This post is regarding
the introduction of the R.
what is R? R is just
like the other programming languages like python, java, c. The main difference
comes here - R is powerful in performing statistical computations. R is a
high-level language and an environment predominantly used for data analysis and
graphics. R is more commonly used by Data Scientists, Statisticians and lot
more who desires to extract the valuable insights from the data.
why is it named R? R
is named after the first letters of the names of creators who crafted it. R was
created by Ross Ihaka and Robert Gentleman at the University of Auckland, New
Zealand.
Why should you want to
use R?
- The main reason for switching to R is to take advantage of its unrivalled coverage and the availability of new, cutting-edge applications in fields such as generalized mixed-effects modelling and generalized additive models.
- The next reason for learning R is that you want to be able to understand the literature. More and more people are reporting their results in the context of R, and it is important to know what they are talking about.
- Third, look around your discipline to see who else is using R: many of the top people will have switched to R already. A large proportion of the world’s leading statisticians use R, and this should tell you something (many, indeed, contribute to R, as you can see below).
- Another reason for changing to R is the quality of back-up and support available. There is a superb network of dedicated R wizards out there on the web, eager to answer your questions. If you intend to invest sufficient effort to become good at statistical computing, then the structure of R and the ease with which you can write your own functions are major attractions. Last, and certainly not least, the product is free. This is some of the finest integrated software in the world, and yet it is yours for absolutely nothing.
Why R for data
analysis?
R is not the only
language that can be used for data analysis. Why R rather than another?
Here is a list:
- interactive language
- data structures
- graphics
- missing values
- functions as first-class objects
- packages
- community
Data analysis is
inherently an interactive process — what you see at one stage determines what
you want to do next. Interactivity is important. Language is
important. The two together — an interactive language — is even more than
their sum.
R has a fantastic
mechanism for creating data structures. Obviously, if you are doing data
analysis, you want to be able to put your data into a natural form. You
don’t have to warp your data into a particular structure because that is
all that is available.
Graphics should be
central to data analysis. Humans are predominantly visual, we don’t
intuitively grasp numbers like we do pictures. It is easy to produce
graphs for exploring data. The default graphs can be tweaked to get
publication-quality graphs.
Real data have missing
values. Missing values are an integral part of the R language. Many
functions have arguments that control how missing values are to be handled.
Functions, like mean
and median, are objects that you can use as data. You can easily change
your analysis to use the median (or some strange estimate you make up on the
spot) rather than the mean.
R has a package system
that makes it extremely easy for people to add their own functionality so it is
indistinguishable from the central part of R. And people have.
There are thousands of packages that do all sorts of extraordinary
things.
The R community is
very strong and quite committed to improving data analysis
There are six key
things to learn to effectively use R:
- how to organize the data
- how to import the data into R
- how to check the data once within R
- how to select the appropriate statistical model and apply it correctly
- how to interpret the output
- how to present the analysis for publication
The hardest part is
selecting the right statistical method to implement on. It all comes by
experience.
Never do an analysis
that is more complicated than it needs to be. KIS - Keep It Simple.
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