Versions Compared

Old Version 4

changes.mady.by.user Former user

Saved on

compared with

New Version 5

changes.mady.by.user Former user

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

Note

If you are not using your own data, information for the login table is obtained from the data provider. Please follow the appropriate procedures to gain clearance to analyse their data.

Anchor
login
login

Log in to the remote servers

Note

Your login details must be loaded via the data() function or read into the R session first.

  • Create a variable called opals that calls the datashield.login function to log into the desired Opal servers. In the DataSHIELD test environment logindata is our login template for the test Opal servers.
Code Block
xml
xml
opals <- datashield.login(logins=logindata,assign=TRUE)
  • The output below indicates that each of the two test Opal servers study1 and study2 contain the same 11 variables listed in capital letters under Variables assigned: .
Code Block
xml
xml
> opals <- datashield.login(logins=logindata,assign=TRUE)
Logging into the collaborating servers

  No variables have been specified.
  All the variables in the opal table
  (the whole dataset) will be assigned to R!

Assigining data:
study1...
study2...

Variables assigned:
study1--LAB_TSC, LAB_TRIG, LAB_HDL, LAB_GLUC_ADJUSTED, PM_BMI_CONTINUOUS, DIS_CVA, MEDI_LPD, DIS_DIAB, DIS_AMI, GENDER, PM_BMI_CATEGORICAL
study2--LAB_TSC, LAB_TRIG, LAB_HDL, LAB_GLUC_ADJUSTED, PM_BMI_CONTINUOUS, DIS_CVA, MEDI_LPD, DIS_DIAB, DIS_AMI, GENDER, PM_BMI_CATEGORICAL
Note

In Horizontal DataSHIELD pooled analysis the data are harmonized and the variables given the same names across the studies, as agreed by all data providers.