R Programming – A Practical Approach
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
- You should have basic knowledge of computers.
- You should have a 32-bit system where you can install R IDE.
- You should have basic problem solving skills and analytical skills.
- 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.
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Click on 'Direct Enrollment ' in the course
Registration is required for course enrollment and scholarship request.
You will get an activation link in the mail. Click on it and follow the instructions to complete your registration.
Login with your username and password. You will be redirected to your dashboard.
You can browse available courses from Home Page or by clicking Courses link on menu bar. Navigating to a course will give you two options: 1.Direct Enrollment 2. Scholarship Enrollment
Logged in user can fill their details in the 'Scholarship Application' available in each course.
We will mail you further details after processing your application based on your academic and professional record.
After processing your scholarship request, we will send you the necessary details.
You can directly enroll for the course by clicking on ‘Direct Enrollment’, if you do not require scholarship.
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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
percentage will be 8.00 X 9.5 X (10/10) = 76%,
or If CGPA is 3.7 out of 4, then Equivalent percentage will be 3.7 X 9.5 X (10/4) = 87.88%
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Steps to Install 'R'
Steps to Install Packages
Default Data-Sets in R
Manual Data Entry
Tabular to Row Data Conversion
R - Colors
Overview - 'Colorbrewer'
Colors in R: Summary
Introduction to Charts
Layering Plots: Summary
Introduction to Statistics
Single Proportion Testing
Single Mean Testing
Descriptive Statistics: Summary
Transformation of Variables
Working with Missing Data
Working with Outliers: Summary
Manging Huge Data
Working with Cases
Working with Subgroups
Working with Files - Merging
Working with Subgroups: Summary
Working with Plots: Summary
Statistics for Bivariate Associations
Association Stats: Summary
Bar Charts for Mean
Scatter Plots for Grouped Data
Scatter Plot Matrices
3D Scatter Plots
Charts for Multiple Variables: Summary
Multiple Variable Statistics
Principal Component / Factor Analysis
Multiple Variable Statistics: Summary