R all-in-one for dummies / Joseph Schmuller.

Format:

Book

Author/Creator:

Schmuller, Joseph, author.

Series:

--For dummies.

For dummies

Language:

English

Subjects (All):

R (Computer program language).

Statistics--Data processing.

Statistics.

Physical Description:

1 online resource (690 pages)

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc., [2023]

Summary:

A deep dive into the programming language of choice for statistics and data With R All-in-One For Dummies, you get five mini-books in one, offering a complete and thorough resource on the R programming language and a road map for making sense of the sea of data we're all swimming in. Maybe you're pursuing a career in data science, maybe you're looking to infuse a little statistics know-how into your existing career, or maybe you're just R-curious. This book has your back. Along with providing an overview of coding in R and how to work with the language, this book delves into the types of projects and applications R programmers tend to tackle the most. You'll find coverage of statistical analysis, machine learning, and data management with R. Grasp the basics of the R programming language and write your first lines of code Understand how R programmers use code to analyze data and perform statistical analysis Use R to create data visualizations and machine learning programs Work through sample projects to hone your R coding skill This is an excellent all-in-one resource for beginning coders who'd like to move into the data space by knowing more about R.

Contents:

Intro

Title Page

Copyright Page

Table of Contents

Introduction

About This All-in-One

Book 1: Introducing R

Book 2: Describing Data

Book 3: Analyzing Data

Book 4: Learning from Data

Book 5: Harnessing R: Some Projects to Keep You Busy

What You Can Safely Skip

Foolish Assumptions

Icons Used in This Book

Beyond This Book

Where to Go from Here

1 Introducing R

Chapter 1 R: What It Does and How It Does It

The Statistical (and Related) Ideas You Just Have to Know

Samples and populations

Variables: Dependent and independent

Types of data

A little probability

Inferential statistics: Testing hypotheses

Null and alternative hypotheses

Two types of error

Getting R

Getting RStudio

A Session with R

The working directory

Getting started

R Functions

User-Defined Functions

Comments

R Structures

Vectors

Numerical vectors

Matrices

Lists

Data frames

for Loops and if Statements

Chapter 2 Working with Packages, Importing, and Exporting

Installing Packages

Examining Data

Heads and tails

Missing data

Subsets

R Formulas

More Packages

Exploring the tidyverse

Importing and Exporting

Spreadsheets

CSV files

Text files

2 Describing Data

Chapter 1 Getting Graphic

Finding Patterns

Graphing a distribution

Bar-hopping

Slicing the pie

The plot of scatter

Of boxes and whiskers

Doing the Basics: Base R Graphics, That Is

Histograms

Graph features

Bar plots

Pie graphs

Dot charts

Bar plots revisited

Scatter plots

A plot twist

Scatter plot matrix

Box plots

Kicking It Up a Notch to ggplot2

Bar plots re-revisited

About that plot twist . . .

Putting a Bow On It.

Chapter 2 Finding Your Center

Means: The Lure of Averages

Calculating the Mean

The Average in R: mean()

What's your condition?

Eliminate signs forthwith()

Explore the data

Outliers: The flaw of averages

Medians: Caught in the Middle

The Median in R: median()

Statistics à la Mode

The Mode in R

Chapter 3 Deviating from the Average

Measuring Variation

Averaging squared deviations: Variance and how to calculate it

Sample variance

Variance in R

Back to the Roots: Standard Deviation

Population standard deviation

Sample standard deviation

Standard Deviation in R

Conditions, conditions, conditions . . .

Chapter 4 Meeting Standards and Standings

Catching Some Zs

Characteristics of z-scores

Bonds versus the Bambino

Exam scores

Standard Scores in R

Where Do You Stand?

Ranking in R

Tied scores

Nth smallest, Nth largest

Percentiles

Percent ranks

Summarizing

Chapter 5 Summarizing It All

How Many?

The High and the Low

Living in the Moments

A teachable moment

Back to descriptives

Skewness

Kurtosis

Tuning in the Frequency

Nominal variables: table() et al.

Numerical variables: hist()

Cumulative frequency

Step by step: The empirical cumulative distribution function

Numerical variables: stem()

Summarizing a Data Frame

Chapter 6 What's Normal?

Hitting the Curve

Digging deeper

Parameters of a normal distribution

Working with Normal Distributions

Distributions in R

Normal density function

Plotting a normal curve

Cumulative density function

Plotting the cdf

Quantiles of normal distributions

Plotting the cdf with quartiles

Random sampling

Meeting a Distinguished Member of the Family

The standard normal distribution in R

Plotting the standard normal distribution

3 Analyzing Data.

Chapter 1 The Confidence Game: Estimation

Understanding Sampling Distributions

An EXTREMELY Important Idea: The Central Limit Theorem

(Approximately) simulating the central limit theorem

Predictions of the central limit theorem

Confidence: It Has Its Limits!

Finding confidence limits for a mean

Using R to find the confidence limits for a mean

Fit to a t

Chapter 2 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

Plotting t in base R graphics

Plotting t in ggplot2

One more thing about ggplot2

Testing a Variance

Manufacturing an Example

Testing in R

Working with Chi-Square Distributions

Visualizing Chi-Square Distributions

Plotting chi-square in base R graphics

Plotting chi-square in ggplot2

Chapter 3 Two-Sample Hypothesis Testing

Hypotheses Built for Two

Sampling Distributions Revisited

Applying the central limit theorem

Zs once more

Z-testing for two samples in R

t for Two

Like Peas in a Pod: Equal Variances

t-Testing in R

Working with two vectors

Working with a data frame and a formula

Visualizing the results

Bar graphs

Like ps and qs: Unequal variances

A Matched Set: Hypothesis Testing for Paired Samples

Paired Sample t-testing in R

Testing Two Variances

F testing in R

F in conjunction with t

Working with F Distributions

Visualizing F Distributions

Chapter 4 Testing More than Two Samples

Testing More than Two

A thorny problem

A solution

Meaningful relationships

ANOVA in R

Plotting a boxplot to visualize the data

After the ANOVA

Planned comparisons

Another word about contrasts

Contrasts in R.

Unplanned comparisons

Another Kind of Hypothesis, Another Kind of Test

Working with repeated measures ANOVA

Repeated measures ANOVA in R

Getting Trendy

Trend Analysis in R

Chapter 5 More Complicated Testing

Cracking the Combinations

Interactions

The analysis

Two-Way ANOVA in R

Visualizing the two-way results

Two Kinds of Variables . . . at Once

Mixed ANOVA in R

Visualizing the mixed ANOVA results

After the Analysis

Multivariate Analysis of Variance

MANOVA in R

Visualizing the MANOVA results

After the MANOVA

Chapter 6 Regression: Linear, Multiple, and the General Linear Model

The Plot of Scatter

Graphing Lines

Regression: What a Line!

Using regression for forecasting

Variation around the regression line

Testing hypotheses about regression

Testing the fit

Testing the slope

Testing the intercept

Linear Regression in R

Features of the linear model

Making predictions

Visualizing the scatterplot and regression line

Plotting the residuals

Juggling Many Relationships at Once: Multiple Regression

Multiple regression in R

Visualizing the 3d scatterplot and regression plane

The scatterplot3d package

car and rgl: A package deal

ANOVA: Another Look

Analysis of Covariance: The Final Component of the GLM

But Wait - There's More

Chapter 7 Correlation: The Rise and Fall of Relationships

Understanding Correlation

Correlation and Regression

Testing Hypotheses about Correlation

Is a correlation coefficient greater than zero?

Do two correlation coefficients differ?

Correlation in R

Calculating a correlation coefficient

Testing a correlation coefficient

Testing the difference between two correlation coefficients

Calculating a correlation matrix.

Visualizing correlation matrices

Multiple Correlation

Multiple correlation in R

Adjusting R-squared

Partial Correlation

Partial Correlation in R

Semipartial Correlation

Semipartial Correlation in R

Chapter 8 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?

Chapter 9 In Due Time

A Time Series and Its Components

Forecasting: A Moving Experience

Forecasting: Another Way

Working with Real Data

Chapter 10 Non-Parametric Statistics

Independent Samples

Two samples: Wilcoxon rank-sum test

More than two samples: Kruskal-Wallis One-Way ANOVA

Matched Samples

Two samples: Wilcoxon matched-pairs signed ranks

More than two samples: Friedman two-way ANOVA

More than two samples: Cochran's Q

Correlation: Spearman's rS

Correlation: Kendall's Tau

A Heads-Up

Chapter 11 Introducing Probability

What Is Probability?

Experiments, trials, events, and sample spaces

Sample spaces and probability

Compound Events

Union and intersection

Intersection, again

Conditional Probability

Working with the probabilities

The foundation of hypothesis testing

Large Sample Spaces

Permutations

Combinations

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

Binomial distribution

Negative binomial distribution

Hypothesis Testing with the Binomial Distribution

More on Hypothesis Testing: R versus Tradition

Chapter 12 Probability Meets Regression: Logistic Regression

Getting the Data

Doing the Analysis

Visualizing the Results.

4 Learning from Data.

Notes:

Includes index.

Description based on print version record.

ISBN:

1-119-98370-3

1-119-98371-1

OCLC:

1363838906