Learning Path: R: Powerful Data Analysis with R

Course Curriculum

Chapter 1: Learning Data Analysis with R

Lecture 1: The Course Overview

Lecture 2: Importing Data from Tables (read.table)

Lecture 3: Downloading Open Data from FTP Sites

Lecture 4: Fixed-Width Format

Lecture 5: Importing with read.lines (The Last Resort)

Lecture 6: Cleaning Your Data

Lecture 7: Loading the Required Packages

Lecture 8: Importing Vector Data (ESRI shp and GeoJSON)

Lecture 9: Transforming from data.frame to SpatialPointsDataFrame

Lecture 10: Understanding Projections

Lecture 11: Basic time/dates formats

Lecture 12: Introducing the Raster Format

Lecture 13: Reading Raster Data in NetCDF

Lecture 14: Mosaicking

Lecture 15: Stacking to Include the Temporal Component

Lecture 16: Exporting Data in Tables

Lecture 17: Exporting Vector Data (ESRI shp File)

Lecture 18: Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids)

Lecture 19: Exporting Data for WebGIS Systems (GeoJSON, KML)

Lecture 20: Preparing the Dataset

Lecture 21: Measuring Spread (Standard Deviation and Standard Distance)

Lecture 22: Understanding Your Data with Plots

Lecture 23: Plotting for Multivariate Data

Lecture 24: Finding Outliers

Lecture 25: Introduction

Lecture 26: Re-Projecting Your Data

Lecture 27: Intersection

Lecture 28: Buffer and Distance

Lecture 29: Union and Overlay

Lecture 30: Introduction

Lecture 31: Converting Vector/Table Data into Raster

Lecture 32: Subsetting and Selection

Lecture 33: Filtering

Lecture 34: Raster Calculator

Lecture 35: Plotting Basics

Lecture 36: Adding Layers

Lecture 37: Color Scale

Lecture 38: Creating Multivariate Plots

Lecture 39: Handling the Temporal Component

Lecture 40: Introduction

Lecture 41: Plotting Vector Data on Google Maps

Lecture 42: Adding Layers

Lecture 43: Plotting Raster Data on Google Maps

Lecture 44: Using Leaflet to Plot on Open Street Maps

Lecture 45: Introduction

Lecture 46: Importing Data from the World Bank

Lecture 47: Adding Geocoding Information

Lecture 48: Concluding Remarks

Lecture 49: Theoretical Background

Lecture 50: Introduction

Lecture 51: Intensity and Density

Lecture 52: Spatial Distribution

Lecture 53: Modelling

Lecture 54: Theoretical Background

Lecture 55: Data Preparation

Lecture 56: K-Means Clustering

Lecture 57: Optimal Number of Clusters

Lecture 58: Hierarchical Clustering

Lecture 59: Concluding

Lecture 60: Theoretical Background

Lecture 61: Reading Time-Series in R

Lecture 62: Subsetting and Temporal Functions

Lecture 63: Decomposition and Correlation

Lecture 64: Forecasting

Lecture 65: Theoretical Background

Lecture 66: Data Preparation

Lecture 67: Mapping with Deterministic Estimators

Lecture 68: Analyzing Trend and Checking Normality

Lecture 69: Variogram Analysis

Lecture 70: Mapping with kriging

Lecture 71: Theoretical Background

Lecture 72: Dataset

Lecture 73: Linear Regression

Lecture 74: Regression Trees

Lecture 75: Support Vector Machines

Chapter 2: Mastering Data Analysis with R

Lecture 1: The Course Overview

Lecture 2: Getting Started and Data Exploration with R/RStudio

Lecture 3: Introduction to Visualization

Lecture 4: Interactive Visualization

Lecture 5: Geographic Plots

Lecture 6: Advanced Visualization

Lecture 7: Getting Introductory Concepts

Lecture 8: Data Partitioning with R

Lecture 9: Multiple Linear Regression with R

Lecture 10: Multicollinearity Issues

Lecture 11: Logistic Regression with Categorical Response Variables at two Levels

Lecture 12: Logistic Regression Model and Interpretation

Lecture 13: Misclassification Error and Confusion Matrix

Lecture 14: ROC Curves

Lecture 15: Prediction and Model Assessment

Lecture 16: Multinomial Logistic Regression with Categorical Response Variables at 3Levels

Lecture 17: Multinomial Logistic Regression Model and Its Interpretation

Lecture 18: Misclassification Error and Confusion Matrix

Lecture 19: Prediction and Model Assessment

Lecture 20: Ordinal Logistic Regression with R

Lecture 21: Ordinal Logistic Regression Model and Interpretation

Lecture 22: The Misclassification Error and Confusion Matrix