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R for Data Science

r4ds.had.co.nz

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Re: R for Data Science

#51

Post by baldfat »

danso wrote: post #41
baldfat wrote: post #20

Earlier quoted context omitted.

In R we also have notebooks that does this just it is slightly different. RMarkdown is how we do this same thing. At first I missed the different code and text blocks but it is just easier to work with when it is all a text file. You can make the RMarkdown for reports and then just run them from the command line and never have to open RStudio or R. Saves me a ton of time. You use back ticks to make your code chunks.…

I agree, though I don't know if I'm just missing something when working in Jupyter, or if Python has an equivalent of RMarkdown? Jupyter has some conveniences, but the tradeoffs aren't worth it for me. Working in a web browser has much less power, when it comes to keyboarding, than Atom/Sublime. And I generally don't need to interact with my data; I know what I'm outputting, I just want to show the results to readers…

>Jupyter, or if Python has an equivalent of RMarkdown?

You can convert the Notebooks to different formats. Though they tend to have to be fixed up a bit for a script (Which I also might need to in R to turn it into a script)

https://ipython.org/ipython-doc/3/notebook/nbconvert.html

Re: R for Data Science

#52

Post by hadley »

DashRattlesnake wrote: post #48
hadley wrote: post #23

Earlier quoted context omitted.

No, but I'll probably crowd source when the book is final.

Just curious, but why crowd source? You're the author, I assume you wrote the questions, didn't you solve them when you wrote them?

I love your sweet naivety about the process of writing exercises :P

Re: R for Data Science

#53

Post by nthot »

hadley wrote: post #2

I'm the author, and I'm happy to answer any questions. The book should be in print by (hopefully) the end of this year, or definitely by Jan 2017. The content will not change significantly, but there is will be minor fixes and a lot of proof reading.

I've always had a hard time understanding pipes, so it may just be that the concept will take more time for me to grok. I thought that the little bunny foo foo example in section 18.2 was really hard to grasp. I think that doing a similarly in depth example with numerical data, while more boring, may make the concept easier to understand.

I've been using r4ds for the past couple weeks. This is the first time I've really understood how everything in R and the tidyverse fits together. I am really enjoying the book. It has already helped me immensely.

Seriously, thank you for writing this.

Re: R for Data Science

#54

Post by yodsanklai »

I'm learning R for fun at the moment. I'm sure it's super useful for statisticians but it's quite an intricate language! It's an unlikely mix of different paradigms and features mixed together. Not something I'd recommend to a beginner programmer, yet it seems that people love it (even non-programmers).

I looked at several tutorials and what worked for me the best so far are the official manuals https://cran.r-project.org/manuals.html (esp. the language definition and the "introduction to R").

Moreover, for the programming languages enthusiasts, the following article is pretty interesting:

Evaluating the Design of the R Language (Morandat, Hill, Osvald, Vitek).

"R is a dynamic language for statistical computing that combines lazy functional features and object-oriented programming. This rather unlikely linguistic cocktail would probably never have been prepared by computer scientists, yet the language has become surprisingly popular. With millions of lines of R code available in repositories, we have an opportunity to evaluate the fundamental choices underlying the R language design. Using a combination of static and dynamic program analysis we can assess the impact and success of different language features."

Re: R for Data Science

#55

Post by [deleted] »

hadley wrote: post #2

I'm the author, and I'm happy to answer any questions. The book should be in print by (hopefully) the end of this year, or definitely by Jan 2017. The content will not change significantly, but there is will be minor fixes and a lot of proof reading.

[deleted]

Re: R for Data Science

#56

Post by hadley »

yodsanklai wrote: post #54

I'm learning R for fun at the moment. I'm sure it's super useful for statisticians but it's quite an intricate language! It's an unlikely mix of different paradigms and features mixed together. Not something I'd recommend to a beginner programmer, yet it seems that people love it (even non-programmers). I looked at several tutorials and what worked for me the best so far are the official manuals https://cran.r-projec…

You might enjoy " rel="nofollow">http://adv-r.had.co.nz/>, which discusses R from more of a programming language perspective (albeit a programming language that is chiefly used for data analysis). There are a lot of misunderstanding about R the language.

Re: R for Data Science

#57

Post by dfsegoat »

yodsanklai wrote: post #54

I'm learning R for fun at the moment. I'm sure it's super useful for statisticians but it's quite an intricate language! It's an unlikely mix of different paradigms and features mixed together. Not something I'd recommend to a beginner programmer, yet it seems that people love it (even non-programmers). I looked at several tutorials and what worked for me the best so far are the official manuals https://cran.r-projec…

It can be challenging for sure.

I will say, having struggled to use R and finally prevailing, that I will NEVER use anything else for generating figures for academic publications or internal reports (ggplot2 - same as the author of course).

Nothing comes close to the composable, functional way that it "just works" -- I don't use it every day - but it is my "go to" for data exploration etc. -- more than pandas / ipython.

Re: R for Data Science

#58

Post by [deleted] »

hadley wrote: post #49
Eridrus wrote: post #45

Earlier quoted context omitted.

I'm a software engineer who is already quite comfortable with Python and has more of an interest in machine learning than data science (as I understand it), is there any reason for me to learn R?

Probably not any strong reasons. That said, if you're a software engineer, you shouldn't find it too hard to pick up enough R to be useful. You might enjoy " rel="nofollow">http://adv-r.had.co.nz> which describes R from more of a programming language perspective.

[deleted]

Re: R for Data Science

#59

Post by dthal »

Eridrus wrote: post #45
hadley wrote: post #2

I'm the author, and I'm happy to answer any questions. The book should be in print by (hopefully) the end of this year, or definitely by Jan 2017. The content will not change significantly, but there is will be minor fixes and a lot of proof reading.

I'm a software engineer who is already quite comfortable with Python and has more of an interest in machine learning than data science (as I understand it), is there any reason for me to learn R?

I use both R and python quite a bit. I prefer python as a programming language. Here's my take on 'Why learn R?': (1) R/ggplot is hands-down better for plotting than anything in python. I also think that R is better for EDA generally. (2) Many smart, knowledgable people use R and publish their code. To learn from it, you need to know enough R to read and modify it. (3) R has better package support than python in several common data analysis domains. For example, in forecasting and in graph analysis, the best R packages available are much better than the best python packages.

Re: R for Data Science

#60

Post by sonabinu »

danso wrote: post #26
hadley wrote: post #14

Earlier quoted context omitted.

What would you recommend for visualisation?

I actually have no idea about that. I don't think there's an equivalent to R's base graphics, so that would seem to make matplotlib the closest thing to a standard -- seaborn [0], which I've seen used a lot lately for more advanced dataviz, lives atop it, but it's also relatively new. People seem to have conflicted feelings about matplotlib, maybe because of its origin in MATLAB? Not that Matlab itself is bad, but I…

What are your thoughts on bokeh? I seem to always revert to R for visualizations
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