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

• You will learn about RMarkdown later today

• rstudio.calvin.edu
  • You were sent an email with userid (e.g., rstudio199) and password.
  • Change passwords here

• Files, Edit, Code, View, etc.
• Tools > Options for customization

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

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

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

RStudio Environment Tab

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

RStudio History Tab

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

RStudio Packages Tab

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

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?

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".

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

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."

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)

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

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)

Other versions:

# 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

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

xyplot( births ~ dayofyear, data = Births78) 

Two Questions?

1. Command: bwplot()

2. The data: HELPrct

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

bwplot( age ~ substance, data = HELPrct)

1. Command: bwplot()

2. The data: HELPrct

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

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 ) 
freqpolygon( ~ age, data = HELPrct ) 
    ashplot( ~ age, data = HELPrct ) 
     qqmath( ~ age, data = HELPrct ) 
     bwplot( ~ 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(Births)      # like Births78, but more years
?Births

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

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

ashplot( ~ age | sex, groups = substance,
         data = HELPrct, width = 5, auto.key = TRUE)   

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

My approach:

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

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

xyplot( births ~ date, data = Births,  
  groups = wday, type = 'spline', npoints = 1001,
  auto.key = list(columns = 4, lines = T, points = F),
  par.settings = list(
    superpose.line = list( lty = 1 ) ))

The mplot() function will help you create plots interactively and then show you the code used to make them when you hit "Show Expression".

mplot(HELPrct)

Big idea: Replace plot name with summary name

• Nothing else changes

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

## [1] 35.7

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

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 
##    7.65    6.69    7.99

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

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.

• single and multiple variable graphical summaries
• single and multiple variable 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

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

1. Are boys feet larger than girls feet? (KidsFeet)

2. Do boys and girls differently shaped feet? (KidsFeet)

The mosaic package includes some other things, too

• Data sets (you've already seen some of them)
• xtras: xchisq.test(), xpnorm(), xqqmath()
• mplot()
• mplot(HELPrct) interactive plot creation
• replacements for plot() in some situations
• 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) = 0.977
##  P(X >  700) = P(Z >  2) = 0.0228

## [1] 0.977

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] 0.0228 0.9772

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]
##  <Pearson 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.

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

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)

• Often used on first day of class

• Story

• woman claims she can tell whether milk has been poured into tea or vice versa.

• Question: How do we test this claim?

Use rflip() to simulate flipping coins

rflip()

## 
## Flipping 1 coin [ Prob(Heads) = 0.5 ] ...
## 
## H
## 
## Number of Heads: 1 [Proportion Heads: 1]

Faster if we flip multiple coins at once:

rflip(10)

## 
## Flipping 10 coins [ Prob(Heads) = 0.5 ] ...
## 
## H H H T T T H H H T
## 
## Number of Heads: 6 [Proportion Heads: 0.6]

• easier to consider heads = correct; tails = incorrect than to compare with a given pattern
• this switch bothers me more than it bothers my students

rflip(10) simulates 1 lady tasting 10 cups 1 time.

We can do that many times to see how guessing ladies do:

do(2) * rflip(10)

##    n heads tails prop
## 1 10     6     4  0.6
## 2 10     5     5  0.5

• do() is clever about what it remembers
• 2 isn't many – we'll do many next.

Ladies <- do(5000) * rflip(10)
head(Ladies, 1)

##    n heads tails prop
## 1 10     4     6  0.4

histogram( ~ heads, data = Ladies, width = 1 )

tally( ~ (heads >= 9) , data = Ladies)

## 
##  TRUE FALSE 
##    52  4948

tally( ~ (heads >= 9) , data = Ladies, format = "prop")

## 
##   TRUE  FALSE 
## 0.0104 0.9896

tally( ~ (heads >= 9) , data = Ladies, format = "prop")

## 
##   TRUE  FALSE 
## 0.0104 0.9896

 prop( ~ (heads >= 9), data = Ladies)

##   TRUE 
## 0.0104

1. Do it for your data
2. Do it for "random" data
3. Do it lots of times for "random" data

• definition of "random" is important, but can often be handled by shuffle() or resample()

diffmean(age ~ sex, data = HELPrct)

## diffmean 
##   -0.784

do(1) * 
  diffmean(age ~ shuffle(sex), data = HELPrct)

##   diffmean
## 1    0.109

Null <- do(5000) * 
  diffmean(age ~ shuffle(sex), data = HELPrct)

prop( ~ (abs(diffmean) > 0.7841), data = Null )

##  TRUE 
## 0.362

histogram( ~ diffmean, data = Null, v = -.7841) 

Bootstrap <- do(5000) * 
  diffmean(age ~ sex, data= resample(HELPrct))

histogram( ~ diffmean, data = Bootstrap, 
                      v = -.7841, glwd = 4 )

cdata( ~ diffmean, data = Bootstrap, p = .95)

##       low        hi central.p 
##    -2.479     0.889     0.950

confint(Bootstrap, method = "quantile")

##       name lower upper level     method estimate
## 1 diffmean -2.48 0.889  0.95 percentile   -0.784

confint(Bootstrap)  # default uses st. err

##       name lower upper level     method estimate
## 1 diffmean -2.48 0.889  0.95 percentile   -0.784

do(1) * lm(width ~ length, data = KidsFeet)

## Source: local data frame [1 x 9]
## 
##   Intercept length sigma r.squared     F numdf dendf  .row .index
##       (dbl)  (dbl) (dbl)     (dbl) (dbl) (dbl) (dbl) (int)  (dbl)
## 1      2.86  0.248 0.396     0.411  25.8     1    37     1      1

do(3) * lm( width ~ shuffle(length), data = KidsFeet)

## Source: local data frame [3 x 9]
## 
##   Intercept  length sigma r.squared     F numdf dendf  .row .index
##       (dbl)   (dbl) (dbl)     (dbl) (dbl) (dbl) (dbl) (int)  (dbl)
## 1     11.64 -0.1071 0.496   0.07664 3.071     1    37     1      1
## 2     11.54 -0.1031 0.498   0.07110 2.832     1    37     1      2
## 3      9.75 -0.0308 0.515   0.00635 0.236     1    37     1      3

do(1) * 
  lm(width ~ length + sex, data = KidsFeet)

## Source: local data frame [1 x 10]
## 
##   Intercept length   sexG sigma r.squared     F numdf dendf  .row .index
##       (dbl)  (dbl)  (dbl) (dbl)     (dbl) (dbl) (dbl) (dbl) (int)  (dbl)
## 1      3.64  0.221 -0.233 0.385      0.46  15.3     2    36     1      1

do(3) * 
  lm( width ~ length + shuffle(sex), data = KidsFeet)

## Source: local data frame [3 x 10]
## 
##   Intercept length    sexG sigma r.squared     F numdf dendf  .row .index
##       (dbl)  (dbl)   (dbl) (dbl)     (dbl) (dbl) (dbl) (dbl) (int)  (dbl)
## 1      2.94  0.243  0.0779 0.400     0.417  12.9     2    36     1      1
## 2      3.45  0.228 -0.2083 0.388     0.451  14.8     2    36     1      2
## 3      2.84  0.248  0.0157 0.402     0.411  12.6     2    36     1      3

Null <- do(5000) * 
  lm( width ~ length + shuffle(sex), data = KidsFeet)
histogram( ~ sexG, data = Null, 
           v = -0.2325, glwd = 4)