From a8723e84ecc61def3de0dcc004cc24fcc1293f98 Mon Sep 17 00:00:00 2001
From: agaye <ag13748@it017324.(none)>
Date: Thu, 1 Jan 2015 02:38:08 -0500
Subject: [PATCH] All changes completed.

---
 DESCRIPTION                 |   2 +-
 R/ds.cor.R                  |  65 +++++++++---------------
 R/ds.corTest.R              |  42 ++++++----------
 R/ds.cov.R                  |  60 +++++++++-------------
 R/ds.tTest.R                |  96 +++++++++++++++---------------------
 R/ds.var.R                  |  40 +++++----------
 data/login_remoteServer.rda | Bin 284 -> 290 bytes
 man/ds.cor.Rd               |  36 +++++++-------
 man/ds.corTest.Rd           |  20 ++++----
 man/ds.cov.Rd               |  36 +++++++-------
 man/ds.tTest.Rd             |  79 ++++++++++++++---------------
 man/ds.var.Rd               |  22 ++++-----
 man/findLoginObjects.Rd     |  26 ++++++++++
 man/login_remoteServer.Rd   |  10 ++--
 14 files changed, 243 insertions(+), 291 deletions(-)
 create mode 100644 man/findLoginObjects.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 894d4ad..586383a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: dsStatsClient
 Maintainer: <datashield@obiba.org>
 Author: <datashield@obiba.org>
-Version: 3.0.1
+Version: 4.0.0
 License: GPL-3
 Title: DataSHIELD client site stattistical functions
 Description: DataSHIELD client site stattistical functions
diff --git a/R/ds.cor.R b/R/ds.cor.R
index 56c1c55..852de32 100644
--- a/R/ds.cor.R
+++ b/R/ds.cor.R
@@ -1,5 +1,5 @@
 #' 
-#' @title Computes the correlation between two or more vectors
+#' @title Computes correlation between two or more vectors
 #' @description This is similar to the R base function 'cor'.
 #' @details In addition to computing correlations this function, unlike
 #' the R base function 'cor', produces a table outlining the number of complete cases 
@@ -19,54 +19,39 @@
 #' @export
 #' @examples {
 #' 
-#' # load that contains the login details
-#' data(logindata)
+#'   # load that contains the login details
+#'   data(logindata)
 #' 
-#' # login and assign specific variable(s)
-#' # (by default the assigned dataset is a dataframe named 'D')
-#' myvar <- list('LAB_HDL', 'LAB_TSC', 'GENDER')
-#' opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
+#'   # login and assign specific variable(s)
+#'   # (by default the assigned dataset is a dataframe named 'D')
+#'   myvar <- list('LAB_HDL', 'LAB_TSC', 'GENDER')
+#'   opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
 #' 
-#' # Example 1: generate the correlation matrix for the assigned dataset 'D' 
-#' # which contains 4 vectors (2 continuous and 1 categorical)
-#' ds.cor(x='D')
+#'   # Example 1: generate the correlation matrix for the assigned dataset 'D' 
+#'   # which contains 4 vectors (2 continuous and 1 categorical)
+#'   ds.cor(x='D')
 #' 
-#' # Example 2: calculate the correlation between two vectors (first assign some vectors from the dataframe 'D')
-#' ds.assign(newobj='labhdl', toAssign='D$LAB_HDL')
-#' ds.assign(newobj='labtsc', toAssign='D$LAB_TSC')
-#' ds.assign(newobj='gender', toAssign='D$GENDER')
-#' ds.cor(x='labhdl', y='labtsc')
-#' ds.cor(x='labhdl', y='gender')
+#'   # Example 2: calculate the correlation between two vectors (first assign some vectors from the dataframe 'D')
+#'   ds.assign(newobj='labhdl', toAssign='D$LAB_HDL')
+#'   ds.assign(newobj='labtsc', toAssign='D$LAB_TSC')
+#'   ds.assign(newobj='gender', toAssign='D$GENDER')
+#'   ds.cor(x='labhdl', y='labtsc')
+#'   ds.cor(x='labhdl', y='gender')
 #' 
-#' # clear the Datashield R sessions and logout
-#' datashield.logout(opals)
+#'   # clear the Datashield R sessions and logout
+#'   datashield.logout(opals)
 #' 
 #' }
 #' 
-ds.cor = function(x=NULL, y=NULL, naAction='pairwise.complete.obs', datasources=NULL)
-{
+ds.cor = function(x=NULL, y=NULL, naAction='pairwise.complete.obs', datasources=NULL){
   
-  # if no opal login details were provided look for 'opal' objects in the environment
+  # if no opal login details are provided look for 'opal' objects in the environment
   if(is.null(datasources)){
-    findLogin <- getOpals()
-    if(findLogin$flag == 1){
-      datasources <- findLogin$opals
-    }else{
-      if(findLogin$flag == 0){
-        stop(" Are yout logged in to any server? Please provide a valid opal login object! ", call.=FALSE)
-      }else{
-        message(paste0("More than one list of opal login object were found: '", paste(findLogin$opals,collapse="', '"), "'!"))
-        userInput <- readline("Please enter the name of the login object you want to use: ")
-        datasources <- eval(parse(text=userInput))
-        if(class(datasources[[1]]) != 'opal'){
-          stop("End of process: you failed to enter a valid login object", call.=FALSE)
-        }
-      }
-    }
+    datasources <- findLoginObjects()
   }
     
   if(is.null(x)){
-    stop("x=NULL. Please provide the names of two numeric vectors!", call.=FALSE)
+    stop("x must be the name of a numeric vector or a table structure (matrix or data frame)", call.=FALSE)
   }else{
     defined <- isDefined(datasources, x)
   }
@@ -76,9 +61,7 @@ ds.cor = function(x=NULL, y=NULL, naAction='pairwise.complete.obs', datasources=
   
   if(typ=='numeric' | typ=='integer' | typ=='factor'){
     if(is.null(y)){
-      message(" ALERT!")
-      message(" y is set to NULL whilst x is a numeric vector, please provide a second vector")
-      stop(" End of process!", call.=FALSE)
+      stop("If x is a numeric vector, y must be a numeric vector!", call.=FALSE)
     }else{
       defined2 <- isDefined(datasources, y)
       typ2 <- checkClass(datasources, y)
@@ -87,7 +70,7 @@ ds.cor = function(x=NULL, y=NULL, naAction='pairwise.complete.obs', datasources=
   
   if(typ=='matrix' | typ=='data.frame' & !(is.null(y))){
     y <- NULL
-    warning(" x is a matrix or a dataframe; y will be ignored and a correlation matrix computed for x!")
+    warning("x is a matrix or a data frame; y will be ignored and a correlation matrix computed for x!")
   }
   
   # name of the studies to be used in the output
diff --git a/R/ds.corTest.R b/R/ds.corTest.R
index 02977fc..7f982e9 100644
--- a/R/ds.corTest.R
+++ b/R/ds.corTest.R
@@ -11,48 +11,34 @@
 #' @export
 #' @examples {
 #' 
-#' # load that contains the login details
-#' data(logindata)
+#'   # load that contains the login details
+#'   data(logindata)
 #' 
-#' # login and assign specific variable(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)
+#'   # login and assign specific variable(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)
 #' 
-#' # test for correlation between the variables 'LAB_TSC' and 'LAB_HDL'
-#' ds.corTest(x='D$LAB_TSC', y='D$LAB_HDL')
+#'   # test for correlation between the variables 'LAB_TSC' and 'LAB_HDL'
+#'   ds.corTest(x='D$LAB_TSC', y='D$LAB_HDL')
 #' 
-#' # clear the Datashield R sessions and logout
-#' datashield.logout(opals)
+#'   # clear the Datashield R sessions and logout
+#'   datashield.logout(opals)
 #' 
 #' }
 #' 
 ds.corTest = function(x=NULL, y=NULL, datasources=NULL){
   
-  # if no opal login details were provided look for 'opal' objects in the environment
+  # if no opal login details are provided look for 'opal' objects in the environment
   if(is.null(datasources)){
-    findLogin <- getOpals()
-    if(findLogin$flag == 1){
-      datasources <- findLogin$opals
-    }else{
-      if(findLogin$flag == 0){
-        stop(" Are yout logged in to any server? Please provide a valid opal login object! ", call.=FALSE)
-      }else{
-        message(paste0("More than one list of opal login object were found: '", paste(findLogin$opals,collapse="', '"), "'!"))
-        userInput <- readline("Please enter the name of the login object you want to use: ")
-        datasources <- eval(parse(text=userInput))
-        if(class(datasources[[1]]) != 'opal'){
-          stop("End of process: you failed to enter a valid login object", call.=FALSE)
-        }
-      }
-    }
+    datasources <- findLoginObjects()
   }
   
   if(is.null(x)){
-    stop("x=NULL. Please provide the names of two numeric vectors!", call.=FALSE)
+    stop("x=NULL. Please provide the names of the 1st vector!", call.=FALSE)
   }
   if(is.null(y)){
-    stop("y=NULL. Please provide the names of two numeric vectors!", call.=FALSE)
+    stop("y=NULL. Please provide the names of the 2nd numeric vector!", call.=FALSE)
   }
   
   # the input variable might be given as column table (i.e. D$object)
diff --git a/R/ds.cov.R b/R/ds.cov.R
index 16cc8c3..15605b1 100644
--- a/R/ds.cov.R
+++ b/R/ds.cov.R
@@ -19,50 +19,36 @@
 #' @export
 #' @examples {
 #' 
-#' # load that contains the login details
-#' data(logindata)
+#'   # load that contains the login details
+#'   data(logindata)
 #' 
-#' # login and assign specific variable(s)
-#' # (by default the assigned dataset is a dataframe named 'D')
-#' myvar <- list('LAB_HDL', 'LAB_TSC', 'GENDER')
-#' opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
+#'   # login and assign specific variable(s)
+#'   # (by default the assigned dataset is a dataframe named 'D')
+#'   myvar <- list('LAB_HDL', 'LAB_TSC', 'GENDER')
+#'   opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
 #' 
-#' # Example 1: generate the covariance matrix for the assigned dataset 'D' 
-#' # which contains 4 vectors (2 continuous and 1 categorical)
-#' ds.cov(x='D')
+#'   # Example 1: generate the covariance matrix for the assigned dataset 'D' 
+#'   # which contains 4 vectors (2 continuous and 1 categorical)
+#'   ds.cov(x='D')
 #' 
-#' # Example 2: calculate the covariance between two vectors 
-#' # (first assign the vectors from 'D')
-#' ds.assign(newobj='labhdl', toAssign='D$LAB_HDL')
-#' ds.assign(newobj='labtsc', toAssign='D$LAB_TSC')
-#' ds.assign(newobj='gender', toAssign='D$GENDER')
-#' ds.cov(x='labhdl', y='labtsc')
-#' ds.cov(x='labhdl', y='gender')
+#'   # Example 2: calculate the covariance between two vectors 
+#'   # (first assign the vectors from 'D')
+#'   ds.assign(newobj='labhdl', toAssign='D$LAB_HDL')
+#'   ds.assign(newobj='labtsc', toAssign='D$LAB_TSC')
+#'   ds.assign(newobj='gender', toAssign='D$GENDER')
+#'   ds.cov(x='labhdl', y='labtsc')
+#'   ds.cov(x='labhdl', y='gender')
 #' 
-#' # clear the Datashield R sessions and logout
-#' datashield.logout(opals)
+#'   # clear the Datashield R sessions and logout
+#'   datashield.logout(opals)
 #' 
 #' }
 #' 
 ds.cov = function(x=NULL, y=NULL, naAction='pairwise.complete.obs', datasources=NULL){
   
-  # if no opal login details were provided look for 'opal' objects in the environment
+  # if no opal login details are provided look for 'opal' objects in the environment
   if(is.null(datasources)){
-    findLogin <- getOpals()
-    if(findLogin$flag == 1){
-      datasources <- findLogin$opals
-    }else{
-      if(findLogin$flag == 0){
-        stop(" Are yout logged in to any server? Please provide a valid opal login object! ", call.=FALSE)
-      }else{
-        message(paste0("More than one list of opal login object were found: '", paste(findLogin$opals,collapse="', '"), "'!"))
-        userInput <- readline("Please enter the name of the login object you want to use: ")
-        datasources <- eval(parse(text=userInput))
-        if(class(datasources[[1]]) != 'opal'){
-          stop("End of process: you failed to enter a valid login object", call.=FALSE)
-        }
-      }
-    }
+    datasources <- findLoginObjects()
   }
   
   if(is.null(x)){
@@ -76,9 +62,7 @@ ds.cov = function(x=NULL, y=NULL, naAction='pairwise.complete.obs', datasources=
   
   if(typ=='numeric' | typ=='integer' | typ=='factor'){
     if(is.null(y)){
-      message(" ALERT!")
-      message(" y is set to NULL whilst x is a numeric vector, please provide a second vector")
-      stop(" End of process!", call.=FALSE)
+      stop("If x is a numeric vector, y must be a numeric vector!", call.=FALSE)
     }else{
       defined2 <- isDefined(datasources, y)
       typ2 <- checkClass(datasources, y)
@@ -87,7 +71,7 @@ ds.cov = function(x=NULL, y=NULL, naAction='pairwise.complete.obs', datasources=
   
   if(typ=='matrix' | typ=='data.frame' & !(is.null(y))){
     y <- NULL
-    warning(" x is a matrix or a dataframe; y will be ignored and a covariance matrix computed for x!")
+    warning("x is a matrix or a dataframe; y will be ignored and a covariance matrix computed for x!")
   }
   
   # name of the studies to be used in the output
diff --git a/R/ds.tTest.R b/R/ds.tTest.R
index 51aa989..aebc174 100644
--- a/R/ds.tTest.R
+++ b/R/ds.tTest.R
@@ -21,80 +21,67 @@
 #' @param datasources a list of opal object(s) obtained after login in to opal servers;
 #' these objects hold also the data assign to R, as \code{dataframe}, from opal datasources.
 #' @return a list containing the following elements:
-#' \code{statistic} the value of the t-statistic 
-#' \code{parameter} the degrees of freedom for the t-statistic 
-#' \code{p.value} p.value the p-value for the test 
-#' \code{conf.int} a confidence interval for the mean appropriate to the specified alternative hypothesis 
-#' \code{estimate} the estimated mean or difference in means depending on whether it was a one-sample test or a two-sample test 
-#' \code{null.value} the specified hypothesized value of the mean or mean difference depending on whether it was a one-sample test or a two-sample test 
+#' \code{statistic} the value of the t-statistic. 
+#' \code{parameter} the degrees of freedom for the t-statistic. 
+#' \code{p.value} p.value the p-value for the test. 
+#' \code{conf.int} a confidence interval for the mean appropriate to the specified alternative hypothesis. 
+#' \code{estimate} the estimated mean or difference in means depending on whether it was a one-sample test 
+#' or a two-sample test. 
+#' \code{null.value} the specified hypothesized value of the mean or mean difference depending on whether it 
+#' was a one-sample test or a two-sample test.
 #' \code{alternative} a character string describing the alternative hypothesis 
 #' \code{method} a character string indicating what type of t-test was performed 
 #' @author Isaeva, J.; Gaye, A.
 #' @export
-#' @examples
-#' {
+#' @examples {
 #' 
-#' # load that contains the login details
-#' data(logindata)
+#'   # load that contains the login details
+#'   data(logindata)
 #' 
-#' # login and assign specific variable(s)
-#' myvar <- list("LAB_HDL", "LAB_TSC")
-#' opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
+#'   # login and assign specific variable(s)
+#'   myvar <- list("LAB_HDL", "LAB_TSC")
+#'   opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
 #' 
-#' # Example 1: Run a t.test of the pooled data for the variables 'LAB_HDL' and 'LAB_TSC' - default
-#' ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC')
+#'   # Example 1: Run a t.test of the pooled data for the variables 'LAB_HDL' and 'LAB_TSC' - default
+#'   ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC')
 #' 
-#' # Example 2: Run a t.test for each study separately for the same variables as above
-#' ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', type='split')
+#'   # Example 2: Run a t.test for each study separately for the same variables as above
+#'   ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', type='split')
 #' 
-#' # Example 3: Run a paired t.test of the pooled data
-#' ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', paired=TRUE)
+#'   # Example 3: Run a paired t.test of the pooled data
+#'   ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', paired=TRUE)
 #' 
-#' # Example 4: Run a paired t.test for each study separately
-#' ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', paired=TRUE, type='split')
+#'   # Example 4: Run a paired t.test for each study separately
+#'   ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', paired=TRUE, type='split')
 #' 
-#' # Example 5: Run a t.test of the pooled data with different alternatives
-#' ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', alternative='greater')
-#' ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', alternative='less')
+#'   # Example 5: Run a t.test of the pooled data with different alternatives
+#'   ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', alternative='greater')
+#'   ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', alternative='less')
 #' 
-#' # Example 6: Run a t.test of the pooled data with mu different from zero
-#' ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', mu=-4)
+#'   # Example 6: Run a t.test of the pooled data with mu different from zero
+#'   ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', mu=-4)
 #' 
-#' # Example 7: Run a t.test of the pooled data assuming that variances of variables are equal
-#' ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', var.equal=TRUE)
+#'   # Example 7: Run a t.test of the pooled data assuming that variances of variables are equal
+#'   ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', var.equal=TRUE)
 #' 
-#' # Example 8: Run a t.test of the pooled data with 90% confidence interval
-#' ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', conf.level=0.90)
+#'   # Example 8: Run a t.test of the pooled data with 90% confidence interval
+#'   ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', conf.level=0.90)
 #' 
-#' # Example 9: Run a one-sample t.test of the pooled data
-#' ds.tTest(x='D$LAB_HDL')
-#' # the below example should not work, paired t.test is not possible if the 'y' variable is missing
-#' # ds.tTest(x='D$LAB_HDL', paired=TRUE) 
+#'   # Example 9: Run a one-sample t.test of the pooled data
+#'   ds.tTest(x='D$LAB_HDL')
+#'   # the below example should not work, paired t.test is not possible if the 'y' variable is missing
+#'   # ds.tTest(x='D$LAB_HDL', paired=TRUE) 
 #' 
-#' # clear the Datashield R sessions and logout
-#' datashield.logout(opals)
+#'   # clear the Datashield R sessions and logout
+#'   datashield.logout(opals)
 #' 
 #'}
 #'
 ds.tTest <- function (x=NULL, y=NULL, type="combine", alternative="two.sided", mu=0, paired=FALSE, var.equal=FALSE, conf.level=0.95, datasources=NULL) {
   
-  # if no opal login details were provided look for 'opal' objects in the environment
+  # if no opal login details are provided look for 'opal' objects in the environment
   if(is.null(datasources)){
-    findLogin <- getOpals()
-    if(findLogin$flag == 1){
-      datasources <- findLogin$opals
-    }else{
-      if(findLogin$flag == 0){
-        stop(" Are yout logged in to any server? Please provide a valid opal login object! ", call.=FALSE)
-      }else{
-        message(paste0("More than one list of opal login object were found: '", paste(findLogin$opals,collapse="', '"), "'!"))
-        userInput <- readline("Please enter the name of the login object you want to use: ")
-        datasources <- eval(parse(text=userInput))
-        if(class(datasources[[1]]) != 'opal'){
-          stop("End of process: you failed to enter a valid login object", call.=FALSE)
-        }
-      }
-    }
+    datasources <- findLoginObjects()
   }
   
   if(is.null(x)){
@@ -187,9 +174,8 @@ ds.tTest <- function (x=NULL, y=NULL, type="combine", alternative="two.sided", m
       cally = paste0("subsetDS('",  x, "', not.na.x)")
       datashield.assign(datasources, 'xok', as.symbol(cally))
       cally = paste0("as.null(",  x, ")")
-      datashield.assign(datasources, 'yok', as.symbol(cally)) # does not matter that as.null(x) since we just want to make y to be NULL
-    }
-    
+      datashield.assign(datasources, 'yok', as.symbol(cally)) # does not matter that as.null(x) since we just want to set y to NULL
+    }    
     
     if (paired) {
       cally = paste0("(yok)","*(",-1,")")
diff --git a/R/ds.var.R b/R/ds.var.R
index 4d8fd83..90c093a 100644
--- a/R/ds.var.R
+++ b/R/ds.var.R
@@ -13,43 +13,29 @@
 #' @export
 #' @examples {
 #' 
-#' # load that contains the login details
-#' data(logindata)
+#'   # load that contains the login details
+#'   data(logindata)
 #' 
-#' # login and assign specific variable(s)
-#' myvar <- list('LAB_TSC')
-#' opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
+#'   # login and assign specific variable(s)
+#'   myvar <- list('LAB_TSC')
+#'   opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
 #' 
-#' # Example 1: compute the pooled variance of the variable 'LAB_TSC' - default behaviour
-#' ds.var(x='D$LAB_TSC')
+#'   # Example 1: compute the pooled variance of the variable 'LAB_TSC' - default behaviour
+#'   ds.var(x='D$LAB_TSC')
 #' 
-#' # Example 2: compute the variance of each study separately
-#' ds.var(x='D$LAB_TSC', type='split')
+#'   # Example 2: compute the variance of each study separately
+#'   ds.var(x='D$LAB_TSC', type='split')
 #' 
-#' # clear the Datashield R sessions and logout
-#' datashield.logout(opals)
+#'   # clear the Datashield R sessions and logout
+#'   datashield.logout(opals)
 #' 
 #' }
 #' 
 ds.var = function(x=NULL, type='combine', datasources=NULL){
   
-  # if no opal login details were provided look for 'opal' objects in the environment
+  # if no opal login details are provided look for 'opal' objects in the environment
   if(is.null(datasources)){
-    findLogin <- getOpals()
-    if(findLogin$flag == 1){
-      datasources <- findLogin$opals
-    }else{
-      if(findLogin$flag == 0){
-        stop(" Are yout logged in to any server? Please provide a valid opal login object! ", call.=FALSE)
-      }else{
-        message(paste0("More than one list of opal login object were found: '", paste(findLogin$opals,collapse="', '"), "'!"))
-        userInput <- readline("Please enter the name of the login object you want to use: ")
-        datasources <- eval(parse(text=userInput))
-        if(class(datasources[[1]]) != 'opal'){
-          stop("End of process: you failed to enter a valid login object", call.=FALSE)
-        }
-      }
-    }
+    datasources <- findLoginObjects()
   }
   
   if(is.null(x)){
diff --git a/data/login_remoteServer.rda b/data/login_remoteServer.rda
index 7720b6a152ddce046e3e71edf338a80a622b9570..721c514cb9661e789165a3b587343b5316007426 100644
GIT binary patch
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zcmV+%0ptE3iwFP!000001C3I#PQx$|y~IgHBLyL`@ejBqQ`Hp~1{h%J>NIYN<iyq4
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diff --git a/man/ds.cor.Rd b/man/ds.cor.Rd
index 4540fd2..9264cb3 100644
--- a/man/ds.cor.Rd
+++ b/man/ds.cor.Rd
@@ -1,6 +1,6 @@
 \name{ds.cor}
 \alias{ds.cor}
-\title{Computes the correlation between two or more vectors}
+\title{Computes correlation between two or more vectors}
 \usage{
 ds.cor(x = NULL, y = NULL, naAction = "pairwise.complete.obs",
   datasources = NULL)
@@ -43,27 +43,27 @@ calculations.
 \examples{
 {
 
-# load that contains the login details
-data(logindata)
+  # load that contains the login details
+  data(logindata)
 
-# login and assign specific variable(s)
-# (by default the assigned dataset is a dataframe named 'D')
-myvar <- list('LAB_HDL', 'LAB_TSC', 'GENDER')
-opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
+  # login and assign specific variable(s)
+  # (by default the assigned dataset is a dataframe named 'D')
+  myvar <- list('LAB_HDL', 'LAB_TSC', 'GENDER')
+  opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
 
-# Example 1: generate the correlation matrix for the assigned dataset 'D'
-# which contains 4 vectors (2 continuous and 1 categorical)
-ds.cor(x='D')
+  # Example 1: generate the correlation matrix for the assigned dataset 'D'
+  # which contains 4 vectors (2 continuous and 1 categorical)
+  ds.cor(x='D')
 
-# Example 2: calculate the correlation between two vectors (first assign some vectors from the dataframe 'D')
-ds.assign(newobj='labhdl', toAssign='D$LAB_HDL')
-ds.assign(newobj='labtsc', toAssign='D$LAB_TSC')
-ds.assign(newobj='gender', toAssign='D$GENDER')
-ds.cor(x='labhdl', y='labtsc')
-ds.cor(x='labhdl', y='gender')
+  # Example 2: calculate the correlation between two vectors (first assign some vectors from the dataframe 'D')
+  ds.assign(newobj='labhdl', toAssign='D$LAB_HDL')
+  ds.assign(newobj='labtsc', toAssign='D$LAB_TSC')
+  ds.assign(newobj='gender', toAssign='D$GENDER')
+  ds.cor(x='labhdl', y='labtsc')
+  ds.cor(x='labhdl', y='gender')
 
-# clear the Datashield R sessions and logout
-datashield.logout(opals)
+  # clear the Datashield R sessions and logout
+  datashield.logout(opals)
 
 }
 }
diff --git a/man/ds.corTest.Rd b/man/ds.corTest.Rd
index c477501..8f4d0fd 100644
--- a/man/ds.corTest.Rd
+++ b/man/ds.corTest.Rd
@@ -28,19 +28,19 @@ Runs a two sided pearson test with a 0.95 confidence level.
 \examples{
 {
 
-# load that contains the login details
-data(logindata)
+  # load that contains the login details
+  data(logindata)
 
-# login and assign specific variable(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)
+  # login and assign specific variable(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)
 
-# test for correlation between the variables 'LAB_TSC' and 'LAB_HDL'
-ds.corTest(x='D$LAB_TSC', y='D$LAB_HDL')
+  # test for correlation between the variables 'LAB_TSC' and 'LAB_HDL'
+  ds.corTest(x='D$LAB_TSC', y='D$LAB_HDL')
 
-# clear the Datashield R sessions and logout
-datashield.logout(opals)
+  # clear the Datashield R sessions and logout
+  datashield.logout(opals)
 
 }
 }
diff --git a/man/ds.cov.Rd b/man/ds.cov.Rd
index 055ee95..01a5570 100644
--- a/man/ds.cov.Rd
+++ b/man/ds.cov.Rd
@@ -43,28 +43,28 @@ calculations.
 \examples{
 {
 
-# load that contains the login details
-data(logindata)
+  # load that contains the login details
+  data(logindata)
 
-# login and assign specific variable(s)
-# (by default the assigned dataset is a dataframe named 'D')
-myvar <- list('LAB_HDL', 'LAB_TSC', 'GENDER')
-opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
+  # login and assign specific variable(s)
+  # (by default the assigned dataset is a dataframe named 'D')
+  myvar <- list('LAB_HDL', 'LAB_TSC', 'GENDER')
+  opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
 
-# Example 1: generate the covariance matrix for the assigned dataset 'D'
-# which contains 4 vectors (2 continuous and 1 categorical)
-ds.cov(x='D')
+  # Example 1: generate the covariance matrix for the assigned dataset 'D'
+  # which contains 4 vectors (2 continuous and 1 categorical)
+  ds.cov(x='D')
 
-# Example 2: calculate the covariance between two vectors
-# (first assign the vectors from 'D')
-ds.assign(newobj='labhdl', toAssign='D$LAB_HDL')
-ds.assign(newobj='labtsc', toAssign='D$LAB_TSC')
-ds.assign(newobj='gender', toAssign='D$GENDER')
-ds.cov(x='labhdl', y='labtsc')
-ds.cov(x='labhdl', y='gender')
+  # Example 2: calculate the covariance between two vectors
+  # (first assign the vectors from 'D')
+  ds.assign(newobj='labhdl', toAssign='D$LAB_HDL')
+  ds.assign(newobj='labtsc', toAssign='D$LAB_TSC')
+  ds.assign(newobj='gender', toAssign='D$GENDER')
+  ds.cov(x='labhdl', y='labtsc')
+  ds.cov(x='labhdl', y='gender')
 
-# clear the Datashield R sessions and logout
-datashield.logout(opals)
+  # clear the Datashield R sessions and logout
+  datashield.logout(opals)
 
 }
 }
diff --git a/man/ds.tTest.Rd b/man/ds.tTest.Rd
index 8729b66..6ac6f59 100644
--- a/man/ds.tTest.Rd
+++ b/man/ds.tTest.Rd
@@ -46,18 +46,19 @@ ds.tTest(x = NULL, y = NULL, type = "combine",
 }
 \value{
 a list containing the following elements: \code{statistic}
-the value of the t-statistic \code{parameter} the degrees
-of freedom for the t-statistic \code{p.value} p.value the
-p-value for the test \code{conf.int} a confidence interval
+the value of the t-statistic. \code{parameter} the degrees
+of freedom for the t-statistic. \code{p.value} p.value the
+p-value for the test. \code{conf.int} a confidence interval
 for the mean appropriate to the specified alternative
-hypothesis \code{estimate} the estimated mean or difference
-in means depending on whether it was a one-sample test or a
-two-sample test \code{null.value} the specified
-hypothesized value of the mean or mean difference depending
-on whether it was a one-sample test or a two-sample test
-\code{alternative} a character string describing the
-alternative hypothesis \code{method} a character string
-indicating what type of t-test was performed
+hypothesis. \code{estimate} the estimated mean or
+difference in means depending on whether it was a
+one-sample test or a two-sample test. \code{null.value} the
+specified hypothesized value of the mean or mean difference
+depending on whether it was a one-sample test or a
+two-sample test. \code{alternative} a character string
+describing the alternative hypothesis \code{method} a
+character string indicating what type of t-test was
+performed
 }
 \description{
 Performs one and two sample t-tests on vectors of data.
@@ -73,45 +74,45 @@ used for performing t-test.
 \examples{
 {
 
-# load that contains the login details
-data(logindata)
+  # load that contains the login details
+  data(logindata)
 
-# login and assign specific variable(s)
-myvar <- list("LAB_HDL", "LAB_TSC")
-opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
+  # login and assign specific variable(s)
+  myvar <- list("LAB_HDL", "LAB_TSC")
+  opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
 
-# Example 1: Run a t.test of the pooled data for the variables 'LAB_HDL' and 'LAB_TSC' - default
-ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC')
+  # Example 1: Run a t.test of the pooled data for the variables 'LAB_HDL' and 'LAB_TSC' - default
+  ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC')
 
-# Example 2: Run a t.test for each study separately for the same variables as above
-ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', type='split')
+  # Example 2: Run a t.test for each study separately for the same variables as above
+  ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', type='split')
 
-# Example 3: Run a paired t.test of the pooled data
-ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', paired=TRUE)
+  # Example 3: Run a paired t.test of the pooled data
+  ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', paired=TRUE)
 
-# Example 4: Run a paired t.test for each study separately
-ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', paired=TRUE, type='split')
+  # Example 4: Run a paired t.test for each study separately
+  ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', paired=TRUE, type='split')
 
-# Example 5: Run a t.test of the pooled data with different alternatives
-ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', alternative='greater')
-ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', alternative='less')
+  # Example 5: Run a t.test of the pooled data with different alternatives
+  ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', alternative='greater')
+  ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', alternative='less')
 
-# Example 6: Run a t.test of the pooled data with mu different from zero
-ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', mu=-4)
+  # Example 6: Run a t.test of the pooled data with mu different from zero
+  ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', mu=-4)
 
-# Example 7: Run a t.test of the pooled data assuming that variances of variables are equal
-ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', var.equal=TRUE)
+  # Example 7: Run a t.test of the pooled data assuming that variances of variables are equal
+  ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', var.equal=TRUE)
 
-# Example 8: Run a t.test of the pooled data with 90\% confidence interval
-ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', conf.level=0.90)
+  # Example 8: Run a t.test of the pooled data with 90\% confidence interval
+  ds.tTest(x='D$LAB_HDL', y='D$LAB_TSC', conf.level=0.90)
 
-# Example 9: Run a one-sample t.test of the pooled data
-ds.tTest(x='D$LAB_HDL')
-# the below example should not work, paired t.test is not possible if the 'y' variable is missing
-# ds.tTest(x='D$LAB_HDL', paired=TRUE)
+  # Example 9: Run a one-sample t.test of the pooled data
+  ds.tTest(x='D$LAB_HDL')
+  # the below example should not work, paired t.test is not possible if the 'y' variable is missing
+  # ds.tTest(x='D$LAB_HDL', paired=TRUE)
 
-# clear the Datashield R sessions and logout
-datashield.logout(opals)
+  # clear the Datashield R sessions and logout
+  datashield.logout(opals)
 
 }
 }
diff --git a/man/ds.var.Rd b/man/ds.var.Rd
index 192be6c..2f149c3 100644
--- a/man/ds.var.Rd
+++ b/man/ds.var.Rd
@@ -32,21 +32,21 @@ It is a wrapper for the server side function
 \examples{
 {
 
-# load that contains the login details
-data(logindata)
+  # load that contains the login details
+  data(logindata)
 
-# login and assign specific variable(s)
-myvar <- list('LAB_TSC')
-opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
+  # login and assign specific variable(s)
+  myvar <- list('LAB_TSC')
+  opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
 
-# Example 1: compute the pooled variance of the variable 'LAB_TSC' - default behaviour
-ds.var(x='D$LAB_TSC')
+  # Example 1: compute the pooled variance of the variable 'LAB_TSC' - default behaviour
+  ds.var(x='D$LAB_TSC')
 
-# Example 2: compute the variance of each study separately
-ds.var(x='D$LAB_TSC', type='split')
+  # Example 2: compute the variance of each study separately
+  ds.var(x='D$LAB_TSC', type='split')
 
-# clear the Datashield R sessions and logout
-datashield.logout(opals)
+  # clear the Datashield R sessions and logout
+  datashield.logout(opals)
 
 }
 }
diff --git a/man/findLoginObjects.Rd b/man/findLoginObjects.Rd
new file mode 100644
index 0000000..5062bb3
--- /dev/null
+++ b/man/findLoginObjects.Rd
@@ -0,0 +1,26 @@
+\name{findLoginObjects}
+\alias{findLoginObjects}
+\title{searches for opal login object in the environment}
+\usage{
+findLoginObjects()
+}
+\value{
+returns a list of opal login objects or stops the process
+}
+\description{
+This is an internal function required by a client function
+}
+\details{
+
+
+if the user does not set the argument 'datasources', this
+function is called to searches for opal login objects in
+the environment. If more than one login object is found a
+prompt asks the user to choose one and if none is found the
+process stops.
+}
+\author{
+Gaye, A.
+}
+\keyword{internal}
+
diff --git a/man/login_remoteServer.Rd b/man/login_remoteServer.Rd
index 2b2b7be..6bb3c96 100755
--- a/man/login_remoteServer.Rd
+++ b/man/login_remoteServer.Rd
@@ -1,16 +1,16 @@
-\name{logindata}
+\name{login_remoteServer}
 
-\alias{logindata}
+\alias{login_remoteServer}
 
 \docType{data}
 
-\title{Information required to login to opal servers for the GLM test data}
+\title{Information required to login to opal servers}
 
 \description{
     A table of with 5 columns: study name, URL, username, password and opal datasource.
 }
 
-\usage{data(logindata)}
+\usage{data(login_remoteServer)}
 
 \format{
   A data frame where the number of servers corresponds to the number of rows
@@ -26,4 +26,4 @@
   }
 }
 
-\examples{data(logindata)}
+\examples{data(login_remoteServer)}