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Flexible scatter plots

Source: R/scatterPlot.R
scatterPlot.Rd

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.title = group,
  key.columns = 1,
  key.position = "right",
  log.x = FALSE,
  log.y = FALSE,
  x.inc = NULL,
  y.inc = NULL,
  limits = NULL,
  windflow = NULL,
  ref.x = NULL,
  ref.y = NULL,
  k = NA,
  dist = 0.02,
  auto.text = TRUE,
  plot = TRUE,
  key = NULL,
  ...
)

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, an average of 2 months would be avg.time = "2 month". In addition, avg.time can equal "season", in which case 3-month seasonal values are calculated with spring defined as March, April, May and so on.

Period boundary behaviour: how bin boundaries are determined depends on the type of period:

  • Single-unit periods ("hour", "day", "week", etc.) are floored to the start of the enclosing unit in the data's timezone (e.g. "day" floors to midnight).

  • Multi-unit fixed-length periods ("3 day", "6 hour", "2 week", etc.) use epoch-aligned arithmetic: bin boundaries are fixed multiples of the period length counted from 1970-01-01, so the same calendar dates always fall in the same bin regardless of where the data starts, and bins run continuously across month boundaries without resetting at the start of each month. For example, with avg.time = "3 day" a bin that begins on 29 January will end on 31 January, and the next bin begins on 1 February — the month boundary does not start a new bin.

  • Calendar periods ("month", "quarter", "year") are floored to the start of the enclosing calendar unit, so they correctly respect variable month and year lengths.

Note that avg.time can be less than the time interval of the original series, in which case the series is expanded to the new time interval. This is useful, for example, for calculating a 15-minute time series from an hourly one where an hourly value is repeated for each new 15-minute period. Note that when expanding data in this way it is necessary to ensure that the time interval of the original series is an exact multiple of avg.time e.g. hour to 10 minutes, day to hour. Also, the input time series must have consistent time gaps between successive intervals so that timeAverage() can work out how much 'padding' to apply. To pad-out data in this way choose fill = TRUE.

data.thresh

The data capture threshold to use (%). 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. See also interval, start.date and end.date to see whether it is advisable to set these other options.

statistic

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 — see below. Not used if avg.time = "default".

percentile

The percentile level used when statistic = "percentile". The default is 95%.

type

Character string(s) defining how data should be split/conditioned before plotting. "default" produces a single panel using the entire dataset. Any other options will split the plot into different panels - a roughly square grid of panels if one type is given, or a 2D matrix of panels if two types are given. type is always passed to cutData(), and can therefore be any of:

  • A built-in type defined in cutData() (e.g., "season", "year", "weekday", etc.). For example, type = "season" will split the plot into four panels, one for each season.

  • The name of a numeric column in mydata, which will be split into n.levels quantiles (defaulting to 4).

  • The name of a character or factor column in mydata, which will be used as-is. Commonly this could be a variable like "site" to ensure data from different monitoring sites are handled and presented separately. It could equally be any arbitrary column created by the user (e.g., whether a nearby possible pollutant source is active or not).

Most openair plotting functions can take two type arguments. If two are given, the first is used for the columns and the second for the rows.

smooth

A smooth line is fitted to the data if TRUE; optionally with 95 percent 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 percent 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).

cols

Colours to use for plotting. Can be a pre-set palette (e.g., "turbo", "viridis", "tol", "Dark2", etc.) or a user-defined vector of R colours (e.g., c("yellow", "green", "blue", "black") - see colours() for a full list) or hex-codes (e.g., c("#30123B", "#9CF649", "#7A0403")). Alternatively, can be a list of arguments to control the colour palette more closely (e.g., palette, direction, alpha, etc.). See openColours() and colourOpts() for more details.

plot.type

Type of plot: “p” (points, default), “l” (lines) or “b” (both points and lines).

key.title

Used to set the title of the legend. The legend title is passed to quickText() if auto.text = TRUE.

key.columns

Number of columns to be used in a categorical legend. With many categories a single column can make to key too wide. The user can thus choose to use several columns by setting key.columns to be less than the number of categories.

key.position

Location where the legend is to be placed. Allowed arguments include "top", "right", "bottom", "left" and "none", the last of which removes the legend entirely.

log.x, log.y

Should the x-axis/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, y.inc

The x/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

If TRUE, the vector-averaged wind speed and direction will be plotted using arrows. Alternatively, can be a list of arguments to control the appearance of the arrows (colour, linewidth, alpha value, etc.). See windflowOpts() for details.

ref.x

Either a single value or values representing the x axis intercepts to draw lines, or a list such as that provided by refOpts() to customise the colour/width/type/etc. of each line. See refOpts() for more details.

ref.y

Either a single value or values representing the y axis intercepts to draw lines, or a list such as that provided by refOpts() to customise the colour/width/type/etc. of each line. See refOpts() for more details.

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.

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". Passed to quickText().

plot

When openair plots are created they are automatically printed to the active graphics device. plot = FALSE deactivates this behaviour. This may be useful when the plot data is of more interest, or the plot is required to appear later (e.g., later in a Quarto document, or to be saved to a file).

key

Deprecated; please use key.position. If FALSE, sets key.position to "none".

...

Addition options are passed on to cutData() for type handling. Some additional arguments are also available, varying somewhat in different plotting functions:

  • title, subtitle, caption, xlab and ylab control the plot title, subtitle, caption, x-axis label and y-axis label. All of these are passed through to quickText() if auto.text = TRUE.

  • xlim, ylim and limits control the limits of the x-axis, y-axis and colorbar scales.

  • ncol and nrow set the number of columns and rows in a faceted plot.

  • fontsize overrides the overall font size of the plot by setting the text argument of ggplot2::theme(). It may also be applied proportionately to any openair annotations (e.g., N/E/S/W labels on polar coordinate plots).

  • Various graphical parameters are also supported: linewidth, linetype, shape, size, border, and alpha. Not all parameters apply to all plots. These can take a single value, or a vector of multiple values - e.g., shape = c(1, 2) - which will be recycled to the length of values needed.

  • For method = "hexbin" a log-scale fill is applied by default; pass trans = NULL to disable or provide custom trans and inv transform functions. bins controls the number of bins.

  • date.format controls the format of date-time x-axes.

Value

an openair object

Details

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.

See also

timePlot() and timeAverage() for details on selecting averaging times and other statistics in a flexible way

Author

David Carslaw

Examples

# load openair data if not loaded already
dat2004 <- selectByDate(mydata, year = 2004)

# basic use, single pollutant

scatterPlot(dat2004, x = "nox", y = "no2")
#> Warning: Removed 20 rows containing missing values or values outside the scale range
#> (`geom_point()`).

if (FALSE) { # \dontrun{
# scatterPlot by year
scatterPlot(mydata, x = "nox", y = "no2", type = "year")
} # }

# scatterPlot by day of the week, removing key at bottom
scatterPlot(dat2004,
  x = "nox", y = "no2", type = "weekday", key =
    FALSE
)
#> Warning: The `key` argument is deprecated. Please use `key.position = "none"` to remove
#> a legend.
#> Warning: Removed 20 rows containing missing values or values outside the scale range
#> (`geom_point()`).


# 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 (shape = 16)
# select only 2004
if (FALSE) { # \dontrun{

scatterPlot(dat2004, x = "nox", y = "no2", z = "co", avg.time = "day", shape = 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
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
scatterPlot(mydata,
  x = "nox", y = "no2", z = "o3", method = "level",
  x.inc = 10, y.inc = 2, smooth = TRUE
)
} # }