| Description |
1 online resource (254 pages) |
| Contents |
Intro; 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 |
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VarietyWhy 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 |
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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 |
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ConclusionReferences; 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 |
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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 |
| Summary |
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 Learn Discover 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 |
| Notes |
Wilcoxon Signed Rank Test |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
R
|
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Machine learning.
|
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MATHEMATICS -- Applied.
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MATHEMATICS -- Probability & Statistics -- General.
|
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Machine learning
|
| Form |
Electronic book
|
| ISBN |
9781484242001 |
|
1484242009 |
|
9781484242018 |
|
1484242017 |
|
9781484246344 |
|
1484246349 |
|
1484241991 |
|
9781484241998 |
|
