How to learn popular analytics tools on your own – ‘R’ Statistical Software


‘R’ Statistical Software

Known as the open statistical software, the learning curve for ‘R’ is pretty easy even for non-geeks and non-statistical professionals. This is one software you can definitely master on your own. So to learn the ‘lingua franca’ of data science, let’s get cracking!

R is multi-platform compatible – Windows PC, Mac, Linux, name it and you get it. You don’t need to have any specific hardware requirements in the beginning when you are working with small datasets.

You can download R from its home page that helps you choose your preferred CRAN Mirror and walks you through the entire process.  When installing, you can probably just accept the default settings.

For Ubuntu Linux or other Debian-related Operating Systems, a more direct method is:

% sudo apt-get install r-base

If you experience problems installing R, you can consult the R FAQ.

Once you have installed ‘R’, start the program from the system menu that shows you the console area where you write your commands.

This starter manual from the R project will guide you to exporting and importing of data into R.

You will need to work with some datasets. So here are some resources that you can quickly download as CSV files.  Although you may find most datasets are dated, it works fine if you are looking for some data to play with.  While this is a more comprehensive and updated list of data.

Start simple, and refine gradually, is the key to R. You can also key in “R tutorial” into Google to check out a tutorial that suits your knowledge skills.

Begin with creating simple frequency distributions and then move on to bivariate expressions with scatter plots, Peason’s R and Linear regression Models. Once you get the hang of it, it is indeed very simple.

The capabilities of R are unlimited with a strong developer community and more than 300 active R-bloggers who are constantly sharing innovative uses and applications.

So if you want to master this leading open source statistical and data analysis language join the community of data scientists and analysts. Learn R and develop your own libraries. It is fast becoming ‘the hottest software‘ to work with.

Here are some additional resources for the keen learner who wants to make a career in data science, or for that matter enhance his job prospects equipped with ‘R skills’.

While this blog gives you ‘R’ updates, this is a comprehensive overview of how R can be a part of your learning curve or current job.

And if you have any problem beginning with ‘R’ feel free to contact me.

Highlights (updated 27/12):

~ For a windows system you need to choose the binary version.

~ Once you have installed R you can keep adding on free extensions called packages to the core program. Packages are developed continuously by developers and added / improvised. So you have plenty to choose from.

~ R offers you the flexibility to work with various packages or commands to achieve your desired result.

~ R has a command line interface. You type in command/s into a window called the R console, that follow certain grammatical rules called syntax.