Course Outline

Introduction

Core Programming and Syntax in R

  • Variables
  • Loops
  • Conditional statements

Fundamentals of R

  • What are vectors?
  • Functions and packages in R

Preparing the Development Environment

  • Installing and configuring R

R and Excel

  • Moving data between R and Excel
  • Working with bivarite analysis in R and Excel

DescTools

  • Controlling output in R
  • Running DescTools functions on variables

R Tidyverse

  • Loading and filtering data
  • Pivoting with R
  • Using Power Query

Data Visualization with R

  • Creating static visualizations with GGPlot
  • Layering multiple charts
  • Transforming visualizations into HTML widgets with Plotly
  • Working with interactive tables
  • Using R Markdown

Summary and Conclusion

Requirements

  • Experience with Excel

Audience

  • Data Analysts
  21 Hours
 

Testimonials

Related Courses

Programming with Big Data in R

  21 hours

Neural Network in R

  14 hours

Introduction to R

  21 hours

Marketing Analytics using R

  21 hours

Advanced R Programming

  7 hours

Building Web Applications in R with Shiny

  7 hours

R Programming for Data Analysis

  14 hours

Forecasting with R

  14 hours

R for Data Analysis and Research

  7 hours

R

  21 hours

Data Mining with R

  14 hours

Introductory R for Biologists

  28 hours

Machine Learning Fundamentals with R

  14 hours

Data Analytics With R

  21 hours

Introduction to Data Visualization with Tidyverse and R

  7 hours