Diurnal, day of the week and monthly variation

Plots the diurnal, day of the week and monthly variation for different variables, typically pollutant concentrations. Four separate plots are produced.

Usage

trend_variation(
  mydata,
  pollutant = "nox",
  local_tz = NULL,
  normalise = FALSE,
  type = "default",
  group = NULL,
  difference = FALSE,
  statistic = "mean",
  conf_int = 0.95,
  b = 100,
  ci = TRUE,
  alpha = 0.3,
  return = "ensemble"
)

Arguments

mydata

A data frame of hourly (or higher temporal resolution data). 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.

local_tz

Should the results be calculated in local time that includes a treatment of daylight savings time (DST)? The default is not to consider DST issues, provided the data were imported without a DST offset. Emissions activity tends to occur at local time e.g. rush hour is at 8 am every day. When the clocks go forward in spring, the emissions are effectively released into the atmosphere typically 1 hour earlier during the summertime i.e. when DST applies. When plotting diurnal profiles, this has the effect of “smearing-out” the concentrations. Sometimes, a useful approach is to express time as local time. This correction tends to produce better-defined diurnal profiles of concentration (or other variables) and allows a better comparison to be made with emissions/activity data. If set to FALSE then GMT is used. Examples of usage include local.tz = "Europe/London", local.tz = "America/New_York". See cutData and import for more details.

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.

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.

Only one type is allowed intimeVariation.

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. See examples below.

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\ the 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 timeVariation with the option group = "split.by" and difference = TRUE.

statistic

Can be “mean” (default) or “median”. If the statistic is ‘mean’ then the mean line and the 95\ 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.

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. conf.int can be of length 2, which is most useful for showing quantiles. For example conf.int = c(0.75, 0.99) will yield a plot showing the median, 25/75 and 5/95th quantiles.

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\ interval calculations by much.

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.

alpha

The alpha transparency used for plotting confidence intervals. 0 is fully transparent and 1 is opaque. The default is 0.4

return

What should the function return? One of: * "ensemble" --- all four time variation panels assembled as a patchwork object (default). * "day_hour", "day", "hour", "month" --- a single time variation panel. * "list" --- a list of the four time variation panels, which may be useful if users wish to assemble them in a different way or with other plots entirely. * "data" --- the raw data used to create the time variation panels.

Details

The variation of pollutant concentrations by hour of the day and day of the week, etc., can reveal many interesting features that relate to source types and meteorology. For traffic sources, there are often important differences in the way vehicles vary by vehicles type, e.g., less heavy vehicles at weekends.

The plots also show the 95\ confidence intervals in the mean are calculated through bootstrap simulations, which will provide more robust estimates of the confidence intervals (particularly when there are relatively few data).

The function can handle multiple pollutants and uses the flexible type option to provide separate panels for each 'type' --- see cutData for more details. It can also accept a group option which is useful if data are stacked. This will work in a similar way to having multiple pollutants in separate columns.

The option difference will calculate the difference in means of two pollutants together with bootstrap estimates of the 95\ in the difference in the mean. 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 as such. Considering differences in this way can provide many useful insights and is particularly useful for model evaluation when information is needed about where a model differs from observations by many different time scales. The manual contains various examples of using difference = TRUE.

Note also that the timeVariation function works well on a subset of data and in conjunction with other plots. For example, a polarPlot may highlight an interesting feature for a particular wind speed/direction range. By filtering for those conditions timeVariation can help determine whether the temporal variation of that feature differs from other features --- and help with source identification.

In addition, timeVariation will work well with other variables if available. Examples include meteorological and traffic flow data.

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.

Usage

Arguments

Details

See also