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Time series plot with categories shown as a stacked bar chart

trend_prop.Rd

This function shows time series plots as stacked bar charts. The different categories in the bar chart are made up from a character or factor variable in a data frame. The function is primarily developed to support the plotting of cluster analysis output from polar_cluster() and openair::trajCluster() that consider local and regional (back trajectory) cluster analysis respectively. However, the function has more general use for understanding time series data.

Usage

trend_prop(
  data,
  pollutant,
  proportion,
  avg_time = "month",
  facet = "default",
  normalise = FALSE
)

Arguments

data

A data frame containing the fields date, pollutant and a splitting variable proportion

pollutant

Name of the pollutant to plot contained in mydata.

proportion

The splitting variable that makes up the bars in the bar chart e.g. proportion = "cluster" if the output from polarCluster is being analysed. If proportion is a numeric variable it is split into 4 quantiles (by default) by cutData. If proportion is a factor or character variable then the categories are used directly.

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".

Note that avg_time when used in timeProp should be greater than the time gap in the original data. For example, avg_time = "day" for hourly data is OK, but avg_time = "hour" for daily data is not.

facet

facet 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, facet = "season" will produce four plots --- one for each season.

It is also possible to choose facet 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 facet 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.

facet must be of length one.

normalise

If normalise = TRUE then each time interval is scaled to 100. This is helpful to show the relative (percentage) contribution of the proportions.

Value

A ggplot2::ggplot2 figure

Details

In order to plot time series in this way, some sort of time aggregation is needed, which is controlled by the option avg_time.

The plot shows the value of pollutant on the y-axis (averaged according to avg_time). The time intervals are made up of bars split according to proportion. The bars therefore show how the total value of pollutant is made up for any time interval.

See also

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

Other cluster analysis functions: polar_cluster()