R Programming for Data Science

available upon request
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Course Objectives

  • Identify types of data
  • Define steps in cleaning data
  • Deploy R language to clean data
  • Define the common approach in analyzing data
  • Using R for analytics tasks


This course aims at providing participants with an overview of what R can offer in real-world data analysis and analytics tasks.

Target Audience

This course is suggested for programmer who wishes to get data into R, get it into the most useful structure, transform it, visualise it and model it.

Training Outline

Day 1
  • Overview of R and data analytics
  • Installing and setting up the R Environment
  • R data types and objects
  • Reading and writing data
  • List, matrix and data frame
  • Sub-setting data
  • Exercise – Working with Data Frame
Day 2
  • Control structures
  • Creating and using functions
  • Scoping rules, manipulating dates and times
  • Laboratory exercise – Working with dates
  • Using the R “apply” functions
  • Simulation, debugging and profiling in R
  • Exercise – “R Programming Exercise”
Day 3
  • Introduction to getting, collecting and sharing data
  • Data sources: Getting data from web and JSON in R
  • Reshaping data, sub-setting observations & variables, summarizing data
  • Creating new variables, grouping data and merging data
  • R base plotting system and ggplot plotting system
  • Visualizing data
  • Exercise – ggplot Plot


Basic knowledge of programming is preferable.