Less Volume, More Creativity

Randy Pruim
eCOTS 2014

Focusing on R Essentials

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.“

Mike McCarthy, Head Coach, Green Bay Packers

SIBKIS: See It Big, Keep It Simple

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)

Less Volume, More Creativity

One key to successfully introducing R is finding a set of commands that is

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

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

The mosaic package offers one way to do this.

A few little details

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

The Most Important R Template

 

goal ( yyy ~ xxx , data = mydata )

 

The Most Important R Template

 

goal (  y  ~  x  , data = mydata )

The Most Important R Template

 

goal (  y  ~  x  , data = mydata , …)

 

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?

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How do we make this plot?

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What is the Goal?

What does R need to know?

How do we make this plot?

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What is the Goal?

  • a scatter plot

What does R need to know?

  • which variable goes where
  • which data set

How do we make this plot?

 

goal (  y  ~  x  , data = mydata )

 

xyplot (  births  ~  dayofyear  , data = Births78 )

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Your turn: How do you make this plot?

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Two Questions?

Your turn: How do you make this plot?

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The data: HELPrct

Variables: age, substance

Command: bwplot()

Raise your hand when you have created this plot

Your turn: How do you make this plot?

bwplot( age ~ substance, data=HELPrct)

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Your turn: How about this one?

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Raise your hand when you have created this plot.

Your turn: How about this one?

bwplot( substance ~ age, data=HELPrct )

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Graphical Summaries: One Variable

histogram( ~ age, data=HELPrct) 

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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

plotname (  y  ~  x  , data = mydata , …)

 

plotname ( ~  x  , data = mydata , …)

 

One variable

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

Two Variables

  • 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

Raise your hand when you have made a plot or two.

Type a question if you have trouble.

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)   

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Bells & Whistles

  • titles
  • axis labels
  • colors
  • sizes
  • transparency
  • etc, etc.

My approach:

  • Let the students ask or
  • Let the data analysis drive

Numerical Summaries: One Variable

Big idea:

  • replace plot name with summary name
  • nothing else changes
histogram( ~ age, data=HELPrct )
     mean( ~ age, data=HELPrct )
[1] 35.65

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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 35.65 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 
  7.652   6.693   7.986 

Numerical Summaries: Tables

tally( sex ~ substance, data=HELPrct )
        substance
sex      alcohol cocaine heroin
  female  0.2034  0.2697 0.2419
  male    0.7966  0.7303 0.7581
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 
39.17 34.85 34.67 37.95 34.36 33.05 36.25 35.47 
  • 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 
 36.25  35.47 
(Intercept)     sexmale 
    36.2523     -0.7841 
  • We will return to modeling shortly.

Some other things

The mosaic package includes some other things, too

  • Data sets (you've already seen some of them)
  • xtras: xchisq.test(), xpnorm(), xqqmath()
  • mPlot() – interactive plot design
  • 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

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[1] 0.9772

xpnorm()

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

If X ~ N(500,100), then 

    P(X <= 300) = P(Z <= -2) = 0.0228
    P(X <= 700) = P(Z <= 2) = 0.9772
    P(X >  300) = P(Z >  -2) = 0.9772
    P(X >  700) = P(Z >  2) = 0.0228