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, ...)

Arguments

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, x can be one of the openair built in types such as "year" or "season".

y

Name of the numeric y-variable to plot.

z

Name of the numeric z-variable to plot for method = "scatter" or method = "level". Note that for method = "scatter" points will be coloured according to a continuous colour scale, whereas for method = "level" the surface is coloured.

method

Methods include “scatter” (conventional scatter plot), “hexbin” (hexagonal binning using the hexbin package). “level” for a binned or smooth surface plot and “density” (2D kernel density estimates).

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 period = "2 month". See function timeAverage for further details on this. This option se useful as one method by which the number of points plotted is reduced i.e. by choosing a longer averaging time.

data.thresh

The data capture threshold to use ( the data using 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".

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 ( using the "percentile" option - see below. Not used if avg.time = "default".

percentile

The percentile level in % used when statistic = "percentile" and when aggregating the data with avg.time. The default is 95. Not used if avg.time = "default".

type

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.

smooth

A smooth line is fitted to the data if TRUE; optionally with 95% confidence intervals shown. For method = "level" a smooth surface will be fitted to binned data.

spline

A smooth spline is fitted to the data if TRUE. This is particularly useful when there are fewer data points or when a connection line between a sequence of points is required.

linear

A linear model is fitted to the data if TRUE; optionally with 95% confidence intervals shown. The equation of the line and R2 value is also shown.

ci

Should the confidence intervals for the smooth/linear fit be shown?

mod.line

If TRUE three lines are added to the scatter plot to help inform model evaluation. The 1:1 line is solid and the 1:0.5 and 1:2 lines are dashed. Together these lines help show how close a group of points are to a 1:1 relationship and also show the points that are within a factor of two (FAC2). mod.line is appropriately transformed when x or y axes are on a log scale.

cols

Colours to be used for plotting. Options include “default”, “increment”, “heat”, “jet” and 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")

plot.type

lattice plot type. Can be “p” (points --- default), “l” (lines) or “b” (lines and points).

key

Should a key be drawn? The default is TRUE.

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 columns to be less than the number of pollutants.

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 TRUE.

log.x

Should the x-axis appear on a log scale? The default is FALSE. If TRUE a well-formatted log10 scale is used. This can be useful for checking linearity once logged.

log.y

Should the y-axis appear on a log scale? The default is FALSE. If TRUE a well-formatted log10 scale is used. This can be useful for checking linearity once logged.

x.inc

The x-interval to be used for binning data when method = "level".

y.inc

The y-interval to be used for binning data when method = "level".

limits

For method = "level" the function does its best to choose sensible limits automatically. However, there are circumstances when the user will wish to set different ones. The limits are set in the form c(lower, upper), so limits = c(0, 100) would force the plot limits to span 0-100.

windflow

This option allows a scatter plot to show the wind speed/direction shows as an arrow. The option is a list e.g. 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.

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 for details.

ref.y

A list with details of the horizontal lines to be added representing reference line(s). For example, 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.

k

Smoothing parameter supplied to gam for fitting a smooth surface when method = "level".

dist

When plotting smooth surfaces (method = "level" and smooth = TRUE, dist controls how far from the original data the predictions should be made. See exclude.too.far from the mgcv package. Data are first transformed to a unit square. Values should be between 0 and 1.

map

Should a base map be drawn? This option is under development.

auto.text

Either 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.

...

Other graphical parameters are passed onto cutData and an appropriate lattice plot function (xyplot, levelplot or hexbinplot depending on method). For example, scatterPlot passes the option hemisphere = "southern" on to cutData to provide southern (rather than default northern) hemisphere handling of type = "season". Similarly, for the default case method = "scatter" common axis and title labelling options (such as xlab, ylab, main) are passed to xyplot via quickText to handle routine formatting. Other common graphical parameters, e.g. layout for panel arrangement, pch for plot symbol and lwd and lty for line width and type, as also available (see examples below).

For method = "hexbin" it can be useful to transform the scale if it is dominated by a few very high values. This is possible by supplying two functions: one that that applies the transformation and the other that inverses it. For log scaling (the default) for example, trans = function(x) log(x) and inv = function(x) exp(x). For a square root transform use trans = sqrt and inv = function(x) x^2. To not carry out any transformation the options trans = NULL and inv = NULL should be used.

Value

As well as generating the plot itself, scatterPlot 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 <- scatterPlot(mydata, "nox", "no2"), 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.

Details

The 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.

See also

linearRelation, timePlot and timeAverage for details on selecting averaging times and other statistics in a flexible way

Examples

# load openair data if not loaded already data(mydata) # basic use, single pollutant scatterPlot(mydata, x = "nox", y = "no2")
# scatterPlot by year scatterPlot(mydata, x = "nox", y = "no2", type = "year")
# scatterPlot by day of the week, removing key at bottom scatterPlot(mydata, 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 { dat2004 <- selectByDate(mydata, year = 2004) 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) # }