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

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, windflow = NULL, smooth = FALSE, ci = TRUE,
y.relation = "same", ref.x = NULL, ref.y = NULL, key.columns = 1,
key.position = "bottom", 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. 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. 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 on the same plot with the same scale. 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. 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 normalise = "mean" then each time series is divided by its mean value. If a date is chosen, then values at that date are set to 100 and the rest of the data scaled accordingly. Choosing a date (say at the beginning of a time series) is very useful for showing how trends diverge over time. Setting group = TRUE is often useful too to show all time series together in one panel. 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". See function timeAverage for further details on this. 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 of 100 will mean that all data will need to be present for the average to be calculated, else it is recorded as NA. Not used if avg.time = "default". 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 records in the period. “percentile” is the percentile level (%) between 0-100, which can be set using the “percentile” option - see below. Not used if avg.time = "default". 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). Not used if avg.time = "default". 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 with a line connecting each chunk. For irregular data, set to FALSE. Note, this should not be set for type other than default. 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 currently allowed in timePlot. 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") The lattice plot type, which is a line (plot.type = "l") by default. Another useful option is plot.type = "h", which draws vertical lines. Should a key be drawn? The default is TRUE. 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. It is therefore useful to use log = TRUE together with group = TRUE. This option allows a scatter plot to show the wind speed/direction as an arrow. The option is a list e.g. windflow = list(col = "grey", lwd = 2, scale = 0.1). This option requires wind speed (ws) and wind direction (wd) to be available. The maximum length of the arrow plotted is a fraction of the plot dimension with the longest arrow being scale of the plot x-y dimension. Note, if the plot size is adjusted manually by the user it should be re-plotted to ensure the correct wind angle. The list may contain other options to panel.arrows in the lattice package. Other useful options include length, which controls the length of the arrow head and angle, which controls the angle of the arrow head. This option works best where there are not too many data to ensure over-plotting does not become a problem. Should a smooth line be applied to the data? The default is FALSE. If a smooth fit line is applied, then ci determines whether the 95% confidence intervals are shown. This determines how the y-axis scale is plotted. "same" ensures all panels use the same scale and "free" will use panel-specific scales. The latter is a useful setting when plotting data with very different values. See ref.y for details. In this case the correct date format should be used for a vertical line e.g. ref.x = list(v = as.POSIXct("2000-06-15"), lty = 5). A list with details of the horizontal lines to be added representing reference line(s). For example, ref.y = list(h = 50, lty = 5) will add a dashed horizontal line at 50. Several lines can be plotted e.g. ref.y = list(h = c(50, 100), lty = c(1, 5), col = c("green", "blue")). See panel.abline in the lattice package for more details on adding/controlling lines. 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. Location where the scale key is to plotted. Can include “top”, “bottom”, “right” and “left”. 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"). 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. 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". 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 than default northern) hemisphere handling of type = "season". Similarly, most common plotting parameters, such as layout for panel arrangement and pch and cex for plot symbol type and size and lty and lwd for line type and width, as passed to xyplot, although some maybe locally managed by openair on route, e.g. axis and title labelling options (such as xlab, ylab, main) are passed via quickText to handle routine formatting. See examples below.

## 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.

## 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 the 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.

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

## Examples



# basic use, single pollutant
timePlot(mydata, pollutant = "nox")
# two pollutants in separate panels
# NOT RUN {
timePlot(mydata, pollutant = c("nox", "no2"))
# }
# two pollutants in the same panel with the same scale
# NOT RUN {
timePlot(mydata, pollutant = c("nox", "no2"), group = TRUE)
# }
# alternative by normalising concentrations and plotting on the same
scale#> function (x, center = TRUE, scale = TRUE)
#> UseMethod("scale")
#> <bytecode: 0x000000002df38040>
#> <environment: namespace:base># NOT RUN {
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
# NOT RUN {
timePlot(selectByDate(mydata, year = 1999), pollutant = "nox")
# }
# select specific date range for two pollutants
# NOT RUN {
timePlot(selectByDate(mydata, start = "6/8/2003", end = "13/8/2003"),
pollutant = c("no2", "o3"))
# }
# choose different line styles etc
# NOT RUN {
timePlot(mydata, pollutant = c("nox", "no2"), lty = 1)
# }
# choose different line styles etc
# NOT RUN {
timePlot(selectByDate(mydata, year = 2004, month = 6), pollutant =
c("nox", "no2"), lwd = c(1, 2), col = "black")
# }
# different averaging times

#daily mean O3
# NOT RUN {
timePlot(mydata, pollutant = "o3", avg.time = "day")
# }
# daily mean O3 ensuring each day has data capture of at least 75%
# NOT RUN {
timePlot(mydata, pollutant = "o3", avg.time = "day", data.thresh = 75)
# }
# 2-week average of O3 concentrations
# NOT RUN {
timePlot(mydata, pollutant = "o3", avg.time = "2 week")
# }