Learning Path R Master R Data Analysis And Visualization

What you'll learn
Import and export data in various formats in R
Perform advanced statistical data analysis
Visualize your data on Google or Open Street maps
Create simple and quick visualizations using the basic graphic tools in R
Implement interactive visualizations using ggplot2.
Add elements, text, animation, and colors to your plot to make sense of data
Master network, radial, and coxcomb plots
Requirements
Basic programming knowledge of R
Basic knowledge of Math and Statistics
Description
R is one of the most comprehensible statistical tool for managing and manipulating data. With the ever increasing number of data, there is a very high demand of professionals who have got skills to analyze these data. If you're looking forward to becoming an expert data analyst, then go for this Learning Path.
Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The highlights of this Learning Path are
Manipulate and analyze small and large sets of data with R
Practice with real-world examples of data analysis and visualization
Let's take a quick look at your learning journey! This Learning Path begins with familiarizing you with the programming and statistics aspects of R. You will learn how CRAN works and why to use it. Acquire the ability to conduct data analysis in practical contexts with R, using core language packages and tools. You will then generate various plots in R using the basic R plotting techniques. Learn how to make plots, charts, and maps in step-by-step manner. Utilize R packages to add context and meaning to your data.
Moving ahead, the Learning Path will gradually take you through creating interactive maps using the googleVis package. Finally, you will generate chloropleth maps and contouring maps, bubble plots, and pie charts.
By the end of this Learning Path, you will be equipped with all data analysis and visualization techniques and build a strong foundation for moving into data science.
About the Author
We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth
Dr. Samik Sen is a theoretical physicist and loves thinking about hard problems. After his PH.D. in developing computational methods to solve problems for which no solutions existed, he began thinking about how to tackle math problems while lecturing. He has a YouTube channel associated with data science, which also provides a valuable engagement with people round the world who look at problems from a different perspective.Fabio Veronesi obtained a Ph.D. in digital soil mapping from Cranfield University and then moved to ETH Zurich, where he has been working for the past three years as a postdoc. In his career, Dr. Veronesi worked at several topics related to environmental research: digital soil mapping, cartography and shaded relief, renewable energy and transmission line siting. During this time, he specialized in the application of spatial statistical techniques to environmental data.Atmajit Singh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst. He writes about data manipulation, data exploration, visualization, and basic R plotting functions on his blog. He has a master's degree in financial economics from the State University of New York (SUNY), Buffalo. He also graduated with a Master of Arts degree in economics from the University of Pune, India.
Overview
Section 1: Speaking 'R' - The Language of Data Science
Lecture 1 The Course Overview
Lecture 2 What Is R?
Lecture 3 Getting and Setting Up R/Rstudio
Lecture 4 Using RStudio
Lecture 5 Packages
Lecture 6 A Lot Is the Same
Lecture 7 Familiar Building Programming Blocks
Lecture 8 Putting It All Together
Lecture 9 Core R Types
Lecture 10 Some Useful Operations
Lecture 11 More Useful Operations
Lecture 12 Titanic
Lecture 13 Tennis
Lecture 14 It's Mostly Cleaning Up
Lecture 15 The Most Widely Used Statistical Package
Lecture 16 Distributions
Lecture 17 Time to Get Graphical
Lecture 18 Plotting to Another Dimension
Lecture 19 Facets
Section 2: Learning Data Analysis with R
Lecture 20 The Course Overview
Lecture 21 Importing Data from Tables (read.table)
Lecture 22 Downloading Open Data from FTP Sites
Lecture 23 Fixed-Width Format
Lecture 24 Importing with read.lines (The Last Resort)
Lecture 25 Cleaning Your Data
Lecture 26 Loading the Required Packages
Lecture 27 Importing Vector Data (ESRI shp and GeoJSON)
Lecture 28 Transforming from data.frame to SpatialPointsDataFrame
Lecture 29 Understanding Projections
Lecture 30 Basic time/dates formats
Lecture 31 Introducing the Raster Format
Lecture 32 Reading Raster Data in NetCDF
Lecture 33 Mosaicking
Lecture 34 Stacking to Include the Temporal Component
Lecture 35 Exporting Data in Tables
Lecture 36 Exporting Vector Data (ESRI shp File)
Lecture 37 Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids)
Lecture 38 Exporting Data for WebGIS Systems (GeoJSON, KML)
Lecture 39 Preparing the Dataset
Lecture 40 Measuring Spread (Standard Deviation and Standard Distance)
Lecture 41 Understanding Your Data with Plots
Lecture 42 Plotting for Multivariate Data
Lecture 43 Finding Outliers
Lecture 44 Introduction
Lecture 45 Re-Projecting Your Data
Lecture 46 Intersection
Lecture 47 Buffer and Distance
Lecture 48 Union and Overlay
Lecture 49 Introduction
Lecture 50 Converting Vector/Table Data into Raster
Lecture 51 Subsetting and Selection
Lecture 52 Filtering
Lecture 53 Raster Calculator
Lecture 54 Plotting Basics
Lecture 55 Adding Layers
Lecture 56 Color Scale
Lecture 57 Creating Multivariate Plots
Lecture 58 Handling the Temporal Component
Lecture 59 Introduction
Lecture 60 Plotting Vector Data on Google Maps
Lecture 61 Adding Layers
Lecture 62 Plotting Raster Data on Google Maps
Lecture 63 Using Leaflet to Plot on Open Street Maps
Lecture 64 Introduction
Lecture 65 Importing Data from the World Bank
Lecture 66 Adding Geocoding Information
Lecture 67 Concluding Remarks
Lecture 68 Theoretical Background
Lecture 69 Introduction
Lecture 70 Intensity and Density
Lecture 71 Spatial Distribution
Lecture 72 Modelling
Lecture 73 Theoretical Background
Lecture 74 Data Preparation
Lecture 75 K-Means Clustering
Lecture 76 Optimal Number of Clusters
Lecture 77 Hierarchical Clustering
Lecture 78 Concluding
Lecture 79 Theoretical Background
Lecture 80 Reading Time-Series in R
Lecture 81 Subsetting and Temporal Functions
Lecture 82 Decomposition and Correlation
Lecture 83 Forecasting
Lecture 84 Theoretical Background
Lecture 85 Data Preparation
Lecture 86 Mapping with Deterministic Estimators
Lecture 87 Analyzing Trend and Checking Normality
Lecture 88 Variogram Analysis
Lecture 89 Mapping with kriging
Lecture 90 Theoretical Background
Lecture 91 Dataset
Lecture 92 Linear Regression
Lecture 93 Regression Trees
Lecture 94 Support Vector Machines
Section 3: R Data Visualization - Basic Plots, Maps, and Pie Charts
Lecture 95 The Course Overview
Lecture 96 Installing Packages and Getting Help in R
Lecture 97 Data Types and Special Values in R
Lecture 98 Matrices and Editing a Matrix in R
Lecture 99 Data frames and Editing a data frame in R
Lecture 100 Importing and Exporting Data in R
Lecture 101 Writing a Function and if else Statement in R
Lecture 102 Basic and Nested Loops in R
Lecture 103 The apply, lapply, sapply, and tapply Functions
Lecture 104 Using and Saving Par to Beautify a Plot in R
Lecture 105 Introducing a Scatter Plot with Texts, Labels, and Lines
Lecture 106 Connecting Points and Generating an Interactive Scatter Plot
Lecture 107 A Simple and Interactive Bar Plot
Lecture 108 Introduction to Line Plot and Its Effective Story
Lecture 109 Generating an Interactive Gantt/Timeline Chart in R
Lecture 110 Merging Histograms
Lecture 111 Making an Interactive Bubble Plot
Lecture 112 Constructing a Waterfall Plot in R
Lecture 113 Constructing a Simple Dendrogram
Lecture 114 Creating Dendrograms with Colors and Labels
Lecture 115 Creating Heat Maps
Lecture 116 Generating a Heat Map with Customized Colors
Lecture 117 Generating an Integrated Dendrogram and a Heat Map
Lecture 118 Creating a Three- Dimensional Heat Map and Stereo Map
Lecture 119 Constructing a Tree Map in R
Lecture 120 Introducing Regional Maps
Lecture 121 Introducing Choropleth Maps
Lecture 122 A Guide to Contour Maps
Lecture 123 Constructing Maps with bubbles
Lecture 124 Integrating Text with Maps
Lecture 125 Introducing Shapefiles
Lecture 126 Creating Cartograms
Lecture 127 Generating a Simple Pie Chart
Lecture 128 Constructing Pie Charts with Labels
Lecture 129 Creating Donut Plots and Interactive Plots
Lecture 130 Generating a Slope Chart
Lecture 131 Constructing a Fan Plot
This Learning Path is aimed at aspiring or professional statisticians, data analysts, or data scientists who want to analyze and visualize data for gaining deeper insights of it.


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