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R Programming – A Practical Approach

R Programming – A Practical Approach

R Programming – A Practical Approach

Course Objectives:

As per the official website(www.r-project.org/) - “R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.”

R was developed by the statisticians Ross Ihaka and Robert Gentleman in the year 1993 at University of Auckland, New Zealand.

R language is an interpreted language and it supports looping and branching along with modular programming using functions. It is highly integrable and we integrate R with the procedures written in C, C++, .Net, Python or FORTRAN.

Anyone can download R for free under the terms of GNU GPL (General Public License). It is a simple and effective language with effective data handling and storage facility including looping statements, other user defined recursive functions and input-output facilities. Also, it includes a variety of operators for calculation of arrays, lists, conditional statements, vectors and matrices. Apart from this, R includes a wide range of coherent and integrated collection of tools for data analysis.

R is one of the most popular data programming language and as per the Rexer Data Science Survey, 76% of analytic professionals reported using R and 36% selected R as their primary tool. Many other reputed surveys such as KDnuggets data science software poll has shown increasing popularity of R. With the comments such as ‘Data is the new Oil of the Digital Economy’ and rising demand for data scientists all over the world, R has gained traction in recent times and is currently supported by a vibrant and talented community of contributors.

In the introductory ‘R-Programming’ course, Academy has focused on getting the course takers familiar with the basic commands, plots and data presentation. We have taken real life example as well as the default datasets provided within the R for demonstrating various functions and for doing the analysis.

Pre-requisites for learning R Programming

  1. You should have basic knowledge of computers.
  2. You should have a 32-bit system where you can install R IDE.
  3. You should have basic problem solving skills and analytical skills.
  4. Knowledge of basics of probability and statistics would be very helpful.

1. R - Basics

Learning Objectives: We have started with step-by-step Installation of R and the R Studio which is a GUI based IDE for R language. We have also explained package installation on R, built in datasets in R, manual data entry, data importing, tabular to row data conversion. We have also looked at the default colors present in the data and a more elaborate color options named ‘Colorbrewer’.
Topics: Steps to Install ‘R’, Introduction to ‘RStudio’, RStudio Interface, Steps to install packages, Default Data-sets in R, Manual Data entry, Data Importing, Tabular to row data conversion, R - colors, Overview - 'Colorbrewer’.

2. Introduction to Charts

Learning Objectives: This chapter covers the details about various charts in R. R programming has multiple libraries which can be used to create charts like Bar charts, pie charts, histograms, box-plots etc. A bar graph or a bar chart is the representation of data in bars. On other hand, a pie chart is the representation of data or values as sectors within the circle each represented with a different color to distinguish them. Box-plot is used for getting information about possible outliers in the data sample. Various ways to save plots as images has also been explained in the unit.
Topics: Bar charts, Pie charts, Histograms, Box-plots, Multiple graphs, Images.

3. Introduction to Statistics

Learning Objectives: This chapter covers the basic concept of statistics viz frequencies, descriptives, hypothesis testing and chi-square testing in R programming. The frequency distribution of a data variable is the count of data that is occurring within a collection of non-repeated categories. Descriptive statistics gives summary statistics of the data and is the basis of advanced analysis of data. We then had a look on inferential statistics methods. In this unit, we have explained single proportion testing, single mean testing and Chi-square testing, which is used to infer results based on the sample data characteristics and hypothesized values. We have also done a univariate analysis to find patterns in the data.
Topics: Frequencies, Descriptives, Single proportion testing, Single mean testing, Chi-square Test, Univariate Analysis, Problem Statement 3: Descriptive statistics, Solution 3: Descriptive statistics.

4. Working with Data

Learning Objectives:This chapter covers the details of working with data. We can have outliers in the data and its treatment is explained. Outliers are those observations which occur very infrequently and might be the result of errors while observing. Proper treatment of data is necessary for the unbiased result.This might includes subsetting, sorting, extracting unique observations renaming variables, creating new variables etc. Each of these tasks can be accomplished using set of newly introduced packages.
Topics:Outliers, Transformation of variables, Composite variables, working with missing data.

5. Managing Huge Data

Learning Objectives:IThis chapter covers the details of working with cases and subgroups and files. Any data set is like an enclosed or shelled collection. It consists of cases which are exactly the objects in the same collection with each case having one or more attributes or qualities known as variables. This lesson covers working with subgroups, and merging files. Merging means that different datasets or files are combined together within a single dataset or file. R programming includes method to merge the files.
Topics:Working with cases, Working with subgroups, Working with Files - Merging.

6. Association: Presentation

Learning Objectives:In this chapter, Bar charts, Box plots and scatter plots are demonstrated. A bar chart or a bar graph represents the the data with the help of bars or rectangles. The values of the variables are determined by the height or length of the rectangle be it vertical or horizontal. A box plot is an exploratory graphic which enables us to encapsulate the features of quantitative variables. A scatter plot pairs up the values of two quantitative variables in a dataset and represent them as geometric points in the cartesian diagram.
Topics:Bar charts, Box plots, Scatter plots.

7. Association: Statistics

Learning Objectives: This chapter is all about statistical concepts like correlation, regression, proportions etc. A correlation is a statistical method or technique to display if there is a relation between pairs of variables or how strongly the pairs of variables are related. Regression is the most critical fundamental tool for statistical analysis frequently used in various research fields. Bivariate regression is the simplest linear regression procedure. Then we also demonstrated few tests as well in the later part of the chapter such as T test, one-factor analysis of variance, proportions etc.
Topics:Correlation, Bivariate Regression, T-test, Paired t-test, ANOVA, Proportions, Working with crosstabs, Statistics for bivariate associations.

8. Complex Charts

Learning Objectives:This chapter covers in detail the method of creating bar charts for mean, scatter plots for grouped data, scatter plot matrices and a very interesting and visual 3D scatter plot.
Topics:Bar charts for mean, scatter plots for grouped data, Scatter plot matrices, 3D scatter plots.

9. Multiple Variable Statistics

Learning Objectives:This chapter includes some relatively advanced topics such as multiple regression, two factor ANOVA, cluster analysis and principal component & factor analysis. These topics are very important specially multiple regression which is used very extensively in research papers and industry to establish relationship between variables.
Topics:Multiple Regression, Two-factor ANOVA, Cluster Analysis, Principal component / factor Analysis.

Q1. Who is our Instructor?

Ans. All our instructors are domain experts from the Industry or are from world-renowned academic Institutes and have at least 10-12 yrs of relevant experience in their domains. They are subject matter experts and are trained by Electronics & ICT Academy for providing online training so that participants get a great learning experience.

Q2. What are the payment options?

Ans. You can pay by Credit Card, Debit Card or Net Banking from all the leading banks. We use a SBI Payment Gateway. Additionally, you may send us a cheque with appropriate details or use wire transfer.

Q3. How to get my queries resolved?

Ans. You can email us at ict@iitk.ac.in

Q4. What internet speed is required to attend the LIVE classes?

Ans. 1Mbps of internet speed is recommended to attend the LIVE classes. However, we have seen people attending the classes from a much slower internet.

Q5. How soon after signing up would I get access to the learning content?

Ans. As soon as your payment is verified, you will immediately get access to our course content in the form of a complete set of previous class recordings, PPTs, PDFs, assignments and access to our 24x7-support team. You can start learning right away.

Q6. What are the system requirements?

Ans. Your system should have a 4GB RAM, a processor better than core 2 duo and operating system can be of 32bit or 64 bit.

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Equivalent Percentage = CGPA obtained X 9.5 X (10/CGPA Scale)
Example: If CGPA obtained is 8.00 on the scale of 10, then Equivalent
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  1. R- Basics

    1. Steps to Install 'R'

    2. R-Studio Installation

    3. R-Studio Interface

    4. Steps to Install Packages

    5. Default Data-Sets in R

    6. Manual Data Entry

    7. Data Importing

    8. Tabular to Row Data Conversion

    9. R - Colors

    10. Overview - 'Colorbrewer'

    11. Colors in R: Summary

  2. Introduction to Charts

    1. Bar Charts

    2. Pie Charts

    3. Histograms

    4. Box-Plots

    5. Customized Graphs

    6. Images

    7. Layering Plots: Summary

  3. Introduction to Statistics

    1. Frequencies

    2. Descriptives

    3. Single Proportion Testing

    4. Single Mean Testing

    5. Chi-Square Test

    6. Univariate Analysis

    7. Descriptive Statistics: Summary

  4. Manipulating Data

    1. Outliers

    2. Transformation of Variables

    3. Composite Variables

    4. Working with Missing Data

    5. Working with Outliers: Summary

  5. Manging Huge Data

    1. Working with Cases

    2. Working with Subgroups

    3. Working with Files - Merging

    4. Working with Subgroups: Summary

  6. Association: Presentation

    1. Bar Charts

    2. Box Plots

    3. Scatter Plots

    4. Working with Plots: Summary

  7. Associations: Statistics

    1. Correlation

    2. Bivariate Regression

    3. T-Test

    4. Paired T-Test

    5. ANOVA

    6. Proportions

    7. Chi-Square Test

    8. Statistics for Bivariate Associations

    9. Association Stats: Summary

  8. Complex Charts

    1. Bar Charts for Mean

    2. Scatter Plots for Grouped Data

    3. Scatter Plot Matrices

    4. 3D Scatter Plots

    5. Charts for Multiple Variables: Summary

  9. Multiple Variable Statistics

    1. Multiple Regression

    2. Two-Factor ANOVA

    3. Cluster Analysis

    4. Principal Component / Factor Analysis

    5. Multiple Variable Statistics: Summary