# implemented classes:
Desc(d.pizza$wrongpizza) # logical
Desc(d.pizza$driver) # factor
Desc(d.pizza$quality) # ordered factor
Desc(as.character(d.pizza$driver)) # character
Desc(d.pizza$week) # integer
Desc(d.pizza$delivery_min) # numeric
Desc(d.pizza$date) # Date
Desc(d.pizza)
Desc(d.pizza$wrongpizza, main="The wrong pizza delivered", digits=5)
Desc(table(d.pizza$area)) # 1-dim table
Desc(table(d.pizza$area, d.pizza$operator)) # 2-dim table
Desc(table(d.pizza$area, d.pizza$operator, d.pizza$driver)) # n-dim table
# expressions
Desc(log(d.pizza$temperature))
Desc(d.pizza$temperature > 45)
# supported labels
Label(d.pizza$temperature) <- "This is the temperature in degrees Celsius
measured at the time when the pizza is delivered to the client."
Desc(d.pizza$temperature)
# try as well: Desc(d.pizza$temperature, wrd=GetNewWrd())
z <- Desc(d.pizza$temperature)
print(z, digits=1, plotit=FALSE)
# plot (additional arguments are passed on to the underlying plot function)
plot(z, main="The pizza's temperature in Celsius", args.hist=list(breaks=50))
# bivariate
Desc(price ~ operator, data=d.pizza) # numeric ~ factor
Desc(driver ~ operator, data=d.pizza) # factor ~ factor
Desc(driver ~ area + operator, data=d.pizza) # factor ~ several factors
Desc(driver + area ~ operator, data=d.pizza) # several factors ~ factor
Desc(driver ~ week, data=d.pizza) # factor ~ integer
Desc(driver ~ operator, data=d.pizza, rfrq=("111")) # alle rel. frequencies
Desc(driver ~ operator, data=d.pizza, rfrq=("000"),
verbose="high") # no rel. frequencies
Desc(price ~ delivery_min, data=d.pizza) # numeric ~ numeric
Desc(price + delivery_min ~ operator + driver + wrongpizza,
data=d.pizza, digits=c(2,2,2,2,0,3,0,0) )
Desc(week ~ driver, data=d.pizza, digits=c(2,2,2,2,0,3,0,0)) # define digits
Desc(delivery_min + weekday ~ driver, data=d.pizza)
# without defining data-parameter
Desc(d.pizza$delivery_min ~ d.pizza$driver)
# with functions and interactions
Desc(sqrt(price) ~ operator : factor(wrongpizza), data=d.pizza)
Desc(log(price+1) ~ cut(delivery_min, breaks=seq(10,90,10)),
data=d.pizza, digits=c(2,2,2,2,0,3,0,0))
# response versus all the rest
Desc(driver ~ ., data=d.pizza[, c("temperature","wine_delivered","area","driver")])
# all the rest versus response
Desc(. ~ driver, data=d.pizza[, c("temperature","wine_delivered","area","driver")])
# pairwise Descriptions
p <- CombPairs(c("area","count","operator","driver","temperature","wrongpizza","quality"), )
for(i in 1:nrow(p))
print(Desc(formula(gettextf("%s ~ %s", p$X1, p$X2)), data=d.pizza))
# get more flexibility, create the table first
tab <- as.table(apply(HairEyeColor, c(1,2), sum))
tab <- tab[,c("Brown","Hazel","Green","Blue")]
# diplay only absolute values, row and columnwise percentages
Desc(tab, row.vars=c(3, 1), rfrq="011", plotit=FALSE)
# do the plot by hand, while setting the colours for the mosaics
cols1 <- SetAlpha(c("sienna4", "burlywood", "chartreuse3", "slategray1"), 0.6)
cols2 <- SetAlpha(c("moccasin", "salmon1", "wheat3", "gray32"), 0.8)
plot(tab, col1=cols1, col2=cols2)
# use global format options for presentation
Fmt(abs=as.fmt(digits=0, big.mark=""))
Fmt(per=as.fmt(digits=2, fmt="%"))
Desc(area ~ driver, d.pizza, plotit=FALSE)
Fmt(abs=as.fmt(digits=0, big.mark="'"))
Fmt(per=as.fmt(digits=3, leading="drop"))
Desc(area ~ driver, d.pizza, plotit=FALSE)
# plot arguments can be fixed in detail
z <- Desc(BoxCox(d.pizza$temperature, lambda = 1.5))
plot(z, mar=c(0, 2.1, 4.1, 2.1), args.rug=TRUE, args.hist=list(breaks=50),
args.dens=list(from=0))
# Output into word document (Windows-specific example) -----------------------
# by simply setting wrd=GetNewWrd()
## Not run:
#
# # create a new word instance and insert title and contents
# wrd <- GetNewWrd(header=TRUE)
#
# # let's have a subset
# d.sub <- d.pizza[,c("driver", "date", "operator", "price", "wrongpizza")]
#
# # do just the univariate analysis
# Desc(d.sub, wrd=wrd)
# ## End(Not run)
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