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R for Data Science

r4ds.had.co.nz

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Re: R for Data Science

#61

Post by dandermotj »

hadley wrote: post #36
dreww2 wrote: post #27

Hadley, can you share a bit more about your plans for modelr and what need(s) the package will be designed to solve? Congrats on your book btw, I've been reading it for a few weeks and it's quite simply excellent.

I don't think modelr is going to change significantly in the future. It solved a pressing problem (fitting models as part of a pipeline) so I could teach modelling using the same interface as everything else in the book. However, the modelling infrastructure in R is generally showing it's age, and thinking about how to make modelling easier is something that I will be working on in the coming months.

I saw the vctrs package repo the other day on your GitHub. What's your plan with that? I guess your covering all of R's base data types (dplyr:data frames, purrr:lists, forcats:factors, vctrs:vectors)? Also do you plan on developing further functional programming packages?

Re: R for Data Science

#62

Post by xaa »

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'm in bioinformatics. R is huge, Python is a distant second. So I am forced to use both.

R is a terrible programming language. It's slow and syntactically inconsistent. For interactive statistical analysis it can be OK, but anything beyond a small program becomes unmanageable quickly. In particular, if you want to manipulate strings or hierarchical data structures quickly in R, good luck.

For ML, scikit-learn is almost always sufficient. For statistics, statsmodels is OK but very underdeveloped compared to what is available in R. IMO plotting is equally painful in both Python and R (seaborn is a good Python library to ease the pain if you haven't seen it).

Personally, I write everything in Python and call out to R as infrequently as possible using rpy2, usually only for specific statistical routines or bioinformatics-specific libraries.

Re: R for Data Science

#63

Post by mooneater »

hadley, I love your book, and I learn a lot from your preferences in R packages. Now is there a general source for determining the "best" packages for various tasks?

CRAN has task views, but they are long lists and don't clearly show popularity or feature matrices. There are just so many options.

Im thinking something like https://djangopackages.org , for example see https://djangopackages.org/grids/g/commenting/

Re: R for Data Science

#64

Post by IndianAstronaut »

baldfat wrote: post #16
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 Report…

Whatever the data problem, I have found R to much more likely have a solution than Python or anything else.

Re: R for Data Science

#65

Post by [deleted] »

hadley wrote: post #56
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.

[deleted]

Re: R for Data Science

#67

Post by siddboots »

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.

Hey Hadley. Huge fan of your work! Many of the libraries you have authored or co-authored have had a big influence on how I think about building tools. I looks forward to getting a hard copy of the book!

I have a bit of a nitpick about chapter 13 on "relational data", in which I believe you are consistently misusing the technical term "relation" to refer to the relationship between two data sets. In the context of relational database theory, "relation" is just another word for "table" (although it connotes more mathematical formalism).

I think it is worth respecting the precise technical usage in this case: Consider a student who might read your book and be told that "relations are always defined between a pair of tables," and that "a primary key and the corresponding foreign key in another table form a relation." The same student might also stumble across the wikipedia page for the relational model and learn that "a relation is defined as a set of tuples that have the same attributes," and that the relational model "organizes data into one or more tables (or relations)."

Re: R for Data Science

#68

Post by nrjames »

hadley wrote: post #14
danso wrote: post #8

Earlier quoted context omitted.

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?

I've used R and Python. I stick with Python whenever possible because IMO it supports the non-modeling parts of data science more effectively. ETL scripts, API creation, Flask for hosting simple websites, etc. yHat makes python-ggplot and Rodeo, similar to RStudio. I explore and develop algorithms in Jupyter notebooks, documenting along the way, while running "hardened" code from the command line, often nohup'ing it on a Linux box, for services that run perpetually, keep me updated via Slack/SMS/email, etc.

For visualization, almost everything I do is in D3, p5.js, or in Processing (Java), which has a Python interpreter, for those interested. There are some great Processing books and Daniel Shiffman is the Hadley of that world. Tons of engaging resources from him. There are tons and tons of good D3 books and online resources. bl.ocks and Mike Bostock's other online articles are wonderful.

Every organization with data scientists defines "data science" differently. People with a modeling and stats focus probably should stick with R. If you find yourself in a position with a wider scope, you simply must have more tools in your tool belt, and in my opinion, R, Python, and JavaScript all are part of that package. For me, personally, Processing is, too. Have a look at Ben Fry's work to understand why. I also use openFrameworks when the volume of data to visualize and performance concerns require it.

Re: R for Data Science

#69

Post by hadley »

mooneater wrote: post #63

hadley, I love your book, and I learn a lot from your preferences in R packages. Now is there a general source for determining the "best" packages for various tasks? CRAN has task views, but they are long lists and don't clearly show popularity or feature matrices. There are just so many options. Im thinking something like https://djangopackages.org , for example see https://djangopackages.org/grids/g/commenting/

Not yet. It's a hard problem.

Re: R for Data Science

#70

Post by crdb »

siddboots wrote: post #67
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.

Hey Hadley. Huge fan of your work! Many of the libraries you have authored or co-authored have had a big influence on how I think about building tools. I looks forward to getting a hard copy of the book! I have a bit of a nitpick about chapter 13 on "relational data", in which I believe you are consistently misusing the technical term "relation" to refer to the relationship between two data sets. In the context of re…

Technically, the relation (value) is the set of tuples (i.e. the data itself, "the true statements", the rows in the table), the relation variable is what is defined by CREATE TABLE (i.e. how the data is constrained, "what can be true") and what most people are trying to model.

The relational data model - the set of relation variables - is thus the "equation that defines what is going on in your company" and what the DBMS puts in - the set of relation values, usually abbreviated to relations - can be seen as "the history of what happened at your company".

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