Introduction to R for Excel Users

Length: 3 Day(s)     Cost:$2350 + GST

= Scheduled class     = Guaranteed to run     = Fully booked

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LOCATION April May June July
Virtual Class

R is a programming language designed for processing, analyzing and visualizing data. But do not think of R as a replacement for Excel! Instead think of R as another tool that will enable you to work on bigger and complex projects.

The focus of this course will be on data manipulation: importing, manipulating, transforming, combining and summarizing.

If you are experiencing any of the below scenarios, then this course is for you:

  • Excel does not handle big data sets well
  • Excel does not do well with complex/unstructured data
  • Excel does not offer robust statistical functionality

Students will need to be familiar with data analysis in excel.

Students who complete this course will be able to use R to help them get the best out of Excel and their data.

Module 1: Introduction and Set-Up

  • Dowloading the Software
  • Navigating the Software
  • Using Other Software
  • Libraries
  • Getting Help

Module 2: Programming Basics

  • Assigning Variables
  • Comparison Operators
  • Data Structures
  • Special Values

Module 3: Quick Start (Analysis Examples)

  • General Data Analysis
  • Using SQL in R
  • Multiple Regression Model

Module 4: Cells are Vectors

  • Individual Cells
  • Cell Ranges
  • Working with Vectors
  • Access Values of a Vector
  • How Vector Operations Work
  • Recycling

Module 5: Formulas are Functions

  • Inputs
  • Outputs
  • Some base Functions

Module 6: Import and Create Data Sets

  • Importing from .csv files
  • Importing from .xlsx files
  • Creating a Data Frame
  • Deleting a Data Frame

Module 7: Inspecting Your Data

  • Print first few rows and columns
  • Print an arbitrary subset
  • Printing all rows or all columns
  • Using head() and tail() functions
  • Using the view() function
  • Structure (str) of the Data Frame
  • Print column names() of the Data Frame
  • Get certain column names
  • Print summary() of the table (partial)
  • See unique() values of a column
  • Visually understand the Data

Module 8 : Working with Columns

  • Refer to Columns
  • Create Columns
  • Reformat Columns
  • Converting strings to numbers with as numeric()
  • Rename Columns
  • Remove Columns
  • Unbound Columns

Module 9: Working with Rows

  • Filtering with brackets
  • Filtering with subset()
  • Filtering uniques with unique()
  • Sorting with order()

Module 10: Manipulating Rows and Columns with dplyr

  • Select() to select columns
  • Mutate()
  • Filter() for filtering rows
  • Arrange() for sorting rows
  • Rename() for renaming columns
  • Application: by-group processing with dplyr
  • Chaining

Module 11: Shape your Dataset

  • Combine Data Tables
  • PivotTables – Summarize and Transpose your Data

Module 12: Working with Lists

  • What is a list?
  • Two common uses
  • How to get things from lists
  • Creating and modifying lists
  • Basic list functions

Module 13: Programming: Loops and Control Flow

  • While() Loop
  • For() Loop
  • Breaking out of or skipping Loops
  • If()

Module 14: Writing your own functions

  • Simple function and more advanced functions (Apply()/ Sapply()/ Mapply() etc)
  • Regular Expression Text Extraction