timeVariation: Diurnal, day of the week and monthly variation

Description

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

Usage

timeVariation(mydata,
pollutant = "nox",
local.time = FALSE,
normalise = FALSE,
ylab = pollutant,
xlab = c("hour", "hour", "month", "weekday"),
name.pol = pollutant,
type = "default",
group = NULL,
ci = TRUE,
cols = "hue",
main = "",
key = NULL,
key.columns = 1,
auto.text = TRUE,
alpha = 0.4,...)

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.time

Should the results be calculated in local time? The default is FALSE. 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 rele

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.

ylab

Name of y-axis variable. By default will use the name of pollutant.

xlab

x-axis label; one for each sub-plot.

name.pol

Names to be given to the pollutant(s). This is useful if you want to give a fuller description of the variables, maybe also including subscripts etc.

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"

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.

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 be used for plotting. Options include "default", "increment", "heat", "spectral", "hue" (default) and user defined (see manual for more details).

main

The plot title; default is no title.

key

By default timeVariation produces four plots on one page. While it is useful to see these plots together, it is sometimes necessary just to use one for a report. If key is TRUE, a key is added to all plots a

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.

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.

alpha

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

...

Other graphical parameters.

Value

As well as generating the plot itself, timeVariation also returns an object of class ``openair''. The object includes three main components: call, the command used to generate the plot; data, the data used to make the four components of the plot (or subplots); and plot, the associated subplots. If retained, e.g. using output <- timeVariation(mydata, "nox"), this output can be used to recover the data, reproduce or rework the original plot or undertake further analysis. An openair output can be manipulated using a number of generic operations, including print, plot and summarise. See openair.generics for further details. The four components of timeVariation are: day.hour, hour, day and month. Associated data.frames can be extracted directly using the $data operator, e.g. as output$data$day.hour, output$data$hour, output$data$day and output$data$month, respectively for output <- timeVariation(mydata, "nox")

Details

The variation of pollutant concentrations by hour of the day and day of the week can reveal many interesting features that relate to source types. For traffic sources, there are often important differences in the way vehicles vary by vehicles type e.g. less heavy vehicles at weekends. The timeVariation function makes it easy to see how concentrations (and many other variable types) vary by hour of the day and day of the week. The plots also show the 95% confidence intervals in the mean, which is particularly useful for comparing two different pollutants whose concentrations have been normalised. 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.

Examples

Run this code

# basic use
timeVariation(mydata, pollutant = "nox")

# for a subset of conditions
timeVariation(subset(mydata, ws > 3 & wd > 100 & wd < 270),
pollutant = "pm10", ylab = "pm10 (ug/m3)")

# multiple pollutants with concentrations normalised
timeVariation(mydata, pollutant = c("nox", "co"), normalise = TRUE)

# show BST/GMT variation (see ?cutData for more details)
# the NOx plot shows the profiles are very similar when expressed in
# local time, showing that the profile is dominated by a local source
# that varies by local time and not by GMT i.e. road vehicle emissions

timeVariation(mydata, pollutant = "nox", type = "gmtbst")

## In this case it is better to group the results for clarity:
timeVariation(mydata, pollutant = "nox", group = "gmtbst")

# By contrast, a variable such as wind speed shows a clear shift when
#  expressed in local time. These two plots can help show whether the
#  variation is dominated by man-made influences or natural processes

timeVariation(mydata, pollutant = "ws", group = "gmtbst")

## It is also possible to plot several variables and set type. For
## example, consider the NOx and NO2 split by levels of O3:

timeVariation(mydata, pollutant = c("nox", "no2"), type = "o3", normalise = TRUE)

## sub plots can be extracted from the openair object
results <- timeVariation(mydata, pollutant = "no2")
plot(results, subset = "day.hour") # top weekday and plot

## plot(results, subset="hour") is the diurnal plot
## plot(results, subset="day") is the weekday plot
## plot(results, subset="month") is the monthly plot

## numerical results (mean, lower/upper uncertainties)
## results$data$day.hour are the weekday and hour results
## results$data$hour are the diurnal results
## results$data$day are the weekday results
## results$data$month are the monthly results