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https://github.com/tidyverse/rvest

Simple web scraping for R
https://github.com/tidyverse/rvest

html r web-scraping

Last synced: 3 days ago
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Simple web scraping for R

Awesome Lists containing this project

README

        

---
output: github_document
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```

# rvest rvest website

[![CRAN status](https://www.r-pkg.org/badges/version/rvest)](https://cran.r-project.org/package=rvest)
[![R-CMD-check](https://github.com/tidyverse/rvest/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidyverse/rvest/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/tidyverse/rvest/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidyverse/rvest?branch=main)

## Overview

rvest helps you scrape (or harvest) data from web pages.
It is designed to work with [magrittr](https://github.com/tidyverse/magrittr) to make it easy to express common web scraping tasks, inspired by libraries like [beautiful soup](https://www.crummy.com/software/BeautifulSoup/) and [RoboBrowser](http://robobrowser.readthedocs.io/en/latest/readme.html).

If you're scraping multiple pages, I highly recommend using rvest in concert with [polite](https://dmi3kno.github.io/polite/).
The polite package ensures that you're respecting the [robots.txt](https://en.wikipedia.org/wiki/Robots_exclusion_standard) and not hammering the site with too many requests.

## Installation

```{r, eval = FALSE}
# The easiest way to get rvest is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just rvest:
install.packages("rvest")
```

## Usage

```{r, message = FALSE}
library(rvest)

# Start by reading a HTML page with read_html():
starwars <- read_html("https://rvest.tidyverse.org/articles/starwars.html")

# Then find elements that match a css selector or XPath expression
# using html_elements(). In this example, each corresponds
# to a different film
films <- starwars %>% html_elements("section")
films

# Then use html_element() to extract one element per film. Here
# we the title is given by the text inside


title <- films %>%
html_element("h2") %>%
html_text2()
title

# Or use html_attr() to get data out of attributes. html_attr() always
# returns a string so we convert it to an integer using a readr function
episode <- films %>%
html_element("h2") %>%
html_attr("data-id") %>%
readr::parse_integer()
episode
```

If the page contains tabular data you can convert it directly to a data frame with `html_table()`:

```{r}
html <- read_html("https://en.wikipedia.org/w/index.php?title=The_Lego_Movie&oldid=998422565")

html %>%
html_element(".tracklist") %>%
html_table()
```