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R for Data Science

r4ds.had.co.nz

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Re: R for Data Science

#11

Post by baldfat »

nickdavidhaynes wrote: post #7
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.

Is there (or are there plans for) a version of this in Jupyter notebooks?

I don't see how there would be a need for that. The code will work in Jupyter Notebooks just as it will work in a different IDE.

I came from Python using iPython. I missed them for the first few weeks, but now I can't switch from RStudio. It really is just such a great tool for data science.

Re: R for Data Science

#12

Post by hadley »

facorreia wrote: post #5
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'll be sure to buy a copy -- I'd just rather wait for the final version.

If you pre-order on amazon, you'll get the final version as soon as it's available.

Re: R for Data Science

#13

Post by nickdavidhaynes »

danso wrote: post #8
Rekushi wrote: post #6

What is the equivalent book for Python and data science?

There's a large number of such books, though none that are as authoritative with respect to Python (this is a statement about the size of Python's community vs. R, not necessarily about the authors): - via Wes McKinney, creator of pandas (which makes Python about as close to R as you can get): https://www.amazon.com/Python-Data-Analysis-Wrangling-IPytho... - http://joelgrus.com/2015/04/26/data-science-from-scratch-fi…

Wes's book is definitely the standard. But I would hold off buying one right now - he's currently working on a (much-needed) second edition, coming out next year (http://wesmckinney.com/).

Joel's book is a great resource for preparing for interviews or learning really basic stuff and less of an introduction to the tools.

Re: R for Data Science

#14

Post by hadley »

danso wrote: post #8
Rekushi wrote: post #6

What is the equivalent book for Python and data science?

There's a large number of such books, though none that are as authoritative with respect to Python (this is a statement about the size of Python's community vs. R, not necessarily about the authors): - via Wes McKinney, creator of pandas (which makes Python about as close to R as you can get): https://www.amazon.com/Python-Data-Analysis-Wrangling-IPytho... - http://joelgrus.com/2015/04/26/data-science-from-scratch-fi…

What would you recommend for visualisation?

Re: R for Data Science

#15

Post by nickdavidhaynes »

baldfat wrote: post #11
nickdavidhaynes wrote: post #7

Earlier quoted context omitted.

Is there (or are there plans for) a version of this in Jupyter notebooks?

I don't see how there would be a need for that. The code will work in Jupyter Notebooks just as it will work in a different IDE. I came from Python using iPython. I missed them for the first few weeks, but now I can't switch from RStudio. It really is just such a great tool for data science.

The advantage of Jupyter notebooks over RStudio is having text and code in the same place. Personally, I find that being able to run+modify the code in a textbook is much more informative than simply reading the syntax (for example - https://github.com/CamDavidsonPilon/Probabilistic-Programmin...). Sure, I could just copy and paste from the website into an IDE, but notebooks are a more natural way of communicating code and prose, IMO.

Re: R for Data Science

#16

Post by baldfat »

Rekushi wrote: post #6

What is the equivalent book for Python and data science?

I LOVE Python and really am pleased with Pandas, but ...

I use R exclusively for data science. Really encourage you to just give it a try. The tools, packages, community and the industry support is just awesome.

I did my reports for the end of the year and people loved the reports but the office is so MS Office focused that they wanted them in Word and PowerPoint (UGH), R has great tools for that RMarkdown and ReportRs library convinced me to switch. Also index being 1 is super strong selling point from now on for doing data science. http://davidgohel.github.io/ReporteRs/

Re: R for Data Science

#17

Post by crdb »

danso wrote: post #3
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.

Trivial, self-serving question: is there a library for generating the diagram of table relationships here (13.2 nycflights13) http://r4ds.had.co.nz/relational-data.html And of course, thanks for another great book, it's helpful for learning R but I'm always enlightened by how thoroughly you explain the general concepts (e.g. Relational data and joins). Have heard a few people on faculty speak enthusiastically about t…

It won't be as pretty, but I deal with large data models all the time, and like to use SchemaSpy [1] which generates an interactive page in HTML and can be used on the command line (I guess you could always modify the CSS to make it pretty). It's literally one of the most useful tools in my life, and the output is good enough to show to clients.

If I'm designing a DB or even just an SQL example, I'll run the code on my local machine (psql + the Postgres app [2]) or if I'm lucky, the client already has a server running Postgres and I can run it there instead. All SchemaSpy then needs is access to the DB and voila, interactive example.

[1] http://schemaspy.sourceforge.net/

[2] http://postgresapp.com/

Re: R for Data Science

#18

Post by Kevin_S »

Seems interesting. Quick question:

Some background on myself first. I am a financial consultant (only 1 year since graduating) and am planning to do a PhD in Accounting in the next 3 years. Currently working through the GMAT, but once that is complete, I will find myself with 2 or so years to do things that will help prepare me for research. One thing I have considered is taking a course/reading books on data science and such to prepare me for the advanced stats/data analysis that will go on during research. As someone with no coding experience, and with solid quant background (I was an economics undergrad), would this book be a good starting point for getting experience with this stuff? And is R the appropriate language to learn? I don't mind learning to code, but it is intimidating.

Thanks!

Re: R for Data Science

#19

Post by glogla »

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.

Are there solutions for the exercises?

Lot of the exercises, especially in the exploratory data analysis part are "why is blah?" or "is there a relationship in blah?"

I think I know the answers, but it would be nice to be able to check if I see what I'm supposed to se in the data.

Re: R for Data Science

#20

Post by baldfat »

nickdavidhaynes wrote: post #15
baldfat wrote: post #11

Earlier quoted context omitted.

I don't see how there would be a need for that. The code will work in Jupyter Notebooks just as it will work in a different IDE. I came from Python using iPython. I missed them for the first few weeks, but now I can't switch from RStudio. It really is just such a great tool for data science.

The advantage of Jupyter notebooks over RStudio is having text and code in the same place. Personally, I find that being able to run+modify the code in a textbook is much more informative than simply reading the syntax (for example - https://github.com/CamDavidsonPilon/Probabilistic-Programmin... ). Sure, I could just copy and paste from the website into an IDE, but notebooks are a more natural way of communicating c…

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.

For example: Some random Markdown text here is treated as a text block in Juypter

```{r}

summary(cars)

```

Then some further text goes here.

http://rmarkdown.rstudio.com/index.html

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