"R is an interpreted computer programming language which was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand." The R Development Core Team currently develops R. It is also a software environment used to analyze statistical information, graphical representation, reporting, and data modeling. R is the implementation of the S programming language, which is combined with lexical scoping semantics. R not only allows us to do branching and looping but also allows to do modular programming using functions. R allows integration with the procedures written in the C, C++, .Net, Python, and FORTRAN languages to improve efficiency. In the present era, R is one of the most important tool which is used by researchers, data analyst, statisticians, and marketers for retrieving, cleaning, analyzing, visualizing, and presenting data.
The history of R goes back about 20-30 years ago. R was developed by Ross lhaka and Robert Gentleman in the University of Auckland, New Zealand, and the R Development Core Team currently develops it. This programming language name is taken from the name of both the developers. The first project was considered in 1992. The initial version was released in 1995, and in 2000, a stable beta version was released.
Data science deals with identifying, extracting, and representing meaningful information from the data source. R, Python, SAS, SQL, Tableau, MATLAB, etc. are the most useful tools for data science. R and Python are the most used ones. But still, it becomes confusing to choose the better or the most suitable one among the two, R and Python.
"R is an interpreted computer programming language which was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand ." The R Development Core Team currently develops R. R is also a software environment which is used to analyze statistical information, graphical representation, reporting, and data modeling. R packages have advanced techniques which are very useful for statistical work. The CRAN text view is provided by many useful R packages. These packages cover everything from Psychometrics to Genetics to Finance. For data analysis, R has inbuilt functionalities Data visualization is a key aspect of analysis. R packages such as ggplot2, ggvis, lattice, etc. make data visualization easier. There are hundreds of packages and ways to accomplish needful data science tasks.
Python is an Interpreted high-level programming language used for general-purpose programming. Guido Van Rossum created it, and it was first released in 1991. Python has a very simple and clean code syntax. It emphasizes the code readability and debugging is also simple and easier in Python For finding outliers in a data set both R and Python are equally good. But for developing a web service to allow peoples to upload datasets and find outliers, Python is better Most of the data analysis functionalities are not inbuilt. They are available through packages like Numpy and Pandas Most of the data analysis functionalities are not inbuilt. They are available through packages like Numpy and Pandas Python has few main packages such as viz, Sccikit learn, and Pandas for data analysis of machine learning, respectively.
R is the most popular programming language for statistical modeling and analysis. Like other programming languages, R also has some advantages and disadvantages. It is a continuously evolving language which means that many cons will slowly fade away with future updates to R.
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