ds.table1D.RdThe function ds.table1D is a client-side wrapper function. It calls the server-side function table1DDS to generate 1-dimensional tables for all data sources.
ds.table1D(
x = NULL,
type = "combine",
warningMessage = TRUE,
datasources = NULL
)a character, the name of a numerical vector with discrete values - usually a factor.
a character which represent the type of table to ouput: pooled table or one table for each
data source. If type is set to 'combine', a pooled 1-dimensional table is returned; if If type
is set to 'split' a 1-dimensional table is returned for each data source.
a boolean, if set to TRUE (deafult) a warning is displayed if any returned table is invalid. Warning messages are suppressed if this parameter is set to FALSE. However the analyst can still view 'validity' information which are stored in the output object 'validity' - see the list of output objects.
a list of DSConnection-class objects obtained after login. If the <datasources>
the default set of connections will be used: see datashield.connections_default.
A list object containing the following items:
table(s) that hold counts for each level/category. If some cells counts are invalid (see 'Details' section) only the total (outer) cell counts are displayed in the returned individual study tables or in the pooled table.
table(s) that hold percentages for each level/category. Here also inner cells are reported as missing if one or more cells are 'invalid'.
a text that informs the analyst about the validity of the output tables. If any tables are invalid the studies they are originated from are also mentioned in the text message.
The table returned by the server side function might be valid (non disclosive - no table cell have counts between 1 and the minimal number agreed by the data owner and set in the data repository) or invalid (potentially disclosive - one or more table cells have a count between 1 and the minimal number agreed by the data owner). If a 1-dimensional table is invalid all the cells are set to NA except the total count. This way it is possible the know the total count and combine total counts across data sources but it is not possible to identify the cell(s) that had the small counts which render the table invalid.
ds.table2D for cross-tabulating two vectors.
if (FALSE) { # \dontrun{
# load the file that contains the login details
data(logindata)
# login and assign all the stored variables to R
conns <- datashield.login(logins=logindata,assign=TRUE)
# Example 1: generate a one dimensional table, outputting combined (pooled) contingency tables
output <- ds.table1D(x='D$GENDER')
output$counts
output$percentages
output$validity
# Example 2: generate a one dimensional table, outputting study specific contingency tables
output <- ds.table1D(x='D$GENDER', type='split')
output$counts
output$percentages
output$validity
# Example 3: generate a one dimensional table, outputting study specific and combined
# contingency tables - see what happens if the reruened table is 'invalid'.
output <- ds.table1D(x='D$DIS_CVA')
output$counts
output$percentages
output$validity
# clear the Datashield R sessions and logout
datashield.logout(conns)
} # }