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Learning Path: R: Powerful Data Analysis with R

Video Description

Unlock advanced data analysis techniques

In Detail

There’s an increasing number of data being produced every day which has led to the demand for skilled professionals who can analyze these data and make decisions. R is one of the popular tool which is widely used by data analysts for performing data analysis on real-world data.

This Learning Path is the complete learning process to play with data. You will start with the most basic importing techniques, for downloading compressed data from the Web. You will learn advanced data analysis concepts such as cluster analysis, time-series analysis, association mining, PCA, handling missing data, sentiment analysis, spatial data analysis with R and QGIS, and advanced data visualization with R’s ggplot2 library.

By the end of this Learning Path, you will learn how to perform data analysis on real-world data.

Prerequisites: Basic knowledge on R

Resources: Code downloads and errata:

  • Learning Data Analysis with R

  • Mastering Data Analysis with R

  • PATH PRODUCTS

    This path navigates across the following products (in sequential order):

  • Learning Data Analysis with R (6h 7m)

  • Mastering Data Analysis with R (3h 43m)

  • Table of Contents

    1. Chapter 1 : Learning Data Analysis with R
      1. The Course Overview 00:04:16
      2. Importing Data from Tables (read.table) 00:02:31
      3. Downloading Open Data from FTP Sites 00:04:03
      4. Fixed-Width Format 00:04:25
      5. Importing with read.lines (The Last Resort) 00:03:21
      6. Cleaning Your Data 00:02:37
      7. Loading the Required Packages 00:04:09
      8. Importing Vector Data (ESRI shp and GeoJSON) 00:04:03
      9. Transforming from data.frame to SpatialPointsDataFrame 00:02:50
      10. Understanding Projections 00:03:06
      11. Basic time/dates formats 00:03:51
      12. Introducing the Raster Format 00:04:59
      13. Reading Raster Data in NetCDF 00:06:10
      14. Mosaicking 00:02:53
      15. Stacking to Include the Temporal Component 00:04:11
      16. Exporting Data in Tables 00:03:12
      17. Exporting Vector Data (ESRI shp File) 00:02:21
      18. Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids) 00:02:43
      19. Exporting Data for WebGIS Systems (GeoJSON, KML) 00:02:40
      20. Preparing the Dataset 00:07:44
      21. Measuring Spread (Standard Deviation and Standard Distance) 00:03:23
      22. Understanding Your Data with Plots 00:05:51
      23. Plotting for Multivariate Data 00:03:02
      24. Finding Outliers 00:03:50
      25. Introduction 00:03:37
      26. Re-Projecting Your Data 00:02:54
      27. Intersection 00:03:07
      28. Buffer and Distance 00:03:22
      29. Union and Overlay 00:03:32
      30. Introduction 00:04:44
      31. Converting Vector/Table Data into Raster 00:04:00
      32. Subsetting and Selection 00:03:16
      33. Filtering 00:04:58
      34. Raster Calculator 00:04:44
      35. Plotting Basics 00:05:15
      36. Adding Layers 00:05:45
      37. Color Scale 00:04:52
      38. Creating Multivariate Plots 00:09:10
      39. Handling the Temporal Component 00:03:20
      40. Introduction 00:02:33
      41. Plotting Vector Data on Google Maps 00:05:46
      42. Adding Layers 00:04:41
      43. Plotting Raster Data on Google Maps 00:04:19
      44. Using Leaflet to Plot on Open Street Maps 00:09:04
      45. Introduction 00:02:22
      46. Importing Data from the World Bank 00:05:09
      47. Adding Geocoding Information 00:05:38
      48. Concluding Remarks 00:03:49
      49. Theoretical Background 00:07:31
      50. Introduction 00:07:37
      51. Intensity and Density 00:07:39
      52. Spatial Distribution 00:10:02
      53. Modelling 00:06:42
      54. Theoretical Background 00:04:31
      55. Data Preparation 00:05:51
      56. K-Means Clustering 00:05:27
      57. Optimal Number of Clusters 00:05:18
      58. Hierarchical Clustering 00:06:34
      59. Concluding 00:04:33
      60. Theoretical Background 00:04:34
      61. Reading Time-Series in R 00:06:38
      62. Subsetting and Temporal Functions 00:05:15
      63. Decomposition and Correlation 00:07:33
      64. Forecasting 00:04:32
      65. Theoretical Background 00:04:42
      66. Data Preparation 00:06:21
      67. Mapping with Deterministic Estimators 00:06:57
      68. Analyzing Trend and Checking Normality 00:04:58
      69. Variogram Analysis 00:05:53
      70. Mapping with kriging 00:06:18
      71. Theoretical Background 00:04:09
      72. Dataset 00:02:37
      73. Linear Regression 00:06:07
      74. Regression Trees 00:04:13
      75. Support Vector Machines 00:05:06
    2. Chapter 2 : Mastering Data Analysis with R
      1. The Course Overview 00:03:24
      2. Getting Started and Data Exploration with R/RStudio 00:28:17
      3. Introduction to Visualization 00:20:29
      4. Interactive Visualization 00:10:35
      5. Geographic Plots 00:10:04
      6. Advanced Visualization 00:11:00
      7. Getting Introductory Concepts 00:06:46
      8. Data Partitioning with R 00:13:49
      9. Multiple Linear Regression with R 00:11:59
      10. Multicollinearity Issues 00:07:31
      11. Logistic Regression with Categorical Response Variables at two Levels 00:13:46
      12. Logistic Regression Model and Interpretation 00:04:24
      13. Misclassification Error and Confusion Matrix 00:06:41
      14. ROC Curves 00:06:02
      15. Prediction and Model Assessment 00:08:43
      16. Multinomial Logistic Regression with Categorical Response Variables at 3Levels 00:07:29
      17. Multinomial Logistic Regression Model and Its Interpretation 00:08:14
      18. Misclassification Error and Confusion Matrix 00:06:34
      19. Prediction and Model Assessment 00:09:55
      20. Ordinal Logistic Regression with R 00:12:55
      21. Ordinal Logistic Regression Model and Interpretation 00:04:41
      22. The Misclassification Error and Confusion Matrix 00:04:28
      23. Prediction and Model Assessment 00:05:52