scatterPlot | R Documentation |
Scatter plots with conditioning and three main approaches: conventional scatterPlot, hexagonal binning and kernel density estimates. The former also has options for fitting smooth fits and linear models with uncertainties shown.
scatterPlot(
mydata,
x = "nox",
y = "no2",
z = NA,
method = "scatter",
group = NA,
avg.time = "default",
data.thresh = 0,
statistic = "mean",
percentile = NA,
type = "default",
smooth = FALSE,
spline = FALSE,
linear = FALSE,
ci = TRUE,
mod.line = FALSE,
cols = "hue",
plot.type = "p",
key = TRUE,
key.title = group,
key.columns = 1,
key.position = "right",
strip = TRUE,
log.x = FALSE,
log.y = FALSE,
x.inc = NULL,
y.inc = NULL,
limits = NULL,
windflow = NULL,
y.relation = "same",
x.relation = "same",
ref.x = NULL,
ref.y = NULL,
k = NA,
dist = 0.02,
map = FALSE,
auto.text = TRUE,
plot = TRUE,
...
)
mydata |
A data frame containing at least two numeric variables to plot. |
x |
Name of the x-variable to plot. Note that x can be a date field or a
factor. For example, |
y |
Name of the numeric y-variable to plot. |
z |
Name of the numeric z-variable to plot for |
method |
Methods include “scatter” (conventional scatter plot),
“hexbin” (hexagonal binning using the |
group |
The grouping variable to use, if any. Setting this to a variable in the data frame has the effect of plotting several series in the same panel using different symbols/colours etc. If set to a variable that is a character or factor, those categories or factor levels will be used directly. If set to a numeric variable, it will split that variable in to quantiles. |
avg.time |
This defines the time period to average to. Can be
“sec”, “min”, “hour”, “day”, “DSTday”,
“week”, “month”, “quarter” or “year”. For much
increased flexibility a number can precede these options followed by a
space. For example, a timeAverage of 2 months would be |
data.thresh |
The data capture threshold to use (\
the data using |
statistic |
The statistic to apply when aggregating the data; default is
the mean. Can be one of "mean", "max", "min", "median", "frequency", "sd",
"percentile". Note that "sd" is the standard deviation and "frequency" is
the number (frequency) of valid records in the period. "percentile" is the
percentile level (\
"percentile" option - see below. Not used if |
percentile |
The percentile level in percent used when |
type |
It is also possible to choose Type can be up length two e.g. |
smooth |
A smooth line is fitted to the data if |
spline |
A smooth spline is fitted to the data if |
linear |
A linear model is fitted to the data if |
ci |
Should the confidence intervals for the smooth/linear fit be shown? |
mod.line |
If |
cols |
Colours to be used for plotting. Options include
“default”, “increment”, “heat”, “jet” and
|
plot.type |
|
key |
Should a key be drawn? The default is |
key.title |
The title of the key (if used). |
key.columns |
Number of columns to be used in the key. With many
pollutants a single column can make to key too wide. The user can thus
choose to use several columns by setting |
key.position |
Location where the scale key is to plotted. Allowed arguments currently include “top”, “right”, “bottom” and “left”. |
strip |
Should a strip be drawn? The default is |
log.x |
Should the x-axis appear on a log scale? The default is
|
log.y |
Should the y-axis appear on a log scale? The default is
|
x.inc |
The x-interval to be used for binning data when |
y.inc |
The y-interval to be used for binning data when |
limits |
For |
windflow |
This option allows a scatter plot to show the wind
speed/direction shows as an arrow. The option is a list e.g. The maximum length of the arrow plotted is a fraction of the plot dimension
with the longest arrow being This option works best where there are not too many data to ensure over-plotting does not become a problem. |
y.relation |
This determines how the y-axis scale is plotted. “same” ensures all panels use the same scale and “free” will use panel-specific scales. The latter is a useful setting when plotting data with very different values. |
x.relation |
This determines how the x-axis scale is plotted. “same” ensures all panels use the same scale and “free” will use panel-specific scales. The latter is a useful setting when plotting data with very different values. |
ref.x |
See |
ref.y |
A list with details of the horizontal lines to be added
representing reference line(s). For example, |
k |
Smoothing parameter supplied to |
dist |
When plotting smooth surfaces ( |
map |
Should a base map be drawn? This option is under development. |
auto.text |
Either |
plot |
Should a plot be produced? |
... |
Other graphical parameters are passed onto For |
scatterPlot()
is the basic function for plotting scatter plots in flexible
ways in openair
. It is flexible enough to consider lots of
conditioning variables and takes care of fitting smooth or linear
relationships to the data.
There are four main ways of plotting the relationship between two variables,
which are set using the method
option. The default "scatter"
will plot a conventional scatterPlot. In cases where there are lots of data
and over-plotting becomes a problem, then method = "hexbin"
or
method = "density"
can be useful. The former requires the
hexbin
package to be installed.
There is also a method = "level"
which will bin the x
and
y
data according to the intervals set for x.inc
and
y.inc
and colour the bins according to levels of a third variable,
z
. Sometimes however, a far better understanding of the relationship
between three variables (x
, y
and z
) is gained by
fitting a smooth surface through the data. See examples below.
A smooth fit is shown if smooth = TRUE
which can help show the overall
form of the data e.g. whether the relationship appears to be linear or not.
Also, a linear fit can be shown using linear = TRUE
as an option.
The user has fine control over the choice of colours and symbol type used.
Another way of reducing the number of points used in the plots which can
sometimes be useful is to aggregate the data. For example, hourly data can be
aggregated to daily data. See timePlot()
for examples here.
By default plots are shown with a colour key at the bottom and in the case of
conditioning, strips on the top 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.
an openair object
David Carslaw
linearRelation
, timePlot
and
timeAverage()
for details on selecting averaging times and other
statistics in a flexible way
# load openair data if not loaded already
dat2004 <- selectByDate(mydata, year = 2004)
# basic use, single pollutant
scatterPlot(dat2004, x = "nox", y = "no2")
## Not run:
# scatterPlot by year
scatterPlot(mydata, x = "nox", y = "no2", type = "year")
## End(Not run)
# scatterPlot by day of the week, removing key at bottom
scatterPlot(dat2004, x = "nox", y = "no2", type = "weekday", key =
FALSE)
# example of the use of continuous where colour is used to show
# different levels of a third (numeric) variable
# plot daily averages and choose a filled plot symbol (pch = 16)
# select only 2004
## Not run:
scatterPlot(dat2004, x = "nox", y = "no2", z = "co", avg.time = "day", pch = 16)
# show linear fit, by year
scatterPlot(mydata, x = "nox", y = "no2", type = "year", smooth =
FALSE, linear = TRUE)
# do the same, but for daily means...
scatterPlot(mydata, x = "nox", y = "no2", type = "year", smooth =
FALSE, linear = TRUE, avg.time = "day")
# log scales
scatterPlot(mydata, x = "nox", y = "no2", type = "year", smooth =
FALSE, linear = TRUE, avg.time = "day", log.x = TRUE, log.y = TRUE)
# also works with the x-axis in date format (alternative to timePlot)
scatterPlot(mydata, x = "date", y = "no2", avg.time = "month",
key = FALSE)
## multiple types and grouping variable and continuous colour scale
scatterPlot(mydata, x = "nox", y = "no2", z = "o3", type = c("season", "weekend"))
# use hexagonal binning
library(hexbin)
# basic use, single pollutant
scatterPlot(mydata, x = "nox", y = "no2", method = "hexbin")
# scatterPlot by year
scatterPlot(mydata, x = "nox", y = "no2", type = "year", method =
"hexbin")
## bin data and plot it - can see how for high NO2, O3 is also high
scatterPlot(mydata, x = "nox", y = "no2", z = "o3", method = "level", dist = 0.02)
## fit surface for clearer view of relationship - clear effect of
## increased O3
scatterPlot(mydata, x = "nox", y = "no2", z = "o3", method = "level",
x.inc = 10, y.inc = 2, smooth = TRUE)
## End(Not run)
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