Interactive Graphics
Stat 579
Outline
• (Time Series)
• Interactive Approaches
• iplots
• rggobi
NASA Meteorological
Data
24 x 24 grid across Central
America
•
satellite captured data:
temperature,
near surface temperature
(surftemp)
pressure,
ozone,
cloud coverage:
low (cloudlow)
medium (cloudmid)
high (cloudhigh)
•
for each location monthly
averages for Jan 1995 to Dec
2000
Gridx 1 to 24
Gridy 1 to 24
•
What is a Time Series?
305
300
295
ts
for each location multiple
measurements
290
285
280
qplot(time, temperature,
geom="point", data=subset(nasa,
(x==1) & (y==1)))
275
10
20
30
40
50
60
70
40
50
60
70
40
50
60
70
TimeIndx
305
300
ts
connected by a line
295
290
285
qplot(time, temperature,
geom="line", data=subset(nasa,
(x==1) & (y==1)))
280
275
10
20
30
TimeIndx
305
qplot(time, temperature, geom="line",
data=subset(nasa, (x==1) & (y %in% c
(1,15))), group=y)
300
295
ts
but only connect the
right points
290
285
280
275
10
20
30
TimeIndx
Practice
each location, draw a time series for pressure.
• For
What do you expect? Are there surprising values? Which
are they?
near surface temperatures for each location
• Plot
Which locations show the highest range in temperatures?
Which locations show the highest overall increase in
temperatures?
use ddply to get these summaries
Interactive Graphics
• Based on Linked Graphics
• Most important common tools:
Selection & Highlighting, Identifying Points
• Plot-specific interactive tools
Install iplots
• For Windows:
install.packages(“iplots”)
• For Macs: download & install JGR from
http://rosuda.org/JGR/down.shtml
(includes iplots and depending packages)
• Help files & documentation:
http://www.rosuda.org/iplots/
iplots Graphics
help(library= “iplots”)
• iplot: Scatterplot
• ihist: Histogram
• ibar: Barchart
• ibox: Boxplot
• imosaic: Mosaicplot
• ipcp: Parallel Coordinate Plot
Select & Identify
• iset.selected()
gives index vector of current selection
• iset.select(indices)
highlights specified values in current data set
Practice
• load the iplots package
near surface temperatures for each location
• Plot
Which locations show the highest range in temperatures?
Which locations show the highest overall increase in
temperatures?
package “rggobi”
• developed by Duncan Temple-Lang,
Debby Swayne, Michael Lawrence, Hadley
Wickham
• Gtk based plots
• allows interactive link between R and
ggobi
gd<-ggobi(nasa.wide[,1:5])[1]
clust9<-cutree(nasa.dend,k=9)
glyph_type(gd)<-4
glyph_size(gd)<-3
glyph_color(gd) <- clust9+1
which(selected.GGobiData(gd))
summary(gd[,1:5])
gd$clusters <- clust9
Practice
• Use a different clustering algorithm (e.g. try
method= “Complete” or “Average”)
• visualize spatial distribution of clustering results
using ggobi
• compare the clustering results - what is your
interpretation?
Mosaicplots
• Area representation of contingency table
• Interactive tools: arrows re-organize order of
variables and #dimensions shown
Your turn: Whodunnit
• Dataset whodunnit contains “story”
• Load data set
• Use interactive tools to figure out what
happened
• Once you know (or suspect) the story behind
this data, collect graphical evidence for your
theory.