Learning Path: R: Master R Data Analysis And Visualization

Harness the power of R for effective data analysis and visualizations

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.