A note about these slides

The support for documentation creation in RStudio is great.

These slides are HTML, but I created them in RMarkdown (+ a little bit of HTML fiddling)
A single RMarkdown file can generate PDF, HTML, or Word
No need to know HTML, LateX or Word
But if you do, you can take advantage
We’ll say more about RMarkdown later

Getting Oriented to RStudio

Access to RStudio Sever

rstudio.calvin.edu
Use username and password as provided to you.

Panes and Tabs

4 panes (but some may be minimized)
Each pane can have multiple tabs
Options let you decide which tabs go in which panes

The Console – ephemeral, interactive commands

R is case sensitive

many students are not case sensitive

Arrows and Tab

up/down arrows scroll through history
TAB completion can simplify typing

If all else fails, try ESC

If you see a + prompt, it means R is waiting for more input
If this is unintentional, you probably have a typo
ESC will get you pack to the command prompt

Caclulation and Assignment

Basic calculation is similar to a calculator
Results can be stored in named variables with <- (or ->)
Good place to get started with R (but we will learn other options shortly)

product <- 5 * 3 * 27
product

## [1] 405

sqrt(100)

## [1] 10

log10(product)

## [1] 2.607455

Environment Tab

Shows the objects in the working environment
Broom icon clears the working environment

RStudio Environment Tab

History Tab

Searchable list of previous commands
Commands can be copied to console or to files

RStudio History Tab

Packages Tab

List all installed packages
Install new packages
Load/unload packages by clicking check boxes
Click on name of package to find vignettes and other info

RStudio History Tab

Help!

RStudio provides several ways to get help

Help tab documents functions and data sets in packages
? followed by name of function or data set
Package vignettes
TAB completion provides useful hints

RStudio History Tab

Less Volume, More Creativity

Mike McCarthy

Head coach, Green Bay Packers (NFL Football)

Packers subscribe to “draft and develop”
Among the youngest teams in the league every year
Coaching staff constantly teaching young players

Mike McCarthy

Head coach, Green Bay Packers (NFL Football)

Packers subscribe to “draft and develop”
Among the youngest teams in the league every year
Coaching staff constantly teaching young players

Joe from Fitchburg, WI:

Do you have a favorite Mike McCarthy quote?

Mike McCarthy

Head coach, Green Bay Packers (NFL Football)

Packers subscribe to “draft and develop”
Among the youngest teams in the league every year
Coaching staff constantly teaching young players

Joe from Fitchburg, WI:

Do you have a favorite Mike McCarthy quote? Mine is “statistics are for losers”.

Mike McCarthy

Head coach, Green Bay Packers (NFL Football)

Packers subscribe to “draft and develop”
Among the youngest teams in the league every year
Coaching staff constantly teaching young players

Joe from Fitchburg, WI:

Do you have a favorite Mike McCarthy quote? Mine is “statistics are for losers”.

Vic Ketchman (packers.com):

“Less volume, more creativity.”

Source: Ask Vic @ packers.com

More Mike McCarthy Quotes

You’ve got to watch that you don’t do too much. We have a philosophy on our coaching staff about less volume, more creativity.

A lot of times you end up putting in a lot more volume, because you are teaching fundamentals and you are teaching concepts that you need to put in, but you may not necessarily use because they are building blocks for other concepts and variations that will come off of that … In the offseason you have a chance to take a step back and tailor it more specifically towards your team and towards your players."

More Mike McCarthy Quotes

Q. (for McCarthy) How many offensive and defensive plays might you have coming into a game on average?

A. That’s an excellent question because years ago when I first got into the NFL we had 150 passes in our game plan. I’ve put a sign on all of the coordinators’ doors - less volume, more creativity. We function with more concepts with less volume. We’re more around 50 (passes) into a game plan.

Source: http://www.jsonline.com/packerinsider/106968233.html (Nov 10, 2010)

The Minimal R Exercise

List every R command used throughout a course

Organize by syntactic similarity and by purpose

Scratch everything you could have done without

Replace dissimilar tools with more similar tools

Aim for a set of commands that is

small: fewer is better
coherent: commands should be as similar as possible
powerful: can do what needs doing

Result: Minimal R for Intro Stats

Less Volume, More Creativity

It is not enough to use R, it must be used efficiently and elegantly.

The mosaic package attempts to be part of one solution.

Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.

— Antoine de Saint-Exupery (writer, poet, pioneering aviator)

Make sure the mosaic package is loaded

happens automatically on our RStudio server
use checkboxes in Packages tab to load/unload packages
packages can also be loaded from the command line

require(mosaic)

Note: R purists might prefer

library(mosaic)

but require() is easier for novices to remember.

The Most Important Template

goal ( yyy ~ xxx , data = mydata )

The Most Important Template

goal ( y ~ x , data = mydata )

The Most Important Template

goal ( y ~ x , data = mydata , …)

Other versions:

# simpler version
goal( ~ x, data = mydata )          
# fancier version
goal( y ~ x | z , data = mydata )   
# unified version
goal( formula , data = mydata )

2 Questions

goal ( y ~ x , data = mydata )

What do you want R to do? (goal)

What must R know to do that?

2 Questions

goal ( y ~ x , data = mydata )

What do you want R to do? (goal)

This determines the function to use

What must R know to do that?

This determines the inputs to the function
Must identify the variables and data frame

How do we make this plot?

What is the Goal?

What does R need to know?

How do we make this plot?

What is the Goal?

a scatter plot

What does R need to know?

which variable goes where
which data set

How do we tell R to make this plot?

What is the Goal?

a scatter plot (xyplot())

What does R need to know?

which variable goes where (births ~ dayofyear)
which data set (data=Births78)
- use ?Births78 for documentation

How do we make this plot?

goal ( y ~ x , data = mydata )

xyplot( births ~ dayofyear, data=Births78)

Your turn: How do you make this plot?

Two Questions?

Your turn: How do you make this plot?

Command: bwplot()
The data: HELPrct

Variables: age, substance
use ?HELPrct for info about data

Your turn: How do you make this plot?

bwplot( age ~ substance, data=HELPrct)

Your turn: How about this one?

Command: bwplot()
The data: HELPrct

Variables: age, substance
use ?HELPrct for info about data

Your turn: How about this one?

bwplot( substance ~ age, data=HELPrct )

Graphical Summaries: One Variable

histogram( ~ age, data=HELPrct)

Note: When there is one variable it is on the right side of the formula.

Graphical Summaries: Overview

One Variable

  histogram( ~age, data=HELPrct ) 
densityplot( ~age, data=HELPrct ) 
     bwplot( ~age, data=HELPrct ) 
     qqmath( ~age, data=HELPrct ) 
freqpolygon( ~age, data=HELPrct ) 
   bargraph( ~sex, data=HELPrct )

Two Variables

xyplot(  i1 ~ age,       data=HELPrct ) 
bwplot( age ~ substance, data=HELPrct ) 
bwplot( substance ~ age, data=HELPrct )

i1 average number of drinks (standard units) consumed per day in the past 30 days (measured at baseline)

The Graphics Template

One variable

plotname ( ~ x , data = mydata , …)

histogram(), qqmath(), densityplot(), freqpolygon(), bargraph()

Two Variables

plotname ( y ~ x , data = mydata , …)

xyplot(), bwplot()

Your turn

Create a plot of your own choosing with one of these data sets

names(KidsFeet)    # 4th graders' feet
?KidsFeet

names(Utilities)   # utility bill data
?Utilities

names(NHANES)      # body shape, etc.
?NHANES

groups and panels

Add groups =group to overlay.
Use y ~ x | z to create multipanel plots.

densityplot( ~ age | sex, data=HELPrct,  
               groups=substance,  auto.key=TRUE)

Bells & Whistles

titles
axis labels
colors
sizes
transparency
etc, etc.

My approach:

Let the students ask or
Let the data analysis drive

Bells and Whistles

xyplot( births ~ dayofyear, data=Births78,  
  groups=dayofyear %% 7, type='l',
  auto.key=list(columns=4, lines=T, points=F),
  par.settings=list(
    superpose.line=list( lty=1 ) ))

Numerical Summaries: One Variable

Big idea: Replace plot name with summary name

Nothing else changes

histogram( ~ age, data=HELPrct )
     mean( ~ age, data=HELPrct )

## [1] 35.7

Other Summaries

The mosaic package includes formula aware versions of mean(), sd(), var(), min(), max(), sum(), IQR(), …

Also provides favstats() to compute our favorites.

favstats( ~ age, data=HELPrct )

##  min Q1 median Q3 max mean   sd   n missing
##   19 30     35 40  60 35.7 7.71 453       0

Tallying

tally( ~ sex, data=HELPrct)

## 
## female   male 
##    107    346

tally( ~ substance, data=HELPrct)

## 
## alcohol cocaine  heroin 
##     177     152     124

Numerical Summaries: Two Variables

Three ways to think about this. All do the same thing.

sd(   age ~ substance, data=HELPrct )
sd( ~ age | substance, data=HELPrct )
sd( ~ age, groups=substance, data=HELPrct )

## alcohol cocaine  heroin 
##    7.65    6.69    7.99

Numerical Summaries: Tables

tally( sex ~ substance, data=HELPrct )

##         substance
## sex      alcohol cocaine heroin
##   female      36      41     30
##   male       141     111     94

tally( ~ sex + substance, data=HELPrct )

##         substance
## sex      alcohol cocaine heroin
##   female      36      41     30
##   male       141     111     94

Numerical Summaries

mean( age ~ substance | sex, data=HELPrct )

##  A.F  C.F  H.F  A.M  C.M  H.M    F    M 
## 39.2 34.9 34.7 38.0 34.4 33.1 36.3 35.5

mean( age ~ substance | sex, data=HELPrct, .format="table" )

##   substance sex mean
## 1         A   F 39.2
## 2         A   M   38
## 3         C   F 34.9
## 4         C   M 34.4
## 5         H   F 34.7
## 6         H   M 33.1

I’ve abbreviated the names to make things fit on slide
Also works for median(), min(), max(), sd(), var(), favstats(), etc.

One Template to Rule a Lot

single and multiple variable graphical summaries
single and multiple variabble numerical summaries
linear models

  mean( age ~ sex, data=HELPrct )
bwplot( age ~ sex, data=HELPrct ) 
    lm( age ~ sex, data=HELPrct )

## female   male 
##   36.3   35.5

## (Intercept)     sexmale 
##      36.252      -0.784

Exercises

Answer each question with both a numberical summary and a graphical summary.

Are boys feet larger than girls feet? (KidsFeet)
Do boys and girls differently shaped feet? (KidsFeet)

Some Other Things in mosaic

Some other things

The mosaic package includes some other things, too

Data sets (you’ve already seen some of them)
expanded formula interface to additional functions (e.g. t.test())
xtras: xchisq.test(), xpnorm(), xqqmath()
mplot()
mplot(HELPrct) interactive plot creation
replacements for plot() in some situations
additional plot types: dotPlot(), ashplot()
simplified histogram() controls (e.g., width)
simplified ways to add onto lattice plots

xpnorm()

xpnorm( 700, mean=500, sd=100)

## 
## If X ~ N(500,100), then 
## 
##  P(X <= 700) = P(Z <= 2) = 0.9772
##  P(X >  700) = P(Z >  2) = 0.0228

## [1] 0.977

xpnorm()

xpnorm( c(300, 700), mean=500, sd=100)

# text output supressed

Other distributions, too

xpbinom( 40, size = 100, prob = 0.5)

## [1] 0.0284

xchisq.test()

xchisq.test(phs)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  x
## X-squared = 20, df = 1, p-value = 8e-07
## 
##    104.00   10933.00 
## (  146.52) (10890.48)
## [12.05]  [ 0.16] 
## <-3.51>  < 0.41> 
##    
##    189.00   10845.00 
## (  146.48) (10887.52)
## [12.05]  [ 0.16] 
## < 3.51>  <-0.41> 
##    
## key:
##  observed
##  (expected)
##  [contribution to X-squared]
##  <residual>

Modeling

Modeling is really the starting point for the mosaic design.

linear models (lm() and glm()) defined the template
lattice graphics use the template (so we chose lattice)
we added functionality so numerical summaries can be done with the same template
additional things added to make modeling easier for beginners.

Models as Functions

model <- lm(width ~ length * sex, 
            data=KidsFeet)
Width <- makeFun(model)
Width( length=25, sex="B")

##    1 
## 9.17

Width( length=25, sex="G")

##    1 
## 8.94

Models as Functions – Plotting

xyplot( width ~ length, data=KidsFeet, 
        groups=sex, auto.key=TRUE )
plotFun( Width(length, sex="B") ~ length, 
         col=1, add=TRUE)
plotFun( Width(length, sex="G") ~ length, 
         col=2, add=TRUE)

Models as Functions – Auto Plotting

This is still experimental, but works in many simple situations.

model <- lm(width ~ length * sex, data=KidsFeet)
plotModel(model)

Less Volume, More Creativity

A note about these slides

Getting Oriented to RStudio

Access to RStudio Sever

Menu

Panes and Tabs

The Console – ephemeral, interactive commands

R is case sensitive

Arrows and Tab

If all else fails, try ESC

Caclulation and Assignment

Environment Tab

History Tab

Packages Tab

Help!

Less Volume, More Creativity

Less Volume, More Creativity

Mike McCarthy

Mike McCarthy

Mike McCarthy

Mike McCarthy

More Mike McCarthy Quotes

More Mike McCarthy Quotes

The Minimal R Exercise

Less Volume, More Creativity

Make sure the mosaic package is loaded

The Most Important Template

goal ( yyy ~ xxx , data = mydata )

The Most Important Template

goal ( y ~ x , data = mydata )

The Most Important Template

goal ( y ~ x , data = mydata , …)

Other versions:

2 Questions

goal ( y ~ x , data = mydata )

What do you want R to do? (goal)

What must R know to do that?

2 Questions

goal ( y ~ x , data = mydata )

What do you want R to do? (goal)

What must R know to do that?

How do we make this plot?

How do we make this plot?

What is the Goal?

What does R need to know?

How do we make this plot?

What is the Goal?

What does R need to know?

How do we tell R to make this plot?

What is the Goal?

What does R need to know?

How do we make this plot?

goal ( y ~ x , data = mydata )

Your turn: How do you make this plot?

Two Questions?

Your turn: How do you make this plot?

Your turn: How do you make this plot?

Your turn: How about this one?

Your turn: How about this one?

Graphical Summaries: One Variable

Graphical Summaries: Overview

One Variable

Two Variables

The Graphics Template

One variable

plotname ( ~ x , data = mydata , …)

Two Variables

plotname ( y ~ x , data = mydata , …)

Your turn

groups and panels

Bells & Whistles

Bells and Whistles

Numerical Summaries: One Variable

Other Summaries

Tallying

Numerical Summaries: Two Variables

Numerical Summaries: Tables

Numerical Summaries

One Template to Rule a Lot

Exercises