R Programming for Data Science and Machine Learning

R Programming course for newbies. R installation, data types, statements, functions, conditions, packages, data frames.

A warm welcome to the R Programming for Beginners course by Uplatz.

R is a programming language regularly used for arithmetical computing and graphical demonstration to examine and visualize data. R is a software environment which is used to analyze numerical data and graphical representation. R lets us to do sectional programming using functions. R programming language was named R, founded on the first name letter of the two authors - Robert Gentleman and Ross Ihaka. An overall understanding of R should cover topics such as introduction, features, installation, RStudio ide, variables, datatypes, operators, if statement, vector, data handing, graphics, statistical modelling, etc. 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.

R is one of the most significant instrument which is used by investigators, data analyst, statisticians, and marketers for retrieving, cleaning, analyzing, visualizing, and presenting data. R is available for Linux, MacOS, and Windows. R's binary and logical operators will look very acquainted to programmers. R can be used to compute a large variety of classical statistic tests. With R, it’s also possible to perform classification analysis such as Principal component analysis and clustering. Many types of graphs can be drawn using R, including, box plot, histogram, density curve, scatter plot, line plot, bar plot, etc.

R Programming for Beginners - Course Syllabus

1. Introduction to R Language

2. R Installation

3. R Data Structures

4. R Data Types

5. R Packages

6. Simple Calculator using R

7. R as a Calculator

8. Condition Statements in R

9. Looping in R

10. Repeat Statement in R

11. Sum of N Natural Numbers

12. Sum Recursion

13. Switch Statement in R

14. Data Pre-processing

15. Functions in R

16. Factors in R

17. Data frames in R

18. Data Reshaping

19. R Math built-in Functions

20. Melting and Casting in R