Last chance! 50% off unlimited learning
Sale ends in
polarFreq primarily plots wind speed-direction frequencies in
‘bins’. Each bin is colour-coded depending on the frequency of
measurements. Bins can also be used to show the concentration of pollutants
using a range of commonly used statistics.polarFreq(mydata, pollutant = "", statistic = "frequency", ws.int = 1,
grid.line = 5, breaks = seq(0, 5000, 500), cols = "default",
trans = TRUE, type = "default", min.bin = 1, ws.upper = NA,
offset = 10, border.col = "transparent", key.header = statistic,
key.footer = pollutant, key.position = "right", key = TRUE,
auto.text = TRUE, ...)ws, wd and
date.pollutant = "nox"cutData for further
details.breaks expects a
sequence of numbers that define the range of the scale. The sequence
could represent one with equal spacing e.g. breaks = seq(0, 100,
10) - a scale from 0-10 in intervals of 10, or a more flexible sequence
e.g. breaks = c(0, 1, 5, 7, 10), which may be useful for some
situations.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")TRUE and a square-root transform is applied.
This results in a non-linear scale and (usually) a better representation
of the distribution. If set to FALSE a linear scale is used.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.
Type can be up length two e.g. type = c("season", "weekday") will
produce a 2x2 plot split by season and day of the week. Note, when two
types are provided the first forms the columns and the second the rows.
polarPlot function can be of use in such
circumstances.ws.int = 10.offset controls the size of the ‘hole’
in the middle and is expressed as a percentage of the maximum wind
speed. Setting a higher offset e.g. 50 is useful for
statistic = "weighted.mean" when ws.int is greater
than the maximum wind speed. See example below.key.header = "header", key.footer =
"footer" adds addition text above and below the scale key. These
arguments are passed to drawOpenKey via quickText, applying
the auto.text argument, to handle formatting."top", "right", "bottom"
and "left".drawOpenKey. See
drawOpenKey for further details.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, polarFreq passes the option
hemisphere = "southern" on to cutData to provide southern
(rather than default northern) hemisphere handling of type = "season".
Similarly, common axis and title labelling options (such as xlab,
ylab, main) are passed to xyplot via quickText
to handle routine formatting.polarFreq 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 <- polarFreq(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.polarFreq is its default use provides details of wind speed and
direction frequencies. In this respect it is similar to
windRose, but considers wind direction intervals of 10
degrees and a user-specified wind speed interval. The frequency of wind
speeds/directions formed by these ‘bins’ is represented on a colour
scale. The polarFreq function is more flexible than either
windRose or polarPlot. It can, for example,
also consider pollutant concentrations (see examples below). Instead of the
number of data points in each bin, the concentration can be shown. Further,
a range of statistics can be used to describe each bin - see
statistic above. Plotting mean concentrations is useful for source
identification and is the same as polarPlot but without
smoothing, which may be preferable for some data. Plotting with
statistic = "weighted.mean" is particularly useful for understanding
the relative importance of different source contributions. For example,
high mean concentrations may be observed for high wind speed conditions,
but the weighted mean concentration may well show that the contribution to
overall concentrations is very low. polarFreq also offers great flexibility with the scale used and the
user has fine control over both the range, interval and colour.windRose, polarPlot
# basic wind frequency plot
polarFreq(mydata)
# wind frequencies by year
## Not run: polarFreq(mydata, type = "year")
# mean SO2 by year, showing only bins with at least 2 points
## Not run: polarFreq(mydata, pollutant = "so2", type = "year", statistic = "mean", min.bin = 2)
# weighted mean SO2 by year, showing only bins with at least 2 points
## Not run: ------------------------------------
# polarFreq(mydata, pollutant = "so2", type = "year", statistic = "weighted.mean",
# min.bin = 2)
## ---------------------------------------------
#windRose for just 2000 and 2003 with different colours
## Not run: ------------------------------------
# polarFreq(subset(mydata, format(date, "%Y") %in% c(2000, 2003)),
# type = "year", cols = "jet")
## ---------------------------------------------
# user defined breaks from 0-700 in intervals of 100 (note linear scale)
## Not run: polarFreq(mydata, breaks = seq(0, 700, 100))
# more complicated user-defined breaks - useful for highlighting bins
# with a certain number of data points
## Not run: polarFreq(mydata, breaks = c(0, 10, 50, 100, 250, 500, 700))
# source contribution plot and use of offset option
## Not run: ------------------------------------
# polarFreq(mydata, pollutant = "pm25", statistic
# ="weighted.mean", offset = 50, ws.int = 25, trans = FALSE)
## ---------------------------------------------
Run the code above in your browser using DataLab