R

R Programming Made Easy (Live)

1.     Course Scope (80/20 rule – you can do 80% of all your work with 20% of the features).

2.     Learning Outcomes:

a.     Conceptual similarities between Excel & R

b.     The relationship between R & RStudio

c.     Installing R & RStudio

d.     How to load tables of data in RStudio

e.     Wrangle and analyze data w/ R’s common functions

f.      Wrangle and analyze tables w/ dplyr

g.     Visualize tables with ggplot2

3.     The Data:

a.     The Iris data set

 50 rows for each of 3 species of Iris. Each row (observation) has 5 variables (4 numeric, 1

categorical)

b.     The Kaggle Titanic data set

      A widely known and used data set with interesting analytical properties. The data is unclean

         Question to analyze = which factor(s) accurately predict survival?

4.     A Brief History of R ( a language for data analysis):

a.     Open-source software ecosystem

b.     Initial focus was statistical analysis & data visualization

c.     Created in 1992, first stable version released in 2000

d.     Available and used on all major operating systems

e.     Originally a niche language for statisticians and academia

f.      Popularity exploded in 2012 with the rise of Data Science

g.     RStudio is the leading IDE (integrated development environment)

5.     Excel and R

a.     Map of Excel features to R features

b.     Excel supports multiple programming languages (VBA,M,DAX) that are masked by its interpreter

c.     R is all about coding

d.     Excel’s functionality can be extended with Add-ins (Data Analysis & Solver)

e.     R’s functionality can be extended through 18,000+ packages

f.      Excel can do statistical, predictive, and prescriptive analyses

g.     R can do all of these and much, much more

6.     Objects

a.     Decomposing Excel [Worksheet, Workbook, Table, Cell, Cell Range, Function, Code]

b.     Decomposing R [IDE, Data Frame, Vector, Function, Package, Command Line, Code]

7.     Tables

8.     Vectors

9.     Vector Types

a.     Data Formats

b.     Math w/ Vectors

c.     Data Frame filtering with logic

d.     Filtering Data w/ R

10.  R Functions & Missing Data

a.     Common statistical functions

b.     Summary Functions

c.     data.frame function

d.     cbind & rbind functions

e.     Aggregate function

11.  Introducing dplyr

a.     Tibbles

b.     Mutating Data

c.     Selecting and Filtering Data

d.     Grouping an Summarizing Data

e.     Joining Data

f.      Arranging Data

12.  Introducing ggplot2

a.     Boxplots

b.     Histograms

c.     Bar charts

d.     Scatterplots