[R] why change days of the week from a factor to an ordered factor?
David Winsemius
dwinsemius at comcast.net
Wed Dec 4 00:20:09 CET 2013
On Dec 2, 2013, at 6:58 PM, Bill wrote:
> Duncan,
> Thanks. Why doesn't
> coloursf2 <- factor(1:8, levels = 8:1)
>
> give an ordering when you do str(coloursf2) like
> "8"<"7"<"6" ...
Because the default for 'ordered' in factor is FALSE:
> coloursf2 <- factor(1:8, levels = 8:1, ordered=TRUE)
> coloursf2
[1] 1 2 3 4 5 6 7 8
Levels: 8 < 7 < 6 < 5 < 4 < 3 < 2 < 1
>
> Bill
>
>
> On Mon, Dec 2, 2013 at 6:29 PM, Duncan Mackay <dulcalma at bigpond.com> wrote:
>
>> Hi Bill
>>
>> eg
>>
>>> colours = 1:8
>>> coloursf = factor(1:8)
>>> colourso = ordered(1:8)
>>> str(coloursf)
>> Factor w/ 8 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8
>>> str(colourso)
>> Ord.factor w/ 8 levels "1"<"2"<"3"<"4"<..: 1 2 3 4 5 6 7 8
>>
>> coloursf2 <- factor(1:8, levels = 8:1)
>> str(coloursf2)
>>
>> Duncan
>>
>> Duncan
>> Duncan Mackay
>> Department of Agronomy and Soil Science
>> University of New England
>> Armidale NSW 2351
>> Email: home: mackay at northnet.com.au
>>
>>
>> ordered used in
>> used in MASS::polr and GEE for polytomous logistic regression
>>
>> -----Original Message-----
>> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
>> On
>> Behalf Of Bill
>> Sent: Monday, 2 December 2013 21:24
>> To: r-help at r-project.org
>> Subject: [R] why change days of the week from a factor to an ordered
>> factor?
>>
>> 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))
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
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>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
David Winsemius
Alameda, CA, USA
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