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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
```
# readr <a href="https://readr.tidyverse.org"><img src="man/figures/logo.png" align="right" height="138" alt = ""/></a>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=readr)
[](https://github.com/tidyverse/readr/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/tidyverse/readr?branch=main)
<!-- badges: end -->
## 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](https://r4ds.hadley.nz/data-import) in R for Data Science.
## Installation
```{r, eval = FALSE}
# The easiest way to get readr is to install the whole tidyverse:
install.packages("tidyverse")
# Alternatively, install just readr:
install.packages("readr")
```
::: .pkgdown-devel
```{r, eval = FALSE}
# Or you can install the development version from GitHub:
# install.packages("pak")
pak::pak("tidyverse/readr")
```
:::
## Cheatsheet
<a href="https://raw.githubusercontent.com/rstudio/cheatsheets/main/data-import.pdf"><img src="https://github.com/rstudio/cheatsheets/raw/main/pngs/thumbnails/data-import-cheatsheet-thumbs.png" height="252" alt="thumbnail of tidyverse data import cheatsheet"//></a>
## Usage
readr is part of the core tidyverse, so you can load it with:
```{r}
library(tidyverse)
```
Of course, you can also load readr as an individual package:
```{r eval = FALSE}
library(readr)
```
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:
* `read_csv()`: comma-separated values (CSV)
* `read_tsv()`: tab-separated values (TSV)
* `read_csv2()`: semicolon-separated values with `,` as the decimal mark
* `read_delim()`: delimited files (CSV and TSV are important special cases)
* `read_fwf()`: fixed-width files
* `read_table()`: whitespace-separated files
* `read_log()`: web log files
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:
```{r}
(chickens <- read_csv(readr_example("chickens.csv")))
```
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.
```{r}
spec(chickens)
```
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.
```{r}
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
```
`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](https://github.com/Rdatatable/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](https://github.com/jcheng5) for showing me the beauty of
deterministic finite automata for parsing, and for teaching me why I
should write a tokenizer.
* [JJ Allaire](https://github.com/jjallaire) for helping me come up with a
design that makes very few copies, and is easy to extend.
* [Dirk Eddelbuettel](http://dirk.eddelbuettel.com) for coming up with the
name!