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What is R Programming?
R is a language as well as an environment for statistical computing as well as graphics.
GNU Project, similar to the S language as well as an environment which developed at Bell Laboratories by John Chambers as well as colleagues.
What Does GNU Means?
GNU is a recursive acronym for “GNU’s Not Unix!” chosen because GNU’s design is Unix-like, but differs from UNIX by being free software as well as containing no UNIX code.
The GNU project includes an operating system kernel, GNU Hurd, which was the original focus of the Free Software Foundation (FSF).
R considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
R provides a wide variety of statistical i.e. linear as well as nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc. as well as graphical techniques, as well as is highly extensible.
The S language is often the vehicle of choice for research in statistical methodology as well as R provides an Open Source route to participation in that activity.
What is the Strength of R Programming?
One of R’s strengths with which well-designed publication-quality plots produced, including mathematical symbols as well as formulae where needed.
Great care taken over the defaults for the minor design choices in graphics, but the user retains full control.
R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public Licence in source code form.
It compiles as well as runs on a wide variety of UNIX platforms as well as similar systems including FreeBSD as well as Linux, Windows as well as MacOS.
R- An In-Depth Learning
R is an integrated suite of software facilities for data manipulation, calculation, as well as a graphical display. It includes:
- An effective data handling as well as a storage facility,
- A suite of operators for calculations on arrays, in particular, matrices,
- A large, coherent, integrated collection of intermediate tools for data analysis,
- Graphical facilities for data analysis as well as display either on-screen or on hardcopy, as well as
- A well-developed, simple as well as an effective programming language which includes conditionals, loops, user defined recursive functions as well as input as well as output facilities.
R- designed around a true computer language, as well as it allows users to add additional functionality by defining new functions.
Much of the system itself written in the R dialect, which makes it easy for users to follow the algorithmic choices made.
For computationally-intensive tasks, C, C++ as well as FORTRAN code can be linked as well as called at run time.
Advanced users can write C code to manipulate R objects directly.
What do other users think about R?
Many users think of R as a statistics system.
We prefer to think of it as an environment within which statistical techniques- implemented.
R- extended easily via packages.
There about eight packages supplied with the R distribution as well as much more available through the CRAN family of Internet sites covering a very wide range of modern statistics.
What R has?
R has its own Latex-like documentation format, which used to supply comprehensive documentation, both online in a number of formats as well as in hardcopy.
Why do we use R?
- Statistical inference
- Data analysis
- Machine learning algorithm
Data science is shaping the way companies run their businesses.
Without a doubt, staying away from Artificial Intelligence as well as Machine will lead the company to fail. The big question is which tool/language should you use?
Should you choose R?
A data scientist can use two excellent tools: R and Python.
You do not have time to learn them both, especially if you get start to learn data science.
Learning statistical modeling, as well as the algorithm, is far more important than to learn a programming language.
A programming language is a tool to compute as well as communicate your discovery.
The most important task in data science is the way you deal with the data: import, clean, prep, feature engineering, feature selection.
This should be your primary focus.
If you are trying to learn R as well as Python at the same time without a solid background in statistics, it’s plain stupid.
Data scientist are not programmers.
Their job is to understand the data, manipulate it as well as expose the best approach.
Do you think R is difficult?
Years ago, R was a difficult language to master.
The language was confusing as well as not as structured as the other programming tools.
To overcome this major issue, a collection of packages called tidy verse developed.
The best algorithms for machine learning, implemented with R.
Packages like Keras as well as Tensor Flow allow creating high-end machine learning technique.
R also has a package to perform Xgboost, one the best algorithm for Kaggle competition.
Which all languages can communicate with R?
R can communicate with the other language.
It is possible to call Python, Java, C++ in R.
The world of big data is also accessible to R.
You can connect R with different databases like Spark or Hadoop.
Evolution of R
Finally, R has evolved as well as allowed a parallelizing operation to speed up the computation.
In fact, R, criticized for using only one CPU at a time.
The parallel package lets you perform tasks in different cores of the machine.
Let’s create the next big thing together!
Coming together is a beginning. Keeping together is progress. Working together is success.