Learning Data Analysis with R [Video]

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Learning Data Analysis with R [Video]

Learning
Dr. Fabio Veronesi

Find, process, analyze, manipulate, and crunch data in R.

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Video Details

ISBN 139781785889868
Course Length2 hours

Video Description

R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

This video delivers viewers the ability to conduct data analysis in practical contexts with R, using core language packages and tools. The end goal is to provide analysts and data scientists a comprehensive learning course on how to manipulate and analyse small and large sets of data with R. It will introduce how CRAN works and will demonstrate why viewers should use them.

You will start with the most basic importing techniques, to downloading compressed data from the web and learn of more advanced ways to handle even the most difficult datasets to import. Next, you will move on to create static plots, while the second will show how to plot spatial data on interactive web platforms such as Google Maps and Open Street maps. Finally, you will learn to implement your learning with real-world examples of data analysis.

This video will lay the foundations for deeper applications of data analysis, and pave the way for advanced data science.

Style and Approach

In this practical video, you will learn to perform a range of data analysis tasks along with real world data sets. This video delivers viewers the ability to conduct data analysis in practical contexts with R, using core language packages and tools.

The overall vision of this video is structured as a series of practical tutorials which cover single tasks, with their appropriate R packages, in detail.

Table of Contents

Importing Data in Table Format
The Course Overview
Importing Data from Tables
Downloading Data from FTP
Fixed-Width Format
Importing with ReadLines
Cleaning Your Data
Handling the Temporal Component
Loading the Required Packages
Importing Vector Data (ESRI shp and GeoJSON)
Transforming from data.frame to SpatialPointsDataFrame
Understanding Projections
Basic time/dates formats
Importing Raster Data
Introducing the Raster Format
Reading Raster Data
Mosaicking
Stacking to Include the Temporal Component
Exporting Data
Exporting Data in Tables
Exporting Vector Data (ESRI shp File)
Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids)
Exporting Data for WebGIS Systems (GeoJSON, KML)
Descriptive Statistics
Preparing the Dataset
Measuring Spread (Standard Deviation and Standard Distance)
Understanding Your Data with Plots
Plotting for Multivariate Data
Finding Outliers
Manipulating Vector Data
Introduction
Re-Projecting Your Data
Intersection
Buffer and Distance
Union and Overlay
Manipulating Raster Data
Introduction
Converting Vector/Table Data into Raster
Subsetting and Selection
Filtering
Raster Calculator
Visualizing Spatial Data
Plotting Basics
Adding Layers
Color Scale
Creating Multivariate Plots
Interactive Maps
Introduction
Plotting Vector Data on Google Maps
Adding Layers
Plotting Raster Data on Google Maps
Using Leaflet to Plot on Open Street Maps
Creating Global Economic Maps with Open Data
Introduction
Importing Data from the World Bank
Adding Geocoding Information
Concluding Remarks
Point Pattern Analysis of Crime in the UK
Theoretical Background
Introduction
Intensity and Density
Spatial Distribution
Modelling
Cluster Analysis of Earthquake Data
Theoretical Background
Data Preparation
K-Means Clustering
Optimal Number of Clusters
Hierarchical Clustering
Concluding
Time Series Analysis of Wind Speed Data
Theoretical Background
Reading Time-Series in R
Subsetting and Temporal Functions
Decomposition and Correlation
Forecasting
Time Series Analysis of Wind Speed Data
Theoretical Background
Data Preparation
Mapping with Deterministic Estimators
Analyzing Trend and Checking Normality
Variogram Analysis
Mapping with kriging
Regression and Statistical Learning
Theoretical Background
Dataset
Linear Regression
Regression Trees
Support Vector Machines

What You Will Learn

  • Import and export data in various formats in R
  • Perform advanced statistical data analysis
  • Visualize your data on Google or Open Street maps
  • Enhance your data analysis skills and learn to handle even the most complex datasets
  • Learn how to handle vector and raster data in R

Authors

Table of Contents

Importing Data in Table Format
The Course Overview
Importing Data from Tables
Downloading Data from FTP
Fixed-Width Format
Importing with ReadLines
Cleaning Your Data
Handling the Temporal Component
Loading the Required Packages
Importing Vector Data (ESRI shp and GeoJSON)
Transforming from data.frame to SpatialPointsDataFrame
Understanding Projections
Basic time/dates formats
Importing Raster Data
Introducing the Raster Format
Reading Raster Data
Mosaicking
Stacking to Include the Temporal Component
Exporting Data
Exporting Data in Tables
Exporting Vector Data (ESRI shp File)
Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids)
Exporting Data for WebGIS Systems (GeoJSON, KML)
Descriptive Statistics
Preparing the Dataset
Measuring Spread (Standard Deviation and Standard Distance)
Understanding Your Data with Plots
Plotting for Multivariate Data
Finding Outliers
Manipulating Vector Data
Introduction
Re-Projecting Your Data
Intersection
Buffer and Distance
Union and Overlay
Manipulating Raster Data
Introduction
Converting Vector/Table Data into Raster
Subsetting and Selection
Filtering
Raster Calculator
Visualizing Spatial Data
Plotting Basics
Adding Layers
Color Scale
Creating Multivariate Plots
Interactive Maps
Introduction
Plotting Vector Data on Google Maps
Adding Layers
Plotting Raster Data on Google Maps
Using Leaflet to Plot on Open Street Maps
Creating Global Economic Maps with Open Data
Introduction
Importing Data from the World Bank
Adding Geocoding Information
Concluding Remarks
Point Pattern Analysis of Crime in the UK
Theoretical Background
Introduction
Intensity and Density
Spatial Distribution
Modelling
Cluster Analysis of Earthquake Data
Theoretical Background
Data Preparation
K-Means Clustering
Optimal Number of Clusters
Hierarchical Clustering
Concluding
Time Series Analysis of Wind Speed Data
Theoretical Background
Reading Time-Series in R
Subsetting and Temporal Functions
Decomposition and Correlation
Forecasting
Time Series Analysis of Wind Speed Data
Theoretical Background
Data Preparation
Mapping with Deterministic Estimators
Analyzing Trend and Checking Normality
Variogram Analysis
Mapping with kriging
Regression and Statistical Learning
Theoretical Background
Dataset
Linear Regression
Regression Trees
Support Vector Machines

Video Details

ISBN 139781785889868
Course Length2 hours
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