tidyr VS dplyr

tidyr

This was fun to play around with. I made some very minor changes and posted at https://gist.github.com/hadley/d54895557fbb0fe0402d2277b9011....

It revealed to me that there's a buglet in `forcats::last()` (https://github.com/tidyverse/forcats/issues/303) and made me wonder if `pivot_longer()` should be able to rename the columns as you pivot them (https://github.com/tidyverse/tidyr/issues/1338)

I originally wrote tidytable because dtplyr was missing a lot of functionality my coworkers and I needed, and at the time dtplyr looked like a "forgotten package" (lots of open issues/bugs, very infrequent updates). Hadley Wickham also mentioned at one point [he had no plans for adding tidyr functions)[https://github.com/tidyverse/tidyr/issues/1015#issuecomment-682977139]. He changed his mind on that one - tidyr functions are the ones that I'm in the process of contributing to dtplyr now.

dplyr

One pandas limitation is that it has its own API that does not quite map back to relational algebra. Ibis is such a library that's literally built by people who built pandas to provide a sane expressions system that can be mapped back to multiple SQL backends. Ibis takes inspiration from the dplyr R package to build a new expression system that can easily map back to relational algebra and thus compile to SQL. It also is declarative in style, enabling us to apply database style optimizations on the complete logical plan or the expression. Ibis is a key component for enabling composability as highlighted in the excellent composable codex.

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!