Last chance! 50% off unlimited learning
Sale ends in
The gvisTreeMap function reads a data.frame and creates text output referring to the Google Visualisation API, which can be included into a web page, or as a stand-alone page. The actual chart is rendered by the web browser.
gvisTreeMap(
data,
idvar = "",
parentvar = "",
sizevar = "",
colorvar = "",
options = list(),
chartid
)paste(gvisChartName) returns list
of class
paste(readLines(file.path(".", "inst", "mansections", "gvisOutputStructure.txt")))
a data.frame. The data has to have at least four columns.
Each row in the data table describes one node (a rectangle in the graph).
Each node (except the root node) has one or more parent nodes. Each node is
sized and colored according to its values relative to the other nodes
currently shown.
column name of data describing the ID for each node. It
can be any valid JavaScript string, including spaces, and any length that a
string can hold. This value is displayed as the node header.
column name of data that match to entries in
idvar. If this is a root node, leave this NA. Only one root is
allowed per treemap.
column name of data with positive values to define the
size of maps. Any positive value is allowed. This value determines the size
of the node, computed relative to all other nodes currently shown. This
value is ignored for non-leaf nodes (it is actually calculated from the size
of all its children).
column name of data with values to define range of
color. The value is used to calculate a color for this node. Any value,
positive or negative, is allowed. The color value is first recomputed on a
scale from minColorValue to maxColorValue, and then the node
is assigned a color from the gradient between minColor and
maxColor.
list of configuration options, see:
gsub("CHARTNAME", googleChartName, readLines(file.path(".", "inst", "mansections", "GoogleChartToolsURLConfigOptions.txt")))
paste(readLines(file.path(".", "inst", "mansections", "gvisOptions.txt")))
character. If missing (default) a random chart id will be
generated based on chart type and tempfile
Tree maps display a tree like structure where every child has to have a unique parent.
Values in column sizevar should be greater than zero and finite.
Markus Gesmann markus.gesmann@gmail.com,
Diego de Castillo decastillo@gmail.com
A tree map is a visual representation of a data tree, where each node can have zero or more children, and one parent (except for the root, which has no parents). Each node is displayed as a rectangle, sized and colored according to values that you assign. Sizes and colors are valued relative to all other nodes in the graph. You can specify how many levels to display simultaneously, and optionally to display deeper levels in a hinted fashion. If a node is a leaf node, you can specify a size and color; if it is not a leaf, it will be displayed as a bounding box for leaf nodes. The default behavior is to move down the tree when a user left-clicks a node, and to move back up the tree when a user right-clicks the graph.
The total size of the graph is determined by the size of the containing element that you insert in your page. If you have leaf nodes with names too long to show, the name will be truncated with an ellipsis (...).
Google Chart Tools API: gsub("CHARTNAME", googleChartName, readLines(file.path(".", "inst", "mansections", "GoogleChartToolsURL.txt")))
See also print.gvis, plot.gvis for printing and
plotting methods.
Please note that the treemap package offeres a static version of tree
maps via its tmPlot function.
## Please note that by default the googleVis plot command
## will open a browser window and requires Internet
## connection to display the visualisation.
Tree <- gvisTreeMap(Regions, idvar="Region", parentvar="Parent",
sizevar="Val", colorvar="Fac")
plot(Tree)
Tree2 <- gvisTreeMap(Regions, "Region", "Parent", "Val", "Fac",
options=list(width=600, height=500,
fontSize=16,
minColor='#EDF8FB',
midColor='#66C2A4',
maxColor='#006D2C',
headerHeight=20,
fontColor='black',
showScale=TRUE))
plot(Tree2)
## Simple static treemap with no drill down options based on US states
## and their area. However we still have to create a parent id to use
## gvisTreeMap
require(datasets)
states <- data.frame(state.name, state.area)
## Create parent variable
total=data.frame(state.area=sum(states$state.area), state.name="USA")
my.states <- rbind(total, states)
my.states$parent="USA"
## Set parent variable to NA at root level
my.states$parent[my.states$state.name=="USA"] <- NA
my.states$state.area.log=log(my.states$state.area)
statesTree <- gvisTreeMap(my.states, "state.name", "parent",
"state.area", "state.area.log")
plot(statesTree)
## We add US regions to the above data set to enable drill down capabilities
states2 <- data.frame(state.region, state.name, state.area)
regions <- aggregate(list(region.area=states2$state.area),
list(region=state.region), sum)
my.states2 <- data.frame(regionid=c("USA",
as.character(regions$region),
as.character(states2$state.name)),
parentid=c(NA, rep("USA", 4),
as.character(states2$state.region)),
state.area=c(sum(states2$state.area),
regions$region.area, states2$state.area))
my.states2$state.area.log=log(my.states2$state.area)
statesTree2 <- gvisTreeMap(my.states2, "regionid", "parentid",
"state.area", "state.area.log")
plot(statesTree2)
## Now we add another layer with US divisions
states3 <- data.frame(state.region, state.division, state.name, state.area)
regions <- aggregate(list(region.area=states3$state.area),
list(region=state.region), sum)
divisions <- aggregate(list(division.area=states3$state.area),
list(division=state.division, region=state.region),
sum)
my.states3 <- data.frame(regionid=c("USA",
as.character(regions$region),
as.character(divisions$division),
as.character(states3$state.name)),
parentid=c(NA, rep("USA", 4),
as.character(divisions$region),
as.character(states3$state.division)),
state.area=c(sum(states3$state.area),
regions$region.area,
divisions$division.area,
states3$state.area))
my.states3$state.area.log=log(my.states3$state.area)
statesTree3 <- gvisTreeMap(my.states3, "regionid", "parentid",
"state.area", "state.area.log")
plot(statesTree3)
Run the code above in your browser using DataLab