dplyr VS r4ds

That's great feedback, thanks!

This tool definitely comes from a place of personal need - beyond just handling large files, I've also never really gelled well with the Excel/Google Sheet model of changing data in place as if you were editing text. I'm a Data Scientist and always preferred the chained data transforms you see in things like dplyr (https://dplyr.tidyverse.org/) or Polars (https://pola.rs/) and I feel this tool maps very closely to the chained model.

Also, thank you for the feature requests! Those would all be very useful - we'll put them on the roadmap.

There's an example of how to use zzz.R with a .onload() function to set options in the dplyr code base: https://github.com/tidyverse/dplyr/blob/bbcfe99e29fe737d456b0d7adc33d3c445a32d9d/R/zzz.r

Look at the tidyverse, especially the case_when or mutate functions.

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.

It looks like your syntax is wrong. I think you’re trying to calculate a new variables in your data frame, or alter an existing column in a data frame. Have a look at the select() function in this reference for the proper syntax to use. https://dplyr.tidyverse.org/ Does that help?

A lot of populat libraries have their own logos. Maybe one of them would be good. Check out dplyr for example: https://dplyr.tidyverse.org/

I hope you give it a try when you get a chance: https://dplyr.tidyverse.org/

You can read up on the dplyr-verbs here, which I strongly suggest for your exam! In the code examples, you can simply click on any function you don't understand and it will take you directly to the documentation. Good Luck!

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/