The Complete R Handbook
(R-BASIC.AE1) / ISBN : 978-1-64459-542-8
Dieser Kurs beinhaltet
Lessons
Hands-On Labs
AI Tutor (Hinzufügen Auf)
Über diesen Kurs
Der Kurs „Complete R Handbook“ soll Ihnen die Fähigkeiten und Kenntnisse vermitteln, die Sie brauchen, um R für statistische Analysen, Datenmanipulation, Visualisierung und mehr zu nutzen. Der Kurs hilft Ihnen dabei, in die Grundlagen der R-Programmierung einzutauchen, einschließlich Datentypen, Variablen, Funktionen und Kontrollstrukturen, und Sie lernen, wie Sie Daten in R mithilfe von Paketen wie dplyr und tidyr für eine effiziente Datenbereinigung manipulieren. Der Kurs hilft Ihnen dabei, statistische Analysetechniken in R zu erkunden, einschließlich Hypothesentests, Regressionsanalyse und ANOVA.
Fähigkeiten, die Sie erwerben werden
Holen Sie sich die Unterstützung, die Sie brauchen. Melden Sie sich für unseren Kurs mit Lehrer an.
Unterricht
31+ Unterricht | 34+ Übungen | 174+ Tests | 109+ Karteikarten | 109+ Glossar der Begriffe
Praktische Übungen
57+ LiveLab | 57+ Videoanleitungen | 02:39+ Hours
1
Introduction
- About This All-in-One
- What You Can Safely Skip
- Icons Used in This Course
- Where to Go from Here
2
R: What It Does and How It Does It
- The Statistical (and Related) Ideas You Just Have to Know
- Getting R
- Getting RStudio
- A Session with R
- R Functions
- User-Defined Functions
- Comments
- R Structures
- for Loops and if Statements
3
Working with Packages, Importing, and Exporting
- Installing Packages
- Examining Data
- R Formulas
- More Packages
- Exploring the tidyverse
- Importing and Exporting
4
Getting Graphic
- Finding Patterns
- Doing the Basics: Base R Graphics, That Is
- Kicking It Up a Notch to ggplot2
- Putting a Bow On It
5
Finding Your Center
- Means: The Lure of Averages
- Calculating the Mean
- The Average in R: mean()
- Medians: Caught in the Middle
- The Median in R: median()
- Statistics à la Mode
- The Mode in R
6
Deviating from the Average
- Measuring Variation
- Back to the Roots: Standard Deviation
- Standard Deviation in R
7
Meeting Standards and Standings
- Catching Some Zs
- Standard Scores in R
- Where Do You Stand?
- Summarizing
8
Summarizing It All
- How Many?
- The High and the Low
- Living in the Moments
- Tuning in the Frequency
- Summarizing a Data Frame
9
What’s Normal?
- Hitting the Curve
- Working with Normal Distributions
- Meeting a Distinguished Member of the Family
10
The Confidence Game: Estimation
- Understanding Sampling Distributions
- An EXTREMELY Important Idea: The Central Limit Theorem
- Confidence: It Has Its Limits!
- Fit to a t
11
One-Sample Hypothesis Testing
- Hypotheses, Tests, and Errors
- Hypothesis Tests and Sampling Distributions
- Catching Some Z’s Again
- Z Testing in R
- t for One
- t Testing in R
- Working with t-Distributions
- Visualizing t-Distributions
- Testing a Variance
- Working with Chi-Square Distributions
- Visualizing Chi-Square Distributions
12
Two-Sample Hypothesis Testing
- Hypotheses Built for Two
- Sampling Distributions Revisited
- t for Two
- Like Peas in a Pod: Equal Variances
- t-Testing in R
- A Matched Set: Hypothesis Testing for Paired Samples
- Paired Sample t-testing in R
- Testing Two Variances
- Working with F Distributions
- Visualizing F Distributions
13
Testing More than Two Samples
- Testing More than Two
- ANOVA in R
- Another Kind of Hypothesis, Another Kind of Test
- Getting Trendy
- Trend Analysis in R
14
More Complicated Testing
- Cracking the Combinations
- Two-Way ANOVA in R
- Two Kinds of Variables … at Once
- After the Analysis
- Multivariate Analysis of Variance
15
Regression: Linear, Multiple, and the General Linear Model
- The Plot of Scatter
- Graphing Lines
- Regression: What a Line!
- Linear Regression in R
- Juggling Many Relationships at Once: Multiple Regression
- ANOVA: Another Look
- Analysis of Covariance: The Final Component of the GLM
- But Wait — There’s More
16
Correlation: The Rise and Fall of Relationships
- Understanding Correlation
- Correlation and Regression
- Testing Hypotheses about Correlation
- Correlation in R
- Multiple Correlation
- Partial Correlation
- Partial Correlation in R
- Semipartial Correlation
- Semipartial Correlation in R
17
Curvilinear Regression: When Relationships Get Complicated
- What Is a Logarithm?
- What Is e?
- Power Regression
- Exponential Regression
- Logarithmic Regression
- Polynomial Regression: A Higher Power
- Which Model Should You Use?
18
In Due Time
- A Time Series and Its Components
- Forecasting: A Moving Experience
- Forecasting: Another Way
- Working with Real Data
19
Non-Parametric Statistics
- Independent Samples
- Matched Samples
- Correlation: Spearman’s rS
- Correlation: Kendall’s Tau
- A Heads-Up
20
Introducing Probability
- What Is Probability?
- Compound Events
- Conditional Probability
- Large Sample Spaces
- R Functions for Counting Rules
- Random Variables: Discrete and Continuous
- Probability Distributions and Density Functions
- The Binomial Distribution
- The Binomial and Negative Binomial in R
- Hypothesis Testing with the Binomial Distribution
- More on Hypothesis Testing: R versus Tradition
21
Probability Meets Regression: Logistic Regression
- Getting the Data
- Doing the Analysis
- Visualizing the Results
22
Tools and Data for Machine Learning Projects
- The UCI (University of California-Irvine) ML Repository
- Introducing the Rattle package
- Using Rattle with iris
23
Decisions, Decisions, Decisions
- Decision Tree Components
- Decision Trees in R
- Decision Trees in Rattle
- Project: A More Complex Decision Tree
- Suggested Project: Titanic
24
Into the Forest, Randomly
- Growing a Random Forest
- Random Forests in R
- Project: Identifying Glass
- Suggested Project: Identifying Mushrooms
25
Support Your Local Vector
- Some Data to Work With
- Separability: It’s Usually Nonlinear
- Support Vector Machines in R
- Project: House Parties
26
K-Means Clustering
- How It Works
- K-Means Clustering in R
- Project: Glass Clusters
27
Neural Networks
- Networks in the Nervous System
- Artificial Neural Networks
- Neural Networks in R
- Project: Banknotes
- Suggested Projects: Rattling Around
28
Exploring Marketing
- Analyzing Retail Data
- Enter Machine Learning
- Suggested Project: Another Data Set
29
From the City That Never Sleeps
- Examining the Data Set
- Warming Up
- Quick Suggested Project: Airline Names
- Suggested Project: Departure Delays
- Quick Suggested Project: Analyze Weekday Differences
- Suggested Project: Delay and Weather
30
Working with a Browser
- Getting Your Shine On
- Creating Your First shiny Project
- Working with ggplot
- Another shiny Project
- Suggested Project
31
Dashboards — How Dashing!
- The shinydashboard Package
- Exploring Dashboard Layouts
- Working with the Sidebar
- Interacting with Graphics
1
R: What It Does and How It Does It
- Performing Basic Operations
- Creating and Using Custom Functions
- Creating and Working with Data Frames
- Working with Matrices
- Using for Loops and if-else Statements
2
Working with Packages, Importing, and Exporting
- Analyzing Data
3
Getting Graphic
- Creating a Scatter Plot and a Box Plot
- Creating a Bar Plot and a Pie Graph
- Creating a Histogram and a Density Plot
- Creating a Grouped Bar Plot with ggplot2
4
Finding Your Center
- Calculating the Mean, Median, and Mode
5
Deviating from the Average
- Finding Variance and Standard Deviation
6
Meeting Standards and Standings
- Calculating Percentiles
- Finding Nth Smallest and Nth Largest Elements
- Handling Tied Ranks
7
Summarizing It All
- Calculating Skewness and Kurtosis in Data
- Analyzing Frequency in Data
8
What’s Normal?
- Exploring Quantiles of a Normal Distribution
- Visualizing the Normal Distribution Curve
9
The Confidence Game: Estimation
- Simulating the Central Limit Theorem
- Calculating Confidence Intervals Using the T-Distribution
10
One-Sample Hypothesis Testing
- Performing the Z-Test
- Analyzing a T-Distribution
11
Two-Sample Hypothesis Testing
- Performing a Z-Test for Two Samples
- Performing a T-Test for Two Samples
- Visualizing F Distributions
12
Testing More than Two Samples
- Performing Repeated Measures ANOVA
- Performing Trend Analysis
13
More Complicated Testing
- Performing Two-Way ANOVA
- Performing Mixed ANOVA
14
Regression: Linear, Multiple, and the General Linear Model
- Creating a Linear Regression Model
- Creating a Multiple Regression Model
- Performing ANCOVA
15
Correlation: The Rise and Fall of Relationships
- Performing Correlation Analysis
- Performing Partial Correlation Analysis
16
Curvilinear Regression: When Relationships Get Complicated
- Creating a Power Regression Model
- Creating an Exponential Regression Model
- Creating a Logarithmic Regression Model
- Creating a Polynomial Regression Model
17
In Due Time
- Analyzing Time Series Data
- Creating Forecasts Using Moving Averages
18
Non-Parametric Statistics
- Performing the Kruskal-Wallis Rank-Sum Test
- Performing the Wilcoxon Rank-Sum Test
- Performing the Cochran’s Q Test
- Performing the Friedman Rank-Sum Test
19
Introducing Probability
- Exploring Binomial Distribution
20
Probability Meets Regression: Logistic Regression
- Creating a Logistic Regression Model
21
Tools and Data for Machine Learning Projects
- Performing EDA
22
Decisions, Decisions, Decisions
- Creating a Decision Tree Model
23
Into the Forest, Randomly
- Creating a Random Forest Model
24
Support Your Local Vector
- Creating an SVM Model
25
K-Means Clustering
- Creating Clusters
26
Neural Networks
- Creating a Neural Network Model
27
Exploring Marketing
- Performing RFM Analysis
28
From the City That Never Sleeps
- Performing Advanced Data Analysis
29
Working with a Browser
- Analyzing Data Using the shiny App
30
Dashboards — How Dashing!
- Creating a shiny Dashboard