Complete Guide to 3D Plots in R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Complete Guide to 3D Plots in R write by Alboukadel KASSAMBARA. This book was released on . Complete Guide to 3D Plots in R available in PDF, EPUB and Kindle. This book provides a complete guide for visualizing a data in 3 dimensions (3D) using R software. It contains 2 main parts and 7 chapters describing how to draw static and interactive 3D plots. - The chapter 1 is about data preparation for 3D plot - In chapter 2, we describe how to create easily basic static 3D scatter plots. We provide R codes for changing: 1) main and axis titles; 2) the appearance of the plot (point colors, labels and shapes, legend position, ...) - Chapter 3 presents how to create advanced static 3D plots including 3D scatter plots with confidence interval, 3D line plots, 3D texts, 3D barplots, 3D histograms and 3D arrows. - Chapter 4 describes the required package for drawing interactive 3D plots. - In chapter 5, we show how to transform easily an existing static 3D plot into aninteractive 3D plot. - Chapter 6 provides many examples of R codes for creating interactive 3D scatter plotswith 3D regression surfaces and concentration ellipsoids. We describe also how to exportthese graphs as png or pdf files. - Chapter 7 presents a complete guide to RGL 3D visualization device system. We provide also R codes for creating a movie from RGL 3D scene and for exporting plot into an interactive HTML web file. Each chapter is organized as an independent quick start guide. This means that, you don’tneed to read the different chapters in sequence.
Complete Guide to 3D Plots in R
Complete Guide to 3D Plots in R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Complete Guide to 3D Plots in R write by Alboukadel Kassambara. This book was released on 2015. Complete Guide to 3D Plots in R available in PDF, EPUB and Kindle. This book provides a complete guide for visualizing a data in 3 dimensions (3D) using R software. It contains 2 main parts and 7 chapters describing how to draw static and interactive 3D plots. The chapter 1 is about data preparation for 3D plot. In chapter 2, we describe how to create and customize easily basic static 3D scatter plots. Chapter 3 presents how to create advanced static 3D plots including 3D scatter plots with confidence interval, 3D line plots, 3D texts, 3D bar plots, 3D histograms and 3D arrows. Chapter 4 describes the required package for drawing interactive 3D plots. In chapter 5, we show how to transform easily an existing static 3D plot into an interactive 3D plot. Chapter 6 provides many examples of R codes for creating interactive 3D scatter plots with 3D regression surfaces and concentration ellipsoids. We describe also how to export these graphs as PNG or PDF files. Chapter 7 presents a complete guide to RGL 3D visualization device system. We provide also R codes for creating a movie from RGL 3D scene and for exporting plot into an interactive HTML web file.
Practical Guide to Cluster Analysis in R
Practical Guide to Cluster Analysis in R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Practical Guide to Cluster Analysis in R write by Alboukadel Kassambara. This book was released on 2017-08-23. Practical Guide to Cluster Analysis in R available in PDF, EPUB and Kindle. Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.
Practical Guide To Principal Component Methods in R
Practical Guide To Principal Component Methods in R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Practical Guide To Principal Component Methods in R write by Alboukadel KASSAMBARA. This book was released on 2017-08-23. Practical Guide To Principal Component Methods in R available in PDF, EPUB and Kindle. Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced. This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra. Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). In Part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables.
Learn R Programming in 24 Hours
Learn R Programming in 24 Hours - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Learn R Programming in 24 Hours write by Alex Nordeen. This book was released on 2020-10-31. Learn R Programming in 24 Hours available in PDF, EPUB and Kindle. R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference. R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple. R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze. Table of Contents Chapter 1: What is R Programming Language? Introduction & Basics Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows Chapter 3: R Data Types, Arithmetic & Logical Operators with Example Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice Chapter 5: Factor in R: Categorical & Continuous Variables Chapter 6: R Data Frame: Create, Append, Select, Subset Chapter 7: List in R: Create, Select Elements with Example Chapter 8: R Sort a Data Frame using Order() Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread) Chapter 10: Merge Data Frames in R: Full and Partial Match Chapter 11: Functions in R Programming (with Example) Chapter 12: IF, ELSE, ELSE IF Statement in R Chapter 13: For Loop in R with Examples for List and Matrix Chapter 14: While Loop in R with Example Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example Chapter 20: R Aggregate Function: Summarise & Group_by() Example Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example Chapter 22: Scatter Plot in R using ggplot2 (with Example) Chapter 23: How to make Boxplot in R (with EXAMPLE) Chapter 24: Bar Chart & Histogram in R (with Example) Chapter 25: T Test in R: One Sample and Paired (with Example) Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples) Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example] Chapter 28: Decision Tree in R with Example Chapter 29: R Random Forest Tutorial with Example Chapter 30: Generalized Linear Model (GLM) in R with Example Chapter 31: K-means Clustering in R with Example Chapter 32: R Vs Python: What's the Difference? Chapter 33: SAS vs R: What's the Difference?
