ds.contourPlot: Generates a contour plot
In datashield/dsGraphicsClient: DataSHIELD client site functions for graphics

Description Usage Arguments Details Value Author(s) Examples

Generates a countour plot of the pooled data or one plot for each dataset.

1 2	ds.contourPlot(x = NULL, y = NULL, type = "combine", show = "all", numints = 20, datasources = NULL)

`x`	a character, the name of a numerical vector.
`y`	a character, the name of a numerical vector.
`type`	a character which represents the type of graph to display. If `type` is set to 'combine', a combined contour plot displayed and if `type` is set to 'split', each conntour is plotted separately.
`show`	a character which represents where the plot should focus. If `show` is set to 'all', the ranges of the variables are used as plot limits. If `show` is set to 'zoomed', the plot is zoomed to the region where the actual data are.
`numints`	a number of intervals for a density grid object.
`datasources`	a list of opal object(s) obtained after login in to opal servers; these objects hold also the data assign to R, as `dataframe`, from opal datasources.

The function first generates a density grid and uses it to plot the graph. Cells of the grid density matrix that hold a count of less than the filter set by DataSHIELD (usually 5) are considered invalid and turned into 0 to avoid potential disclosure. A message is printed to inform the user about the number of invalid cells. The ranges returned by each study and used in the process of getting the grid density matrix are not the exact minumum and maximum values but rather close approximates of the real minimum and maximum value. This was done to reduce the risk of potential disclosure.

a contour plot

Isaeva, J.; Gaye, A.; Burton, P.

{

  # load the file that contains the login details
  data(logindata)

  # login and assign specific variables(s)
  # (by default the assigned dataset is a dataframe named 'D')
  myvar <- list("LAB_TSC","LAB_HDL")
  opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)

  # Example 1: generate a contour plot of the pooled data (default)
  ds.contourPlot(x='D$LAB_TSC', y='D$LAB_HDL')
  # now produce the same plot but zoom in
  ds.contourPlot(x='D$LAB_TSC', y='D$LAB_HDL', show='zoomed')

  # Example 2: generate a contour plot where each study is plotted seaparately
  ds.contourPlot(x='D$LAB_TSC', y='D$LAB_HDL', type='split')
  # now produce the same plots but zoom in
  ds.contourPlot(x='D$LAB_TSC', y='D$LAB_HDL', type='split', show='zoomed')

  # Example 3: generate a contour plot with a less dense grid (default numints is 20)
  ds.contourPlot(x='D$LAB_TSC', y='D$LAB_HDL', type='split', numints=15)

  # clear the Datashield R sessions and logout
  datashield.logout(opals)

}