v6.0 DataSHIELD Training Part 1: Introduction and logging in

Owned by Alex Westerberg (Unlicensed)
Last updated: 05 Nov, 2020
5 min read


 Click here for page contents...

Prerequisites

It is recommended that you familiarise yourself with R first by sitting our Introduction to R tutorial.

It also requires that you have the DataSHIELD training environment installed on your machine, see our Installation Instructions for Linux, Windows, or Mac.

Help

DataSHIELD support is freely available in the DataSHIELD forum by the DataSHIELD community. Please use this as the first port of call for any problems you may be having, it is monitored closely for new threads.

DataSHIELD bespoke user support and also user training classes are offered on a fee-paying basis. Please enquire at datashield@newcastle.ac.uk for current prices. 

Introduction

This tutorial introduces users to DataSHIELD commands and syntax. Throughout this document we refer to R, but all commands are run in the same way in Rstudio. This tutorial contains a limited number of examples; further examples are available in each DataSHIELD function manual page that can be accessed via the help function.


The DataSHIELD approach: aggregate and assign functions

How assign and aggregate functions work

DataSHIELD commands call functions that range from carrying out pre-requisite tasks such as login to the datasources, to generating basic descriptive statistics, plots and tabulations. More advance functions allow for users to fit generalized linear models and generalized estimating equations models. R can list all functions available in DataSHIELD.

This section explains the functions we will call during this tutorial. Although this knowledge is not required to run DataSHIELD analyses it helps to understand the output of the commands. It can explain why some commands call functions that return nothing to the user, but rather store the output on the server of the data provider for use in a second function.

In DataSHIELD the person running an analysis (the client) uses client-side functions to issue commands (instructions). These commands initiate the execution (running) of server-side functions that run the analysis server-side (behind the firewall of the data provider). There are two types of server-side function: assign functions and aggregate functions.

Assign functions do not return an output to the client, with the exception of error or status messages. Assign functions create new objects and store them server-side either because the objects are potentially disclosive, or because they consist of the individual-level data which, in DataSHIELD, is never seen by the analyst. These new objects can include:

  • new transformed variables (e.g. mean centred or log transformed variables)
  • a new variable of a modified class (e.g. a variable of class numeric may be converted into a factor which R can then model as having discrete categorical levels)
  • a subset object (e.g. a dataframe including gender as a variable may be split into males and females).

Assign functions return no output to the client except to indicate an error or useful messages about the object store on server-side.

Aggregate functions analyse the data server-side and return an output in the form of aggregate data (summary statistics that are not disclosive) to the client. The help page for each function tells us what is returned and when not to expect an output on client-side.



Start your training Virtual Machines

Please follow instructions to Start the Opal VMs.

Recall from the installation instructions, the Opal web interface:

is a simple check to tell if the VMs have started.

Start R/RStudio

Load Packages

  • The following relevant R packages are required for analysis:

DSI to login and logout.

DSOpal used by DSI to access the Opal server.

dsBaseClient containing all DataSHIELD functions referred to in this tutorial.

  • To load the R packages, type the library function into the command line as given in the example below:
#load libraries
library(DSI)
library(DSOpal)
library(dsBaseClient)

Build your login dataframe 

DataSHIELD cloud IP addresses

The DataSHIELD cloud training environment does not use fixed IP addresses, the client and opal training server addresses change each training session. As part of the user tutorial you learn how to build a DataSHIELD login dataframe. In a real world instance of DataSHIELD this is populated with secure certificates not text based usernames and passwords.

Login Dataframe

  • Build login dataframe.
Build your login dataframe
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://192.168.56.101:8080/",
               user = "administrator", password = "datashield_test&",
               table = "CNSIM.CNSIM2", driver = "OpalDriver")

logindata <- builder$build()

Log onto the remote Opal training servers

  • Assign to "connections" the DSI function to log into the desired Opal servers. In the DataSHIELD test environment logindata is our login dataframe for the Opal training servers.
connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D")

  • The output below indicates that each of the two Opal training servers dstutorial-100 and dstutorial-101 contain the same 11 variables listed in capital letters under Variables assigned:

Logging into the collaborating servers
  Logged in all servers [================================================================] 100% /14s

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

Assigning table data...
  Assigned all tables [==================================================================] 100% /13s

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
  • Command to logout:
DSI::datashield.logout(connections)

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.

How datashield.login works

The datashield.login function from the R package opal allows users to login and assign data to analyse from the Opal server in a server-side R session created behind the firewall of the data provider.

All the commands sent after login are processed within the server-side R instance only allows a specific set of commands to run (see the details of a typical horizontal DataSHIELD process). The server-side R session is wiped after logging out.

If we do not specify individual variables to assign to the server-side R session, all variables held in the Opal servers are assigned. Assigned data are kept in a data frame named D by default. Each column of that data frame represents one variable and the rows are the individual records.

Assign individual variables on login

Users can specify individual variables to assign to the server-side R session. It is best practice to first create a list of the Opal variables you want to analyse.

  • The example below creates a new variable myvar that lists the Opal variables required for analysis: LAB_HDL and GENDER
  • The variables argument in the function datashield.login uses myvar, which then will call only this list.
myvar <- list('LAB_HDL', 'GENDER')
connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D", variables=myvar)

Logging into the collaborating servers
  Logged in all servers [================================================================] 100% / 4s

Assigning table data...
  Assigned all tables [==================================================================] 100% / 7s

Variables assigned:
study1 -- LAB_HDL, GENDER
study2 -- LAB_HDL, GENDER

The format of assigned data frames

Assigned data are kept in a data frame (table) named D by default. Each row of the data frame are the individual records and each column is a separate variable.

  • The example below uses the argument symbol in the datashield.login function to change the name of the data frame from D to mytable
myvar <- list('LAB_HDL', 'GENDER')
connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol='mytable', variables=myvar)

Logging into the collaborating servers
  Logged in all servers [================================================================] 100% / 4s

Assigning table data...
  Assigned all tables [==================================================================] 100% / 6s

Variables assigned:
study1 -- LAB_HDL, GENDER
study2 -- LAB_HDL, GENDER

Conclusion

The other parts in this DataSHIELD tutorial series are:

Also remember you can:

DataSHIELD Wiki by DataSHIELD is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Based on a work at http://www.datashield.ac.uk/wiki