Using R for Statistics

Using R for Statistics will get you the answers to most of the problems you are likely to encounter when using a variety of statistics. This book is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background. After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics. What you’ll learn How to apply statistical concepts using R and some R programming How to work with data files, prepare and manipulate data, and combine and restructure datasets How to summarize continuous and categorical variables What is a probability distribution How to create and customize plots How to do hypothesis testing How to build and use regression and linear models Who this book is for No prior knowledge of R or of programming is assumed, making this book ideal if you are more accustomed to using point-and-click style statistical packages. You should have some prior experience with statistics, however. Table of Contents1. R Fundamentals 2. Working with Data Files 3. Preparing and Manipulating Data 4. Combining and Restructuring Data Sets 5. Continuous Variables 6. Tabular Data 7. Probability Distribution 8. Creating Plots 9. Customizing Plots 10. Hypothesis Tests 11. Regression and Linear Models 12. Appendix A: Basic Programming with R 13. Appendix B: Add-on Packages 14: Appendix C: Data Sets Contents at a Glance Contents About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1: R Fundamentals Downloading and Installing R Getting Orientated The R Console and Command Prompt Functions Objects Simple Objects Vectors Data Frames The Data Editor Workspaces Error Messages Script Files Summary Chapter 2: Working with Data Files Entering Data Directly Importing Plain Text Files CSV and Tab-Delimited Files DIF Files Other Plain Text Files Importing Excel Files Importing Files from Other Software Using Relative File Paths Exporting Datasets Summary Chapter 3: Preparing and Manipulating Your Data Variables Rearranging and Removing Variables Renaming Variables Variable Classes Calculating New Numeric Variables Dividing a Continuous Variable into Categories Working with Factor Variables Manipulating Character Variables Concatenating Character Strings Extracting a Substring Searching a Character Variable Working with Dates and Times Adding and Removing Observations Adding New Observations Removing Specific Observations Removing Duplicate Observations Selecting a Subset of the Data Selecting a Subset According to Selection Criteria Selecting a Random Sample from a Dataset Sorting a Dataset Summary Chapter 4: Combining and Restructuring Datasets Appending Rows Appending Columns Merging Datasets by Common Variables Stacking and Unstacking a Dataset Stacking Data Unstacking Data Reshaping a Dataset Summary Chapter 5: Summary Statistics for Continuous Variables Univariate Statistics Statistics by Group Measures of Association Covariance Pearson’s Correlation Coefficient Spearman’s Rank Correlation Coefficient Hypothesis Test of Correlation Comparing a Sample with a Specified Distribution Shapiro-Wilk Test Kolmogorov-Smirnov Test Confidence Intervals and Prediction Intervals Summary Chapter 6: Tabular Data Frequency Tables Creating Tables Displaying Tables Creating Tables from Count Data Creating a Table Directly Chi-Square Goodness-of-Fit Test Tests of Association Between Categorical Variables Chi-Square Test of Association Fisher’s Exact Test Proportions Test Summary Chapter 7: Probability Distributions Probability Distributions in R Probability Density Functions and Probability Mass Functions Finding Probabilities Finding Quantiles Generating Random Numbers Summary Chapter 8: Creating Plots Simple Plots Histograms Normal Probability Plots Stem-and-Leaf Plots Bar Charts Pie Charts Scatter Plots Scatterplot Matrices Box Plots Plotting a Function Exporting and Saving Plots Summary Chapter 9: Customizing Your Plots Titles and Labels Axes Colors Plotting Symbols Plotting Lines Shaded Areas Adding Items to Plots Adding Straight Lines Adding a Mathematical Function Curve Adding Labels and Text Adding a Grid Adding Arrows Overlaying Plots Adding a Legend Multiple Plots in the Plotting Area Changing the Default Plot Settings Summary Chapter 10: Hypothesis Testing Student’s T-Tests One-Sample T-Test Two-Sample T-Test Paired T-Test Wilcoxon Rank-Sum Test Analysis of Variance Kruskal-Wallis Test Multiple Comparison Methods Tukey’s HSD Test Other Pairwise T-Tests Pairwise Wilcoxon Rank-Sum Tests Hypothesis Tests for Variance F-Test Bartlett’s Test Summary Chapter 11: Regression and General Linear Models Building the Model Simple Linear Regression Multiple Linear Regression Interaction Terms Polynomial Terms Transformations The Intercept Term Including Factor Variables Updating a Model Stepwise Model Selection Procedures Assessing the Fit of the Model Coefficient Estimates Plotting the Line of Best Fit Model Diagnostics Residual Analysis Leverage Cook’s Distances Making Predictions Summary Appendix A: Add-On Packages Viewing a List of Available Add-on Packages Installing and Loading Add-On Packages Windows Users Mac Users Linux Users Appendix B: Basic Programming with R Creating New Functions Conditional Statements Conditions If Statement If/else Statement The switch Function Loops For Loop While Loop Summary Appendix C: Datasets apartments bigcats bottles brains CIAdata1, CIAdata2 coffeeshop concrete CPIdata customers endangered fiveyearreport flights fruit grades1 people people2 powerplant pulserates resistance supermarkets vitalsigns WHOdata Index " R is a popular and growing open source statistical analysis and graphics environment as well as a programming language and platform. If you need to use a variety of statistics, then Using R for Statistics will get you the answers to most of the problems you are likely to encounter. Using R for Statistics is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background. After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics. " R is a popular and growing open source statistical analysis and graphics environment as well as a programming language and platform. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics. What you'll learn: How to apply statistical concepts using R and some R programming; How to work with data files, prepare and manipulate data, and combine and restructure datasets; How to summarize continuous and categorical variables; What is a probability distribution; How to create and customize plots; How to do hypothesis testing; How to build and use regression and linear models