Function to to draw and visualise correlation matrices using lattice. The primary purpose is as a tool for exploratory data analysis. Hierarchical clustering is used to group similar variables.

## Usage

```
corPlot(
mydata,
pollutants = NULL,
type = "default",
cluster = TRUE,
method = "pearson",
dendrogram = FALSE,
lower = FALSE,
cols = "default",
r.thresh = 0.8,
text.col = c("black", "black"),
auto.text = TRUE,
...
)
```

## Arguments

- mydata
A data frame which should consist of some numeric columns.

- pollutants
the names of data-series in

`mydata`

to be plotted by`corPlot`

. The default option`NULL`

and the alternative “all” use all available valid (numeric) data.- 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”, “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.- cluster
Should the data be ordered according to cluster analysis. If

`TRUE`

hierarchical clustering is applied to the correlation matrices using`hclust`

to group similar variables together. With many variables clustering can greatly assist interpretation.- method
The correlation method to use. Can be “pearson”, “spearman” or “kendall”.

- dendrogram
Should a dendrogram be plotted? When

`TRUE`

a dendrogram is shown on the right of the plot. Note that this will only work for`type = "default"`

.- lower
Should only the lower triangle be plotted?

- cols
Colours to be used for plotting. Options include “default”, “increment”, “heat”, “spectral”, “hue”, “greyscale” and user defined (see

`openColours`

for more details).- r.thresh
Values of greater than

`r.thresh`

will be shown in bold type. This helps to highlight high correlations.- text.col
The colour of the text used to show the correlation values. The first value controls the colour of negative correlations and the second positive.

- 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 passed onto

`lattice:levelplot`

, with common axis and title labelling options (such as`xlab`

,`ylab`

,`main`

) being passed via`quickText`

to handle routine formatting.

## Value

As well as generating the plot itself, `corPlot`

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 <- corPlot(mydata)`

, this output can be used to recover the
data, reproduce or rework the original plot or undertake further analysis.
Note the denogram when `cluster = TRUE`

can aslo be returned and
plotted. See examples.

An openair output can be manipulated using a number of generic operations,
including `print`

, `plot`

and `summary`

.

## Details

The `corPlot`

function plots correlation matrices. The implementation
relies heavily on that shown in Sarkar (2007), with a few extensions.

Correlation matrices are a very effective way of understating relationships
between many variables. The `corPlot`

shows the correlation coded in
three ways: by shape (ellipses), colour and the numeric value. The ellipses
can be thought of as visual representations of scatter plot. With a perfect
positive correlation a line at 45 degrees positive slope is drawn. For zero
correlation the shape becomes a circle. See examples below.

With many different variables it can be difficult to see relationships
between variables i.e. which variables tend to behave most like one another.
For this reason hierarchical clustering is applied to the correlation
matrices to group variables that are most similar to one another (if
`cluster = TRUE`

).

If clustering is chosen it is also possible to add a dendrogram using the
option `dendrogram = TRUE`

. Note that dendrogramscan only be plotted
for `type = "default"`

i.e. when there is only a single panel. The
dendrogram can also be recovered from the plot object itself and plotted
more clearly; see examples below.

It is also possible to use the `openair`

type option to condition the
data in many flexible ways, although this may become difficult to visualise
with too many panels.

## References

Sarkar, D. (2007). Lattice Multivariate Data Visualization with R. New York: Springer.

Friendly, M. (2002). Corrgrams : Exploratory displays for correlation matrices. American Statistician, 2002(4), 1-16. doi:10.1198/000313002533

## Examples

```
# load openair data if not loaded already
data(mydata)
## basic corrgram plot
corPlot(mydata)
## plot by season ... and so on
corPlot(mydata, type = "season")
## recover dendogram when cluster = TRUE and plot it
res <-corPlot(mydata)
plot(res$clust)
if (FALSE) {
## a more interesting are hydrocarbon measurements
hc <- importAURN(site = "my1", year = 2005, hc = TRUE)
## now it is possible to see the hydrocarbons that behave most
## similarly to one another
corPlot(hc)
}
```