timePlot(mydata,
pollutant = "nox",
group = FALSE,
stack = FALSE,
normalise = FALSE,
avg.time = "default",
data.thresh = 0,
statistic = "mean",
percentile = NA,
date.pad = FALSE,
type = "default",
layout = c(1, 1),
cols = "brewer1",
main = "",
ylab = pollutant,
plot.type = "l",
lty = 1:length(pollutant),
lwd = 1,
pch = NA,
key = TRUE,
strip = TRUE,
log = FALSE,
smooth = FALSE,
ci = TRUE,
key.columns = 1,
name.pol = pollutant,
date.breaks = 7,
auto.text = TRUE, ...)date
field and at least one variable to plot.pollutant = c("nox", "co")
should be used.FALSE, which
means they are plotted in separate panels with their own scaled. If
TRUE then they are plotteTRUE the time series will be stacked by
year. This option can be useful if there are several years worth of
data making it difficult to see much detail when plotted on a single
plot.FALSE. If TRUE then the variable(s) are divided by
their mean values. This helps to compare the shape of the diurnal
trends for variables on very different scales.avg.time. A value of zero means that
all available data will be used in a particular period regardless if
of the number of values available. Conversely, a value ofstatistic =
"percentile" and when aggregating the data with
avg.time. More than one percentile level is allowed for
type = "default" e.g. percentile = c(50, 95date.pad = TRUE the time gaps between
the chunks are shown properly, rather than witype determines how the data are split
i.e. conditioned, and then plotted. The default is will produce a
single plot using the entire data. Type can be one of the built-in
types as detailed in cutData e.g. "season"layout cols = "blapollutant(s).lattice plot type, which is a line
(plot.type = "l") by default. Another useful option is
plot.type = "h", which draws vertical lines.lty = 1, for
example. See lty option for standard plwd = 2. Alternatively,
varying line widths can be chosen depending on the pollutant. For
example, if pollutant = c("nox", TRUE.TRUE.FALSE. If TRUE a well-formatted log10 scale is
used. This can be useful for plotting data for several different
pollutants that exist on very different scales. IFALSE.ci determines
whether the 95% confidence intervals aer shown.columns to be less
than the number of pollutants.TRUE (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.lattice:xyplot and cutData. For example, in the case
of cutData the option hemisphere = "southern".timePlot also returns an object of class
``openair''. The object includes three main components: call, the command used to
generate the plot; data, the data frame of summarised information used to make the
plot; and plot, the plot itself. If retained, e.g. using
output <- timePlot(mydata, "nox"), this output can be used to recover the data, reproduce
or rework the original plot or undertake further analysis.
An openair output can be manipulated using a number of generic operations, including
print, plot and summary. See openair.generics
for further details.timePlot is the basic time series plotting function in
openair. Its purpose is to make it quick and easy to plot time
series for pollutants and other variables. The other purpose is to plot
potentially many variables together in as compact a way as possible.
The function is flexible enough to plot more than one variable at
once. If more than one variable is chosen plots it can either show all
variables on the same plot (with different line types) on the
same scale, or (if group = FALSE) each variable in its own
panels with its own scale.
The general preference is not to plot two variables on the same graph
with two different y-scales. It can be misleading to do so and
difficult with more than two variables. If there is in interest in
plotting several variables together that have very different scales,
then it can be useful to normalise the data first, which can be down be
setting normalise = TRUE. This option ensures that each variable
is divided by its mean and makes it easy to plot two or more variables
on the same plot - generally with group = TRUE.
The user has fine control over the choice of colours, line width and
line types used. This is useful for example, to emphasise a particular
variable with a specific line type/colour/width.
timePlot works very well with selectByDate,
which is used for selecting particular date ranges quickly and
easily. See examples below.
By default plots are shown with a colour key at the bottom and in teh
case of multiple pollutants or sites, strips on teh left of each
plot. Sometimes this may be overkill and the user can opt to remove
the key and/or the strip by setting key and/or strip to
FALSE. One reason to do this is to maximise the plotting area
and therefore the information shown.MannKendall, smoothTrend,
linearRelation, selectByDate and
timeAverage for details on selecting averaging times
and other statistics in a flexible way# basic use, single pollutant
timePlot(mydata, pollutant = "nox")
# two pollutants in separate panels
timePlot(mydata, pollutant = c("nox", "no2"))
# two pollutants in the same panel with the same scale
timePlot(mydata, pollutant = c("nox", "no2"), group = TRUE)
# alternative by normalising concentrations and plotting on the same
scale
timePlot(mydata, pollutant = c("nox", "no2"), group = TRUE, normalise =
TRUE)
# examples of selecting by date
# plot for nox in 1999
timePlot(selectByDate(mydata, year = 1999), pollutant = "nox")
# select specific date range for two pollutants
timePlot(selectByDate(mydata, start = "6/8/2003", end = "13/8/2003"),
pollutant = c("no2", "o3"))
# choose different line styles etc
timePlot(mydata, pollutant = c("nox", "no2"), lty = 1)
# choose different line styles etc
timePlot(selectByDate(mydata, year = 2004, month = 6), pollutant =
c("nox", "no2"), lwd = c(1, 2), col = "black")
# different averaging times
#daily mean O3
timePlot(mydata, pollutant = "o3", avg.time = "day")
# daily mean O3 ensuring each day has data capture of at least 75\%
timePlot(mydata, pollutant = "o3", avg.time = "day", data.thresh = 75)
# 2-week average of O3 concentrations
timePlot(mydata, pollutant = "o3", avg.time = "2 week")Run the code above in your browser using DataLab