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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
  • Using +, -, ^, sqrt
  • Using exp, log, sin
  • Different ways of invoking log
  • Vectors using seq and length
  • Using 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

  • Storing captaincy information in vectors
  • Constructing a data frame using vectors
  • Plotting one vector of a data frame vs. another one
  • Adding a vector to a data frame
  • Saving a data frame into a csv file
  • Preventing the writing of row numbers into the csv file
  • Changing the contents of a csv file through a text editor
  • Loading a csv file into a data frame
  • Accessing the data sets that come with R

Module 3: Introduction to RStudio

  • Features of RStudio
  • A look at the windows in RStudio interface:
    • Source and Console windows
    • Workspace window
    • Plots and Files window
  • Example to plot a simple data set
  • Introduction to packages in R
  • How to find the list of packages installed in R
  • Installation of R packages in RStudio
  • Loading and using R packages

Module 4: Introduction to R script

  • What is an R script
  • Features of R script
  • How to create and save an R script from the user interface (UI) of RStudio
  • Shortcut keys to create an R script
  • How to use auto-completion of commands
  • How to run an entire script
  • How to run a block of a script
  • How to add comments
  • How to comment an existing line
  • How to load one script into another script

Module 5: Working Directories in RStudio

  • What is working directory in R
  • How to know current working directory
  • How to use getwd function
  • How to set a working directory from the user interface of RStudio
  • How to set a working directory from the Console window of RStudio
  • How to use setwd function
  • How to read and store a csv file in R
  • How to use read.csv function
  • How to view a stored csv file in R
  • How to use View function

Module 6: Indexing and Slicing Data Frames

  • Shortcut key for assignment operator (<-)
  • How to perform numeric indexing
  • How to extract a row or column from a data frame
  • How to retrieve multiple rows from a data frame
  • How to combine objects to form a vector
  • How to perform logical indexing on a data frame
  • How to perform name indexing on a data frame
  • How to slice a data frame using subset function
  • How to select required columns (by name) from a data frame
  • How to retrieve data using double square brackets

Module 7: Creating Matrices using Data Frames

  • Data required in a matrix format
  • Convert a data frame into a matrix
  • Create a matrix with known data
  • Add two matrices
  • Subtract two matrices
  • Multiply two matrices element wise
  • Perform true matrix multiplication
  • Calculate the transpose of a matrix
  • Calculate the determinant of a matrix

Module 8: Operations on Matrices and Data Frames

  • How to find the inverse of a matrix
  • How to calculate the sum of elements in a matrix using for loop
  • How to calculate the sum of elements in a matrix using sum function
  • How to calculate time elapsed in an operation
  • How to find out the sum of rows of a matrix
  • How to find out the sum of columns of a matrix
  • How to add a new column or row to an existing data-frame
  • How to use cbind and rbind function

Module 9: Merging and Importing Data

  • Use of built-in functions in R for exploring a data frame
  • Access help in RStudio
  • Advantages of merging data frames
  • Merge two data frames
  • Import data from command line
  • Import xml file and txt file in R
  • Import data from user interface of RStudio

Module 10: Data Types and Factors

  • What is an object in R
  • Types of R - objects
  • What is an atomic vector in R
  • Types of atomic vectors
  • How to find types of vectors
  • Factors in R
  • Levels of a factor in R
  • Identification of categorical variables
  • How to change the type of a vector
  • How to change the values of levels

Contributors and Content Editors

Manivel, Nancyvarkey, Sudhakarst