Learn R Programming in 24 Hours

Learn R Programming in 24 Hours

By Alex Nordeen

Copyright 2021 - All Rights Reserved – Alex Nordeen

ALL RIGHTS RESERVED. No part of this publication may be reproduced or transmitted in any form whatsoever, electronic, or mechanical, including photocopying, recording, or by any informational storage or retrieval system without express written, dated and signed permission from the author.

Table Of Content

Chapter 1: What is R Programming Language? Introduction & Basics

What is R?

What is R used for?

R by Industry

R package

Communicate with R

Why use R?

Should you choose R?

Is R difficult?

Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows

Install Anaconda

Install R

Install Rstudio

Run Rstudio

Test

Install package

Open a library

Run R code

Chapter 3: R Data Types, Arithmetic & Logical Operators with Example

Basic data types

Variables

Vectors

Arithmetic Operators

Logical Operators

Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice

What is a Matrix?

How to Create a Matrix in R

Add a Column to a Matrix with the cbind()

Slice a Matrix

Chapter 5: Factor in R: Categorical & Continuous Variables

What is Factor in R?

Categorical Variables

Continuous Variables

Chapter 6: R Data Frame: Create, Append, Select, Subset

What is a Data Frame?

How to Create a Data Frame

Slice Data Frame

Append a Column to Data Frame

Select a Column of a Data Frame

Chapter 7: List in R: Create, Select Elements with Example

How to Create a List

Select Elements from List

Built-in Data Frame

Chapter 8: R Sort a Data Frame using Order()

Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread)

Introduction to Data Analysis

Merge with dplyr()

Data Cleaning functions

gather()

spread()

separate()

unite()

Chapter 10: Merge Data Frames in R: Full and Partial Match

Full match

Partial match

Chapter 11: Functions in R Programming (with Example)

What is a Function in R?

R important built-in functions

General functions

Math functions

Statistical functions

Write function in R

When should we write function?

Functions with condition

Chapter 12: IF, ELSE, ELSE IF Statement in R

The if else statement

The else if statement

Chapter 13: For Loop in R with Examples for List and Matrix

For Loop Syntax and Examples

For Loop over a list

For Loop over a matrix

Chapter 14: While Loop in R with Example

Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples

apply() function

lapply() function

sapply() function

Slice vector

tapply() function

Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files

Read CSV

Read Excel files

readxl_example()

read_excel()

excel_sheets()

Import data from other Statistical software

Read sas

Read STATA

Read SPSS

Best practices for Data Import

Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm

Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File

Export to Hard drive

Create data frame

Export CSV

Export to Excel file

Export to different software

Export SAS file

Export STATA file

R

Interact with the Cloud Services

Google Drive

Export to Dropbox

Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example

Pearson Correlation

Spearman Rank Correlation

Correlation Matrix

Visualize Correlation Matrix

Chapter 20: R Aggregate Function: Summarise & Group_by() Example

Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example

select()

Filter()

Pipeline

arrange()

Chapter 22: Scatter Plot in R using ggplot2 (with Example)

ggplot2 package

Scatterplot

Change axis

Scatter plot with fitted values

Add information to the graph

Rename x-axis and y-axis

Control the scales

Theme

Save Plots

Chapter 23: How to make Boxplot in R (with EXAMPLE)

Create Box Plot

Basic box plot

Box Plot with Dots

Control Aesthetic of the Box Plot

Box Plot with Jittered Dots

Notched Box Plot

Chapter 24: Bar Chart & Histogram in R (with Example)

How to create Bar Chart

Bar chart: count

Customize the graph

Histogram

Chapter 25: T Test in R: One Sample and Paired (with Example)

What is Statistical Inference?

What is t-test?

One-sample t-test

Paired t-test

Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples)

What is ANOVA?

One-way ANOVA

Pairwise comparison

Two-way ANOVA

Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example]

Simple Linear regression

Machine learning

Chapter 28: Decision Tree in R with Example

What are Decision Trees?

Step 1) Import the data

Step 2) Clean the dataset

Step 3) Create train/test set

Step 4) Build the model

Step 5) Make a prediction

Step 6) Measure performance

Step 7) Tune the hyper-parameters

Chapter 29: R Random Forest Tutorial with Example

What is Random Forest in R?

Step 1) Import the data

Step 2) Train the model

Step 3) Search the best maxnodes

Step 4) Search the best ntrees

Step 5) Evaluate the model

Step 6) Visualize Result

Appendix

Chapter 30: Generalized Linear Model (GLM) in R with Example

What is Logistic regression?

How to create Generalized Liner Model (GLM)

Chapter 31: K-means Clustering in R with Example

What is Cluster analysis?

K-means algorithm

Optimal k

Chapter 32: R Vs Python: What’s the Difference?

R

Python

Popularity index

Job Opportunity

Analysis done by R and Python

Percentage of people switching

Difference between R and Python

R or Python Usage

Chapter 33: SAS vs R: What's the Difference?

What is SAS?

What is mean by R?

Why use SAS?

Why use R?

History of SAS

History of R

SAS Vs. R

Feature of R

Features of SAS

The Final Verdict

Chapter 1: What is R Programming Language? Introduction & Basics

What is R?

R is a programming language developed by Ross Ihaka and Robert Gentleman in 1993. R possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithm, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational task, C, C++ and Fortran codes are preferred.

R is not only entrusted by academic, but many large companies also use R programming language, including Uber, Google, Airbnb, Facebook and so on.

Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicate the results

Program: R is a clear and accessible programming tool

Transform: R is made up of a collection of libraries designed specifically for data science

Discover: Investigate the data, refine your hypothesis and analyze them

Model: R provides a wide array of tools to capture the right model for your data

Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world

What is R used for?

Statistical inference

Data analysis

Machine learning algorithm

R by Industry

If we break down the use of R by industry, we see that academics come first. R is a language to do statistic. R is the first choice in the healthcare industry, followed by government and consulting.

R package

The primary uses of R is and will always be, statistic, visualization, and machine learning. The picture below shows which R package got the most questions in Stack Overflow. In the top 10, most of them are related to the workflow of a data scientist: data preparation and communicate the results.

All the libraries of R, almost 12k, are stored in CRAN. CRAN is a free and open source. You can download and use the numerous libraries to perform Machine Learning or time series analysis.

Communicate with R

R has multiple ways to present and share work, either through a markdown document or a shiny app. Everything can be hosted in Rpub, GitHub or the business's website.

Below is an example of a presentation hosted on Rpub

Rstudio accepts markdown to write a document. You can export the documents in different formats:

Document :

HTML

PDF/Latex

Word

Presentation

HTML

PDF beamer

Rstudio has a great tool to create an App easily. Below is an example of app with the World Bank data.

Why use R?

Data science is shaping the way companies run their businesses. Without a doubt, staying away from Artificial Intelligence and Machine will lead the company to fail. The big question is which tool/language should you use?

They are plenty of tools available in the market to perform data analysis. Learning a new language requires some time investment. The picture below depicts the learning curve compared to the business capability a language offers. The negative relationship implies that there is no free lunch. If you want to give the best insight from the data, then you need to spend some time learning the appropriate tool, which is R.

On the top left of the graph, you can see Excel and PowerBI. These two tools are simple to learn but don't offer outstanding business capability, especially in term of modeling. In the middle, you can see Python and SAS. SAS is a dedicated tool to run a statistical analysis for business, but it is not free. SAS is a click and run software. Python, however, is a language with a monotonous learning curve. Python is a fantastic tool to deploy Machine Learning and AI but lacks communication features. With an identical learning curve, R is a good trade-off between implementation and data analysis.

When it comes to data visualization (DataViz), you'd probably heard about Tableau. Tableau is, without a doubt, a great tool to discover patterns through graphs and charts. Besides, learning Tableau is not time-consuming. One big problem with data visualization is you might end up never finding a pattern or just create plenty of useless charts. Tableau is a good tool for quick visualization of the data or Business Intelligence. When it comes to statistics and decision-making tool, R is more appropriate.

Stack Overflow is a big community for programming languages. If you have a coding issue or need to understand a model, Stack Overflow is here to help. Over the year, the percentage of question-views has increased sharply for R compared to the other languages. This trend is of course highly correlated with the booming age of data science but, it reflects the demand of R language for data science.

In data science, there are two tools competing with each other. R and Python are probably the programming language that defines data science.

Should you choose R?

Data scientist can use two excellent tools: R and Python. You may not have time to learn them both, especially if you get started to learn data science. Learning statistical modeling and algorithm is far more important than to learn a programming language. A programming language is a tool to compute and communicate your discovery. The most important task in data science is the way you deal with the data: import, clean, prep, feature engineering, feature selection. This should be your primary focus. If you are trying to learn R and Python at the same time without a solid background in statistics, its plain stupid. Data scientist are not programmers. Their job is to understand the data, manipulate it and expose the best approach. If you are thinking about which language to learn, let's see which language is the most appropriate for you.

The principal audience for data science is business professional. In the business, one big implication is communication. There are many ways to communicate: report, web app, dashboard. You need a tool that does all this together.

Is R difficult?

Years ago, R was a difficult language to master. The language was confusing and not as structured as the other programming tools. To overcome this major issue, Hadley Wickham developed a collection of packages called tidyverse. The rule of the game changed for the best. Data manipulation become trivial and intuitive. Creating a graph was not so difficult anymore.

The best algorithms for machine learning can be implemented with R. Packages like Keras and TensorFlow allow to create high-end machine learning technique. R also has a package to perform Xgboost, one the best algorithm for Kaggle competition.

R can communicate with the other language. It is possible to call Python, Java, C++ in R. The world of big data is also accessible to R. You can connect R with different databases like Spark or Hadoop.

Finally, R has evolved and allowed parallelizing operation to speed up the computation. In fact, R was criticized for using only one CPU at a time. The parallel package lets you to perform tasks in different cores of the machine.

Summary

In a nutshell, R is a great tool to explore and investigate the data. Elaborate analysis like clustering, correlation, and data reduction are done with R. This is the most crucial part, without a good feature engineering and model, the deployment of the machine learning will not give meaningful results.

Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows

R is a programming language. To use R, we need to install an Integrated Development Environment (IDE). Rstudio is the Best IDE available as it is user-friendly, open-source and is part of the Anaconda platform.

Install Anaconda

What is Anaconda?

Anaconda free open source is distributing both Python and R programming language. Anaconda is widely used in the scientific community and data scientist to carry out Machine Learning project or data analysis.

Why use Anaconda?

Anaconda will help you to manage all the libraries required for Python, or R. Anaconda will install all the required libraries and IDE into one single folder to simplify package management. Otherwise, you would need to install them separately.

Mac User

Step 1) Go to https://www.anaconda.com/download/ and Download Anaconda for Python 3.6 for your OS.

By default, Chrome selects the downloading page of your system. In this tutorial, installation is done for Mac. If you run on Windows or Linux, download Anaconda 5.1 for Windows installer or Anaconda 5.1 for Linux installer.

Step 2) You are now ready to install Anaconda. Double-click on the downloaded file to begin the installation. It is .dmg for mac and .exe for windows. You will be asked to confirm the installation. Click Continue button.

You are redirected to the Anaconda3 Installer.

Step 3) Next window displays the ReadMe. After you are done reading the document, click Continue

Step 4) This window shows the Anaconda End User License Agreement. Click Continue to agree.

Step 5) You are prompted to agree, click Agree to go to the next step.

Step 6) Click Change Install Location to set the location of Anaconda. By default, Anaconda is installed in the user environment: Users/YOURNAME/.

Select the destination by clicking on Install for me only. It means Anaconda will be accessible only to this user.

Step 7) You can install Anaconda now. Click Install to proceed. Anaconda takes around 2.5 GB on your hard drive.

A message box is prompt. You need to confirm by typing your password. Hit Install Software

The installation may take sometimes. It depends on your machine.

Step 8) Anaconda asks you if you want to install Microsoft VSCode. You can ignore it and hit Continue

Step 9) The installation is completed. You can close the window.

You are asked if you want to move Anaconda3 installer to the Trash. Click Move to Trash

You are done with the installation of Anaconda on a macOS system

Windows User

Step 1) Open the downloaded exe and click Next

Step 2) Accept the License Agreement

Step 3) Select Just Me and click Next

Step 4) Select Destination Folder and Click Next

Step 5) Click Install in next Screen

Step 6) Installation will begin

Once done, Anaconda will be installed.

Install R

Mac users

Step 1) Anaconda uses the terminal to install libraries. The terminal is a quick way to install libraries. We need to be sure to point the installation toward the right path. In our case, we set the location of Anaconda to the Users/USERNAME/. We can confirm this by checking anaconda3 folder.

Open Computer and select Users, USERNAME and anaconda3. It confirms that we installed Anaconda on the right path. Now, let's see how macOS write the path. Right-click, and then Get Info

Select the path Where and click Copy

Step 2) For Mac user: