rvest VS d3

You’re looking for the rvest package

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.

Tools: R + Rvest to scrape and clean the data. D3 to create the map. Svelte to put it all together.

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

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).

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.

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.

I would try some more nuanced web scraping with a package like rvest

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).

Yes this was done with a combination of GSAP Scrolltrigger https://gsap.com/docs/v3/Plugins/ScrollTrigger/ and https://d3js.org/

d3 - very power visualization library enabling dynamic visualizations. docs

Yep, Evidence is doing good work. We were most directly inspired by VitePress; we spent months rewriting both D3’s docs (https://d3js.org) and Observable Plot’s docs (https://observablehq.com/plot) in VitePress, and absolutely loved the experience. But we wanted a tool focused on data apps, dashboards, reports — observability and business intelligence use cases rather than documentation. Compared to Evidence, I’d say we’re trying to target data app developers more than data analysts; we offer a lot of power and expressiveness, and emphasize custom visualizations and interaction (leaning on Observable Plot or D3), as well as polyglot programming with data loaders written in any language (Python, R, not just SQL).

D3.js: A robust library to visualize your data and create interactive data-driven visualizations.

They are images so it could be any number of things, datawrapper, charts.js, d3.js to name a few options.

I made this interactive visualization that attempts to show the real-time frequency and location of births around the world. A country’s annual births (i.e. the country’s population times its birthrate) were distributed across all of the populated locations in each country, weighted by the population distribution (i.e. more populated areas got a greater fraction of the births). Data Sources and Tools Population and birthrate data for 2023 was obtained from Wikipedia (Population and birth rates). Population distribution across the globe was obtained from Socioeconomic Data and Applications Center (sedac) at Columbia University. Data is processed and visualized at a 1 degree x 1 degree resolution, each of which has a different probability of a birth occurring in a specific time period. D3.js was used to create the map elements and html, css and javascript were used to create the user interface.

When I go to the homepage of types/d3 the only hint for any kind of documentation is what seems to be the main github page of d3. It's highly possible I'm missing something here, so sorry if I am but I can't find any documentation of how you are supposed to type these library objects.

Website: D3.js official site

D3js