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R is a programming language. It is also a software environment for statistical statistics, data manipulation, and graphics description.
Robert gentlemen and Ross Ihaka invented r language in August 1993 at Auckland university in New Zealand. And R is named from the first letters of the names of these two.
The R is an open-source language. And R programming language is freely available under the General Public License.
The R language is widely used for statistics and data mining. The software environment in R is written in FORTRAN and C language. R language supports Linux, Windows, Mac, and almost all operating systems/platforms.
Let's take an Example of R programming: To Find Minimum or Maximum number?
Input
Numlist = c(21, 38, 91, 12, 87, 16)
print('Original vector:')
print(Numlist)
print(paste("The Maximum value is:",max(Numlist)))
print(paste("The Minimum value is:",min(Numlist)))
Output
[1] "Original vector:"
[1] 21 38 91 12 87 16
[1] "The Maximum value is: 91"
[1] "The Minimum value is:12"
[Program exited with exit code 0]
What is R-Studio?
We need an IDE (Integrated Development Environment) to install the R programming language. If you have worked on other programming languages, do you know what IDE is? IDE is a platform where we need to install a programming language. For example, in the case of the python programming language, we use jupyter(IDE).
We have various teams of experts with years of experience in various languages. If you are stuck with a python programming assignment, you can take our python assignment help service.
For Java, we use Eclipse IDE. Similarly, the IDE used for the R programming language is R-Studio. It helps you to make your programming task easier.
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Let's have a look at the R-studio window
In this image, we have 4 panes or 4 windows. As mentioned, each of the windows shows you something about your work.
Pane 1:Programmer used to name it Script Window, in this, a programmer used to write code.
Pane 2:Console window, it Shows codes to be executed are written in this console window.
Pane 3: History pane, any cmd that you run can be shown in the history column.
Pane 4: It contains files, plots, packages, help, etc.
R programming language provides nearly 10,000 packages. You can install them from pane 4 easily.
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Simple linear regression is used to analyze large amounts of data
Multiple regression is used in big data and data analysis
Logistic regression is a data analytics technique
Cross-validation is used to improve model performance
R programming is a powerful tool for performing virtual statistical computation. It saves time and produces error-free work.
R is an interpreted language. It simply means it executes code line by line.
R is a vector-based language. It allows the users to perform multiple calculations and complex operations in a single command.
It has the efficiency to run on various operating systems like Windows, Mac OS, and UNIX, as there are several graphical front-ends available.
R programming language supports arrays, data frames, lists, and matrix arithmetic.
It is highly compatible with other programming languages, including C++ programming, Java,.Net, and FORTRAN
R is essential in data science because of its flexibility in statistics. It is normally used in biology, genetics as well as in statistics.
It also facilitates easy debugging of errors in the code
It also provides a Robust facility for data handling and storage.
Disadvantages of R programming language
Weak origin
R language is based on S programming language, it does not support dynamic or 3d graphics.
Security
It lacks basic security because this r programming language can not be embedded into web applications.
Complicated syntax
If we compare it to any other programming language, the syntax or R programming language is a little difficult.
Data Handling
R programming language utilizes more memory when compared to python. R requires the entire data in one single place in the memory. Therefore it creates some issues for handling large amounts of data.
How R language is helpful to you
R programming language will be applied in Bootstrap, clinical trials, and Bayesian.
It is utilized for data mining, logistic regression, and mapping.
Programs in the R language are utilized for modeling, graphics, and microarray analysis.
One can use RGUI.
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