r4ds VS rvest

https://r4ds.hadley.nz is free and very good

R for Data Science teaches you the tidyverse packages, which makes data wrangling so much easier!

One of the best free resources is the R4DS book by Hadley Wickham. You should make sure you start with the in progress second edition. https://r4ds.hadley.nz/

The first edition of R4DS is quite old now. Check out the soon to be released second edition: https://r4ds.hadley.nz/

R for Data Science (2nd Ed), the updated guide from Hadley Wickham

The big skills gap you have is in practical data exploration and transformation, which will be a large part of any data-centric role. As much as people may have distaste for it, there is no avoiding data manipulation as critical foundational enabler of all inferential and predictive modeling work. SQL is the lingua franca here and well worth picking up the basics (joins, window functions, handling dates and times, etc.), plus learning how to implement similar transformations in R and Python. With appropriately transformed data, you then need to be able to visualize it effectively using tools like Tableau or ggplot2 in R. I would not necessarily seek courses or certificates in it but expect to be evaluated on them in technical interview screenings, so self-study accordingly. R for Data Science by Hadley Wickham is a great free resource for these topics for R.

I just looked and there is now a second edition! https://r4ds.hadley.nz/

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).