R: Flexible scatter plots

scatterPlot {openair} | R Documentation

## Flexible scatter plots

### Description

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.

### Usage

    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.

### 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.

### 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`.

### Author(s)

David Carslaw

### 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
    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)

[Package openair version 2.8-5 Index]