I am reading the code below. It acts on a csv file called dodgers.csv with
the following variables.


> print(str(dodgers))  # check the structure of the data frame
'data.frame':   81 obs. of  12 variables:
 $ month      : Factor w/ 7 levels "APR","AUG","JUL",..: 1 1 1 1 1 1 1 1 1
1 ...
 $ day        : int  10 11 12 13 14 15 23 24 25 27 ...
 $ attend     : int  56000 29729 28328 31601 46549 38359 26376 44014 26345
44807 ...
 $ day_of_week: Factor w/ 7 levels "Friday","Monday",..: 6 7 5 1 3 4 2 6 7
1 ...
 $ opponent   : Factor w/ 17 levels "Angels","Astros",..: 13 13 13 11 11 11
3 3 3 10 ...
 $ temp       : int  67 58 57 54 57 65 60 63 64 66 ...
 $ skies      : Factor w/ 2 levels "Clear ","Cloudy": 1 2 2 2 2 1 2 2 2 1
...
 $ day_night  : Factor w/ 2 levels "Day","Night": 1 2 2 2 2 1 2 2 2 2 ...
 $ cap        : Factor w/ 2 levels "NO","YES": 1 1 1 1 1 1 1 1 1 1 ...
 $ shirt      : Factor w/ 2 levels "NO","YES": 1 1 1 1 1 1 1 1 1 1 ...
 $ fireworks  : Factor w/ 2 levels "NO","YES": 1 1 1 2 1 1 1 1 1 2 ...
 $ bobblehead : Factor w/ 2 levels "NO","YES": 1 1 1 1 1 1 1 1 1 1 ...
NULL
>

I don't understand why the author of the code decided to make the factor
days_of_week into an ordered factor. Anyone know why this should be done?
Thank you.

Here is the code:

# Predictive Model for Los Angeles Dodgers Promotion and Attendance

library(car)  # special functions for linear regression
library(lattice)  # graphics package

# read in data and create a data frame called dodgers
dodgers <- read.csv("dodgers.csv")
print(str(dodgers))  # check the structure of the data frame

# define an ordered day-of-week variable
# for plots and data summaries
dodgers$ordered_day_of_week <- with(data=dodgers,
  ifelse ((day_of_week == "Monday"),1,
  ifelse ((day_of_week == "Tuesday"),2,
  ifelse ((day_of_week == "Wednesday"),3,
  ifelse ((day_of_week == "Thursday"),4,
  ifelse ((day_of_week == "Friday"),5,
  ifelse ((day_of_week == "Saturday"),6,7)))))))
dodgers$ordered_day_of_week <- factor(dodgers$ordered_day_of_week,
levels=1:7,
labels=c("Mon", "Tue", "Wed", "Thur", "Fri", "Sat", "Sun"))

# exploratory data analysis with standard graphics: attendance by day of
week
with(data=dodgers,plot(ordered_day_of_week, attend/1000,
xlab = "Day of Week", ylab = "Attendance (thousands)",
col = "violet", las = 1))

# when do the Dodgers use bobblehead promotions
with(dodgers, table(bobblehead,ordered_day_of_week)) # bobbleheads on
Tuesday

# define an ordered month variable
# for plots and data summaries
dodgers$ordered_month <- with(data=dodgers,
  ifelse ((month == "APR"),4,
  ifelse ((month == "MAY"),5,
  ifelse ((month == "JUN"),6,
  ifelse ((month == "JUL"),7,
  ifelse ((month == "AUG"),8,
  ifelse ((month == "SEP"),9,10)))))))
dodgers$ordered_month <- factor(dodgers$ordered_month, levels=4:10,
labels = c("April", "May", "June", "July", "Aug", "Sept", "Oct"))

# exploratory data analysis with standard R graphics: attendance by month
with(data=dodgers,plot(ordered_month,attend/1000, xlab = "Month",
ylab = "Attendance (thousands)", col = "light blue", las = 1))

# exploratory data analysis displaying many variables
# looking at attendance and conditioning on day/night
# the skies and whether or not fireworks are displayed
library(lattice) # used for plotting
# let us prepare a graphical summary of the dodgers data
group.labels <- c("No Fireworks","Fireworks")
group.symbols <- c(21,24)
group.colors <- c("black","black")
group.fill <- c("black","red")
xyplot(attend/1000 ~ temp | skies + day_night,
    data = dodgers, groups = fireworks, pch = group.symbols,
    aspect = 1, cex = 1.5, col = group.colors, fill = group.fill,
    layout = c(2, 2), type = c("p","g"),
    strip=strip.custom(strip.levels=TRUE,strip.names=FALSE, style=1),
    xlab = "Temperature (Degrees Fahrenheit)",
    ylab = "Attendance (thousands)",
    key = list(space = "top",
        text = list(rev(group.labels),col = rev(group.colors)),
        points = list(pch = rev(group.symbols), col = rev(group.colors),
        fill = rev(group.fill))))

# attendance by opponent and day/night game
group.labels <- c("Day","Night")
group.symbols <- c(1,20)
group.symbols.size <- c(2,2.75)
bwplot(opponent ~ attend/1000, data = dodgers, groups = day_night,
    xlab = "Attendance (thousands)",
    panel = function(x, y, groups, subscripts, ...)
       {panel.grid(h = (length(levels(dodgers$opponent)) - 1), v = -1)
        panel.stripplot(x, y, groups = groups, subscripts = subscripts,
        cex = group.symbols.size, pch = group.symbols, col = "darkblue")
       },
    key = list(space = "top",
    text = list(group.labels,col = "black"),
    points = list(pch = group.symbols, cex = group.symbols.size,
    col = "darkblue")))

# specify a simple model with bobblehead entered last
my.model <- {attend ~ ordered_month + ordered_day_of_week + bobblehead}

# employ a training-and-test regimen
set.seed(1234) # set seed for repeatability of training-and-test split
training_test <- c(rep(1,length=trunc((2/3)*nrow(dodgers))),
rep(2,length=(nrow(dodgers) - trunc((2/3)*nrow(dodgers)))))
dodgers$training_test <- sample(training_test) # random permutation
dodgers$training_test <- factor(dodgers$training_test,
  levels=c(1,2), labels=c("TRAIN","TEST"))
dodgers.train <- subset(dodgers, training_test == "TRAIN")
print(str(dodgers.train)) # check training data frame
dodgers.test <- subset(dodgers, training_test == "TEST")
print(str(dodgers.test)) # check test data frame

# fit the model to the training set
train.model.fit <- lm(my.model, data = dodgers.train)
# obtain predictions from the training set
dodgers.train$predict_attend <- predict(train.model.fit)

# evaluate the fitted model on the test set
dodgers.test$predict_attend <- predict(train.model.fit,
  newdata = dodgers.test)

# compute the proportion of response variance
# accounted for when predicting out-of-sample
cat("\n","Proportion of Test Set Variance Accounted for: ",
round((with(dodgers.test,cor(attend,predict_attend)^2)),
  digits=3),"\n",sep="")

# merge the training and test sets for plotting
dodgers.plotting.frame <- rbind(dodgers.train,dodgers.test)

# generate predictive modeling visual for management
group.labels <- c("No Bobbleheads","Bobbleheads")
group.symbols <- c(21,24)
group.colors <- c("black","black")
group.fill <- c("black","red")
xyplot(predict_attend/1000 ~ attend/1000 | training_test,
       data = dodgers.plotting.frame, groups = bobblehead, cex = 2,
       pch = group.symbols, col = group.colors, fill = group.fill,
       layout = c(2, 1), xlim = c(20,65), ylim = c(20,65),
       aspect=1, type = c("p","g"),
       panel=function(x,y, ...)
            {panel.xyplot(x,y,...)
             panel.segments(25,25,60,60,col="black",cex=2)
            },
       strip=function(...) strip.default(..., style=1),
       xlab = "Actual Attendance (thousands)",
       ylab = "Predicted Attendance (thousands)",
       key = list(space = "top",
              text = list(rev(group.labels),col = rev(group.colors)),
              points = list(pch = rev(group.symbols),
              col = rev(group.colors),
              fill = rev(group.fill))))

# use the full data set to obtain an estimate of the increase in
# attendance due to bobbleheads, controlling for other factors
my.model.fit <- lm(my.model, data = dodgers)  # use all available data
print(summary(my.model.fit))
# tests statistical significance of the bobblehead promotion
# type I anova computes sums of squares for sequential tests
print(anova(my.model.fit))

cat("\n","Estimated Effect of Bobblehead Promotion on Attendance: ",
round(my.model.fit$coefficients[length(my.model.fit$coefficients)],
digits = 0),"\n",sep="")

# standard graphics provide diagnostic plots
plot(my.model.fit)

# additional model diagnostics drawn from the car package
library(car)
residualPlots(my.model.fit)
marginalModelPlots(my.model.fit)
print(outlierTest(my.model.fit))

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