Learn R for Applied Statistics: With Data Visualizations, Regressions, and Statistics

Description

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions.  Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations.  What You Will LearnDiscover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics  Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.   Table of Contents About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1: Introduction What Is R? High-Level and Low-Level Languages What Is Statistics? What Is Data Science? What Is Data Mining? Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment What Is Text Mining? Data Acquisition Text Preprocessing Modeling Evaluation/Validation Applications Natural Language Processing Three Types of Analytics Descriptive Analytics Predictive Analytics Prescriptive Analytics Big Data Volume Velocity Variety Why R? Conclusion References Chapter 2: Getting Started What Is R? The Integrated Development Environment RStudio: The IDE for R Installation of R and RStudio Writing Scripts in R and RStudio Conclusion References Chapter 3: Basic Syntax Writing in R Console Using the Code Editor Adding Comments to the Code Variables Data Types Vectors Lists Matrix Data Frame Logical Statements Loops For Loop While Loop Break and Next Keywords Repeat Loop Functions Create Your Own Calculator Conclusion References Chapter 4: Descriptive Statistics What Is Descriptive Statistics? Reading Data Files Reading a CSV File Writing a CSV File Reading an Excel File Writing an Excel File Reading an SPSS File Writing an SPSS File Reading a JSON File Basic Data Processing Selecting Data Sorting Filtering Removing Missing Values Removing Duplicates Some Basic Statistics Terms Types of Data Mode, Median, Mean Mode Median Mean Interquartile Range, Variance, Standard Deviation Range Interquartile Range Variance Standard Deviation Normal Distribution Modality Skewness Binomial Distribution The summary() and str() Functions Conclusion References Chapter 5: Data Visualizations What Are Data Visualizations? Bar Chart and Histogram Line Chart and Pie Chart Scatterplot and Boxplot Scatterplot Matrix Social Network Analysis Graph Basics Using ggplot2 What Is the Grammar of Graphics? The Setup for ggplot2 Aesthetic Mapping in ggplot2 Geometry in ggplot2 Labels in ggplot2 Themes in ggplot2 ggplot2 Common Charts Bar Chart Histogram Density Plot Scatterplot Line chart Boxplot Interactive Charts with Plotly and ggplot2 Conclusion References Chapter 6: Inferential Statistics and Regressions What Are Inferential Statistics and Regressions? apply(), lapply(), sapply() Sampling Simple Random Sampling Stratified Sampling Cluster Sampling Correlations Covariance Hypothesis Testing and P-Value T-Test Types of T-Tests Assumptions of T-Tests Type I and Type II Errors One-Sample T-Test Two-Sample Independent T-Test Two-Sample Dependent T-Test Chi-Square Test Goodness of Fit Test Contingency Test ANOVA Grand Mean Hypothesis Assumptions Between Group Variability Within Group Variability One-Way ANOVA Two-Way ANOVA MANOVA Nonparametric Test Wilcoxon Signed Rank Test Wilcoxon-Mann-Whitney Test Kruskal-Wallis Test Linear Regressions Multiple Linear Regressions Conclusion References Index