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Learn R for Applied Statistics : With Data Visualizations, Regressions, and Statistics.
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. Aft...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress L.P.,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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