-
Notifications
You must be signed in to change notification settings - Fork 23
/
ds.densityGrid.R
192 lines (175 loc) · 7.57 KB
/
ds.densityGrid.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
#'
#' @title Generates a density grid in the client-side
#' @description This function generates a grid density object which can then be used to produced
#' heatmap or contour plots.
#' @details The cells with a count > 0 and < nfilter.tab are considered invalid
#' and the count is set to 0.
#'
#' 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 non-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 \code{x}
#' and \code{y} vectors. These elements are set by the server-side function
#' \code{densityGridDS} to 'valid' values
#' (i.e. values that do not lead to leakage of micro-data to the user).
#'
#' Server function called: \code{densityGridDS}
#' @param x a character string providing the name of the input numerical vector.
#' @param y a character string providing the name of the input numerical vector.
#' @param numints an integer, the number of intervals for the grid density object.
#' The default value is 20.
#' @param type a character string that represents the type of graph to display.
#' If \code{type} is set to
#' \code{'combine'}, a pooled grid density matrix is generated,
#' instead if \code{type} is set to \code{'split'}
#' one grid density matrix is generated. Default \code{'combine'}.
#' @param datasources a list of \code{\link{DSConnection-class}} objects obtained after login.
#' If the \code{datasources} argument is not specified
#' the default set of connections will be used: see \code{\link{datashield.connections_default}}.
#' @return \code{ds.densityGrid} returns a grid density matrix.
#' @author DataSHIELD Development Team
#' @export
#' @examples
#' \dontrun{
#'
#' ## Version 6, for version 5 see the Wiki
#' # Connecting to the Opal servers
#'
#' require('DSI')
#' require('DSOpal')
#' require('dsBaseClient')
#'
#' builder <- DSI::newDSLoginBuilder()
#' builder$append(server = "study1",
#' url = "http://192.168.56.100:8080/",
#' user = "administrator", password = "datashield_test&",
#' table = "CNSIM.CNSIM1", driver = "OpalDriver")
#' builder$append(server = "study2",
#' url = "http://192.168.56.100:8080/",
#' user = "administrator", password = "datashield_test&",
#' table = "CNSIM.CNSIM2", driver = "OpalDriver")
#' builder$append(server = "study3",
#' url = "http://192.168.56.100:8080/",
#' user = "administrator", password = "datashield_test&",
#' table = "CNSIM.CNSIM3", driver = "OpalDriver")
#' logindata <- builder$build()
#'
#' # Log onto the remote Opal training servers
#' connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D")
#'
#' #Generate the density grid
#' # Example1: generate a combined grid density object (default)
#' ds.densityGrid(x="D$LAB_TSC",
#' y="D$LAB_HDL",
#' datasources = connections)#all opal servers are used
#'
#' # Example2: generate a grid density object for each study separately
#' ds.densityGrid(x="D$LAB_TSC",
#' y="D$LAB_HDL",
#' 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
#' ds.densityGrid(x="D$LAB_TSC",
#' y="D$LAB_HDL",
#' type="split",
#' numints=15,
#' datasources = connections)
#'
#' # clear the Datashield R sessions and logout
#' datashield.logout(connections)
#'
#' }
#'
ds.densityGrid <- function(x=NULL, y=NULL, numints=20, type='combine', datasources=NULL){
# look for DS connections
if(is.null(datasources)){
datasources <- datashield.connections_find()
}
if(is.null(x)){
stop("Please provide the name of the numeric vector 'x'!", call.=FALSE)
}
if(is.null(y)){
stop("Please provide the name of the numeric vector 'y'!", call.=FALSE)
}
# the input variable might be given as column table (i.e. D$object)
# or just as a vector not attached to a table (i.e. object)
# we have to make sure the function deals with each case
objects <- c(x, y)
xnames <- extract(objects)
varnames <- xnames$elements
obj2lookfor <- xnames$holders
# check if the input object(s) is(are) defined in all the studies
for(i in 1:length(varnames)){
if(is.na(obj2lookfor[i])){
defined <- isDefined(datasources, varnames[i])
}else{
defined <- isDefined(datasources, obj2lookfor[i])
}
}
# call the internal function that checks the input object(s) is(are) of the same class in all studies.
for(i in 1:length(objects)){
typ <- checkClass(datasources, objects[i])
}
# name of the studies to be used in the plots' titles
stdnames <- names(datasources)
# number of studies
num.sources <- length(datasources)
if(type=="combine"){
# get the range from each study and produce the 'global' range
cally <- paste0('rangeDS(', x, ')')
x.ranges <- DSI::datashield.aggregate(datasources, as.symbol(cally))
cally <- paste0('rangeDS(', y, ')')
y.ranges <- DSI::datashield.aggregate(datasources, as.symbol(cally))
x.minrs <- c()
x.maxrs <- c()
y.minrs <- c()
y.maxrs <- c()
for(i in 1:num.sources){
x.minrs <- append(x.minrs, x.ranges[[i]][1])
x.maxrs <- append(x.maxrs, x.ranges[[i]][2])
y.minrs <- append(y.minrs, y.ranges[[i]][1])
y.maxrs <- append(y.maxrs, y.ranges[[i]][2])
}
x.range.arg <- c(min(x.minrs), max(x.maxrs))
y.range.arg <- c(min(y.minrs), max(y.maxrs))
x.global.min <- x.range.arg[1]
x.global.max <- x.range.arg[2]
y.global.min <- y.range.arg[1]
y.global.max <- y.range.arg[2]
# generate the grid density object to plot
cally <- paste0("densityGridDS(", x, ",", y, ",", limits=T, ",", x.global.min, ",",
x.global.max, ",", y.global.min, ",", y.global.max, ",", numints, ")")
grid.density.obj <- DSI::datashield.aggregate(datasources, as.symbol(cally))
numcol <- dim(grid.density.obj[[1]])[2]
# print the number of invalid cells in each participating study
for (i in 1:num.sources){
message(stdnames[i],': ', names(dimnames(grid.density.obj[[i]])[2]))
}
Global.grid.density <- matrix(0, dim(grid.density.obj[[1]])[1], numcol-2)
for (i in 1:num.sources){
Global.grid.density <- Global.grid.density + grid.density.obj[[i]][,1:(numcol-2)]
names(dimnames(Global.grid.density))[2] <- "Grid Density Matrix of the Pooled Data"
}
# newline for some space between the previous messages and the matrix when it is displayed
message()
return(Global.grid.density)
}else{
if(type=="split"){
# generate the grid density object
num_intervals <- numints
cally <- paste0("densityGridDS(", x, ",", y, ",", 'limits=FALSE', ",", 'x.min=NULL', ",",
'x.max=NULL', ",", 'y.min=NULL', ",", 'y.max=NULL', ",", numints=num_intervals, ")")
grid.density.obj <- DSI::datashield.aggregate(datasources, as.symbol(cally))
numcol <- dim(grid.density.obj[[1]])[2]
# print the number of invalid cells in each participating study
for (i in 1:num.sources){
message(stdnames[i],': ', names(dimnames(grid.density.obj[[i]])[2]))
}
return(grid.density.obj)
}else{
stop('Function argument "type" has to be either "combine" or "split"')
}
}
}