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R data science essentials : learn the essence of data science and visualization using R in no time at all /
Learn the essence of data science and visualization using R in no time at allAbout This Book Become a pro at making stunning visualizations and dashboards quickly and without hassle For better decision making in business, apply the R programming language with the help of useful statistical technique...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2016.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with R; Reading data from different sources; Reading data from a database; Data types in R; Variable data types; Data preprocessing techniques; Performing data operations; Arithmetic operations on the data; String operations on the data; Aggregation operations on the data; Mean; Median; Sum; Maximum and minimum; Standard deviation; Control structures in R; Control structures
- if and else; Control structures
- for; Control structures
- while
- Control structures
- repeat and breakControl structures
- next and return; Bringing data to a usable format; Summary; Chapter 2: Exploratory Data Analysis; The Titanic dataset; Descriptive statistics; Box plot; Exercise; Inferential statistics; Univariate analysis; Bivariate analysis; Multivariate analysis; Cross-tabulation analysis; Graphical analysis; Summary; Chapter 3: Pattern Discovery; Transactional datasets; Using the built-in dataset; Building the dataset; Apriori analysis; Support, confidence, and lift; Support; Confidence; Lift; Generating filtering rules; Plotting; Dataset; Rules
- Sequential datasetApriori sequence analysis; Understanding the results; Reference; Business cases; Summary; Chapter 4: Segmentation Using Clustering; Datasets; Reading and formatting the dataset in R; Centroid-based clustering and an ideal number of clusters; Implementation using K-means; Visualizing the clusters; Connectivity-based clustering; Visualizing the connectivity; Business use cases; Summary; Chapter 5: Developing Regression Models; Datasets; Sampling the dataset; Logistic regression; Evaluating logistic regression; Linear regression; Evaluating linear regression
- Methods to improve the accuracyEnsemble models; Replacing NA with mean or median; Removing the highly correlated values; Removing outliers; Summary; Chapter 6: Time Series Forecasting; Datasets; Extracting patterns; Forecasting using ARIMA; Forecasting using Holt-Winters; Methods to improve accuracy; Summary; Chapter 7: Recommendation Engine; Dataset and transformation; Recommendations using user-based CF; Recommendations using item-based CF; Challenges and enhancements; Summary; Chapter 8: Communicating Data Analysis; Dataset; Plotting using the googleVis package
- Creating an interactive dashboard using ShinySummary; Index