Autoplay
Autocomplete
Previous Lesson
Complete and Continue
R Programming for Statistics and Data Science
Introduction and Getting Started
What Does this Course Cover? (4:56)
Introduction
Downloading and Installing R and RStudio (3:20)
Quick Guide to the RStudio User Interface (7:37)
Changing the Appearance in RStudio (1:47)
Installing Packages in R and Using the Library (5:10)
The Building Blocks of R
Creating an Object in R (5:21)
Data Types in R - Integers and Doubles (4:40)
Data Types in R – Characters and Logicals (3:18)
Coercion Rules in R (2:39)
Functions in R (3:22)
Functions and Arguments (2:30)
Building a Function in R (Basics) (8:05)
Using the Script Versus Using the Console (2:55)
Vectors and Vector Operations
Introduction (1:10)
Introduction to Vectors (3:31)
Vector Recycling (1:39)
Naming a Vector in R (3:21)
Getting Help with R (6:37)
Slicing and Indexing a Vector in R (7:01)
Changing the Dimensions of an Object in R (4:50)
Matrices
Creating a Matrix in R (6:51)
Faster Code: Creating a Matrix in a Single Line of Code (2:46)
Do Matrices Recycle? (1:36)
Indexing an Element from a Matrix (4:37)
Slicing a Matrix in R (3:33)
Matrix Arithmetic (7:07)
Matrix Operations in R (4:18)
Categorical Data (3:30)
Creating a Factor in R (6:00)
Lists in R (6:01)
Fundamentals of Programming with R
Relational Operators in R (5:06)
Logical Operators in R (3:22)
Vectors and Logicals Operators (2:29)
If, Else, Else If Statements in R (5:48)
If, Else, Else If Statements - Keep-In-Mind’s (3:50)
For Loops in R (6:24)
While Loops in R (4:05)
Repeat Loops in R (3:05)
Building a Function in R 2.0 (4:34)
Building a Function in R 2.0 - Scoping (5:16)
Data Frames
Introduction (0:55)
Creating a Data Frame in R (5:54)
The Tidyverse Package (3:19)
Data Import in R (3:28)
Importing a CSV in R (3:14)
Data Export in R (2:31)
Getting a Sense of Your Data Frame (3:58)
Indexing and Slicing a Data Frame in R (4:09)
Extending a Data Frame in R (4:20)
Dealing with Missing Data in R (4:48)
Manipulating Data
Introduction (1:15)
Data Transformation with R - the Dplyr Package - Part I (5:44)
Data Transformation with R - the Dplyr Package - Part II (3:22)
Sampling Data with the Dplyr Package (1:44)
Using the Pipe Operator in R (3:27)
Tidying Data in R - gather() and separate() (7:27)
Tidying Data in R - unite() and spread() (2:44)
Visualizing Data
Introduction (1:00)
Introduction to Data Visualization (3:59)
Intro to ggplot2 (6:47)
Variables: Revisited (5:51)
Building a Histogram with ggplot2 (6:31)
Building a Bar Chart with ggplot2 (6:29)
Building a Box and Whiskers Plot with ggplot2 (6:17)
Building a Scatterplot with ggplot2 (5:21)
Exploratory Data Analysis
Population Versus sample (4:02)
Mean, Median, Mode (5:04)
Skewness (3:21)
Variance, standard deviation, and coefficient of variability (6:11)
Covariance and Correlation (6:41)
Hypothesis Testing
Distributions (6:32)
Standard Error and Confidence Intervals (8:36)
Hypothesis Testing (8:02)
Type I and Type II Errors (3:22)
Test for the Mean - Population Variance Known (7:00)
The P-Value (4:45)
Test for the Mean - Population Variance Unknown (5:09)
Comparing Two Means - Dependent Samples (6:40)
Comparing Two Means - Independent Samples (5:29)
Linear Regression Analysis
The Linear Regression Model (5:26)
Correlation Versus Regression (1:37)
Geometrical Representation (1:37)
First Regression in R (4:18)
How to Interpret the Regression Table (4:25)
Decomposition of Variability: SST, SSR, SSE (3:15)
R-Squared (5:01)
The Linear Regression Model
This video explains the linear regression model.
Complete and Continue