graphic-walker VS dplyr

[2] https://github.com/Kanaries/graphic-walker/issues/330

GWalkR is an open-source R library that allows you to turn your data frame into a tableau style user interface for data exploration and visualization. It also allows you to analysis your data with natural language questions.

GWalkR is the R binding of graphic-walker: https://github.com/Kanaries/graphic-walker

Big fan of D3.js and now there is Observable Plot! I am building several data visualization software for exploratory data analysis:

RATH, auto exploratory data analysis: https://github.com/Kanaries/Rath

GraphicWalker, embeddable data exploration component: https://github.com/Kanaries/graphic-walker

They are using vega-lite for now. But there is a limit of building more fancy and customized visualizations. It seems Plot has a more flexible layer based visualization system that can support larger design space.

Is Plot stable enough now to migrate from vega-lite based system to Plot based? Are there any large milestone or roadmap of Plot in future?

+ Graphic Walker (https://github.com/Kanaries/graphic-walker): A lite embeddable component for visual analysis.

+ PyGWalker (https://github.com/Kanaries/pygwalker): turning your pandas dataframe into a Tableau-style User Interface for visual exploration.

RATH is a collection of interesting ideas that we think the next generation of data analysis software should be, so there might be many features that not well organized to be a united app. Tell me which feature you prefer and which is not. Looking forward for your ideas and advice.

I know you said you *dont* want a tableau like interface, but in case you do this might be a cool open source project to check out: https://github.com/Kanaries/graphic-walker

> it seems like the heavy lifting is done by the web app here: https://github.com/Kanaries/graphic-walker

FWIW, both are made by the same entity, Kanaries.

You may try to use and modify based on the OpenSource Graphic Walker: https://github.com/Kanaries/graphic-walker

Graphic Walker is designed to be easy to embed in other applications as a React component. Check out the code here: https://github.com/Kanaries/graphic-walker

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!