R Master Guide

Data Science. Just another buzzword you should use to look smart, or is it the real deal?

Maybe a better question is: How can R be your perfect partner to achieve the data science dream?

This master guide will answer all the questions you want to ask about R.

First popularized as a term in 2010 when Google predicts its future, data science has been around much longer.

R is one of the most preferred programming languages for data science in the world. Over 2 million professional data scientists and hobbyists use R worldwide.

Instead of finding help from tutorials and resources all over the internet whenever you get stuck, this article brings you all of it at once.

From beginner to advanced, you will get 120+ free R tutorials, projects, use-cases, applications, interview questions, practicals, and many more to learn R from scratch. I bet you will be a R-ockstar if you follow me till the end.

Already know the basics of R? Jump directly to real-time R Projects

1. What is R?

R is a programming language for data science & machine learning which is open source. Ihaka and Gentleman developed it as a learning aid for statistics students. Due to its easy-to-use nature, it has become a featured tool for data science.

Or a simpler version..

R is the most powerful tool for data analytics as well as visualization. It has thousands of packages that enhance it even further. Packages that enable it to make visual reports, software packages, web apps and much more. It is useful for many disciples like machine learning, data science, and data visualization.

Check out these 126 + Free R tutorials and ease your way to become the next data scientist.

2. Why Learn R?

Because data science is the future and R is among the top tool for it.

Many data scientists use R. Many companies use it as well. All for different reasons and purposes.

Here are a few reasons why R is so popular and why you should learn it:

1. Data analysis tool: Many big and small companies use R for data analysis.
2. Research aid: R can be a tool to analyze test results and outcomes to help in various research fields.
3. Machine learning tool: R is useful for building machine learning models.
4. Data visualization aid: R is the foremost tool for data visualization. Many companies use it to communicate data analysis and statistics to business-oriented minds.
5. Open-source: R is open-source which makes it free to use. You can also modify its base code or make packages of your own to add functionalities you wish to add.
6. So many packages: R has more than 15,000 packages available online. These packages help with a large number of applications.
7. Variety of applications: R is useful for a massive variety of applications. These applications include data analysis, machine learning, web/software development, and much more.

Look what a famous mathematician is saying-

And in the world where stats is the king, It take real guts to ignore R.

Attractive right? Look some more R offerings to why you should learn it.

3. Features of R

There are many useful and amazing features of the R programming environment. Here are a few:

1. Statistical computing: R’s base package already has many useful functions. They allow performing complex statistical operations with ease. It also has several packages that provide even more useful and powerful functions.
2. Data visualization: R is the best when it comes to data visualization. It can produce publication-quality static graphs and plots with its base package. Packages like ggplot2, and plotly enable dynamic and animated graphs as well.
3. Open-source: R is an open-source language and completely free to use and reproduce.
4. Massive community: R has over 2 million users worldwide. The R community helps new and veteran users through forums and discussion rooms. It also organizes conventions, get-togethers, research grants, and scholarships.
5. Wide variety of packages: Many online repositories have packages for R. GitHub, Bioconductor, and CRAN(Comprehensive R Archive Network) are some examples. The CRAN repository houses more than 15,000 R packages.

Before going further, have a look at the latest features of R programming to compare it with other languages.

4. What is use of R in Real-world?

There many varied applications of R programming. Almost every industry sector uses R for one purpose or another.

1. Healthcare: R is used for pre-clinical drug trials and for medical research. Genetic analysis and genetic anomaly detection is another common application of R. Apart from that, R is also used for epidemic prediction and to maintain and use medical records of large populations to analyze effects and side-effects of various drugs.
2. IT: The IT industry uses R for data science and business intelligence for themselves and for their clients as well. It also uses R for software and web development.
3. Finance: the finance industry prefers R for predicting market trends, fraud detection, risk analysis, mortgage rate calculations, and processing various statistic models like linear and nonlinear regression models and time-series models.
4. Academics: R was created for statistical analysis. It is still used for statistical research and for many other research fields as well.
5. Social Media: the social-media sector uses R for machine learning research and to gain insights to improve their user experience.

It's just a few! There are much more Applications of R in real world.

5. Career Opportunities in R

R has a very broad range of applications. Due to this very reason, it is one of the most coveted languages by companies of all sizes and scales. There are more than 3 million job openings for R programmers all over the world.

It is used in every major industry sector from the healthcare industry to manufacturing. Devising marketing strategy, getting business intelligence, calculating financial criteria, detecting anomalies and patterns in transaction records, improving online advertising campaigns, predicting weather patterns, getting damage and loss assessments can all be done with R.

This also means that the need for good R programmers is everywhere. A decent R programmer can earn around $77,520 to $151,716 per year.

Get the detail understanding of R basics to start your data science journey.

6. What are the Job Profiles in R programming?

R programming is used in every industry and, therefore, R programmers can find employment in every industry as well. Companies hire people with skills in R programming for many different roles. Including but not limited to:

1. Data analysts: data analysts clean, organize, analyze and visualize data from multiple sources to get insights and patterns. The average salary of a data analyst is $70,000 per year.
2. Business analyst: business analysts analyze their organization’s financial data and other records to make advice and suggestions to maximize the organization’s profits or to improve results. A business analyst’s average salary is $65,749 per year.
3. Data visualization executive: A visualization executive’s job is to represent stats and figures received after data analysis graphically with crisp clear images that are eye-catching and convey the meaning clearly. A data visualization executive can earn around $112,569 per year.
4. Quantitative analyst: A quantitative analyst is a person with good knowledge of data science as well as finance. Their average salary is around $80,572 per year.
5. Data scientist: A data scientist’s job includes every aspect of data science and analysis. The result of their analysis can have multiple uses for the organization. A data scientist’s average salary is 10,00,000 to $375,700 per year.

NOTE: The above-mentioned salary range has been collected from various job portals and surveys, it could vary based on the experience and skill set.

7. Future Scope of R programming

R has been one of the most popular programming languages for the last two decades. The biggest reason for that is its versatility. Every industry is using R for one purpose or another. With the increasing rate of generation of data and the dependence of industry on data science, R is sure to maintain its popularity in the next decade as well.

The R community is very active and always ensures that R remains the most cutting edge and advanced statistical and analysis tool. In many cases, the R community is the first place where innovative new technology, algorithms or techniques appear.

8. How to Install R?

Now, let’s get our hands dirty and start our journey towards mastery in R programming. The R programming language is cross-platform supportive. You can use it on any OS without any compatibility issues. You can also integrate it with many different programming languages like C, C++, FORTRAN, Java, Python, or Hadoop.

Here is our step-by-step guide to install R for Windows, Linux and mac OS.

9. Basic Concepts of R Programming

Here are the basic concepts of the R programming language:

1. Data types

There are five basic data types in R programming. These are:

• Numeric
• Character
• Complex
• Integers
• Logical

Implement these R data types with examples.

2. Data structures

R has many different data structures that provide specialized properties for different types of data. There are a few basic data structures that are used to build more complex ones. These basic data structures are:

• Vectors
• Factors
• Data-frames
• Arrays
• Matrix

Learn how to work with these data structures.

3. Conditional and loops

R has a few control structures for conditional reasoning and iterative processing. These control structures control the flow of an R program. They are:

• For loops
• While loops
• Break statement
• Next statement
• Repeat loops
• If-else statements
• ifelse() function
• Switch

Implement these Control structure with example.

4. Functions

Functions are blocks of code with a definite, pre-defined purpose. To create functions in R, you can use the function keyword. Functions in R programming take input as arguments and return an output. They have four components, which are:

• Function name
• Arguments
• Function body
• Return statement

Use R functions to solve real-world problems.

5. Packages in R

One of the most exciting features of R is its massive package collection. GitHub and Bioconductor are online repositories where you can find R packages. Maintained by the R development team, CRAN or the Comprehensive R Archive Network is the largest online repository for R packages.

Learn How to Install & Use Packages in R

10. Other Topics in R that You Must Study

After the basics, let’s move on to some advanced topics in R programming, since I’ll talk about these in a separate guide, I’ll only briefly describe them here.

1. Debugging functions: R has many packages and functions that make debugging R code and programs much easier.
2. Performance tuning: It deals with includes habits to avoid to degrade the performance of your R programs and efficient coding practices.
3. Hypothesis testing: This is the process of validating an assumption by using random samples of data to test the hypothesis against and judging its validity based on the results.
4. Principal component analysis: Principal component analysis is used when there are too many variables that affect the required analysis. Using this, you can reduce the number of variables without affecting the information conveyed by the original variables.
5. Factor analysis: Factor analysis is another multivariate analysis technique that reduces the number of variables. This makes the analysis and calculation much easier.
6. Bootstrapping in R: It is a statistical method. In this, we take small samples of the given dataset and perform the required analysis on them. It then makes predictions about the entire data based on the results of the analysis of the samples.
7. Graphical models: Different techniques used to visualize data in graphical formats are called graphical models.
8. Bar charts: these are an important and easier-to-understand way of presenting data graphically.
9. Lattice package: A very popular and powerful graphics package in R programming.
10. Linear regression: In this technique, we find and determine linear relations between two or more variables.
11. Non-linear regression: This technique helps in finding non-linear relationships between independent variables.
12. Logistic regression: It is a type of non-linear analysis that deals with categorical data.
13. Decision trees: Decision trees is a popular data mining technique that uses a tree-like structure to simulate the consequences of various decisions.
14. Random forest: Random forest emulates decision making in complex situations with multiple variables using multiple decision trees.
15. Clustering: This technique involves classifying data into multiple groups based on similarity.
16. Classification: It is a technique that uses certain characteristics to categorize data.
17. SVM training: SVM or a Support Vector Machine learns to classify future examples by studying the current data and its characteristics.
18. Testing models: we use testing models to test machine learning algorithms. The e1071 is an R package that is very useful for this.
19. Bayesian networks: Bayesian networks are useful to answer probabilistic queries. They help in modeling variables and their relationships.
20. Bayesian inference: Bayesian network inferencing uses a Bayesian network to draw insights about the data.
21. Normal distribution: It is a probability distribution that is symmetric about the mean of the data.
22. Binomial distribution: Binomial distribution is a discrete probability distribution technique.
23. Poisson distribution: It shows how many times an event is likely to occur in a given period of time.
24. Predictive analysis: In predictive analysis, we analyze the current data or its sample to make predictions about a larger data.
25. Survival analysis: Survival analysis is used to predict the time at which an event will occur. It is a predictive statistical technique.
26. Chi-square test: It helps in determining the correlation between two variables.
27. T-test: T-tests determine the equality of the means of two data groups.
28. ANOVA algorithm: ANOVA is a statistical algorithm. It measures the difference between the means of two groups.

11. Useful Packages in R

R has a large number of packages available for a variety of tasks. Some of the most recommended R packages are:

1. Tinyverse
2. Ggplot
3. PARTY
4. Devtools
5. MLR
6. R markdown
7. Leaflet
8. e1071
9. Stringr
10. Plotly
11. Caret
12. Dygraphs
13. Sentimentr
14. Shiny
15. Reshape
16. MICE
17. MASS
18. Randomforest
19. Ggmap
20. Dichromat

Use these packages in R Programming with example.

12. Object-Oriented Programming

R is a functional or sequential programming language. This means that an R program is interpreted line-by-line or function wise. In object-oriented programming, the program behaves like a collection of objects with defined behaviors, interacting with each other. Compared to the object-oriented programming paradigm, a functional or sequential approach is obsolete.

However, R also has the option to write and interpret code in an object-oriented way. It has multiple object models that help in this. R’s base package provides the S3, S4, and R5 models. The R6 is another object model for R that is very popular which comes with a package.

Look at the examples of Object-Oriented Programming in R.

13. Data Reshaping

Before performing any kind of analysis, we have to clean the data of unnecessary values and fill the blank places. we have to arrange it into an acceptable format that is easier to process for the analysis. This process of cleaning and formatting the data is called data reshaping.

Data reshaping is the first step in every data analysis. R has a suite of functions that help in this process. Functions like cbind(), t() and rbind() help in arranging the data in any desirable format. It also has packages like reshape, rehsape2 and tidyr that provide even more versatile and powerful functions that make reshaping the data much easier.

Discover 4 major functions to Organise your Data.

14. Useful Functions in R

R has a plethora of functions that make data analysis and statistical computing much easier. These functions can be categorized based on what type of objects or structures they operate on:

1. String manipulation

These functions help in manipulating or changing strings in desired ways. They can split them into substrings, concatenate them into a single string and so on. They can also provide more information on string objects. Eg: substr(), cat(), grep(), nchar(), etc.

2. Data manipulation

These functions take large or small data objects as arguments and change or edit them in the required way. Eg: the sample() function takes a dataset as input and returns a random sample of specified size, the duplicate() function creates a duplicate of the given data with certain modifications if required, etc.

Work on data manipulation in R with examples

3. Input and output functions

These functions are useful for getting user input or for displaying output on the screen. Eg: scan(), readline(), etc or print() and cat().

Learn to Read/write the input/output functions in R

4. Descriptive statistics

These functions describe the given data. They provide further insights into the data and highlight patterns. Example: summary(), name(), apply(), simple complex(), etc.

Implement Descriptive Statistics with Examples

5. Contingency tables

Contingency tables help in condensing large complex data into smaller tables. We use the table() function to create and manipulate them.

Learn to Create Contingency Tables in R

6. Generalized linear models

R has simple functions that create linear and non-linear regression models. The glm() function is the easiest way to create logistic regression or Poisson regression models.

Learn to Build Generalized Linear Models in R

15. Data Visualization in R

R biggest strength is data visualization. It can render publication-quality graphs and plots with simple commands. R’s base package has the functions to make rich static graphics with no fuss.

It also has packages like ggplot2, dygraphs, and plotly that can make dynamic and animated graphics easily. The functions also provide a lot of customizability. Any kind of graph, any kind of data, any kind of colors and visual properties the possibilities are infinite.

Expore the attractive data Visualization in R .

16. R for Data Science

The R programming environment is rich with features that help in processing, transforming, and visualizing data. It provides simple functions for performing complex and multi-tiered calculations. It also has a suite of packages that make these tasks more easier. This makes it the perfect data analysis tool. Data cleaning, data analysis, data modeling, and data visualization are all very simple and easy-to-do with R.

It can also interface with databases to enable data extraction and efficient data management. Advanced data analytics options like image processing and prediction models are also present in R’s environment.

See how R can be your armor when it comes to data science.

17. R for Machine Learning

There are many different packages available for machine learning in R. Packages that can implement single machine learning models as well as ones that can facilitate complete machine learning suites. These packages include:

1. rpart: the rpart package can implement partitioning and repetitive machine learning models like decision trees and classifications.
2. MLR: The MLR package stands for Machine Learning in R and is a complete machine learning package for R.
3. randomforest: The randomforest package helps in implementing the random forest algorithm which is the most popular machine learning algorithm.
4. CARET: Classification And REgression Training
5. Neuralnet: The neuralnet package uses backpropagation and weighted backtracking to help form neural networks in R.

Check all the essential Machine Learning Tools for R.

18. Interesting Projects in R

Learning the topics and the theory of a programming language is a good start. However, the difference between a beginner and an intermediate or advanced programmer is experience and practice.

Developing a project is the best way to improve programming and gain experience at the same time. Starting simple like a calculator and taking on more complex projects as you gain confidence in your skills is the way to go.

After that, you can move on to complete visualization of the analysis using different packages.

For advanced project ideas, you can take a look at the following:

1. Uber data analysis using R: This project analyzes the data of uber rides in new-york in the year of 2014.

Source Code: Uber Data Analysis Project in R

2. Sentiment analysis using R: Processing natural language sentences to extract opinions or emotions from them is a popular technique used in machine learning these days.

Source Code: Data Science Project of Sentiment Analysis

3. Credit card fraud detection system in R: Processing a credit card transaction dataset to identify anomalies and possible credit card frauds.

Source Code: Credit Card Fraud Detection Machine Learning Project

4. Customer Segmentation using R: in customer segmentation, we use clustering algorithms to classify customers in different groups. This helps in identifying relevant customer base.

Source Code: Customer Segmentation with Machine Learning Project

5. Movie recommendation system made in R: Using the recommended lab package, this project aims at building a recommendation system that recommends movies based on their user ratings.

Source Code: Data Science Movie Recommendation Project

19. Best Books to Learn R Programming

Here is a list of must-read books recommended by us to learn R programming:

1. R for Data Science - Hadley Wickham & Garrett Grolemund
2. Practical Data Science with R - Nina Zumel & John Mount
3. The art of R Programming - Norman Matloff
4. Hands-on Programming with R - Garrett Grolemund
5. R for Everyone: Advanced Analytics and Graphics - Jared P. Lander
6. Learning Rstudio for R Statistical Computing - Mark P.J.van der Loo & Edwin de Jonge

Check the summary of these books and decide which is more suitable for you.

20. Interview Questions

After learning the theory and making a few projects, the next step would be to start preparing for the interview for your dream job. Here, we have a detailed list of commonly asked interview questions for jobs for R programmers. We can classify them based on the level of their difficulties.

1. Beginner level R interview questions.
2. Intermediate level R interview questions.
3. Advanced level R interview questions.

21. Use Cases

Here are a few use cases of R in the real world:

1. Microsoft: The statistical engine within the Azure ML framework is built with R. Microsoft also uses it for the Xbox matchmaking service.
2. Bank of America: Bank of America does financial reporting and calculates financial losses with R.
3. Facebook: Facebook uses R to predict colleague interactions and to update its social network graph.
4. The Food and Drug Administration: FDA uses it to predict possible reactions and medical issues caused by various food products. They also use it for pre-clinical trials of medicines and food products.
5. Cornell University: For research involving statistical computing Cornell recommends their researchers and students to use R.
6. Bajaj Allianz Insurance: Bajaj Allianz uses R to generate actionable insights to improve customer experience. They also use it to make their recommendation engines and upsell propensity models.
7. Ford Motor Company: Ford uses R to analyze customer sentiment about its product. This helps them in improving their future designs.
8. Amazon: Amazon uses R to improve their cross-product suggestions.

22. Conclusion

Finally, we have come to the end of our mastery guide for R.

I hope this guide expanded your view a bit and showed you what R really offers.

Serious for data science? Then do follow the material provided in this guide and practice all topics thoroughly.

The interview questions will help you to crack any R interview. With this guide, a glamorous career as an R programmer is sure to be in your future.

Which of these do you remember from back in the day. Have I missed anything?

Let me know your biggest takeaways in the comments.