Skip to contents

Plot time series values in a conventional calendar format

trend_calendar.Rd

With a single year of data, trend_calendar() will plot data in a conventional calendar format, i.e., by month and day of the week. With multiple years of data, a year-month matrix of panels will instead be plotted. Daily statistics are calculated using time_average(), which by default will calculate the daily mean concentration.

Usage

trend_calendar(
  data,
  pollutant,
  statistic = "mean",
  data_thresh = 0,
  border_colour = "white",
  w_shift = 0
)

Arguments

data

A data frame minimally containing date and at least one other numeric variable. The date should be in either Date format or class POSIXct.

pollutant

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

statistic

Statistic passed to time_average().

data_thresh

Data capture threshold passed to time_average(). For example, data_thresh = 75 means that at least 75\ be available in a day for the value to be calculate, else the data is removed.

border_colour

The colour to use for the border of each tile. Defaults to "white". NA removes the border.

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.

Details

trend_calendar() has two accompanying annotation functions. annotate_calendar_text() can write either the day of the month or the average pollutant concentration on the calendar. annotate_calendar_wd() will draw wind speed and direction arrows onto the calendar, assuming columns labelled "ws" and "wd" were present in the original data.

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

See also

annotate_calendar_wd() and annotate_calendar_text() for annotating calendar plots.

Other time series and trend functions: trend_level(), trend_prop(), trend_variation()

Examples

if (FALSE) {
marylebone %>%
  selectByDate(year = 2019) %>%
  trend_calendar("nox") +
  annotate_calendar_text(colour = "white", size = 5, type = "date") +
  annotate_calendar_wd(colour = "black")
}