dplyr VS r-source

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!

Sure, will do my best! I'm not familiar with program verification / Isabelle, however if you take a look at the source code for the optim family of functions, you'll see that the last line of the function calls `.External2()`, which is similar to `.Call()|`. Both these functions allow R to call compiled C code, which means some of the work being done in these functions happens in this C code.

Funny, when I look it up (which I did because I don't want to talk without info), I found this. https://github.com/SurajGupta/r-source/blob/master/src/library/base/R/sapply.R

So you are testing for the same thing, but the TukeyHSD and HSD.test functions use different ways to compensate for unbalanced designs. TukeyHSD applies an adjustment for "mildly unbalanced designs" by effectively padding the sample sizes (at least that is my understanding of the source code at a quick glance) where: