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

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.

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",
  log = FALSE,
  windflow = NULL,
  smooth = FALSE,
  smooth_k = NULL,
  ci = TRUE,
  ref.x = NULL,
  ref.y = NULL,
  key.columns = NULL,
  key.position = "bottom",
  name.pol = pollutant,
  date.breaks = 7,
  date.format = NULL,
  auto.text = TRUE,
  plot = TRUE,
  key = NULL,
  ...
)

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

Controls how multiple lines/series are grouped. Three options are available:

FALSE (default): each pollutant is plotted in its own panel with its own scale.
TRUE: all pollutants are plotted together on the same panel and scale, coloured by pollutant name.
A character string giving the name of a column in mydata (e.g. group = "site" or group = "pollutant"): lines are coloured by the values in that column. With a single pollutant all groups appear in one panel; with multiple pollutants each pollutant gets its own panel and lines within each panel are coloured by the group column. This is particularly useful for long-format data where multiple species are stored in one column.

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 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.

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, an average of 2 months would be avg.time = "2 month". In addition, avg.time can equal "season", in which case 3-month seasonal values are calculated with spring defined as March, April, May and so on.

Period boundary behaviour: how bin boundaries are determined depends on the type of period:

Single-unit periods ("hour", "day", "week", etc.) are floored to the start of the enclosing unit in the data's timezone (e.g. "day" floors to midnight).
Multi-unit fixed-length periods ("3 day", "6 hour", "2 week", etc.) use epoch-aligned arithmetic: bin boundaries are fixed multiples of the period length counted from 1970-01-01, so the same calendar dates always fall in the same bin regardless of where the data starts, and bins run continuously across month boundaries without resetting at the start of each month. For example, with avg.time = "3 day" a bin that begins on 29 January will end on 31 January, and the next bin begins on 1 February — the month boundary does not start a new bin.
Calendar periods ("month", "quarter", "year") are floored to the start of the enclosing calendar unit, so they correctly respect variable month and year lengths.

Note that avg.time can be less than the time interval of the original series, in which case the series is expanded to the new time interval. This is useful, for example, for calculating a 15-minute time series from an hourly one where an hourly value is repeated for each new 15-minute period. Note that when expanding data in this way it is necessary to ensure that the time interval of the original series is an exact multiple of avg.time e.g. hour to 10 minutes, day to hour. Also, the input time series must have consistent time gaps between successive intervals so that timeAverage() can work out how much 'padding' to apply. To pad-out data in this way choose fill = TRUE.

data.thresh

The data capture threshold to use (%). 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. See also interval, start.date and end.date to see whether it is advisable to set these other options.

statistic

The statistic to apply when aggregating the data; default is the mean. Can be one of "mean", "max", "min", "median", "frequency", "sum", "sd", "percentile". Note that "sd" is the standard deviation, "frequency" is the number (frequency) of valid records in the period and "data.cap" is the percentage data capture. "percentile" is the percentile level (%) between 0-100, which can be set using the "percentile" option — see below. Not used if avg.time = "default".

percentile

The percentile level in percent 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".

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 with a line connecting each chunk. For irregular data, set to FALSE. Note, this should not be set for type other than default.

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.

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")). Alternatively, can be a list of arguments to control the colour palette more closely (e.g., palette, direction, alpha, etc.). See openColours() and colourOpts() for more details.

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. It is therefore useful to use log = TRUE together with group = TRUE.

windflow

If TRUE, the vector-averaged wind speed and direction will be plotted using arrows. Alternatively, can be a list of arguments to control the appearance of the arrows (colour, linewidth, alpha value, etc.). See windflowOpts() for details.

smooth

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

smooth_k

An integer controlling the number of basis functions used in the GAM smooth. In a GAM, k sets the maximum degrees of freedom for the smooth term: larger values allow more flexibility and can capture finer structure in the data, while smaller values produce smoother, less wiggly fits. The default (NULL) lets ggplot2 choose automatically (typically k = 10). Increase k if the smooth appears too rigid; decrease it to avoid over-fitting.

ci

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

ref.x

Either a single value or values representing the x axis intercepts to draw lines, or a list such as that provided by refOpts() to customise the colour/width/type/etc. of each line. See refOpts() for more details.

ref.y

Either a single value or values representing the y axis intercepts to draw lines, or a list such as that provided by refOpts() to customise the colour/width/type/etc. of each line. See refOpts() for more details.

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.

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.

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").

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. The user can override this behaviour by adjusting the value of date.breaks up or down.

date.format

This option controls the date format on the x-axis. A sensible format is chosen by default, but the user can set date.format to override this. For format types see strptime(). For example, to format the date like "Jan-2012" set date.format = "\%b-\%Y".

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).

key

Deprecated; please use key.position. If FALSE, sets key.position to "none".

...

Addition options are passed on to cutData() for type handling. Some additional arguments are also available, varying somewhat in different plotting functions:

title, subtitle, caption, tag, xlab and ylab control the plot title, subtitle, caption, tag, x-axis label and y-axis label, passed to ggplot2::labs() via quickText() if auto.text = TRUE.
xlim, ylim and limits control the limits of the x-axis, y-axis and colorbar scales.
ncol and nrow set the number of columns and rows in a faceted plot.
scales can be "fixed", "free_x", "free_y" or "free" to control whether axes are shared across facets when using type. Also supported are the legacy x.relation and y.relation, which can be either "same" or "free" and get remapped to scales automatically.
Similarly, space, axes, axis.labels, switch and strip.position can be used to customise the appearance of faceted plots. See ggplot2::facet_wrap() and ggplot2::facet_grid() for the arguments these take.
fontsize overrides the overall font size of the plot by setting the text argument of ggplot2::theme(). It may also be applied proportionately to any openair annotations (e.g., N/E/S/W labels on polar coordinate plots).
Various graphical parameters are also supported: linewidth, linetype, shape, size, border, and alpha. Not all parameters apply to all plots. These can take a single value, or a vector of multiple values - e.g., shape = c(1, 2) - which will be recycled to the length of values needed.
lineend, linejoin and linemitre tweak the appearance of line plots; see ggplot2::geom_line() for more information.
In polar coordinate plots, annotate = FALSE will remove the N/E/S/W labels and any other annotations.

Value

an openair object

Details

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.

Author

David Carslaw

Jack Davison

Examples

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


# two pollutants in separate panels
if (FALSE) { # \dontrun{
timePlot(mydata, pollutant = c("nox", "no2"))

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

# group by a column (e.g. long-format data with a 'site' column)
d <- rbind(
  cbind(mydata[, c("date", "nox")], site = "London"),
  cbind(transform(mydata[, c("date", "nox")], nox = nox * 1.5), site = "Manchester")
)
timePlot(d, pollutant = "nox", group = "site")

# 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",
  linewidth = 3,
  linetype = 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"), linetype = 1)

# choose different line styles etc
timePlot(
  selectByDate(mydata, year = 2004, month = 6),
  pollutant = c("nox", "no2"),
  linewidth = 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")
} # }