R: Time series plot with categories shown as a stacked bar chart

timeProp {openair} | R Documentation

## Time series plot with categories shown as a stacked bar chart

### Description

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 `polarCluster` and `trajCluster` that consider local and regional (back trajectory) cluster analysis respectively. However, the function has more general use for understanding time series data.

### Usage

    timeProp(
      mydata,
      pollutant = "nox",
      proportion = "cluster",
      avg.time = "day",
      type = "default",
      statistic = "mean",
      normalise = FALSE,
      cols = "Set1",
      date.breaks = 7,
      date.format = NULL,
      key.columns = 1,
      key.position = "right",
      key.title = proportion,
      auto.text = TRUE,
      ...
    )

### Arguments

`mydata` | 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"`. 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. 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.
`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` must be of length one.
`statistic` | Determines how the bars are calculated. The default (“mean”) will provide the contribution to the overall mean for a time interval. `statistic = "frequency"` will give the proportion in terms of counts.
`normalise` | If `normalise = TRUE` then each time interval is scaled to 100. This is helpful to show the relative (percentage) contribution of the proportions.
`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")`
`date.breaks` | Number of major x-axis intervals to use. The function will try and choose a sensible number of dates/times as well as formatting the date/time appropriately to the range being considered. This does not always work as desired automatically. The user can therefore increase or decrease the number of intervals by adjusting the value of `date.breaks` up or down.
`date.format` | This option controls the date format on the x-axis. While `timePlot` generally sets the date format sensibly there can be some situations where the user wishes to have more control. For format types see `strptime`. For example, to format the date like “Jan-2012” set `date.format = "%b-%Y"`.
`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”.
`key.title` | The title of the key.
`auto.text` | Either `TRUE` (default) or `FALSE`. If `TRUE` titles and axis labels etc. will automatically try and format pollutant names and units properly e.g. by subscripting the ‘2’ in NO2.
`...` | Other graphical parameters passed onto `timeProp` and `cutData`. For example, `timeProp` passes the option `hemisphere = "southern"` on to `cutData` to provide southern (rather than default northern) hemisphere handling of `type = "season"`. Similarly, common axis and title labelling options (such as `xlab`, `ylab`, `main`) are passed to `xyplot` via `quickText` to handle routine formatting.

### 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.

### Author(s)

David Carslaw

### See Also

See `timePlot` for time series plotting, `polarCluster` for cluster analysis of bivariate polar plots and `trajCluster` for cluster analysis of HYSPLIT back trajectories.

### Examples

    ## See manual for more examples e.g. related to clustering

    ## monthly plot of SO2 showing the contribution by wind sector
    timeProp(mydata, pollutant = "so2", avg.time = "month", proportion = "wd")

[Package openair version 2.8-5 Index]