Less Volume, More Creativity

Focusing on R Essentials

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.

• Minimal R Vignette

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

Simpler version:

• goal( ~ x, data = mydata )

Fancier version:

• goal( y ~ x | z , data = mydata )

Unified version:

• goal( formula , data = mydata )

What do you want R to do? (goal)

What must R know to do that?

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

What is the Goal?

What does R need to know?

What is the Goal?

• a scatter plot

What does R need to know?

• which variable goes where
  
• which data set

Two Questions?

The data: HELPrct

Variables: age, substance

Command: bwplot()

Raise your hand when you have created this plot

bwplot( age ~ substance, data=HELPrct)

Raise your hand when you have created this plot.

bwplot( substance ~ age, data=HELPrct )

histogram( ~ age, data=HELPrct) 

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

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)

One variable

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

Two Variables

• xyplot(), bwplot()

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.

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

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

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

My approach:

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

Big idea:

• replace plot name with summary name
• nothing else changes

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

[1] 35.65

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

tally( ~ sex, data=HELPrct)

female   male 
   107    346 

tally( ~ substance, data=HELPrct)

alcohol cocaine  heroin 
    177     152     124 

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 

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

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.

• 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.

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( 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] 0.9772

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

[1] 0.02275 0.97725

xchisq.test(phs)

    Pearson's Chi-squared test with Yates' continuity correction

data:  phs
X-squared = 24.43, df = 1, p-value = 7.71e-07

   104.00   10933.00 
(  146.52) (10890.48)
[12.34]  [ 0.17] 
<-3.51>  < 0.41> 

   189.00   10845.00 
(  146.48) (10887.52)
[12.34]  [ 0.17] 
< 3.51>  <-0.41> 

key:
    observed
    (expected)
    [contribution to X-squared]
    <residual>

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.