#########################################
### quick example
###
### data: Gross national income data
#########################################
# load Gross national income data
data(GNI2010)
# create treemap
tmPlot(GNI2010,
index=c("continent", "iso3"),
vSize="population",
vColor="GNI",
type="value")
#########################################
### extended examples
###
### data: fictive structural business statistics data
#########################################
### load fictive structural business statistics data
data(sbsData)
sbsData$employees.growth <- sbsData$employees09 - sbsData$employees08
#########################################
### types
#########################################
# value treemap: the color variable is directly mapped to the colors
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="employees.growth",
type="value")
# comparisson treemaps: colors indicate change of vSize with respect to vColor
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="employees08",
type="comp")
# four comparisson treemaps
tmPlot(sbsData,
index=c("section", "subsection"),
vSize=c("employees09", "value added09", "turnover09", "salaries09"),
vColor=c("employees08", "value added08", "turnover08", "salaries08"),
type="comp")
# density treemaps: colors indicate density (like a population density map)
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="turnover09",
vColor="employees09*1000",
type="dens")
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="turnover09",
type="dens")
# linked treemaps: objects are linked by color over different treemaps
tmPlot(sbsData[sbsData$section=="Manufacturing",],
index="subsection",
vSize=c("income09", "employees09", "expenditures09", "salaries09"),
type="linked")
# index treemap: each aggregation index has distinct color
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
type="index")
# categorical treemap: colors are determined by a categorical variable
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="section",
type="categorical")
#########################################
### layout algorithm
#########################################
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="employees.growth",
type="value",
algorithm="squarified")
#########################################
### graphical options: fontsize
#########################################
# draw labels at fixed fontsize (fit only)
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="employees08",
type="comp",
fontsize.labels=12,
lowerbound.cex.labels=1)
# draw labels at flexible fontsize (skip tiny rectangles)
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="employees08",
type="comp",
fontsize.labels=12,
lowerbound.cex.labels=.6)
# draw labels at maximal fontsize
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="employees08",
type="comp",
fontsize.labels=10,
lowerbound.cex.labels=1,
inflate.labels = TRUE)
# draw all labels at fixed fontsize
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="employees08",
type="comp",
fontsize.labels=10,
lowerbound.cex.labels=1,
force.print.labels=TRUE)
#########################################
### graphical options: color palette
#########################################
# terrain colors
sbsData$employees.growth <- sbsData$employees09 - sbsData$employees08
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="employees.growth",
type="value",
palette=terrain.colors(10))
# Brewer's Red-White-Grey palette reversed with predefined range
tmPlot(sbsData,
index=c("section", "subsection"),
vSize="employees09",
vColor="employees.growth",
type="value",
palette="-RdGy",
range=c(-20000,20000))
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