Merge changes from v6.3.6-dev

Author: StuartWheater

DESCRIPTION

@@ -5,7 +5,7 @@ Description: Base 'DataSHIELD' functions for the client side. 'DataSHIELD' is a
     you to do non-disclosive federated analysis on sensitive data. 'DataSHIELD' analytic functions have
     been designed to only share non disclosive summary statistics, with built in automated output
     checking based on statistical disclosure control. With data sites setting the threshold values for
-    the automated output checks. For more details, see citation("dsBaseClient").
+    the automated output checks. For more details, see citation('dsBaseClient').
 Authors@R: c(person(given = "Paul",
                     family = "Burton",
                     role = c("aut"),

NAMESPACE

@@ -117,6 +117,7 @@ export(ds.unList)
 export(ds.unique)
 export(ds.var)
 export(ds.vectorCalc)
+export(subsetHelper)
 import(DSI)
 import(data.table)
 importFrom(stats,as.formula)

R/ds.boxPlot.R

@@ -21,7 +21,7 @@
 #'   ## Version 6, for version 5 see the Wiki
 #'   
 #'   ### Please ensure you have a training Virtual Machine running,
-#'    or that you have a live connection to a server.
+#'   # or that you have a live connection to a server.
 #'      
 #'   # Connecting to the Opal servers
 #' 
@@ -48,37 +48,37 @@
 #'    symbol = "D") 
 #'   
 #'   ## Create a boxplot of one variable
-#'   ds.boxPlot("D", "LAB_HDL", datasources = connections) 
+#'   ds.boxPlot("D", "LAB_HDL", datasources = connections)
 #'  
 #'   ## Create a boxplot that is split by study:
-#'   ds.boxPlot("D", "LAB_HDL", type= "split", datasources = connections) 
+#'   ds.boxPlot("D", "LAB_HDL", type= "split", datasources = connections)
 #'   
 #'   ## Create a boxplot of two variables variable
-#'   ds.boxPlot("D", c("LAB_HDL", "LAB_TRIG", type="pooled", 
-#'   datasources = connections)  
+#'   ds.boxPlot("D", c("LAB_HDL", "LAB_TRIG"), type="pooled",
+#'   datasources = connections)
 #'   # only one plot is created (of the aggregated results of all servers)
 #'   
 #'   ## Create a boxplot of two variables, which are split by a factor
-#'   ds.boxPlot("D", c("LAB_HDL", "LAB_TRIG"), group = "GENDER", 
+#'   ds.boxPlot("D", c("LAB_HDL", "LAB_TRIG"), group = "GENDER",
 #'   datasources = connections)
 #'   
 #'   ## Create a boxplot with x- and y-axis labels
-#'   ds.boxPlot("D", c("LAB_HDL", "LAB_TRIG"), group = "GENDER", 
+#'   ds.boxPlot("D", c("LAB_HDL", "LAB_TRIG"), group = "GENDER",
 #'   xlabel = "Variable", ylabel = "Measurement", datasources = connections)
 #'   
 #'   ## Improve the presentation of ds.boxplot output using ggplot:
 #'   ### User must save the output, which is in a ggplot format already:
-#'   a <- ds.boxPlot("D", c("LAB_HDL", "LAB_TRIG"), group = "GENDER", 
+#'   a <- ds.boxPlot("D", c("LAB_HDL", "LAB_TRIG"), group = "GENDER",
 #'   xlabel = "Variable", ylabel = "Measurement", datasources = connections)
 #'                  
 #'   ### Then customise output "a" using ggplot tools:
 #'   a + ggplot2::scale_fill_discrete(name = "Gender", labels = c("Male", "Female"))
 #'   
 #'   ### Or use an alternative way, to maintain the aesthetics:
-#'   a + ggplot2::scale_fill_brewer(name = "Gender", labels = c("Male", "Female")) 
+#'   a + ggplot2::scale_fill_brewer(name = "Gender", labels = c("Male", "Female"))
 #'                                                                                 
 #'   # Clear the Datashield R sessions and logout                 
-#'   datashield.logout(connections) 
+#'   datashield.logout(connections)
 #'   
 #' }   
 #'

R/ds.densityGrid.R

@@ -66,11 +66,11 @@
 #'   # Example2: generate a grid density object for each study separately
 #'   ds.densityGrid(x="D$LAB_TSC",
 #'                  y="D$LAB_HDL",
-#'                  type="split"
+#'                  type="split",
 #'                  datasources = connections[1])#only the first Opal server is used ("study1")
 #'
 #'   # Example3: generate a grid density object where the number of intervals is set to 15, for
-#'               each study separately
+#'   #           each study separately
 #'   ds.densityGrid(x="D$LAB_TSC",
 #'                  y="D$LAB_HDL",
 #'                  type="split",

R/ds.heatmapPlot.R

@@ -133,7 +133,7 @@
 #'                  datasources = connections[1]) #only the first server is used (study1)
 #'                  
 #'   # Example 3: Plot a combined heat map plot using the method 'deterministic' centroids of each 
-#'   k = 7 nearest neighbours for numints = 40
+#'   # k = 7 nearest neighbours for numints = 40
 #'   ds.heatmapPlot(x = 'D$LAB_TSC',
 #'                  y = 'D$LAB_HDL', 
 #'                  numints = 40, 

R/ds.histogram.R

@@ -120,7 +120,7 @@
 #'               datasources = connections) #all studies are used
 #'
 #'   # Example 2: generate a combined histogram with the default small cells counts
-#'                suppression rule
+#'   #             suppression rule
 #'   ds.histogram(x = 'D$PM_BMI_CONTINUOUS',
 #'                method = 'smallCellsRule',
 #'                type = 'combine',

R/ds.matrixDiag.R

@@ -112,7 +112,7 @@
 #'                 datasources = connections)
 #'                 
 #'   #Example 2: Create a square matrix with the server-side scalar as all diagonal values 
-#'   and all the other values = 0
+#'   #and all the other values = 0
 #'   
 #'   #Create a scalar in the server-side
 #'   
@@ -152,7 +152,7 @@
 #'                datasources = connections)
 #'                    
 #'  #Example 4: Create a square matrix with the client-side vector as a diagonal
-#'  and all the other values = 0
+#'  #and all the other values = 0
 #'  
 #'  ds.matrixDiag(x1 = c(2,6,9,10),
 #'                aim = "clientside.vector.2.matrix",
@@ -161,7 +161,7 @@
 #'                datasources = connections)
 #'                
 #'  #Example 5: Create a square matrix with the client-side scalar as all diagonal values 
-#'  and all the other values = 0
+#'  #and all the other values = 0
 #'  
 #'  ds.matrixDiag(x1 = 4,
 #'                aim = "clientside.scalar.2.matrix",

R/ds.recodeValues.R

@@ -69,7 +69,7 @@
 #'                   values2replace.vector = c(1,2), 
 #'                   new.values.vector = c(1,2),
 #'                   missing = 99, 
-#'                   newobj = 'bmi_recoded'
+#'                   newobj = 'bmi_recoded',
 #'                   datasources = connections)
 #'                  
 #'   # Clear the Datashield R sessions and logout                 

R/ds.table.R

@@ -1539,6 +1539,12 @@ if(num.table.dims==1)
 
 }#END second dim=1 loop
 
+#################################################
+# Setup on.exit() to restore options 'warn' value
+#################################################
+
+old_warn_option <- base::getOption("warn")
+on.exit(base::options(warn = old_warn_option), add = TRUE)
 
 ################################
 #NOW UNDERTAKE CHISQUARED TESTS#

R/ds.table2D.R

@@ -56,7 +56,7 @@
 #'   # Example 2: generate a two dimensional table, outputting study specific contingency tables
 #'   ds.table2D(x='D$DIS_DIAB', y='D$GENDER', type='split')
 #'   # display the 5 results items, one at a time to avoid having too much information displayed
-#'   at the same time
+#'   # at the same time
 #'   output$counts
 #'   output$rowPercent
 #'   output$colPercent
@@ -112,6 +112,13 @@ ds.table2D <- function(x=NULL, y=NULL, type='both', warningMessage=TRUE, datasou
     stop("Function argument 'type' has to be either 'combine', 'split' or 'both'")
   }
 
+  #################################################
+  # Setup on.exit() to restore options 'warn' value
+  #################################################
+
+  old_warn_option <- base::getOption("warn")
+  on.exit(base::options(warn = old_warn_option), add = TRUE)
+
   # the input variable might be given as column table (i.e. D$x)
   # or just as a vector not attached to a table (i.e. x)
   # we have to make sure the function deals with each case