This function provides a quick graphical and numerical summary of data. The location presence/absence of data are shown, with summary statistics and plots of variable distributions. summaryPlot can also provide summaries of a single pollutant across many sites.

summaryPlot(mydata, na.len = 24, clip = TRUE, percentile = 0.99,
  type = "histogram", pollutant = "nox", period = "years",
  avg.time = "day", print.datacap = TRUE, breaks = NULL,
  col.trend = "darkgoldenrod2", col.data = "lightblue",
  col.mis = rgb(0.65, 0.04, 0.07), col.hist = "forestgreen",
  cols = NULL, date.breaks = 7, auto.text = TRUE, ...)

Arguments

mydata

A data frame to be summarised. Must contain a date field and at least one other parameter.

na.len

Missing data are only shown with at least na.len contiguous missing vales. The purpose of setting na.len is for clarity: with long time series it is difficult to see where individual missing hours are. Furthermore, setting na.len = 96, for example would show where there are at least 4 days of continuous missing data.

clip

When data contain outliers, the histogram or density plot can fail to show the distribution of the main body of data. Setting clip = TRUE, will remove the top 1 yield what is often a better display of the overall distribution of the data. The amount of clipping can be set with percentile.

percentile

This is used to clip the data. For example, percentile = 0.99 (the default) will remove the top 1 percentile of values i.e. values greater than the 99th percentile will not be used.

type

type is used to determine whether a histogram (the default) or a density plot is used to show the distribution of the data.

pollutant

pollutant is used when there is a field site and there is more than one site in the data frame.

period

period is either years (the default) or months. Statistics are calculated depending on the period chosen.

avg.time

This defines the time period to average the time series plots. Can be “sec”, “min”, “hour”, “day” (the default), “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 avg.time = "2 month".

print.datacap

Should the data capture % be shown for each period?

breaks

Number of histogram bins. Sometime useful but not easy to set a single value for a range of very different variables.

col.trend

Colour to be used to show the monthly trend of the data, shown as a shaded region. Type colors() into R to see the full range of colour names.

col.data

Colour to be used to show the presence of data. Type colors() into R to see the full range of colour names.

col.mis

Colour to be used to show missing data.

col.hist

Colour for the histogram or density plot.

cols

Predefined colour scheme, currently only enabled for "greyscale".

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. The user can therefore increase or decrease the number of intervals by adjusting the value of date.breaks up or down.

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. Commonly used examples include the axis and title labelling options (such as xlab, ylab and main), which are all passed to the plot via quickText to handle routine formatting. As summaryPlot has two components, the axis labels may be a vector. For example, the default case (type = "histogram") sets y labels equivalent to ylab = c("", "Percent of Total").

Details

summaryPlot produces two panels of plots: one showing the presence/absence of data and the other the distributions. The left panel shows time series and codes the presence or absence of data in different colours. By stacking the plots one on top of another it is easy to compare different pollutants/variables. Overall statistics are given for each variable: mean, maximum, minimum, missing hours (also expressed as a percentage), median and the 95th percentile. For each year the data capture rate (expressed as a percentage of hours in that year) is also given.

The right panel shows either a histogram or a density plot depending on the choice of type. Density plots avoid the issue of arbitrary bin sizes that can sometimes provide a misleading view of the data distribution. Density plots are often more appropriate, but their effectiveness will depend on the data in question.

summaryPlot will only show data that are numeric or integer type. This is useful for checking that data have been imported properly. For example, if for some reason a column representing wind speed erroneosly had one or more fields with charcters in, the whole column would be either character or factor type. The absence of a wind speed variable in the summaryPlot plot would therefore indicate a problem with the input data. In this particular case, the user should go back to the source data and remove the characters or remove them using R functions.

If there is a field site, which would generally mean there is more than one site, summaryPlot will provide information on a single pollutant across all sites, rather than provide details on all pollutants at a single site. In this case the user should also provide a name of a pollutant e.g. pollutant = "nox". If a pollutant is not provided the first numeric field will automatically be chosen.

It is strongly recommended that the summaryPlot function is applied to all new imported data sets to ensure the data are imported as expected.

Examples

# load example data from package data(mydata) # do not clip density plot data
# NOT RUN { summaryPlot(mydata, clip = FALSE) # }
# exclude highest 5 % of data etc.
# NOT RUN { summaryPlot(mydata, percentile = 0.95) # }
# show missing data where there are at least 96 contiguous missing # values (4 days)
# NOT RUN { summaryPlot(mydata, na.len = 96) # }
# show data in green
# NOT RUN { summaryPlot(mydata, col.data = "green") # }
# show missing data in yellow
# NOT RUN { summaryPlot(mydata, col.mis = "yellow") # }
# show density plot line in black
# NOT RUN { summaryPlot(mydata, col.dens = "black") # }