ds.densityGrid: Generates a density grid with or without a priori defined...
In datashield/dsGraphicsClient: DataSHIELD client site functions for graphics

Description Usage Arguments Details Value Author(s) Examples

This function generates a grid density object which can then be used to produced a heatmap or contourplots. In cells with a count > 0 and < 5 are considered invalid and the count is set to 0. The function prints the number of invalid cells in for participating study.

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

`x`	a character the name of numerical vector
`y`	a character the name of numerical vector
`numints`	an integer, the number of intervals for the grid density object, by default is 20.
`type`	a character which represent the type of graph to display. If `type` is set to 'combine', a pooled grid density matrix is generated and one grid density matrix is generated for each study if `type` is set to 'split'.
`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.

In DataSHIELD the user does not have access to the micro-data so and extreme values such as the maximum and the minimum are potentially disclosive so this function does not allow for the user to set the limits of the density grid and the minimum and maximum values of the x and y vectors. These elements are set by the server side function densitygrid.ds to 'valid' values (i.e. values that do not lead to leakage of micro-data to the user).

a grid density matrix is returned

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 grid density object (the default behaviour)
  ds.densityGrid(x='D$LAB_TSC', y='D$LAB_HDL')

  # Example2: generate a grid density object for each study separately
  ds.densityGrid(x='D$LAB_TSC', y='D$LAB_HDL', type="split")

  # Example3: generate a grid density object where the number of intervals is set to 15, for each study separately
  ds.densityGrid(x='D$LAB_TSC', y='D$LAB_HDL', type="split", numints=15)

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

}