How come there's no source in the git repo? You shouldn't just throw up the PDF and call it a day, github isn't just a trendy file host.
A book to learn R and Python in parallel for Data Science
51–60 of 90 posts
Re: A book to learn R and Python in parallel for Data Science
#52I read a good portion of the first chapter and skimmed the rest. I am very much enjoying this book and hope that you continue to write more chapters.
Re: A book to learn R and Python in parallel for Data Science
#53Earlier quoted context omitted.
Interesting, why do you advise people to stay away from Shiny?
It tries to do html, but it is limited. So I'd rather use Javascript to manipulate the frontend directly. It tries to do functional programming, but the documentation is not satisfying. The responses and behaviour is perplexing. I spent around 5—10 hours trying to get a Shiny GUI to work and eventually got to the conclusion that 1) if you want a big project do all the frontend stuff in something else, like JS and 2)…
Re: A book to learn R and Python in parallel for Data Science
#54Earlier quoted context omitted.
Dash by plotly is also amazing its like shiny but for python! - we were able to whip together an app that would let you drag and drop xyyy data and get a scatter plot instantly - you can try it here (first load takes 1-2s): https://dash-app-dx9g2r0la6-8000.cloud.kyso.io It was also really easy to make it, maybe 250 lines of python in total (guide to making this app is here: https://kyso.io/KyleOS/creating-an-interact…
I thought the Python equivalent to Shiny is Bokeh, see https://bokeh.pydata.org/en/latest/docs/gallery.html#gallery ) ?
Re: A book to learn R and Python in parallel for Data Science
#55Re: A book to learn R and Python in parallel for Data Science
#56If anyone is interested, I also made a 'Learn R by Example' project which attempts to teach R through code comments: https://github.com/photonlines/Learn-R-by-Example
Re: A book to learn R and Python in parallel for Data Science
#57I feel like ever book ive ever read, on any programming language, makes me immediately want to pound my head into my desk. Nothing against the author, its just so clear that as it pertains to programming, being good at programming, and the teaching of it never come hand in hand. Same goes for real life, some of the best data scientists I work with, cant for the life of them explain concepts, and then the ones who are…
Teaching and executing are two separate skills. Fun little anecdote, in high school I had this AWFUL science teacher. He would literally just have us watch Crash Course videos to get the concepts. Turns out he was a relatively distinguished scientist himself..
Re: A book to learn R and Python in parallel for Data Science
#58R has a nice web development framework called Shiny. While it is not comparable to say Django or Flask, Shiny does make it incredibly easy to share data analysis. If one wants to share statistical analysis or create a data oriented dashboard, then there is definitely a reason to consider R and Shiny. Note that Python has Dash, which is comparable to Shiny, but it is less mature as far as I know. While previously Shin…
I maintain R and Shiny where I work. With Shiny, one can now build any web application imaginable, even a completely generic one. R has such a huge library of software now, that it has gone far and wide outside of statistics and analytics - any kind of application can be built in R now. In fact I see no point in using Python for mathematics or number crunching any more as R has it all and performance critical parts c…
Re: A book to learn R and Python in parallel for Data Science
#59I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
I use both Python and R. tidyverse/ggplot2 alone are enough reason to use R, and are substantially faster for tasks that utilize those packages than the equivalent in Python (in my opinion). Although I haven't had as much reason to use base R. For more ML-related tasks I do go back to Python.