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