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

Description Usage Arguments Details Value Author(s) Examples

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

1 2	ds.heatmapPlot(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 heatmap plot displayed and if `type` is set to 'split', each heatmap 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 heatmap plot

Isaeva, J.; Gaye, A.

{

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

  # login and assign the required variables to R
  myvar <- list("LAB_TSC","LAB_HDL")
  opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)

  # Example1: generate a combined (i.e. pooled heatmap plot)
  ds.heatmapPlot(x='D$LAB_TSC', y='D$LAB_HDL')
  ds.heatmapPlot(x='D$LAB_TSC', y='D$LAB_HDL', show='zoomed')

  # Example2: generate a heatmapplot where each study is plotted seaparately
  ds.heatmapPlot(x='D$LAB_TSC', y='D$LAB_HDL', type='split')
  ds.heatmapPlot(x='D$LAB_TSC', y='D$LAB_HDL', type='split', show='zoomed')

  # Example3: generate a heatmap plot with a less dense drid
  ds.heatmapPlot(x='D$LAB_TSC', y='D$LAB_HDL', type='split', numints=15)

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

}