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Plot time series values in a conventional calendar format

Source: R/calendarPlot.R
calendarPlot.Rd

This function will plot data by month laid out in a conventional calendar format. The main purpose is to help rapidly visualise potentially complex data in a familiar way. Users can also choose to show daily mean wind vectors if wind speed and direction are available.

Usage

calendarPlot(
  mydata,
  pollutant = "nox",
  year = NULL,
  month = NULL,
  type = "month",
  statistic = "mean",
  data.thresh = 0,
  percentile = NA,
  annotate = "date",
  windflow = NULL,
  cols = "heat",
  limits = NULL,
  lim = NULL,
  col.lim = c("grey30", "black"),
  col.na = "white",
  font.lim = c(1, 2),
  cex.lim = c(0.6, 0.9),
  cex.date = 0.6,
  digits = 0,
  labels = NULL,
  breaks = NULL,
  w.shift = 0,
  w.abbr.len = 1,
  remove.empty = TRUE,
  show.year = TRUE,
  key.title = paste(statistic, pollutant, sep = " "),
  key.position = "right",
  auto.text = TRUE,
  plot = TRUE,
  key = NULL,
  ...
)

Arguments

mydata

A data frame of time series. Must include a date field and at least one variable to plot.

pollutant

Mandatory. A pollutant name corresponding to a variable in a data frame should be supplied e.g. pollutant = "nox".

year

Year to plot e.g. year = 2003. If not supplied and mydata contains more than one year, the first year of the data will be automatically selected. Manually setting year to NULL will use all available years.

month

If only certain month are required. By default the function will plot an entire year even if months are missing. To only plot certain months use the month option where month is a numeric 1:12 e.g. month = c(1, 12) to only plot January and December.

type

type determines how the data are split, i.e., conditioned, and then plotted. Only one type can be used with this function, as one faceting 'direction' is reserved by the month of the year. If a single type is given, it will form the "rows" of the resulting grid. Alternatively, c(type, "month") can be used can be specified for type to be used as the "columns" instead.

type = "year" is a special case for calendarPlot() and will automatically prevent a single year from being selected (unless specified using the year argument) and set show.year to FALSE.

statistic

Statistic passed to timeAverage(). Note that if statistic %in% c("max", "min") and annotate is "ws" or "wd", the hour corresponding to the maximum/minimum concentration of polluant is used to provide the associated ws or wd and not the maximum/minimum daily ws or wd.

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.

percentile

The percentile level in percent used when statistic = "percentile" and when aggregating the data with avg.time. More than one percentile level is allowed for type = "default" e.g. percentile = c(50, 95). Not used if avg.time = "default".

annotate

This option controls what appears on each day of the calendar. Can be:

  • "date" — shows day of the month

  • "value" — shows the daily mean value

  • "none" — shows no label

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.

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.

limits

Use this option to manually set the colour scale limits. This is useful in the case when there is a need for two or more plots and a consistent scale is needed on each. Set the limits to cover the maximum range of the data for all plots of interest. For example, if one plot had data covering 0–60 and another 0–100, then set limits = c(0, 100). Note that data will be ignored if outside the limits range.

lim

A threshold value to help differentiate values above and below lim. It is used when annotate = "value". See next few options for control over the labels used.

col.lim

For the annotation of concentration labels on each day. The first sets the colour of the text below lim and the second sets the colour of the text above lim.

col.na

Colour to be used to show missing data.

font.lim

For the annotation of concentration labels on each day. The first sets the font of the text below lim and the second sets the font of the text above lim. Note that font = 1 is normal text and font = 2 is bold text.

cex.lim

For the annotation of concentration labels on each day. The first sets the size of the text below lim and the second sets the size of the text above lim.

cex.date

The base size of the annotation text for the date.

digits

The number of digits used to display concentration values when annotate = "value".

breaks, labels

If a categorical colour scale is required, breaks should be specified. This can be either of:

  • A single value, which will divide the scale into breaks levels using the same logic as cutData(). For example, breaks = 5 will split the scale into five quantiles.

  • A numeric vector, which will define the specific breakpoints. For example, c(0, 50, 100) will bin the data into 0 to 50, 50 to 100, and so on. If breaks does not cover the full range of the data, the outer limits will be extended so that the full colour scale is covered while retaining the desired number of breaks.

By default, breaks will generate nicely formatted labels for each category. The labels argument overrides this - for example, a user could define breaks = 3, labels = c("low", "medium", "high"). Care should be taken to provide the appropriate number of labels - it should be equal to breaks if a single value is given, or equal to length(breaks)-1 if breaks is a vector.

w.shift

Controls the order of the days of the week. By default the plot shows Saturday first (w.shift = 0). To change this so that it starts on a Monday for example, set w.shift = 2, and so on.

w.abbr.len

The default (1) abbreviates the days of the week to a single letter (e.g., in English, S/S/M/T/W/T/F). w.abbr.len defines the number of letters to abbreviate until. For example, w.abbr.len = 3 will abbreviate "Monday" to "Mon".

remove.empty

Should months with no data present be removed? Default is TRUE.

show.year

If only a single year is being plotted, should the calendar labels include the year label? TRUE creates labels like "January-2000", FALSE labels just as "January". If multiple years of data are detected, this option is forced to be TRUE.

key.title

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

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.

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, tag, xlab and ylab control the plot title, subtitle, caption, tag, x-axis label and y-axis label, passed to ggplot2::labs() via 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.

  • scales can be "fixed", "free_x", "free_y" or "free" to control whether axes are shared across facets when using type. Also supported are the legacy x.relation and y.relation, which can be either "same" or "free" and get remapped to scales automatically.

  • Similarly, space, axes, axis.labels, switch and strip.position can be used to customise the appearance of faceted plots. See ggplot2::facet_wrap() and ggplot2::facet_grid() for the arguments these take.

  • 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.

  • lineend, linejoin and linemitre tweak the appearance of line plots; see ggplot2::geom_line() for more information.

  • In polar coordinate plots, annotate = FALSE will remove the N/E/S/W labels and any other annotations.

Value

an openair object

Details

calendarPlot() will plot data in a conventional calendar format, i.e., by month and day of the week. Daily statistics are calculated using timeAverage(), which by default will calculate the daily mean concentration.

If wind direction is available it is then possible to plot the wind direction vector on each day. This is very useful for getting a feel for the meteorological conditions that affect pollutant concentrations. Note that if hourly or higher time resolution are supplied, then calendarPlot() will calculate daily averages using timeAverage(), which ensures that wind directions are vector-averaged.

If wind speed is also available, then setting the option annotate = "ws" will plot the wind vectors whose length is scaled to the wind speed. Thus information on the daily mean wind speed and direction are available.

It is also possible to plot categorical scales. This is useful where, for example, an air quality index defines concentrations as bands, e.g., "good", "poor". In these cases users must supply labels and corresponding breaks.

Note that is is possible to pre-calculate concentrations in some way before passing the data to calendarPlot(). For example rollingMean() could be used to calculate rolling 8-hour mean concentrations. The data can then be passed to calendarPlot() and statistic = "max" chosen, which will plot maximum daily 8-hour mean concentrations.

See also

Other time series and trend functions: TheilSen(), smoothTrend(), timePlot(), timeProp(), timeVariation()

Author

David Carslaw

Examples

# basic plot
calendarPlot(mydata, pollutant = "o3", year = 2003)


# show wind vectors
calendarPlot(mydata, pollutant = "o3", year = 2003, annotate = "wd")
#> Warning: ! `annotate` in `openair::calendarPlot()` no longer supports `'ws'` or `'wd'`.
#> ℹ Please use the `windflow` argument instead for more thorough control over the
#>   apperance of the 'windflow' arrow.
#> ℹ Setting `windflow` to TRUE.

if (FALSE) { # \dontrun{
# show wind vectors scaled by wind speed and different colours
calendarPlot(mydata,
  pollutant = "o3", year = 2003, annotate = "ws",
  cols = "heat"
)

# show only specific months with selectByDate
calendarPlot(selectByDate(mydata, month = c(3, 6, 10), year = 2003),
  pollutant = "o3", year = 2003, annotate = "ws", cols = "heat"
)

# categorical scale example
calendarPlot(mydata,
  pollutant = "no2", breaks = c(0, 50, 100, 150, 1000),
  labels = c("Very low", "Low", "High", "Very High"),
  cols = c("lightblue", "green", "yellow", "red"), statistic = "max"
)

# UK daily air quality index
pm10.breaks <- c(0, 17, 34, 50, 59, 67, 75, 84, 92, 100, 1000)
calendarPlot(
  mydata,
  "pm10",
  year = 1999,
  breaks = pm10.breaks,
  labels = c(1:10),
  cols = "daqi",
  statistic = "mean",
  key.title = "PM10 DAQI"
)
} # }