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

Learning Data Analysis with R

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

  • Video Duration:6 Hours
  • Cost: $ 124.99

Course Details

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.

Who all can attend

If you are a statistician, analyst, or a budding data scientist and want to learn how to analyze data with R, then this is the course for you. Some basic knowledge of R and programming is assumed, along with a background in mathematics.

What you will learn from this course

  • 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

Course Content

  1. Importing Data in Table Format
    • The Course Overview
    • Importing Data from Tables (read.table)
    • Downloading Open Data from FTP Sites
    • Fixed-Width Format
    • Importing with read.lines (The Last Resort)
    • Cleaning Your Data

  2. 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

  3. Importing Raster Data
    • Introducing the Raster Format
    • Reading Raster Data
    • Mosaicking
    • Stacking to Include the Temporal Component

  4. 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)

  5. Descriptive Statistics
    • Preparing the Dataset
    • Measuring Spread (Standard Deviation and Standard Distance)
    • Understanding Your Data with Plots
    • Plotting for Multivariate Data
    • Finding Outliers

  6. Manipulating Vector Data
    • Introduction
    • Re-Projecting Your Data
    • Intersection
    • Buffer and Distance
    • Union and Overlay

  7. Manipulating Raster Data
    • Introduction
    • Converting Vector/Table Data into Raster
    • Subsetting and Selection
    • Filtering
    • Raster Calculator

  8. Visualizing Spatial Data
    • Plotting Basics
    • Adding Layers
    • Color Scale
    • Creating Multivariate Plots
    • Handling the Temporal Component

  9. 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

  10. Creating Global Economic Maps with Open Data
    • Introduction
    • Importing Data from the World Bank
    • Adding Geocoding Information
    • Concluding Remarks

  11. Point Pattern Analysis of Crime in the UK
    • Theoretical Background
    • Introduction
    • Intensity and Density
    • Spatial Distribution
    • Modelling

  12. Cluster Analysis of Earthquake Data
    • Theoretical Background
    • Data Preparation
    • K-Means Clustering
    • Optimal Number of Clusters
    • Hierarchical Clustering
    • Concluding

  13. Time Series Analysis of Wind Speed Data
    • Theoretical Background
    • Reading Time-Series in R
    • Subsetting and Temporal Functions
    • Decomposition and Correlation
    • Forecasting

  14. Geostatistics
    • Theoretical Background
    • Data Preparation
    • Mapping with Deterministic Estimators
    • Analyzing Trend and Checking Normality
    • Variogram Analysis
    • Mapping with kriging

  15. Regression and Statistical Learning
    • Theoretical Background
    • Dataset
    • Linear Regression
    • Regression Trees
    • Support Vector Machines

Contact Us

Instructor-led online training is also available for the same course.

For more details, write to us at :

Emailinfo@multisoftvirtualacademy.com

Call(+91) 8130666206/209

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Learning Data Analysis with R
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