)
#> cols(
#>   chicken = col_character(),
#>   sex = col_character(),
#>   eggs_laid = col_double(),
#>   motto = col_character()
#> )

Now you can copy, paste, and tweak this, to create a more explicit readr call that expresses the desired column types. Here we express that sex should be a factor with levels rooster and hen, in that order, and that eggs_laid should be integer.

chickens <- read_csv(
  readr_example("chickens.csv"),
  col_types = cols(
    chicken   = col_character(),
    sex       = col_factor(levels = c("rooster", "hen")),
    eggs_laid = col_integer(),
    motto     = col_character()
  )
)
chickens
#> # A tibble: 5 × 4
#>   chicken                 sex     eggs_laid motto                               
#>   <chr>                   <fct>       <int> <chr>                               
#> 1 Foghorn Leghorn         rooster         0 That's a joke, ah say, that's a jok…
#> 2 Chicken Little          hen             3 The sky is falling!                 
#> 3 Ginger                  hen            12 Listen. We'll either die free chick…
#> 4 Camilla the Chicken     hen             7 Bawk, buck, ba-gawk.                
#> 5 Ernie The Giant Chicken rooster         0 Put Captain Solo in the cargo hold.

vignette("readr") gives an expanded introduction to readr.

Editions

readr got a new parsing engine in version 2.0.0 (released July 2021). In this so-called second edition, readr calls vroom::vroom(), by default.

The parsing engine in readr versions prior to 2.0.0 is now called the first edition. If you’re using readr >= 2.0.0, you can still access first edition parsing via the functions with_edition(1, ...) and local_edition(1). And, obviously, if you’re using readr < 2.0.0, you will get first edition parsing, by definition, because that’s all there is.

We will continue to support the first edition for a number of releases, but the overall goal is to make the second edition uniformly better than the first. Therefore the plan is to eventually deprecate and then remove the first edition code. New code and actively-maintained code should use the second edition. The workarounds with_edition(1, ...) and local_edition(1) are offered as a pragmatic way to patch up legacy code or as a temporary solution for infelicities identified as the second edition matures.

Alternatives

There are two main alternatives to readr: base R and data.table’s fread(). The most important differences are discussed below.

Base R

Compared to the corresponding base functions, readr functions:

  • Use a consistent naming scheme for the parameters (e.g. col_names and col_types not header and colClasses).

  • Are generally much faster (up to 10x-100x) depending on the dataset.

  • 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 locale().

data.table and fread()

data.table has a function similar to read_csv() called fread(). Compared to fread(), readr functions:

  • Are sometimes slower, particularly on numeric heavy data.

  • Can automatically guess some parameters, but basically encourage explicit specification of, e.g., the delimiter, skipped rows, and the header row.

  • Follow tidyverse-wide conventions, such as returning a tibble, a standard approach for column name repair, and a common mini-language for column selection.

Acknowledgements

Thanks to:

  • Joe Cheng for showing me the beauty of deterministic finite automata for parsing, and for teaching me why I should write a tokenizer.

  • JJ Allaire for helping me come up with a design that makes very few copies, and is easy to extend.

  • Dirk Eddelbuettel for coming up with the name!