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Working with R

Course Summary

The Working with R training course is designed to demonstrate an introduction to the fundamentals of the R programming language required to perform data analytics.

The course begins by exploring the language fundamentals, commonly used libraries, and advanced concepts necessary for data analytics. Next, it explores functions and data frames in order to read, write and manipulate data with R. The course concludes by examining common data analytics and graphing best practices so that data insights can be presented to business users in creative and interesting ways.

Learn how to use R as a tool to perform data science, machine learning or statistics on large data sets.
Data Scientists with a statistics background with some programming ability and advanced SQL analysis skills.
Data Scientist
Skill Level
Learning Spikes - Workshops
3 Days
Related Technologies
Big Data Training | R | Data Science


Productivity Objectives
  • Understand how R and the R environment can be leveraged to perform data analytics
  • Install and configure a development environment including R, RStudio, and Github
  • Build a simple application using R
  • Evaluate R against other technologies like SAS, SPSS, etc.

What You'll Learn:

In the Working with R training course, you'll learn:
  • What tools do Data Scientists use?
    • What is R? Why use R?
    • R compared to other programming languages
    • Setting up the R development environment
    • Introduction to R Studio
    • Using Github with your data analytics project
  • R Language Fundamentals
    • Core R syntax concepts
    • Variables and Types
    • Control Structures (Loops / Conditionals)
    • Writing the first R program
  • Working with R
    • R Scalars, Vectors, and Matrices
    • Defining R Vectors
    • String and Text Manipulation
    • File IO
    • Lists
  • Functions
    • Introducing Functions
    • Writing R Functions
    • Closures
    • lapply/sapply functions
  • DataFrames
    • Working with Data
    • DataFrames and File I/O
    • Reading data from files
    • Data Preparation
    • Built-in Datasets
  • Visualization
    • Graphics Package
    • plot() / barplot() / hist() / boxplot() / scatter plot
    • Heat Map
    • ggplot2 package ( qplot(), ggplot())
    • Exploration With Dplyr
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”


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