Iris for Big Data #rstats #bigdata

Quote of the Day-

it is impossible to be a data scientist without knowing iris 

#Anonymous #Quotes

 

Revolution Analytics has been nice enough to provide both datasets and code for analyzing Big Data in R.

http://www.revolutionanalytics.com/subscriptions/datasets/

http://packages.revolutionanalytics.com/datasets/

Site was updated so here are the new links

 

while the Datasets collection is still elementary, as a R Instructor I find this list extremely useful. However I wish they look at some other repositories and make .xdf and “tidy” csv versions. A little bit of RODBC usage should help, and so will some descriptions. Maybe they should partner with Quandl, DataMarket, or Infochimps on this initiative than do it alone.

 

Overall there can be a R package (like a Big Data version of the famous datasets package in R)

But a nice and very useful effort

Revolution R Datasets

More code-

http://blog.revolutionanalytics.com/2013/08/big-data-sets-for-r.html

Also a recent project made by a student of mine on Revolution Datasets and using their blog posts.

Note how much more better the above project is than use the mini and super clean datasets within R (like Boston)

 

Hat TIP- R’s very own Mr Smith
Unrelated-
For more on IRIS

 

Using ifelse in R for creating new variables #rstats #data #manipulation

The ifelse function is simple and powerful and can help in data manipulation within R. Here I create a categoric variable from specific values in a numeric variable

> data(iris)

> iris$Type=ifelse(iris$Sepal.Length<5.8,”Small Flower”,”Big Flower”)
> table(iris$Type)
Big Flower Small Flower
77           73

The parameters  of ifelse is quite simple

Usage

ifelse(test, yes, no)
Arguments

test
an object which can be coerced to logical mode.

yes
return values for true elements of test.

no
return values for false elements of tes

 

So many R Packages Everywhere, which one do I use? #rstats

Some thoughts on R Packages

  • CRAN is no longer the sole repository for many useful R packages. This includes R Forge, Google Code and increasingly Github
  • CRAN lacks the flexibility and social aspect of Github.
  • CRAN Views is the only thing that lists subject wide listing of R packages. The categorization is however done more on methods than on use cases or business domains.
  • Multiple R packages for the same thing. Which one do I use? Only Stack Overflow helps with that. No rating , no recommendation system
  • The packages suggested by R package feature needs better and automatic association analysis . Right now it is manual and dependent on package author and maintainer.
  • Quis custodiet ipsos custodes? Who guards the guardians of R packages. In an era of cyber security, we need better transparency on security measures within R packages especially given the international nature of the project.  I am very sure I ( or anyone) can create R code to communicate discretely especially on Windows

  • I would rather not install anything on my local machine, and read the package directly from the CRAN . CRAN was designed in an era of low bandwidth- this needs to be upgraded.
  • Note I am refraining respectfully from the atrocious nature of aesthetics in the home website. Many statisticians feel no use of making R user friendly. My professors at U tenn (from which I dropped out in 2 sems) were horrified when I took courses in graphic design as I wanted to know more on the A and B, which make the A/B testing of statistical design. Now that I am getting older, I get horrified by the lack of HTML, CSS and JQuery by some of the brightest programmers in this project.
  • Please comment below.