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The Complete Introduction to R Programming Bundle

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Content
12 hours
Lessons
192

Introduction to R Programming

Jump Into One of the Most Effective Statistical Analysis Tools on Earth

By Packt Publishing | in Online Courses

It seems like everything these days is driven by data, and statisticians and analysts across industries need to handle this data efficiently and tactfully. That's where R comes in, a powerful programming language that helps developers solve even the most complex data problems. Data scientists are in constant demand, and this extensive course will give you your first taste of R, enabling you to make statistical inferences and run programs that solve important data problems and turn heads.
  • Access 50 lectures & 3.5 hours of content 24/7
  • Get introduced to the R Studio & programming concepts like variables, vectors, arrays, loops, & matrices
  • Visualize data using R's base graphics
  • Learn the fundamentals of univariate & bivariate analysis, computing confidence intervals, interpreting p values, & working w/ statistical tests
  • Perform a full-scale data analysis project
Selva Prabhakaran is a data scientist with a large E-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Selva lives in Bangalore with his wife.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Installation and Setup
    • The Course Overview (4:54)
    • Installing R (3:45)
    • Installing RStudio (4:35)
    • Installing Packages (4:50)
  • Working with Vectors
    • Data Types and Data Structures (3:04)
    • Vectors (5:44)
    • Random Numbers, Rounding, and Binning
    • Missing Values (2:47)
    • The which() Operator (3:11)
  • R Essentials
    • Lists (4:35)
    • Set Operations (2:08)
    • Sampling and Sorting (2:52)
    • Check Conditions (2:17)
    • For Loops (2:34)
  • Dataframes and Matrices
    • Dataframes (8:30)
    • Importing and Exporting Data (6:29)
    • Matrices and Frequency Tables (3:41)
    • Merging Dataframes (2:26)
    • Aggregation (2:48)
    • Melting and Cross Tabulations with dcast() (3:58)
  • Core Programming
    • Dates (5:35)
    • String Manipulation (5:14)
    • Functions (5:34)
    • Debugging and Error Handling (4:29)
    • Fast Loops with apply() (4:26)
    • Fast Loops with sapply(), lapply() and vapply() (1:59)
  • Making Plots with Base Graphics
    • Creating and Customizing an R Plot (7:03)
    • Drawing Plots with 2 Y Axes (2:23)
    • Multiplots and Custom Layouts (3:08)
    • Creating Basic Graph Types (4:47)
  • Statistical Inference
    • Univariate Analysis (6:16)
    • Normal Distribution, Central Limit Theorem, and Confidence Intervals (5:32)
    • Correlation and Covariance (3:03)
    • Chi-sq Statistic (4:42)
    • ANOVA (4:54)
    • Statistical Tests (5:14)
  • R Very Own Project
    • Project 1 – Data Munging and Summarizing (11:31)
    • Project 2 – Visualization with Base Graphics (5:42)
    • Project 3 – Statistical Inference (3:50)
  • DPlyR and Pipes
    • Pipes with Magrittr (5:21)
    • The 7 Data Manipulation Verbs (5:19)
    • Aggregation and Special Functions (3:36)
    • Two Table Verbs (2:42)
    • Working With Databases (5:30)
  • data.table
    • Understanding Basics, Filter, and Select (7:34)
    • Understanding Syntax, Creating and Updating Columns (4:06)
    • Aggregating Data, .N, and .I (4:20)
    • Chaining, Functions, and .SD (4:17)
    • Fast Loops with set(), Keys, and Joins (9:12)

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Content
2 hours
Lessons
31

Learning R for Data Visualization

Create Interactive Visualizations on the Web with R

By Packt Publishing | in Online Courses

R is one of the top rising tools in the analytics world. At its core, R is a statistical programming language that provides excellent tools for data mining and analysis, but it also has high-level graphics and machine learning capabilities. In this course, you'll learn how to harness those graphics techniques to represent complex sets of data in inspiring ways.

  • Access 31 lectures & 2 hours of content 24/7
  • Create basic plots like histograms, scatterplots & more
  • Import data in R from popular formats like CSV & Excel tables
  • Build a complete website to import & plot data
  • Learn how to the Shiny package to create fully-featured web pages directly from the R console
Dr. Fabio Veronesi obtained a PhD in digital soil mapping from Cranfield University and then moved to Zurich, where he has been working for the past three years as a postdoc at ETH. There, he is working on Geoinformation topics, ranging from the application of mathematical techniques to the improvement of shaded relief representations to the use of machine learning to increase the accuracy of wind speed maps.

During his PhD, he needed to learn a programming language, because commercial applications did not provide the ideal platforms to pursue his research work. Since R has a series of packages created specifically for the application of statistical techniques to soil science, he decided to teach himself this powerful language. Since then, he has been using R every day for his work.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Introducing Scientific Plotting in R
    • The Course Overview (5:32)
    • Preview of R Plotting Functionalities (3:15)
    • Introducing the Dataset (3:21)
    • Loading Tables and CSV Files (4:41)
    • Loading Excel Files (3:33)
    • Exporting Data (4:18)
  • Scientific Plotting in ggplot2
    • Creating Histograms (5:01)
    • The Importance of Box Plots (3:44)
    • Plotting Bar Charts (2:43)
    • Plotting Multiple Variables – Scatterplots (3:06)
    • Dealing with Time – Time-series Plots (2:38)
    • Handling Uncertainty (4:15)
  • Customizing Plots
    • Changing Theme (3:07)
    • Changing Colors (3:19)
    • Modifying Axis and Labels (2:40)
    • Adding Supplementary Elements (4:08)
    • Adding Text Inside and Outside of the Plot (5:02)
    • Multi-plots (3:59)
  • Exporting Plots
    • Exporting Plots as Images (3:24)
    • Adjusting the Page Size (2:32)
  • Interactive Plots in rCharts
    • Getting Started with Interactive Plotting (2:44)
    • Creating Interactive Histograms and Box Plots (4:55)
    • Plotting Interactive Bar Charts (3:12)
    • Creating Interactive Scatterplots (2:58)
    • Developing Interactive Time-series Plots (3:47)
  • Creating a Website with Shiny
    • Getting Started with Shiny (4:09)
    • Creating a Simple Website (4:52)
    • File Input (3:09)
    • Conditional Panels – UI (3:44)
    • Conditional Panels – Servers (5:31)
    • Deploying the Site (5:37)

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Content
2 hours
Lessons
41

R Graph Essentials

Grasp the Basics of Visualizing Data with R Graphs

By Packt Publishing | in Online Courses

R is an ideal tool for organizing and graphing huge datasets, which is especially valuable to businesses that handle a lot of users and financial details on a daily basis. In this beginner's course to R graphics you'll get a solid grounding in the "base" graphics package in R, as well as more sophisticated packages like lattice and ggplot2. By course's end, you'll be ready to extend your R knowledge to more advanced levels.

  • Access 41 lectures & 2 hours of content 24/7
  • Understand the basic functionality of R graphs
  • Explore different types of graphs for visualizing different types of variables
  • Cover bivariate plots, time series, & high dimensional plots
  • Learn the tips & tricks to the most efficient ways of drawing various types of graphs
Ehsan Karim is a statistics Ph.D. candidate at the University of British Columbia. His current research interest is in the methods that deal with time-dependent confounding in longitudinal observational studies. Additionally, he is interested in software implementation of methods related to causal inference. He has been a user of R for more than 15 years, and has more than 5 years of experience in teaching various statistical software packages.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Introducing Plot Functions
    • Introduction (2:02)
    • Generating a Basic Plot with Titles (3:08)
    • Putting Legends and Setting Margins (3:53)
    • Putting Texts and Mathematical Expressions to the Plot (2:29)
    • Symbols and Colors in the Plot (3:15)
    • Saving the Plots in Various Formats (2:22)
  • Further Control Over Plot Function
    • Controlling Axes and Boxes (2:24)
    • Controlling Layouts and Splits (2:36)
    • Controlling the Color of Plot Elements (2:12)
    • Controlling Line Patterns and Width (2:13)
    • Controlling Texts of Plot Elements (2:36)
  • Plots for Categorical Variables
    • Pie Chart for One Variable (2:38)
    • Bar Chart or Pareto Chart for One Variable (2:14)
    • Bar Chart for More Than One Variable (2:04)
    • Labeling the Pie and Bar Charts (2:28)
    • Dot Charts (2:07)
  • Plots for Continuous Variables
    • Stem-and-leaf Plots (3:00)
    • Histogram, Comparison, and Handling Bins (2:54)
    • Density, Rug Representation, and Overlay Plots (2:22)
    • Boxplots and Parameters (2:14)
    • Side-by-side Boxplots and Parameters (3:07)
  • Bivariate Plots for Two Continuous Variables
    • Scatter Plot and Parameters (2:31)
    • Adding Straight Lines and Jitter Points (2:27)
    • Adding Model Summaries in the Plot (3:45)
    • Sub-grouping in a Scatter Plot (3:35)
    • Conditioning Plots (1:32)
  • Time Series Plots
    • Plotting Basic Line Graphs Using a Function (3:16)
    • Default Time Series Plots (4:19)
    • Plotting Date and Time Variables (2:01)
    • Plotting Trend (2:11)
    • Setting Appropriate Time Axes (1:59)
  • Visualizing Contour Plots and Three-dimensional Plots
    • Drawing Contour Plots in Base Package (2:00)
    • Drawing Contour Plots in Lattice (1:45)
    • Drawing Surfaceplot Using Base Graphics (1:48)
    • Drawing Surfaceplot Using Lattice (2:10)
    • Drawing an Interactive 3D Plot (2:10)
  • Miscellaneous Topics
    • Creating Maps (4:24)
    • Interactive Options (4:47)
    • R Commander (3:29)
    • Trees and Clustering (2:42)
    • RStudio Interface for Graphics (4:04)

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Content
2 hours
Lessons
40

Building Interactive Graphs with ggplot2 and Shiny

Go Beyond the Basics of R Graphics to Start Creating Sophisticated, Professional Visuals

By Packt Publishing | in Online Courses

Ggplot2 is one of R's most popular packages, and is an implementation of the grammar of graphic in R. In this course, you'll move beyond the basic, default graphics offered by R and shows you how to create more advanced and publication-ready plots. Soon enough, you'll be separating from other data job seekers with more sophisticated and interactive graphing abilities.

  • Access 40 lectures & 2 hours of content 24/7
  • Start making elegant & publication-ready plots by learning ggplot2
  • Build statistical plots layer by layer
  • Understand how to combine elements to make new graphics
  • Customize your graphs & make interactive web pages to present your work or analyze your data
Christophe Ladroue has many years of experience in machine learning and statistics. Most of his work has been focused on developing tools for the analysis of biological data, from genetics to physiology, and his scientific publications span from medical journals to pure statistics. He has used and has been teaching R and ggplot2 for a few years and he occasionally posts related articles on his personal blog: http://chrisladroue.com/blog/

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Getting Started with ggplot2
    • Setting Up ggplot2 (2:50)
    • Understanding the Structure of a Plot (3:21)
    • Mapping Data to Graphical Elements with Aesthetics (3:01)
    • Understanding Some Subtleties with Aesthetics (3:01)
    • Using ggplot2 in Scripts (2:47)
  • Understanding Basic Plots
    • Drawing Lines (2:28)
    • Drawing Paths (1:46)
    • Bar Charts (2:00)
    • Histograms and Density Plots (2:47)
    • Using Boxplots (2:33)
  • Using Conditional Plots
    • Using Group and Color (2:07)
    • Using Size and Color (1:57)
    • Over Plotting Many Points with Jitter (2:06)
    • Faceting with One Variable (1:39)
    • Faceting with Two Variables (1:43)
  • Using Statistics in Our Plot
    • Linear Trends (1:56)
    • Non-linear Trends (1:53)
    • User-Defined Function (1:46)
    • BigVis: Visualizing Big Data (2:10)
    • BigVis: Smoothing Plots and Peeling Data (2:04)
  • Customizing Your Graphs
    • Controlling the Axes (2:18)
    • Ordering Variables (1:54)
    • Customizing the Color Palette for Categorical Variables (2:01)
    • Customizing the Color Palette for Continuous Variables (2:48)
    • Customizing the Axes Labels and the Legends (2:17)
  • Shiny – Part 1
    • Creating Interactive Web Pages with Shiny (1:48)
    • Understanding the Structure of a Shiny App (3:53)
    • Rendering Text (2:46)
    • Understanding Reactive Programming (3:54)
    • Using a Button to Avoid Frequent Updates (2:18)
  • Shiny – Part 2
    • Creating and Using Tabs (2:10)
    • Scoping (2:52)
    • Uploading a File (2:03)
    • Downloading a File (2:14)
    • Sharing Your Work (3:06)
  • Putting Everything Together
    • Designing an Interactive Dashboard (2:10)
    • Building a Time Series Plot (2:38)
    • Making a Bubble Chart in ggplot2 (2:18)
    • Making Conditional Panels (2:27)
    • Building the Dashboard (2:44)

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2.5 hours
Lessons
30

Learning Data with R Mining

Learn All the Relevant Aspects of Data Mining Using R

By Pakct Publishing | in Online Courses

As the world continues to generate more and more data at a faster pace, the demand for data mining - generating new information by examining large databases - is growing rapidly as well. R is one of the top tools for data mining, and although data mining is a very broad topic, this course will get you up to speed with the mathematical basics. Once you've got that, you'll be able to directly apply your knowledge to working to solve real-world problems with R.

  • Access 30 lectures & 2.5 hours of content 24/7
  • Understand the mathematical basics of data mining & working w/ algorithms
  • Learn how to solve real-world data mining problems
  • Explore the different disciplines of data mining & the algorithms within them
Romeo Kienzler is a Chief Data Scientist at the IBM Watson IoT Division. In his role, he is involved in international data mining and data science projects to ensure that clients get the most out of their data. He works as an Associate Professor for data mining at a Swiss University and his current research focus is on cloud-scale data mining using open source technologies including R, ApacheSpark, SystemML, ApacheFlink, and DeepLearning4J. He also contributes to various open source projects. Additionally, he is currently writing a chapter on Hyperledger for a book on Blockchain technologies.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Getting Started – A Motivating Example
    • The Course Overview (3:30)
    • Getting Started with R (5:05)
    • Data Preparation and Data Cleansing (4:10)
    • The Basic Concepts of R (5:46)
    • Data Frames and Data Manipulation (5:29)
  • Clustering – A Dating App for Your Data Points
    • Data Points and Distances in a Multidimensional Vector Space (3:59)
    • An Algorithmic Approach to Find Hidden Patterns in Data (6:24)
    • A Real-world Life Science Example (4:28)
  • R Deep Dive, Why Is R Really Cool?
    • Example – Using a Single Line of Code in R (4:00)
    • R Data Types (5:44)
    • R Functions and Indexing (4:14)
    • S3 Versus S4 – Object-oriented Programming in R (4:44)
  • Association Rule Mining
    • Market Basket Analysis (3:00)
    • Introduction to Graphs (2:09)
    • Different Association Types (5:27)
    • The Apriori Algorithm (6:38)
    • The Eclat Algorithm (3:53)
    • The FP-Growth Algorithm (3:47)
  • Classification
    • Mathematical Foundations (6:00)
    • The Naive Bayes Classifier (3:50)
    • Spam Classification with Naïve Bayes (3:32)
    • Support Vector Machines (4:29)
    • K-nearest Neighbors (3:20)
  • Clustering
    • Hierarchical Clustering (5:44)
    • Distribution-based Clustering (6:54)
    • Density-based Clustering (3:11)
    • Using DBSCAN to Cluster Flowers Based on Spatial Properties (2:25)
  • Cognitive Computing and Artificial Intelligence in Data Mining
    • Introduction to Neural Networks and Deep Learning (6:09)
    • Using the H2O Deep Learning Framework (2:28)
    • Real-time Cloud Based IoT Sensor Data Analysis (6:17)

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R: Data Analysis and Visualization Book

Master the Art of Building Analytical Models Using R

By Packt Publishing | in Online Courses

This enormous book will take you on a complete journey with the R programming language and its many applications to data analysis. Over five connected modules, you'll dive into statistical reasoning, graphing with R, data mining, the quantitative finance concepts of R, and its machine learning capabilities. Across these lessons, you'll have a fully-fledged, nuanced understanding of the many professional applications of R.

  • Access 1,738 pages 24/7
  • Describe & visualize the behavior of data & relationships between data
  • Handle missing data gracefully using multiple imputation
  • Create diverse types of bar charts using the default R functions
  • Familiarize yourself w/ algorithms written in R for spatial data mining, text mining, & more
  • Harness the power of R to build machine learning algorithms w/ real-world data science applications
  • Learn specialized machine learning techniques for text mining, big data, & more
Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, it has published over 3,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done–whether that’s specific learning on an emerging technology or optimizing key skills in more established tools.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • R: Data Analysis and Visualization
    • R: Data Analysis and Visualization

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R: Unleash Machine Learning Techniques Book

Boot Up R to Solve Interesting Real-World Problems Using Machine Learning

By Packt Publishing | in Online Courses

Machine learning is one of the most important new frontiers in technology, and the R programming language is one of the best ways to optimize machine learning to solve a diverse range of challenges. Starting with a refresher in R, and then delving into real world problems, this course introduces you to an exciting new way to glean information and answer questions with R.

  • Access 1,123 pages 24/7
  • Implement R machine learning algorithms from scratch
  • Solve real-world problems using machine learning algorithms
  • Write reusable code & build complete machine learning systems from the ground up
  • Evaluate & improve the performance of machine learning models
  • Learn specialized machine learning techniques for text mining, social network data, big data, & more
Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, it has published over 3,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done–whether that’s specific learning on an emerging technology or optimizing key skills in more established tools.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • R: Unleash Machine Learning Techniques
    • R: Unleash Machine Learning Techniques

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Data Visualization: Representing Information on Modern Web Book

Unleash the Power of Data by Creating Interactive & Compelling Visualizations for the Web

By Packt Publishing | in Online Courses

One of the most important things any good data science expert or analyst must know how to do is creative intelligent visualizations. Through this book, you'll learn how to effectively design and present large amounts of data to demonstrate key insights. You'll learn how to visualize with HTML5, JavaScript, and D3, three of the top technologies for creating interactive visualizations on the web.

  • Harness the power of D3 by building interactive & real-time data-driven web visualizations
  • Find out how to use JavaScript to create compelling visualizations of social data
  • Apply critical thinking to visualization designs & get intimate w/ your dataset to identify its potential visual characteristics
  • Explore the various features of HTML5 to design creative visualizations
  • Discover what data is available on Stack Overflow, Facebook, Twitty, & Google+
Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, it has published over 3,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done–whether that’s specific learning on an emerging technology or optimizing key skills in more established tools.

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Compatibility

  • Internet required

Course Outline

  • Data Visualization: Representing Information on Modern Web
    • Data Visualization: Representing Information on Modern Web

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We want you to be happy with every course you purchase! If you're unsatisfied for any reason, we will issue a store credit refund within 15 days of purchase.