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# Learning Path: R: Powerful Data Analysis with R

10.99 \$
Video Introducing this tutorial

Chapter 1 : Learning Data Analysis with R
The Course Overview
Fixed-Width Format
Importing with read.lines (The Last Resort)
Importing Vector Data (ESRI shp and GeoJSON)
Transforming from data.frame to SpatialPointsDataFrame
Understanding Projections
Basic time/dates formats
Introducing the Raster Format
Mosaicking
Stacking to Include the Temporal Component
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)
Preparing the Dataset
Measuring Spread (Standard Deviation and Standard Distance)
Plotting for Multivariate Data
Finding Outliers
Introduction
Intersection
Buffer and Distance
Union and Overlay
Introduction
Converting Vector/Table Data into Raster
Subsetting and Selection
Filtering
Raster Calculator
Plotting Basics
Color Scale
Creating Multivariate Plots
Handling the Temporal Component
Introduction
Plotting Vector Data on Google Maps
Plotting Raster Data on Google Maps
Using Leaflet to Plot on Open Street Maps
Introduction
Importing Data from the World Bank
Concluding Remarks
Theoretical Background
Introduction
Intensity and Density
Spatial Distribution
Modelling
Theoretical Background
Data Preparation
K-Means Clustering
Optimal Number of Clusters
Hierarchical Clustering
Concluding
Theoretical Background
Subsetting and Temporal Functions
Decomposition and Correlation
Forecasting
Theoretical Background
Data Preparation
Mapping with Deterministic Estimators
Analyzing Trend and Checking Normality
Variogram Analysis
Mapping with kriging
Theoretical Background
Dataset
Linear Regression
Regression Trees
Support Vector Machines

Chapter 2 : Mastering Data Analysis with R
The Course Overview
Getting Started and Data Exploration with R/RStudio
Introduction to Visualization
Interactive Visualization
Geographic Plots
Getting Introductory Concepts
Data Partitioning with R
Multiple Linear Regression with R
Multicollinearity Issues
Logistic Regression with Categorical Response Variables at two Levels
Logistic Regression Model and Interpretation
Misclassification Error and Confusion Matrix
ROC Curves
Prediction and Model Assessment
Multinomial Logistic Regression with Categorical Response Variables at 3Levels
Multinomial Logistic Regression Model and Its Interpretation
Misclassification Error and Confusion Matrix
Prediction and Model Assessment
Ordinal Logistic Regression with R
Ordinal Logistic Regression Model and Interpretation
The Misclassification Error and Confusion Matrix
Prediction and Model Assessment