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Rvest Alternatives
Similar projects and alternatives to rvest
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r-web-scraping-cheat-sheet
Guide, reference and cheatsheet on web scraping using rvest, httr and Rselenium.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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money_diaries
An interactive web app for searching and filtering money diaries
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pokemon-games-ratings
Dataset and visualizations of Pokemon Game Ratings, from scraping metacritic.com.
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FEH_stats
R scripts to run summoning simulations for the game Fire Emblem Heroes
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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feh-assets-json
Discontinued JSON dumps of Fire Emblem Heroes asset files
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d3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
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Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better rvest alternative or higher similarity.
rvest reviews and mentions
Posts with mentions or reviews of rvest.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-09.
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PSA: You don't need fancy stuff to do good work.
Before diving into advanced machine learning algorithms or statistical models, we need to start with the basics: collecting and organizing data. Fortunately, both Python and R offer a wealth of libraries that make it easy to collect data from a variety of sources, including web scraping, APIs, and reading from files. Key libraries in Python include requests, BeautifulSoup, and pandas, while R has httr, rvest, and dplyr.
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Average price of an ounce of medium/high-quality marijuana in each U.S. state, April 2023 [OC]
Tools: R + Rvest to scrape and clean the data. D3 to create the map. Svelte to put it all together.
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AHR Summoning Statistics: 40 Summons and First Summon
so ik R has packages and native functions to help bypass this manual process. Eg scraping the wiki / gamepress unit list with Rvest may prove easier, furthermore you can specify web based sources when reading data. I'm not giga familiar with doing either myself, but maybe you can scrape data from the wikis or from repositories like the feh assets 1. But if youre able to set up a simple R script to read in new data and transform / clean it and save manual updates every 2 weeks
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Webscraping Google Search results and extracting the urls
There are very similar tools in R that I cover in that tutorial. For example, rvest or xml2 should be able to do the job as both of them support XPath selectors (you can take the ones from the article - they should work in R too).
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Made an app where you can search for money diaries by location or income
To get the data from the website, I need to use the package (a set of R code someone created and shared that's designed for a certain task) rvest, then I did a bunch of data munging in R to pull out the location/salary/age/etc. I saved that in a dataset and then used another package flexdashboard to make a webpage which I can essentially "one-click" publish using a free tool called RPubs.
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Used Cars Data Scraping - R & Github Actions & AWS
It came up with the idea of how to combine Data Engineering with Cloud and automation. I needed to find a data source as it would be an automated pipeline, so I needed a dynamic source. At the same time, I wanted to find a site where I thought retrieving data would not be a problem and do practice with both rvest and dplyr. After I had no problems with my experiments with Carvago, I added the necessary data cleaning steps. Another thing I aimed for in the project was to keep the data in different ways in different environments. While raw (daily CSV) and processed data were written to the Github repo, I wrote the processed data to PostgreSQL on AWS RDS. In addition, I sync the raw and processed data to S3 to be able to use it with Athena. However, I have separated some stages for GitHub Actions to be a good practice. For example, in the first stage, I added synchronization with AWS S3 as a separate action while scraping data, cleaning, and printing fundamental analysis to a simple log file. If there is no error after all this, I added a report with RMarkdown and the action that will be published on github.io. Thus, I created an end-to-end data pipeline where the data from the source is made to offer basic reporting with simple processing.
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How to convert large xml file to csv/sheet format
1) Use rvest to extract the contents of the XML file (i.e. loop over top-level nodes and pull any variable you're interested in into a column).
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A note from our sponsor - WorkOS
workos.com | 16 Apr 2024
Stats
Basic rvest repo stats
13
1,466
7.2
about 2 months ago
tidyverse/rvest is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of rvest is R.
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