Data Science, Analytics and Machine Learning with R
- 1st Edition - January 23, 2023
- Imprint: Academic Press
- Authors: Luiz Paulo Favero, Patricia Belfiore, Rafael de Freitas Souza
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 4 2 7 1 - 1
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 8 5 9 2 3 - 3
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 multivari… Read more
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Request a sales quoteData 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.
- Presents a comprehensive and practical overview of machine learning, data mining and AI techniques for a broad multidisciplinary audience
- Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R
- Teaches readers how to apply machine learning techniques to a wide range of data and subject areas
- Presents data in a graphically appealing way, promoting greater information transparency and interactive learning
Data science and data engineering professionals, as well as researchers and engineers who need to ensure consistent, accurate and reliable data across their company, lab, hospital, etc. Graduate students in data science, compute science, and big data
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Epigraph
- Part I: Introduction
- Chapter 1: Overview of data science, analytics, and machine learning
- Abstract
- Introduction
- Overview of the book
- Final remarks
- References
- Chapter 2: Introduction to R-based language
- Abstract
- Introduction
- How to use this work
- R-based language installation
- Installing RStudio
- Objects
- Functions and arguments
- Packages
- Loading datasets
- Brief notion of data manipulation
- Final remarks
- Supplementary data sets
- References
- Part II: Applied statistics and data visualization
- Chapter 3: Types of variables, measurement scales, and accuracy scales
- Abstract
- Introduction
- Types of variables
- Types of variables and scales of measurement
- Types of variables based on number of categories and scales of accuracy
- Final remarks
- Exercises
- References
- Chapter 4: Univariate descriptive statistics
- Abstract
- Introduction
- Frequency distribution table
- Graphical representation of the results
- The most common summary measures in univariate descriptive statistics
- Final remarks
- Exercises
- Supplementary data sets
- References
- Chapter 5: Bivariate descriptive statistics
- Abstract
- Introduction
- Association between two qualitative variables
- Correlation between two quantitative variables
- Final remarks
- Exercises
- Supplementary data sets
- References
- Chapter 6: Hypotheses tests
- Abstract
- Introduction
- Univariate tests for normality
- Tests for homogeneity of variance
- Hypotheses tests regarding a population mean (μ) from one random sample
- Student’s t-test to compare two population means from two independent random samples
- Student’s t-test to compare two population means from two paired random samples
- Analysis of variance to compare the means of more than two populations
- Final remarks
- Exercises
- Supplementary data sets
- References
- Chapter 7: Data visualization and multivariate graphs
- Abstract
- Introduction
- The library ggplot2
- Bar chart with ggplot2
- Pareto chart with ggplot2
- Line graph with ggplot2
- Scatter plot with ggplot2
- Histogram with ggplot2
- Boxplot with ggplot2
- Final remarks
- Exercises
- Appendix
- Supplementary data sets
- References
- Part III: Data mining and preparation
- Chapter 8: Webscraping and handcrafted robots
- Abstract
- Introduction
- CSS selector and XPATH
- The tool SelectorGadget
- The library rvest
- The library RSelenium
- Requirements necessary for using RSelenium
- Creating a robot with RSelenium
- Final remarks
- Exercises
- Chapter 9: Using application programming interfaces to collect data
- Abstract
- Introduction
- Verbs about API
- Example 1: Who is in the space stations?
- Example 2: Where is the ISS now?
- Example 3: When will the ISS fly over a certain point on the globe?
- Example 4: Health indicators of the World Health Organization
- Final remarks
- Exercises
- Chapter 10: Managing data
- Abstract
- Introduction
- The operator %>%
- The function rename()
- The function mutate()
- The function filter()
- The function arrange()
- The function group_by()
- The function select()
- The function summarise()
- The functions separate() and unite()
- The functions gather() and spread()
- Join functions
- Final remarks
- Exercise
- Supplementary data sets
- References
- Part IV: Unsupervised machine learning techniques
- Chapter 11: Cluster analysis
- Abstract
- Cluster analysis with hierarchical and nonhierarchical agglomeration schedules in R
- Final remarks
- Exercise
- Supplementary data sets
- References
- Chapter 12: Principal component factor analysis
- Abstract
- Principal component factor analysis in R
- Final remarks
- Exercise
- Supplementary data sets
- References
- Chapter 13: Simple and multiple correspondence analysis
- Abstract
- Applications in R
- Final remarks
- Exercises
- Appendix
- Supplementary data sets
- Part V: Supervised machine learning techniques
- Chapter 14: Simple and multiple regression models
- Abstract
- Estimation of regression models in R
- Final remarks
- Exercises
- Supplementary data sets
- References
- Chapter 15: Binary and multinomial logistic regression models
- Abstract
- Estimation of binary and multinomial logistic regression models in R
- Final remarks
- Exercises
- Supplementary data sets
- References
- Chapter 16: Count-data and zero-inflated regression models
- Abstract
- Estimating regression models for count data in R
- Final remarks
- Exercise
- Supplementary data sets
- References
- Chapter 17: Generalized linear mixed models
- Abstract
- Estimation of hierarchical linear models in R
- Final remarks
- Exercise
- Supplementary data sets
- References
- Part VI: Improving performance
- Chapter 18: Support vector machines
- Abstract
- Introduction
- Separating hyperplanes
- Maximal margin classifiers
- Support vector classifiers
- Support vector machines
- Support vector machines in R
- Final remarks
- Exercise
- Supplementary data sets
- References
- Chapter 19: Classification and regression trees
- Abstract
- Introduction
- CARTs estimation methods
- Variance
- Overfitting
- Pruning
- Hyperparameters
- Estimating CART models in R
- Classification trees in R
- Final remarks
- Exercises
- Supplementary data sets
- References
- Chapter 20: Boosting and bagging
- Abstract
- Introduction
- Boosting
- Bagging
- Boosting and bagging applications in R
- Final remarks
- Exercise
- Supplementary data sets
- References
- Chapter 21: Random forests
- Abstract
- Introduction
- Random forests
- Hyperparameters
- Random forests applications in R
- Final remarks
- Exercise
- Supplementary data sets
- References
- Chapter 22: Artificial neural networks
- Abstract
- Introduction
- Artificial neural networks
- Activation functions and estimations of the ouput values of each layer
- Demonstration of calculations of layer output values
- Method of calculation of estimation errors for iteration feeding
- Hyperparameters
- Artificial neural networks applications in R
- Final remarks
- Exercise
- Supplementary data sets
- References
- Part VII: Spatial analysis
- Chapter 23: Working on shapefiles
- Abstract
- Introduction
- Using shapefiles
- Carring a shapefile
- Incorporating information into a shapefile
- Plotting information from a dataset on a map
- Dismembering shapefiles
- Joining shapefiles
- Final considerations
- Supplementary data sets
- References
- Chapter 24: Dealing with simple feature objects
- Abstract
- Introduction
- Working with simple features
- Creating a simple feature object
- Using layers in simple feature objects
- Combining simple feature objects with shapefiles
- Using R like geographic information systems software
- Combining simple feature layers and objects in search of insight
- Example of using a robot to capture space data
- Final considerations
- Supplementary data sets
- References
- Chapter 25: Raster objects
- Abstract
- Introduction
- Loading a raster file
- Plotting the raster file information
- Combining a raster object with a shapefile
- Loading raster objects entirely into the computer’s RAM
- Cutting out raster objects
- Final considerations
- Chapter 26: Exploratory spatial analysis
- Abstract
- Introduction
- Establishing neighborhoods
- Standardization of matrices
- Techniques for verification of spatial autocorrelation
- Final remarks
- Exercise
- Supplementary data sets
- References
- Part VIII: Adding value to your work
- Chapter 27: Enhanced and interactive graphs
- Abstract
- Introduction
- The library plotly
- Scatter plot with plotly
- Line graph with plotly
- Bar chart with plotly
- Pareto chart with plotly
- Histogram with plotly
- Boxplot with plotly
- Pie charts with plotly
- Final remarks
- Exercises
- Supplementary data sets
- References
- Chapter 28: Dashboards with R
- Abstract
- Introduction
- First steps in the library shiny
- Creating the first dashboard in the library shiny
- Reactive programming
- Construction of a complex dashboard
- Final remarks
- Exercise
- Supplementary data sets
- Answers
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 11
- Chapter 12
- Chapter 13
- Chapter 14
- Chapter 15
- Chapter 16
- Chapter 17
- Chapter 18
- Chapter 19
- Chapter 21
- Chapter 22
- Chapter 26
- Chapter 27
- References
- References
- Index
- Edition: 1
- Published: January 23, 2023
- Imprint: Academic Press
- No. of pages: 660
- Language: English
- Paperback ISBN: 9780128242711
- eBook ISBN: 9780323859233
LF
Luiz Paulo Favero
Dr. Fávero is a Full Professor at the Economics, Business Administration and Accounting College and at the Polytechnic School of the University of Sao Paulo (FEAUSP and EPUSP), where he teaches Data Science, Data Analysis, Multivariate Modeling, Machine and Deep Learning and Operational Research to undergraduate, Master’s and Doctorate students. He has a Post-Doctorate degree in Data Analysis and Econometrics from Columbia University in New York. He is a tenured Professor by FEA/USP (with greater focus on Quantitative Modeling). He has a degree in Engineering from USP Polytechnic School, a post-graduate degree in Business Administration from Getúlio Vargas Foundation (FGV/SP), and he has received the titles of Master and PhD in Data Science and Quantitative Methods applied to Organizational Economics from FEA/USP. He is a Visiting Professor at the Federal University of Sao Paulo (UNIFESP), Dom Cabral Foundation, Getúlio Vargas Foundation, FIA, FIPE and MONTVERO. He has authored or co-authored 9 books and he is the founder and former editor-in-chief of the International Journal of Multivariate Data Analysis. He is member and founder of the Latin American Academy of Data Science. He is a consultant to companies operating in sectors such as retail, industry, mining, banks, insurance and healthcare, with the use of Data Analysis, Machine and Deep Learning, Big Data and AI platforms, such as R, Python, SAS, Stata and IBM SPSS. Dr. Fávero is a Full Professor at the Economics, Business Administration and Accounting College and at the Polytechnic School of the University of Sao Paulo (FEAUSP and EPUSP), where he teaches Data Science, Data Analysis, Multivariate Modeling, Machine and Deep Learning and Operational Research to undergraduate, Master’s and Doctorate students. He has a Post-Doctorate degree in Data Analysis and Econometrics from Columbia University in New York. He is a tenured Professor by FEA/USP (with greater focus on Quantitative Modeling). He has a degree in Engineering from USP Polytechnic School, a post-graduate degree in Business Administration from Getúlio Vargas Foundation (FGV/SP), and he has received the titles of Master and PhD in Data Science and Quantitative Methods applied to Organizational Economics from FEA/USP. He is a Visiting Professor at the Federal University of Sao Paulo (UNIFESP), Dom Cabral Foundation, Getúlio Vargas Foundation, FIA, FIPE and MONTVERO. He has authored or co-authored 9 books and he is the founder and former editor-in-chief of the International Journal of Multivariate Data Analysis. He is member and founder of the Latin American Academy of Data Science. He is a consultant to companies operating in sectors such as retail, industry, mining, banks, insurance and healthcare, with the use of Data Analysis, Machine and Deep Learning, Big Data and AI platforms, such as R, Python, SAS, Stata and IBM SPSS.
Affiliations and expertise
Economics, Business Administration and Accounting College of the University of Sao Paulo, Brazil/ Faculdade de Economia, Administracao e Contabilidade, Universidade de Sao Paulo, BrazilPB
Patricia Belfiore
Dr. Belfiore is Associate Professor at the Federal University of ABC (UFABC), where she teaches Data Science, Statistics, Operational Research, Production Planning and Control, and Programming and Algorithms Development to Engineering students. She has a master’s in electrical engineering and a PhD in production engineering from the Polytechnic School of the University of Sao Paulo (EPUSP). She has a post-doctorate degree in Operational Research and Computer Programming from Columbia University in New York. She takes part in several research and consultancy projects in the fields of modeling, optimization and programming. She has taught Operational Research, Multivariate Data Analysis and Operations Research and Logistics to undergraduate and master’s students at FEI University Center and at the Arts, Sciences and Humanities College of the University of Sao Paulo (EACH/USP). Her main research interests are in the fields of modeling, simulation, combinatorial optimization, heuristics and computer programming. She is the author/co-author of 9 books. She is a consultant to companies operating in sectors such as retail, industry, banks, insurance and healthcare, with the use of Process Simulation and Optimization, Data Analysis, and Machine and Deep Learning platforms, such as R, Python, Stata, IBM SPSS and ProModel.
Affiliations and expertise
Associate Professor, Federal University of ABC (UFABC)/ Federal University of ABC, BrazilRd
Rafael de Freitas Souza
Dr. Freitas Souza is Assistant Professor at the Economics, Business Administration and Accounting College of Ribeirao Preto of the University of São Paulo (FEARPUSP), where he teaches Programming Languages, Data Science and Analytics, Algorithm Design and Algorithm Development. He has a PhD in Business Management from the Economics, Business Administration and Accounting College of the University of São Paulo (FEAUSP). His main research interests are in the fields of Performance Management (Private and Public sectors) using Multivariate Modeling, Machine and Deep Learning techniques, including Spatial Analysis.
Affiliations and expertise
Economics, Business Administration and Accounting College of Ribeirao Preto, University of São Paulo, BrazilRead Data Science, Analytics and Machine Learning with R on ScienceDirect