variationPlot: Variation Plot

In openair: Tools for the Analysis of Air Pollution Data

Variation Plot

Description

The variationPlot() function is designed to explore how the distribution of a pollutant (or other variable) changes by another variable (x). For example, it can be used to explore how the distribution of nox varies by season or by weekday. This plot can be extensively conditioned using the type and group arguments, both of which are passed to cutData(). An appropriate plot type will be chosen based on the type of x - e.g., ordered variables will be joined by a line.

Usage

variationPlot(
  mydata,
  pollutant = "nox",
  x = "hour",
  statistic = "mean",
  type = "default",
  group = "default",
  normalise = FALSE,
  difference = FALSE,
  conf.int = NULL,
  B = 100,
  local.tz = NULL,
  ci = TRUE,
  cols = "hue",
  alpha = 0.4,
  strip.position = "top",
  key.position = "top",
  key.columns = NULL,
  name.pol = NULL,
  auto.text = TRUE,
  plot = TRUE,
  ...
)

Arguments

mydata	A data frame of time series. Must include a date field and at least one variable to plot.
pollutant	Name of variable to plot. Two or more pollutants can be plotted, in which case a form like pollutant = c("nox", "co") should be used.
x	A character value to be passed to cutData(); used to define the category by which pollutant will be varied and plotted.
statistic	Can be "mean" (default) or "median". If the statistic is "mean" then the mean line and the 95% confidence interval in the mean are plotted by default. If the statistic is "median" then the median line is plotted together with the 5/95 and 25/75th quantiles are plotted. Users can control the confidence intervals with conf.int.
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.
group	This sets the grouping variable to be used. For example, if a data frame had a column site setting group = "site" will plot all sites together in each panel. Passed to cutData().
normalise	Should variables be normalised? The default is FALSE. If TRUE then the variable(s) are divided by their mean values. This helps to compare the shape of the diurnal trends for variables on very different scales.
difference	If two pollutants are chosen then setting difference = TRUE will also plot the difference in means between the two variables as pollutant[2] - pollutant[1]. Bootstrap 95\ difference in means are also calculated. A horizontal dashed line is shown at y = 0. The difference can also be calculated if there is a column that identifies two groups, e.g., having used splitByDate(). In this case it is possible to call the function with the option group = "split.by" and difference = TRUE.
conf.int	The confidence intervals to be plotted. If statistic = "mean" then the confidence intervals in the mean are plotted. If statistic = "median" then the conf.int and 1 - conf.int quantiles are plotted. Any number of conf.ints can be provided.
B	Number of bootstrap replicates to use. Can be useful to reduce this value when there are a large number of observations available to increase the speed of the calculations without affecting the 95% confidence interval calculations by much.
local.tz	Used for identifying whether a date has daylight savings time (DST) applied or not. Examples include local.tz = "Europe/London", local.tz = "America/New_York", i.e., time zones that assume DST. https://en.wikipedia.org/wiki/List_of_zoneinfo_time_zones shows time zones that should be valid for most systems. It is important that the original data are in GMT (UTC) or a fixed offset from GMT.
ci	Should confidence intervals be shown? The default is TRUE. Setting this to FALSE can be useful if multiple pollutants are chosen where over-lapping confidence intervals can over complicate plots.
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")). See openColours() for more details.
alpha	The alpha transparency used for plotting confidence intervals. 0 is fully transparent and 1 is opaque. The default is 0.4.
strip.position	Location where the facet 'strips' are located when using type. When one type is provided, can be one of "left", "right", "bottom" or "top". When two types are provided, this argument defines whether the strips are "switched" and can take either "x", "y", or "both". For example, "x" will switch the 'top' strip locations to the bottom of the plot.
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.
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.
name.pol	This option can be used to give alternative names for the variables plotted. Instead of taking the column headings as names, the user can supply replacements. For example, if a column had the name "nox" and the user wanted a different description, then setting name.pol = "nox before change" can be used. If more than one pollutant is plotted then use c e.g. name.pol = c("nox here", "o3 there").
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).
...	Addition options are passed on to cutData() for type handling. Some additional arguments are also available: xlab, ylab and main override the x-axis label, y-axis label, and plot title. layout sets the layout of facets - e.g., layout(2, 5) will have 2 columns and 5 rows. lwd and lty control various graphical parameters. fontsize overrides the overall font size of the plot. ylim controls axis limits.

Details

When statistic = "mean", the plot shows the 95% confidence intervals in the mean. The 95% confidence intervals are calculated through bootstrap simulations, which will provide more robust estimates of the confidence intervals (particularly when there are relatively few data).

Users can supply their own ylim, e.g. ylim = c(0, 200).

The difference option calculates the difference in means between two pollutants, along with bootstrap estimates of the 95\ in the difference. This works in two ways: either two pollutants are supplied in separate columns (e.g. pollutant = c("no2", "o3")), or there are two unique values of group. The difference is calculated as the second pollutant minus the first and is labelled accordingly. This feature is particularly useful for model evaluation and identifying where models diverge from observations across time scales.

Depending on the choice of statistic, a subheading is added. Users can control the text in the subheading through the use of sub e.g. sub = "" will remove any subheading.

Value

an openair object.

Author(s)

Jack Davison

David Carslaw

See Also

timeVariation(), which conveniently assembles many time-related variation plots into a single plot

Examples

# example using the 'mydata' dataset
variationPlot(
  mydata,
  pollutant = c("nox", "o3"),
  x = "hour",
  type = "season",
  normalise = TRUE
)

openair documentation built on April 2, 2026, 9:07 a.m.