Fundamentals of R Programming and Statistical Analysis [Video]

Fundamentals of R Programming and Statistical Analysis [Video]

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Radia Johnson

A comprehensive guide to working on statistical data with the R language
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Video Details

ISBN 139781782175247
Course Length6 hours and 46 minutes

Video Description

The R language is widely used among statisticians and data miners to develop statistical software and data analysis.

In this course, we’ll start by diving into the different types of R data structures and you’ll learn how the R programming language handles data. Then we’ll look in-depth at manipulating different datasets in R. After that, we’ll dive into data visualization with R, using basic plots, heat maps, and networks. We’ll explore the different flow control loops of the R programming language, and you’ll learn how to debug your code.

In the second half of the course, you’ll get hands-on working with the various statistical methods in R programming. You’ll find out how to work with different probability distributions, various types of hypothesis testing, and statistical analysis with the R programming language.

By the end of this video course, you will be well-versed in the basics of R programming and the various concepts of statistical data analysis with R.

Style and Approach

This fast-paced, practical guide is filled with real-world examples that will take you on a journey through the various concepts and phases of statistical analysis using the R programming language.

Table of Contents

R Data Structures
The Course Overview
Working with Vectors
Working with Lists and Attributes
Working with Multidimensional Arrays and Matrices
Working with Data Frames and Factors
Loading and Saving Data in R
Manipulating Datasets with R
Working with the Subset() and with() Functions
Working with the which() and grep() Functions
Working with the sort() and order() Functions
Working with sapply() and lapply()
Working with tapply() and table() Functions
Visualizing Data in R
Basic Plots in R
Basic Plots with the ggplot2 Package
Visualizing Heatmaps
Visualizing Networks
Other Visualization Methods
Flow Control and Debugging Tools
for Loops Versus the apply() Function
The if Statement and ifelse() Function
while and repeat Loops and the Break Statement
Writing Your Own Functions
General Programming and Debugging Tools
Evaluating Probability Distributions
Descriptive Statistics
Overview of Probability Distributions
Fitting Probability Distribution
Other Statistical Tests to Fit Distributions
Hypothesis Testing and Statistical Models
Model Formulas
One and Two Sample Tests
Linear Regression
Analysis of Variance
Linear Models for Gene Expression Data

What You Will Learn

  • Work with different data structures such as vectors, lists, attributes, arrays, and matrices
  • Visualize data using basic and advanced plotting techniques and images
  • Control program flow using for, if and else, and while-repeat loops
  • Discover how to make the best use of debugging tools to make your code more efficient
  • Work with the various statistical analysis techniques such as evaluating probability distributions, hypothesis testing, one sample testing, linear regression, and so on

Authors

Table of Contents

R Data Structures
The Course Overview
Working with Vectors
Working with Lists and Attributes
Working with Multidimensional Arrays and Matrices
Working with Data Frames and Factors
Loading and Saving Data in R
Manipulating Datasets with R
Working with the Subset() and with() Functions
Working with the which() and grep() Functions
Working with the sort() and order() Functions
Working with sapply() and lapply()
Working with tapply() and table() Functions
Visualizing Data in R
Basic Plots in R
Basic Plots with the ggplot2 Package
Visualizing Heatmaps
Visualizing Networks
Other Visualization Methods
Flow Control and Debugging Tools
for Loops Versus the apply() Function
The if Statement and ifelse() Function
while and repeat Loops and the Break Statement
Writing Your Own Functions
General Programming and Debugging Tools
Evaluating Probability Distributions
Descriptive Statistics
Overview of Probability Distributions
Fitting Probability Distribution
Other Statistical Tests to Fit Distributions
Hypothesis Testing and Statistical Models
Model Formulas
One and Two Sample Tests
Linear Regression
Analysis of Variance
Linear Models for Gene Expression Data

Video Details

ISBN 139781782175247
Course Length6 hours and 46 minutes
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