The trendLevel function provides a way of rapidly showing a large amount of data in a condensed form. In one plot, the variation in the concentration of one pollutant can to shown as a function of three other categorical properties. The default version of the plot uses y = hour of day, x = month of year and type = year to provide information on trends, seasonal effects and diurnal variations. However, x, y and type and summarising statistics can all be modified to provide a range of other similar plots.
trendLevel(
mydata,
pollutant = "nox",
x = "month",
y = "hour",
type = "year",
rotate.axis = c(90, 0),
n.levels = c(10, 10, 4),
limits = c(0, 100),
cols = "default",
auto.text = TRUE,
key.header = "use.stat.name",
key.footer = pollutant,
key.position = "right",
key = TRUE,
labels = NA,
breaks = NA,
statistic = c("mean", "max", "min", "median", "frequency", "sum", "sd", "percentile"),
percentile = 95,
stat.args = NULL,
stat.safe.mode = TRUE,
drop.unused.types = TRUE,
col.na = "white",
plot = TRUE,
...
)an openair object.
The openair data frame to use to generate the trendLevel()
plot.
The name of the data series in mydata to sample to produce
the trendLevel() plot.
The name of the data series to use as the trendLevel()
x-axis, y-axis or conditioning variable, passed to cutData(). These are
used before applying statistic. trendLevel() does not allow duplication
in x, y and type options.
The rotation to be applied to trendLevel x and y
axes. The default, c(90, 0), rotates the x axis by 90 degrees but does
not rotate the y axis. If only one value is supplied, this is applied to
both axes; if more than two values are supplied, only the first two are
used.
The number of levels to split x, y and type data into
if numeric. The default, c(10, 10, 4), cuts numeric x and y data into
ten levels and numeric type data into four levels. This option is ignored
for date conditioning and factors. If less than three values are supplied,
three values are determined by recursion; if more than three values are
supplied, only the first three are used.
The colour scale range to use when generating the
trendLevel() plot.
The colour set to use to colour the trendLevel() surface.
cols is passed to openColours() for evaluation.
Automatic routine text formatting. auto.text = TRUE passes
common lattice labelling terms (e.g. xlab for the x-axis, ylab for
the y-axis and main for the title) to the plot via quickText() to
provide common text formatting. The alternative auto.text = FALSE turns
this option off and passes any supplied labels to the plot without
modification.
Adds additional text labels above and/or below
the scale key, respectively. For example, passing the options key.header = "", key.footer = c("mean","nox") adds the addition text as a scale footer.
If enabled (auto.text = TRUE), these arguments are passed to the scale
key (drawOpenKey()) via quickText() to handle formatting. The term
"get.stat.name", used as the default key.header setting, is reserved
and automatically adds statistic function names or defaults to "level"
when unnamed functions are requested via statistic.
Location where the scale key should be plotted. Allowed
arguments currently include "top", "right", "bottom", and "left".
Fine control of the scale key via drawOpenKey().
If a categorical colour scale is required then breaks
should be specified. These should be provided as a numeric vector, e.g.,
breaks = c(0, 50, 100, 1000). Users should set the maximum value of
breaks to exceed the maximum data value to ensure it is within the
maximum final range, e.g., 100--1000 in this case. Labels will
automatically be generated, but can be customised by passing a character
vector to labels, e.g., labels = c("good", "bad", "very bad"). In this
example, 0 - 50 will be "good" and so on. Note there is one less label
than break.
The statistic to apply when aggregating the data; default is
the mean. Can be one of "mean", "max", "min", "median",
"frequency", "sum", "sd", "percentile". Note that "sd" is the
standard deviation, "frequency" is the number (frequency) of valid
records in the period and "data.cap" is the percentage data capture.
"percentile" is the percentile level (%) between 0-100, which can be set
using the "percentile" option. Functions can also be sent directly via
statistic; see 'Details' for more information.
The percentile level used when statistic = "percentile".
The default is 95%.
Additional options to be used with statistic if this is a
function. The extra options should be supplied as a list of named
parameters; see 'Details' for more information.
An addition protection applied when using functions
directly with statistic that most users can ignore. This option returns
NA instead of running statistic on binned sub samples that are empty.
Many common functions terminate with an error message when applied to an
empty dataset. So, this option provides a mechanism to work with such
functions. For a very few cases, e.g., for a function that counted missing
entries, it might need to be set to FALSE; see 'Details' for more
information.
Hide unused/empty type conditioning cases. Some
conditioning options may generate empty cases for some data sets, e.g. a
hour of the day when no measurements were taken. Empty x and y cases
generate 'holes' in individual plots. However, empty type cases would
produce blank panels if plotted. Therefore, the default, TRUE, excludes
these empty panels from the plot. The alternative FALSE plots all type
panels.
Colour to be used to show missing data.
Should a plot be produced? FALSE can be useful when analysing
data to extract plot components and plotting them in other ways.
Addition options are passed on to cutData() for type handling
and lattice::levelplot() for finer control of the plot itself.
Karl Ropkins
David Carslaw
Jack Davison
trendLevel() allows the use of third party summarising functions via the
statistic option. Any additional function arguments not included within a
function called using statistic should be supplied as a list of named
parameters and sent using stat.args. For example, the encoded option
statistic = "mean" is equivalent to statistic = mean, stat.args = list(na.rm = TRUE) or the R command mean(x, na.rm = TRUE). Many R
functions and user's own code could be applied in a similar fashion, subject
to the following restrictions: the first argument sent to the function must
be the data series to be analysed; the name 'x' cannot be used for any of the
extra options supplied in stat.args; and the function should return the
required answer as a numeric or NA. Note: If the supplied function returns
more than one answer, currently only the first of these is retained and used
by trendLevel(). All other returned information will be ignored without
warning. If the function terminates with an error when it is sent an empty
data series, the option stat.safe.mode should not be set to FALSE or
trendLevel() may fail. Note: The stat.safe.mode = TRUE option returns an
NA without warning for empty data series.
Other time series and trend functions:
TheilSen(),
calendarPlot(),
runRegression(),
smoothTrend(),
timePlot(),
timeProp(),
timeVariation()
# basic use
# default statistic = "mean"
trendLevel(mydata, pollutant = "nox")
# applying same as 'own' statistic
my.mean <- function(x) mean(x, na.rm = TRUE)
trendLevel(mydata, pollutant = "nox", statistic = my.mean)
# alternative for 'third party' statistic
# trendLevel(mydata, pollutant = "nox", statistic = mean,
# stat.args = list(na.rm = TRUE))
if (FALSE) {
# example with categorical scale
trendLevel(mydata,
pollutant = "no2",
border = "white", statistic = "max",
breaks = c(0, 50, 100, 500),
labels = c("low", "medium", "high"),
cols = c("forestgreen", "yellow", "red")
)
}
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