timePlot: Plot time series

Description

Plot time series quickly, perhaps for multiple pollutants, grouped or in separate panels.

Usage

timePlot(mydata, pollutant = "nox", group = FALSE,
    stack = FALSE, normalise = NULL, avg.time = "default",
    data.thresh = 0, statistic = "mean", percentile = NA,
    date.pad = FALSE, type = "default", cols = "brewer1",
    plot.type = "l", key = TRUE, log = FALSE,
    smooth = FALSE, ci = TRUE, ref.x = NULL, ref.y = NULL,
    key.columns = 1, name.pol = pollutant, date.breaks = 7,
    date.format = NULL, auto.text = 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.

group

If more than one pollutant is chosen, should they all be plotted on the same graph together? The default is FALSE, which means they are plotted in separate panels with their own scaled. If TRUE then they are plotted o

stack

If TRUE the time series will be stacked by year. This option can be useful if there are several years worth of data making it difficult to see much detail when plotted on a single plot.

normalise

Should variables be normalised? The default is is not to normalise the data. normalise can take two values, either "mean" or a string representing a date in UK format e.g. "1/1/1998" (in the format dd/mm/YYYY). If

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 o

data.thresh

The data capture threshold to use (%) when aggregating the data using avg.time. A value of zero means that all available data will be used in a particular period regardless if of the number of values available. Conversely, a value

statistic

The statistic to apply when aggregating the data; default is the mean. Can be one of "mean", "max", "min", "median", "frequency", "sd", "percentile". Note that "sd" is the standard deviation and "frequency" is the number (frequency) of valid r

percentile

The percentile level in % used when statistic = "percentile" and when aggregating the data with avg.time. More than one percentile level is allowed for type = "default" e.g. percentile = c(50, 95)

date.pad

Should missing data be padded-out? This
  is useful where a data frame consists of two or more
  "chunks" of data with time gaps between them. By setting
  date.pad = TRUE the time gaps between the chunks
  are shown properly, rather than

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", "

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 sup

plot.type

The lattice plot type, which is a
  line (plot.type = "l") by default. Another useful
  option is plot.type = "h", which draws vertical
  lines.

key

Should a key be drawn? The default is
  TRUE.

log

Should the y-axis appear on a log scale? The
  default is FALSE. If TRUE a well-formatted
  log10 scale is used. This can be useful for plotting data
  for several different pollutants that exist on very
  different scales. I

smooth

Should a smooth line be applied to the
  data? The default is FALSE.

ci

If a smooth fit line is applied, then ci
  determines whether the 95% confidence intervals aer
  shown.

ref.x

Add a vertical dashed reference line at this
  value.

ref.y

Add a horizontal dashed reference line at
  this value.

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.

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 descript

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. Th

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 ex

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.

...

Other graphical parameters are passed onto
  cutData and lattice:xyplot. For example,
  timePlot passes the option hemisphere =
  "southern" on to cutData to provide southern
  (rather t

`Value`

As well as generating the plot itself, timePlot
  also returns an object of class ``openair''. The object
  includes three main components: call, the command
  used to generate the plot; data, the data frame of
  summarised information used to make the plot; and
  plot, the plot itself. If retained, e.g. using
  output <- timePlot(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 summary. See openair.generics
  for further details.

`Details`

The timePlot is the basic time series plotting
  function in openair. Its purpose is to make it
  quick and easy to plot time series for pollutants and
  other variables. The other purpose is to plot potentially
  many variables together in as compact a way as possible.

  The function is flexible enough to plot more than one
  variable at once. If more than one variable is chosen
  plots it can either show all variables on the same plot
  (with different line types) on the same scale, or
  (if group = FALSE) each variable in its own panels
  with its own scale.

  The general preference is not to plot two variables on
  the same graph with two different y-scales. It can be
  misleading to do so and difficult with more than two
  variables. If there is in interest in plotting several
  variables together that have very different scales, then
  it can be useful to normalise the data first, which can
  be down be setting the normalise option.

  The user has fine control over the choice of colours,
  line width and line types used. This is useful for
  example, to emphasise a particular variable with a
  specific line type/colour/width.

  timePlot works very well with
  selectByDate, which is used for selecting
  particular date ranges quickly and easily. See examples
  below.

  By default plots are shown with a colour key at the
  bottom and in teh case of multiple pollutants or sites,
  strips on the left of each plot. Sometimes this may be
  overkill and the user can opt to remove the key and/or
  the strip by setting key and/or strip to
  FALSE. One reason to do this is to maximise the
  plotting area and therefore the information shown.

`See Also`

MannKendall, smoothTrend,
  linearRelation, selectByDate
  and timeAverage for details on selecting
  averaging times and other statistics in a flexible way

`Examples`

Run this code# basic use, single pollutant
timePlot(mydata, pollutant = "nox")

# two pollutants in separate panels
timePlot(mydata, pollutant = c("nox", "no2"))

# two pollutants in the same panel with the same scale
timePlot(mydata, pollutant = c("nox", "no2"), group = TRUE)

# alternative by normalising concentrations and plotting on the same
  scale
timePlot(mydata, pollutant = c("nox", "co", "pm10", "so2"), group = TRUE, avg.time =
  "year", normalise = "1/1/1998", lwd = 3, lty = 1)

# examples of selecting by date

# plot for nox in 1999
timePlot(selectByDate(mydata, year = 1999), pollutant = "nox")

# select specific date range for two pollutants
timePlot(selectByDate(mydata, start = "6/8/2003", end = "13/8/2003"),
pollutant = c("no2", "o3"))

# choose different line styles etc
timePlot(mydata, pollutant = c("nox", "no2"), lty = 1)

# choose different line styles etc
timePlot(selectByDate(mydata, year = 2004, month = 6), pollutant =
c("nox", "no2"), lwd = c(1, 2), col = "black")

# different averaging times

#daily mean O3
timePlot(mydata, pollutant = "o3", avg.time = "day")

# daily mean O3 ensuring each day has data capture of at least 75\%
timePlot(mydata, pollutant = "o3", avg.time = "day", data.thresh = 75)

# 2-week average of O3 concentrations
timePlot(mydata, pollutant = "o3", avg.time = "2 week")
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