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Introduction to R Programming
Introduction
Welcome to R programming (0:14)
Welcome to the Course
Getting started
Intro (0:53)
Downloading and installing R & RStudio (3:20)
Quick guide to the RStudio user interface (7:37)
Changing the appearance in RStudio (1:47)
Installing packages 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 (3:17)
Coercion rules in R (2:39)
Functions in R (3:22)
Functions and arguments (2:30)
Building a function in R (8:12)
Using the script vs. using the console (2:55)
Vectors and vector operations
Intro (1:10)
Introduction to vectors (3:31)
Vector recycling (1:39)
Naming a vector (3:21)
Getting help with R (6:37)
Slicing and indexing a vector (7:01)
Changing the dimensions of an object in R (4:50)
Matrices
Creating a matrix (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 (3:33)
Matrix arithmetic (7:07)
Matrix operations (4:18)
Categorical data (3:29)
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)
Logical Operators and Vectors (2:29)
If, Else, Else-If Statements (5:47)
If, Else, Else-If Keep-In-Minds (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 (4:33)
Scoping in R | Building a Function in R 2.0 (Ctnd) (5:16)
Data frames
Creating a data frame (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 (4:48)
Manipulating data
Intro (1:15)
Data transformation with R - the Dplyr package - Part I (5:43)
Data transformation with R - the Dplyr package - Part II (3:22)
Sampling data with the Dplyr package (1:44)
Using the pipe operator (3:27)
Tidying your data - gather() and separate() (7:27)
Tidying your data - unite() and spread() (2:44)
Visualizing data
Intro (1:00)
Intro 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 vs. 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 vs. Regression (1:37)
Geometrical Representation (1:37)
Doing the Regression in R (4:18)
How to Interpret the Regression Table (4:25)
Decomposition of Variability (3:15)
R-Squared (4:51)
Data types in R - Integers and doubles
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