R all-in-one for dummies
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&...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc
[2023]
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| Colección: | --For dummies.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009724221206719 |
Tabla de Contenidos:
- 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
- Histograms
- Bar plots
- Dot charts
- Bar plots re-revisited
- Scatter plots
- About that plot twist . . .
- Scatter plot matrix
- Box plots
- 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
- Box plots
- 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
- Visualizing the results
- 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
- Making predictions
- 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.
