ds.dataFrame | R Documentation |
Creates 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
)
x |
a character string that provides the name of the objects to be combined. |
row.names |
NULL, integer or character string that provides the row names of the output data frame. |
check.rows |
logical. If TRUE then the rows are checked for consistency of length and names. Default is FALSE. |
check.names |
logical. If TRUE the column names in the data frame are checked to ensure that is unique. Default is TRUE. |
stringsAsFactors |
logical. If true the character vectors are converted to factors. Default TRUE. |
completeCases |
logical. If TRUE rows with one or more missing values will be deleted from the output data frame. Default is FALSE. |
DataSHIELD.checks |
logical. Default FALSE. If TRUE undertakes all DataSHIELD checks
(time-consuming) which are: |
newobj |
a character string that provides the name for the output data frame
that is stored on the data servers. Default |
datasources |
a list of |
notify.of.progress |
specifies if console output should be produced to indicate progress. Default is FALSE. |
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
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.
DataSHIELD Development Team
## Not run:
## 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)
## End(Not run)
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