timePlot(mydata, pollutant = "nox", group = FALSE, stack = FALSE, normalise = NULL, avg.time = "default", data.thresh = 0, statistic = "mean", percentile = NA, date.pad = FALSE, type = "default", cols = "brewer1", plot.type = "l", key = TRUE, log = FALSE, windflow = NULL, smooth = FALSE, ci = TRUE, y.relation = "same", ref.x = NULL, ref.y = NULL, key.columns = 1, name.pol = pollutant, date.breaks = 7, date.format = NULL, 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 plotted on the same plot with the same scale.TRUE 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.normalise can take two values, either
“mean” or a string representing a date in UK format e.g.
"1/1/1998" (in the format dd/mm/YYYY). If normalise = "mean" then
each time series is divided by its mean value. If a date is chosen, then
values at that date are set to 100 and the rest of the data scaled
accordingly. Choosing a date (say at the beginning of a time series) is
very useful for showing how trends diverge over time. Setting group
= TRUE is often useful too to show all time series together in one
panel.period = "2 month". See
function timeAverage for further details on this.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 of 100 will mean that all data will
need to be present for the average to be calculated, else it is recorded
as NA. Not used if avg.time = "default".avg.time
= "default".statistic =
"percentile" and when aggregating the data with avg.time. More
than one percentile level is allowed for type = "default" e.g.
percentile = c(50, 95). Not used if avg.time = "default".date.pad = TRUE the time gaps between the
chunks are shown properly, rather than with a line connecting each chunk.
For irregular data, set to FALSE. Note, this should not be set for
type other than default.type 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”,
“year”, “weekday” and so on. For example, type
= "season" will produce four plots --- one for each season.It is also possible to choose type as another variable in the data
frame. If that variable is numeric, then the data will be split into four
quantiles (if possible) and labelled accordingly. If type is an existing
character or factor variable, then those categories/levels will be used
directly. This offers great flexibility for understanding the variation
of different variables and how they depend on one another.
Only one type is currently allowed in timePlot.
RColorBrewer colours --- see the openair
openColours function for more details. For user defined the
user can supply a list of colour names recognised by R (type
colours() to see the full list). An example would be
cols = c("yellow", "green", "blue")lattice plot type, which is a line
(plot.type = "l") by default. Another useful option is
plot.type = "h", which draws vertical lines.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. It is therefore useful to use log =
TRUE together with group = TRUE.windflow = list(col = "grey", lwd = 2, scale =
0.1). This option requires wind speed (ws) and wind
direction (wd) to be available.The maximum length of the arrow plotted is a fraction of the plot
dimension with the longest arrow being scale of the plot
x-y dimension. Note, if the plot size is adjusted manually by the
user it should be re-plotted to ensure the correct wind angle. The
list may contain other options to panel.arrows in the
lattice package. Other useful options include
length, which controls the length of the arrow head and
angle, which controls the angle of the arrow head.
This option works best where there are not too many data to ensure over-plotting does not become a problem.
FALSE.ci determines
whether the 95% confidence intervals are shown.ref.y for details. In this case the
correct date format should be used for a vertical line e.g. ref.x
= list(v = as.POSIXct("2000-06-15"), lty = 5).ref.y =
list(h = 50, lty = 5) will add a dashed horizontal line at
50. Several lines can be plotted e.g. ref.y = list(h = c(50,
100), lty = c(1, 5), col = c("green", "blue")). See
panel.abline in the lattice package for more details
on adding/controlling lines.columns to be less than
the number of pollutants.name.pol = "nox before change"
can be used. If more than one pollutant is plotted then use
c e.g. name.pol = c("nox here", "o3 there").date.breaks up or down.timePlot generally sets the date format
sensibly there can be some situations where the user wishes to
have more control. For format types see strptime. For
example, to format the date like “Jan-2012” set
date.format = "%b-%Y".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.cutData and
lattice:xyplot. For example, timePlot passes the option
hemisphere = "southern" on to cutData to provide southern
(rather than default northern) hemisphere handling of type = "season".
Similarly, most common plotting parameters, such as layout for
panel arrangement and pch and cex for plot symbol type and
size and lty and lwd for line type and width, as passed
to xyplot, although some maybe locally managed by openair
on route, e.g. axis and title labelling options (such as xlab,
ylab, main) are passed via quickText
to handle routine formatting. See examples below.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.
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 the
normalise option.
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 the case
of multiple pollutants or sites, strips on the 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.
TheilSen, 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
## Not run: timePlot(mydata, pollutant = c("nox", "no2"))
# two pollutants in the same panel with the same scale
## Not run: timePlot(mydata, pollutant = c("nox", "no2"), group = TRUE)
# alternative by normalising concentrations and plotting on the same
scale
## Not run:
# timePlot(mydata, pollutant = c("nox", "co", "pm10", "so2"), group = TRUE, avg.time =
# "year", normalise = "1/1/1998", lwd = 3, lty = 1)
# ## End(Not run)
# examples of selecting by date
# plot for nox in 1999
## Not run: timePlot(selectByDate(mydata, year = 1999), pollutant = "nox")
# select specific date range for two pollutants
## Not run:
# timePlot(selectByDate(mydata, start = "6/8/2003", end = "13/8/2003"),
# pollutant = c("no2", "o3"))
# ## End(Not run)
# choose different line styles etc
## Not run: timePlot(mydata, pollutant = c("nox", "no2"), lty = 1)
# choose different line styles etc
## Not run:
# timePlot(selectByDate(mydata, year = 2004, month = 6), pollutant =
# c("nox", "no2"), lwd = c(1, 2), col = "black")
# ## End(Not run)
# different averaging times
#daily mean O3
## Not run: timePlot(mydata, pollutant = "o3", avg.time = "day")
# daily mean O3 ensuring each day has data capture of at least 75%
## Not run: timePlot(mydata, pollutant = "o3", avg.time = "day", data.thresh = 75)
# 2-week average of O3 concentrations
## Not run: timePlot(mydata, pollutant = "o3", avg.time = "2 week")
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