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

Fundamentals of R Programming and Statistical Analysis

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

  • Video Duration:07 Hours
  • Cost: $ 124.99

Course Details

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.

Who all can attend

This course is for those who are interested in using R for scientific computing and bioinformatics. For users who want to use R to perform basic and advanced scientific computing that are in turn needed to perform commonly needed scientific data tasks such as looking for statistically significant changes between groups and statistical modeling to look for relationships in the data. Basically, this course is for people who are keen in learning how to analyze scientific data, that is, look for differentially expressed genes and gene set enrichment analysis etc

What you will learn from this course

  • 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

Course Content

  1. 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

  2. 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

  3. Visualizing Data in R
    • Basic Plots in R
    • Basic Plots with the ggplot2 Package
    • Visualizing Heatmaps
    • Visualizing Networks
    • Other Visualization Methods

  4. 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

  5. Evaluating Probability Distributions
    • Descriptive Statistics
    • Overview of Probability Distributions
    • Fitting Probability Distribution
    • Other Statistical Tests to Fit Distributions

  6. Hypothesis Testing and Statistical Models
    • Model Formulas
    • One and Two Sample Tests
    • Linear Regression
    • Analysis of Variance
    • Linear Models for Gene Expression Data

Contact Us

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

For more details, write to us at :


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Fundamentals of R Programming and Statistical Analysis
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