ds.dataFrame.RdCreates a data frame from its elemental components: pre-existing data frames, single variables or matrices.
ds.dataFrame(
x = NULL,
row.names = NULL,
check.rows = FALSE,
check.names = TRUE,
stringsAsFactors = TRUE,
completeCases = FALSE,
DataSHIELD.checks = FALSE,
newobj = NULL,
datasources = NULL,
notify.of.progress = FALSE
)a character string that provides the name of the objects to be combined.
NULL, integer or character string that provides the row names of the output data frame.
logical. If TRUE then the rows are checked for consistency of length and names. Default is FALSE.
logical. If TRUE the column names in the data frame are checked to ensure that is unique. Default is TRUE.
logical. If true the character vectors are converted to factors. Default TRUE.
logical. If TRUE rows with one or more missing values will be deleted from the output data frame. Default is FALSE.
logical. Default FALSE. If TRUE undertakes all DataSHIELD checks
(time-consuming) which are:
1. the input object(s) is(are) defined in all the studies
2. the input object(s) is(are) of the same legal class in all the studies
3. if there are any duplicated column names in the input objects in each study
4. the number of rows of the data frames or matrices and the length of all component variables
are the same
a character string that provides the name for the output data frame
that is stored on the data servers. Default dataframe.newobj.
a list of DSConnection-class objects obtained after login.
If the datasources argument is not specified
the default set of connections will be used: see datashield.connections_default.
specifies if console output should be produced to indicate progress. Default is FALSE.
ds.dataFrame returns the object specified by the newobj argument
which is written to the serverside. Also, two validity messages are returned to the
client-side indicating the name of the newobj that has been created in each data source
and if it is in a valid form.
It creates a data frame by combining pre-existing data frames, matrices or variables.
The length of all component variables and the number of rows of the data frames or matrices must be the same. The output data frame will have the same number of rows.
Server functions called: classDS, colnamesDS, dataFrameDS
if (FALSE) { # \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")
# Create a new data frame
ds.dataFrame(x = c("D$LAB_TSC","D$GENDER","D$PM_BMI_CATEGORICAL"),
row.names = NULL,
check.rows = FALSE,
check.names = TRUE,
stringsAsFactors = TRUE, #character variables are converted to a factor
completeCases = TRUE, #only rows with not missing values are selected
DataSHIELD.checks = FALSE,
newobj = "df1",
datasources = connections[1], #only the first Opal server is used ("study1")
notify.of.progress = FALSE)
# Clear the Datashield R sessions and logout
datashield.logout(connections)
} # }