Background
The R package pems.utils uses two
main data types: pems.elements, data-series with
assigned units, and pems, datasets of simultaneously
logged pems.elements.
This document provides an overview of their plotting in
R.
For a quick and more general introduction to
pems.utils, see [>pems.utils
introduction] or to return to the [>website
index].
If you have any suggestions how to make either
pems.utils or this document better or you have any
problems using either, please let me know [>email me].
Unless you have setup R to automatically load
pems.utils, you will need to load it e.g. using
library(pems.utils) to use the functions described
here.
pems.utils includes a number of specialist graphics
functions developed specifically for use with pems and
pems.element data types. However,
pems.utils is primarily a data handling and analysis
package, and outputs have been designed to allow users the flexibility
to work with other graphics packages.
Generic Plots:
The pems.utils package includes generic plotting
methods for both pems and
pems.elements data types.
So, both data types can be handled using standard
plot(…) calls, e.g.:
plot(pems.1)
plot(pems.1$local.time, pems.1$conc.co)
Here, plot(pems, …) behaves like a
plot(data.frame, …) and generates a co-plot, and
plot(pems.element, …) behaves like plot(vector,
…) and generates a conventional scatterplot.
These generic plots accept most common plot
arguments, e.g. type, col and
lty, making this is simple-to-use option for most
R users.
For more about other generic functions like plot in
pems.utils, see [>pems.utils
generics].
Working with other Graphics Packages
Although there are some important differences, discussed in the
package introduction, pems behave in a similar fashion
to other (row and column) data set objects like
data.frames and data.tables, and
pems.elements behave in a similar fashion to other
vector objects like numerics,
characters and factors when used with
other packages.
So, generally, anything you can do in a graphic package with a
data.frame or a vector you should be
able to do with a pems or a
pems.element, respectively.
For example, pems data sets can be treated like
conventional data.frames in either
lattice or ggplot2 plots:
require(lattice)
xyplot(conc.co~local.time, data=pems.1)
require(ggplot2)
ggplot(pems.1) + geom_point(aes(x=local.time, y=conc.co))
If you are plotting pems.utils outputs with these or
other packages and have any suggestions how to make either plot handling
or this document better or you have any problems, please let me know. [>email me].
pemsPlot
pemsPlot is the main workhorse function for plotting
pems.utils outputs.
It accepts x and y arguments like
plot, which can be supplied directly or sourced from a
data set supplied as data in the same call.
Plot outputs can be easily modified using additional arguments and
functions, e.g.:
#plot 1:default x/y plot
pemsPlot(local.time, velocity, data=pems.1)
#add a 'referencing' term for grouping, etc...
pems.1$ref <- refRow(pems.1, breaks=c(405,660))
#plot 2: grouped line plot with y axis reset to miles per hour
pemsPlot(local.time, velocity, data=pems.1, group=ref,
type="l", grid.lty=3, y.to="mph")
#plot 3: conditioned type h plot with full dataset ghosts plotted
#(type h is a drop line plot; h comes from histogram-like)
p <- pemsPlot(local.time, velocity, data=pems.1,
cond=ref, type="h")
loa::add.XYZGhosts(p)
pemsPlots accepts (optional) arguments:
z which is by default linked to point size and color;
groups for within-plot grouping and
cond for multiple plot panelling.
Like plot, it accepts common plotting arguments,
e.g. type to change between point and line outputs, but
it also accepts loa style commands,
e.g. grid.lty to change the line style of grid lines
without affecting other lines in the plots, and commands like
y.to that access pems information to
change plot element scales and units.
Other functions can also be used to add addition elements to existing
plots, e.g. here add.XYZGhosts from
loa is used to add a ‘ghost’ versions of the full
dataset to all panels in a multiple panel plot.
pemsPlot is highly flexible, and used with other
panel… and loa add…
functions, can be easily used to generate many plots commonly used in
PEMS data analysis with just a few lines of code.
For example, data maps from plots of GPS latitude and longitude
data:
#map 1: CO2 Emissions bubble plot, first 300 measurements
pems.1$em.co2 <- calcEm(conc.co2, data=pems.1)
p <- pemsPlot(longitude, latitude, em.co2,
data=pems.1[1:300,], grid=FALSE,
col.regions=c("red", "black"))
loa::add.LonLatMap(p, map.type="osm")
#map 2: vehicle journey path, first 300 measurements
p <- pemsPlot(longitude, latitude,
data=pems.1[1:300,], grid=FALSE,
panel=panel.routePath)
loa::add.LonLatMap(p, map.type="bing",
recolor.map=c("darkgrey", "white", "white", "white"))
Both examples limit the plot range to the first 300 rows of the
pems.1 dataset.
In map 1 the supplied emission measurement, em.co2,
is used as the z pemsPlot argument to
generate a bubble plot. Other plot arguments are also used to modify the
plot appearance, e.g. col.regions to change the palette
coloring points, and then add.LonLatMap from
loa is used to add a same-location map, in this case an
open street map tile.
In map 2, a route-path panel is used to plot the vehicle journey this
time on a satellite image from bing which was recolored before
plotting.
The map layers were generated using code in
loaMapPlot in loa, and the map layers
are (c) OpenStreetMap contributors.
Plots can be easily extended to include multiple time-series and
axes, e.g.:
#calculate emissions for pems.1 CO, NOx and HCs:
pems.1$em.co <- calcEm(conc.co, data=pems.1)
pems.1$em.nox <- calcEm(conc.nox, data=pems.1)
pems.1$em.hc <- calcEm(conc.hc, data=pems.1)
#multi-species time-series of exhaust emissions
#superimposed on the emitting vehicle's speed profile
p <- pemsPlot(local.time, cpe(em.co, em.nox*5, em.hc*10),
data=pems.1, type="l", ylab="",
col=c("red", "blue", "darkgreen"),
key.groups.main="Emissions\n[g/sec]",
key.position="left")
p <- loa::add.Y2Axis(p, ylab="speed", col="grey", rescale=200)
loa::add.XYPolygon(p, x=local.time, y=velocity,
data=pems.1, col="grey",
y2.scale=TRUE, first=TRUE)
Here, the cpe function is used to group multiple
y axis data-series and scale them manually, and other
arguments are used to control plot appearance and layout,
e.g. col to color y time-series directly and
key… arguments are used to position and label the key.
Then add… functions are used to add a second y-axis,
and a polygon layer, plotted on that second axis scale, showing the
vehicle speed profile.
