polarPlot); identifying clusters in the
original data for subsequent processing.polarCluster(mydata, pollutant = "nox", x = "ws",
wd = "wd", n.clusters = 6, cols = "Paired",
angle.scale = 315, units = x, auto.text = TRUE, ...)wd, another variable to plot in polar coordinates
(the default is a column "ws" --- wind speed) and a
pollutant. Should also contain date if plots by
time period are required.pollutant = "nox". Only one
pollutant can be chosen.n.clusters is more than length 1, then a
lattice panel plot will be output showing the
clusters identified for each one of n.clusters.RColorBrewer colours --- see the openair
openColours function for more details. Useful
schemes include "Accent", "Darangle.scale to another
value (between 0 and 360 degrees) toTRUE (default) or
FALSE. If TRUE titles and axis labels will
automatically try and format pollutant names and units
properly e.g. by subscripting the `2' in NO2.polarPlot, lattice:levelplot and
cutData. Common axis and title labelling options
(such as xlab, ylab, main) are
passed via polarCluster also returns an object of class
``openair''. The object includes three main components:
call, the command used to generate the plot;
data, the original data frame with a new field
cluster identifying the cluster; and plot,
the plot itself. Note that any rows where the value of
pollutant is NA are ignored so that the
returned data frame may have fewer rows than the
original.
An openair output can be manipulated using a number of
generic operations, including print, plot
and summary. See openair.generics
for further details.polarPlot function provide a very useful graphical
technique for identifying and characterising different
air pollution sources. While bivariate polar plots
provide a useful graphical indication of potential
sources, their location and wind-speed or other variable
dependence, they do have several limitations. Often, a
`feature' will be detected in a plot but the subsequent
analysis of data meeting particular wind speed/direction
criteria will be based only on the judgement of the
investigator concerning the wind speed-direction
intervals of interest. Furthermore, the identification of
a feature can depend on the choice of the colour scale
used, making the process somewhat arbitrary.
polarCluster applies Partition Around Medoids
(PAM) clustering techniques to polarPlot surfaces
to help identify potentially interesting features for
further analysis. Details of PAM can be found in the
cluster package (a core R package that will be
pre-installed on all R systems). PAM clustering is
similar to k-means but has several advantages e.g. is
more robust to outliers. The clustering is based on the
equal contribution assumed from the u and v wind
components and the associated concentration. The data are
standardized before clustering takes place.
The function works best by first trying different numbers
of clusters and plotting them. This is achieved by
setting n.clusters to be of length more than 1.
For example, if n.clusters = 2:10 then a plot will
be output showing the 9 cluster levels 2 to 10.
Note that clustering is computationally intensive and the
function can take a long time to run --- particularly
when the number of clusters is increased. For this reason
it can be a good idea to run a few clusters first to get
a feel for it e.g. n.clusters = 2:5.
Once the number of clusters has been decided, the user
can then run polarCluster to return the original
data frame together with a new column cluster,
which gives the cluster number as a character (see
example). Note that any rows where the value of
pollutant is NA are ignored so that the
returned data frame may have fewer rows than the
original.
Note that there are no automatic ways in ensuring the
most appropriate number of clusters as this is
application dependent. However, there is often a-priori
information available on what different features in polar
plots correspond to. Nevertheless, the appropriateness of
different clusters is best determined by post-processing
the data. The Carslaw and Beevers (2012) paper discusses
these issues in more detail.
Note that unlike most other openair functions only
a single type "default" is allowed.polarPlot# load example data from package
data(mydata)
## plot 2-8 clusters. Warning! This can take several minutes...
\dontrun{
polarCluster(mydata, pollutant = "nox", n.clusters = 2:8)
}
# basic plot with 6 clusters
results <- polarCluster(mydata, pollutant = "nox", n.clusters = 6)
## get results, could read into a new data frame to make it easier to refer to
## e.g. results <- results$data...
head(results$data)
## how many points are there in each cluster?
table(results$data$cluster)
## plot clusters 3 and 4 as a timeVariation plot using SAME colours as in
## cluster plot
timeVariation(subset(results$data, cluster %in% c("3", "4")), pollutant = "nox",
group = "cluster", col = openColours("Paired", 6)[c(3, 4)])Run the code above in your browser using DataLab