R Programming for Statistics and Data Science

Video description

Waste no time and jump right into hands-on coding in R. We start off light and teach you all the basics as we go along. An equally satisfying experience for complete beginners and those of you who would just like a refresher on R.

About This Video

  • You will learn descriptive statistics and the fundamentals of inferential statistics
  • Soar above the average data scientist and boost the productivity of your operations
  • Learn to work with R's most comprehensive collection of tools and create meaning-heavy data visualizations and plots

In Detail

R programming is a skill you'll need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn't you, data scientist is the hottest ranked profession in the US. But to do that, you need the tools and the skillset to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream job. This course packs all of this, and more, in one easy-to-handle bundle, and it's the perfect start to your journey. So, welcome to R Programming for Statistics and Data Science, the course that will get you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skillset to tackle any new data science project with confidence and critically assess your and other people's work.

Practicability is the key to this course. Using R, you have a wide variety of options where you can take the code provided within this course and expand on it in any number of directions. You’ll reinforce your learning through numerous practical exercises.


Aspiring data scientists, beginners in programming, people interested in statistics and data analysis, and anyone who wants to learn how to code and apply their skills in practice will find this course useful.

Table of contents

  1. Chapter 1 : Introduction and Getting Started
    1. What Does this Course Cover?
    2. Introduction
    3. Downloading and Installing R and RStudio
    4. Quick Guide to the RStudio User Interface
    5. Changing the Appearance in RStudio
    6. Installing Packages in R and Using the Library
  2. Chapter 2 : The Building Blocks of R
    1. Creating an Object in R
    2. Data Types in R - Integers and Doubles
    3. Data Types in R – Characters and Logicals
    4. Coercion Rules in R
    5. Functions in R
    6. Functions and Arguments
    7. Building a Function in R (Basics)
    8. Using the Script Versus Using the Console
  3. Chapter 3 : Vectors and Vector Operations
    1. Introduction
    2. Introduction to Vectors
    3. Vector Recycling
    4. Naming a Vector in R
    5. Getting Help with R
    6. Slicing and Indexing a Vector in R
    7. Changing the Dimensions of an Object in R
  4. Chapter 4 : Matrices
    1. Creating a Matrix in R
    2. Faster Code: Creating a Matrix in a Single Line of Code
    3. Do Matrices Recycle?
    4. Indexing an Element from a Matrix
    5. Slicing a Matrix in R
    6. Matrix Arithmetic
    7. Matrix Operations in R
    8. Categorical Data
    9. Creating a Factor in R
    10. Lists in R
  5. Chapter 5 : Fundamentals of Programming with R
    1. Relational Operators in R
    2. Logical Operators in R
    3. Vectors and Logicals Operators
    4. If, Else, Else If Statements in R
    5. If, Else, Else If Statements - Keep-In-Mind's
    6. For Loops in R
    7. While Loops in R
    8. Repeat Loops in R
    9. Building a Function in R 2.0
    10. Building a Function in R 2.0 - Scoping
  6. Chapter 6 : Data Frames
    1. Introduction
    2. Creating a Data Frame in R
    3. The Tidyverse Package
    4. Data Import in R
    5. Importing a CSV in R
    6. Data Export in R
    7. Getting a Sense of Your Data Frame
    8. Indexing and Slicing a Data Frame in R
    9. Extending a Data Frame in R
    10. Dealing with Missing Data in R
  7. Chapter 7 : Manipulating Data
    1. Introduction
    2. Data Transformation with R - the Dplyr Package - Part I
    3. Data Transformation with R - the Dplyr Package - Part II
    4. Sampling Data with the Dplyr Package
    5. Using the Pipe Operator in R
    6. Tidying Data in R - gather() and separate()
    7. Tidying Data in R - unite() and spread()
  8. Chapter 8 : Visualizing Data
    1. Introduction
    2. Introduction to Data Visualization
    3. Intro to ggplot2
    4. Variables: Revisited
    5. Building a Histogram with ggplot2
    6. Building a Bar Chart with ggplot2
    7. Building a Box and Whiskers Plot with ggplot2
    8. Building a Scatterplot with ggplot2
  9. Chapter 9 : Exploratory Data Analysis
    1. Population Versus sample
    2. Mean, Median, Mode
    3. Skewness
    4. Variance, standard deviation, and coefficient of variability
    5. Covariance and Correlation
  10. Chapter 10 : Hypothesis Testing
    1. Distributions
    2. Standard Error and Confidence Intervals
    3. Hypothesis Testing
    4. Type I and Type II Errors
    5. Test for the Mean - Population Variance Known
    6. The P-Value
    7. Test for the Mean - Population Variance Unknown
    8. Comparing Two Means - Dependent Samples
    9. Comparing Two Means - Independent Samples
  11. Chapter 11 : Linear Regression Analysis
    1. The Linear Regression Model
    2. Correlation Versus Regression
    3. Geometrical Representation
    4. First Regression in R
    5. How to Interpret the Regression Table
    6. Decomposition of Variability: SST, SSR, SSE
    7. R-Squared

Product information

  • Title: R Programming for Statistics and Data Science
  • Author(s): 365 Careers Ltd.
  • Release date: October 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781789950298