Data science, analytics and machine learning with R / Luiz Paulo Fávero, Patrícia Belfiore, and Rafael de Freitas Souza.

Includes bibliographic references and index.

I. Introduction -- Overview of data science, analytics, and machine learning -- Introduction to R-based language -- II. Applied statistics and data visualization -- Types of variables, measurement scales, and accuracy scales -- Univariate descriptive statistics -- Bivariate descriptive statistics -- Hypotheses tests -- Data visualization and multivariate graphs -- III. Data mining and preparation -- Webscraping and handcrafted robots -- Using application programming interfaces to collect data -- Managing data -- IV. Unsupervised machine learning techniques -- Cluster analysis -- Principal component factor analysis -- Simple and multiple correspondence analysis - V. Supervised machine learning techniques -- Simple and multiple regression models -- Binary and multinomial logistics -- Count-data and zero-inflated -- Generalized linear mixed models -- VI. Improving performance -- Support vector machines -- Classification and regression trees -- Boosting and bagging -- Random forests -- Artificial neural networks -- VII. Spatial analysis -- Working on shapefiles -- Dealing with simple feature objects -- Raster objects -- Exploratory spatial analysis -- VIII. Adding value to your work -- Enhanced and interactive graphs -- Dashboards with R.

Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear. -- Provided by publisher.