ds.sqrt | R Documentation |
Computes the square root values for a specified numeric or integer vector.
This function is similar to R function sqrt
.
ds.sqrt(x = NULL, newobj = NULL, datasources = NULL)
x |
a character string providing the name of a numeric or an integer vector. |
newobj |
a character string that provides the name for the output variable
that is stored on the data servers. Default name is set to |
datasources |
a list of |
The function calls the server-side function sqrtDS
that computes the
square root values of the elements of a numeric or integer vector and assigns a new vector
with those square root values on the server-side. The name of the new generated vector is
specified by the user through the argument newobj
, otherwise is named by default to
sqrt.newobj
.
ds.sqrt
assigns a vector for each study that includes the square root values of
the input numeric or integer vector specified in the argument x
. The created vectors
are stored in the servers.
Demetris Avraam for DataSHIELD Development Team
## Not run:
# 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")
# Example 1: Get the square root of LAB_HDL variable
ds.sqrt(x='D$LAB_HDL', newobj='LAB_HDL.sqrt', datasources=connections)
# compare the mean of LAB_HDL and of LAB_HDL.sqrt
# Note here that the number of missing values is bigger in the LAB_HDL.sqrt
ds.mean(x='D$LAB_HDL', datasources=connections)
ds.mean(x='LAB_HDL.sqrt', datasources=connections)
# Example 2: Generate a repeated vector of the squares of integers from 1 to 10
# and get their square roots
ds.make(toAssign='rep((1:10)^2, times=10)', newobj='squares.vector', datasources=connections)
ds.sqrt(x='squares.vector', newobj='sqrt.vector', datasources=connections)
# check the behavior of that operation by comparing the tables of squares.vector and sqrt.vector
ds.table(rvar='squares.vector')$output.list$TABLE_rvar.by.study_counts
ds.table(rvar='sqrt.vector')$output.list$TABLE_rvar.by.study_counts
# clear the Datashield R sessions and logout
datashield.logout(connections)
## End(Not run)
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