Difference between revisions of "R"

Revision as of 15:42, 18 April 2019

R ( http://www.r-project.org/) is an open source software - a well organized and sophisticated package - that facilitates data analysis, modeling, inferential testing and forecasting. It is a user friendly software which allows to create new function commands to solve statistical problems. It runs on a variety of UNIX platforms (and similar systems such as LINUX), Windows and Mac OS.

R is the most preferred open source language for analytics and data science. At Microsoft, R is used by its data scientists, who apply machine learning to data from Bing, Azure, Office, and the Sales, Marketing, and Finance departments. Twitter has been using R for measuring user-experience. On the other hand, the cross-platform compatibility of R and its capacity to handle large and complex data sets make it an ideal tool for academicians to analyze data in their labs.

R can be used for simple calculations, matrix calculations, differential equations, optimisation, statistical analysis, plotting graphs, etc. Also, it is useful to anybody who wishes to undertake extensive statistical computations and data visualization.

Note: Each numbered topic corresponds to a single spoken tutorial. Each bulleted point corresponds to a command or topic that must be covered in the given spoken tutorial.

Module 1: Introduction to basics of R

• Version of R and RStudio used

• Operating systems on which these run

• Quick intro to R and RStudio

• Resizing the font and window size

• , +, -, ^, sqrt

• exp, log, sin

• Different ways of invoking log

• Vectors using seq and length

• pi

• Plotting a sine function

• Defining more points to get a smooth plot

• Plotting with points and as line

• Introduction to help

Module 2: Introduction to data frames in R

Module 3: Introduction to RStudio

Module 4: Introduction to R script

Module 5: Working Directories in RStudio

Module 6: Indexing and Slicing Data Frames

Module 7: Creating Matrices using Data Frames

Module 8: Operations on Matrices and Data Frames

Module 9: Merging and Importing Data

Module 10: Data Types and Factors