Local DataSHIELD Training Environment v5

This tutorial assumes you have already installed the DataSHIELD training environment (installation takes around half an hour) or that you have a login for a DataSHIELD cloud training server.

It is recommended that you familiarise yourself with R first by sitting our Session 1: Introduction to R tutorial

Getting ready

Start the Opal Servers

  • Open VirtualBox. Left click on an Opal test server and select the green Start arrow (or double click on the Opal test server name).
  • The Opal test servers take a few minutes to boot. Leave them running for a few minutes to ensure Opal has started. You do not need to login.

You can check whether the Opal test servers are ready by typing the following into your navigation bar

http://192.168.56.100:8080 and http://192.168.56.101:8080 or

https://192.168.56.100:8443 and https://192.168.56.101:8443

Start R/RStudio and load packages

  • Start R (or RStudio, if using).
  • Check for dataSHIELD package updates
  • The following relevant R packages are required for analysis
    • opal to login and logout
    • dsBaseClient dsStatsClient ds.GraphicsClient and dsModellingClient 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:
#update dataSHIELD packages
update.packages(repos='http://cran.obiba.org') 
 
#load libraries
library(opal)
library(dsBaseClient)
library(dsStatsClient)
library(dsGraphicsClient)
library(dsModellingClient)

The output in R/RStudio will look as follows:

library(opal)
#Loading required package: RCurl
#Loading required package: bitops
#Loading required package: rjson
library(dsBaseClient)
#Loading required package: fields
#Loading required package: spam
#Loading required package: grid
library(dsStatsClient)
library(dsGraphicsClient)
library(dsModellingClient)

You might see the following status message that you can ignore. The message refers to the blocking of functions within the package.
The following objects are masked from ‘package:xxxx’

Login template

A Horizontal-DataSHIELD process starts with a login to one or more Opal servers that hold the data behind the data provider firewall. Formatting of the login details is required to log into Opal servers:

  • Login details are held in a data frame (table). The example below shows the format of the login data frame for DataSHIELD training VMs which consists of two datasets held by two Opal servers. This login file logindata is built into the DataSHIELD test environment.
  • The DataSHIELD test environment login details are loaded using the data function. Calling logindata allows users to view the login data on the screen:
data(logindata)
logindata

# server url                 user          password table
# study1 192.168.56.100:8080 administrator password CNSIM.CNSIM
# study2 192.168.56.101:8080 administrator password CNSIM.CNSIM

You can create a login table to use with /wiki/spaces/DSDEV/pages/12943489 or live research data.

path<-"/FILEPATH/login.txt"
my_login<-read.table(path, sep="", header=TRUE)

The login details for live research data will have the same format as the login template except:

  • user must contain the file path to an ssl certificate
  • password must contain the file path to an ssl key
  • server must be the formal name of the study
  • url is the url of the remote server or the test environment
  • table indicates the location of the dataset in Opal, e.g. project_name.table_name

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.

Log in to the remote servers

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.
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: .
> 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

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.

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')
opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)

#Logging into the collaborating servers

#Assigining data:
#study1...
#study2...

#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')
opals <- datashield.login(logins=logindata,assign=TRUE,variables=myvar, symbol='mytable')

#Logging into the collaborating servers

#Assigining data:
#study1...
#study2...

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

Only DataSHIELD developers will need to change the default value of the last argument, directory, of the datashield.login function.

Basic statistics and data manipulations

Descriptive statistics: variable dimensions and class

Almost all functions in DataSHIELD can display split results (results separated for each study) or pooled results (results for all the studies combined). This can be done using the type='split' and type='combine' argument in each function. The majority of DataSHIELD functions have a default of type='combined'. The default for each function can be checked in the function help page.

It is possible to get some descriptive or exploratory statistics about the assigned variables held in the server-side R session such as number of participants at each data provider, number of participants across all data providers and number of variables. Identifying parameters of the data will facilitate your analysis.

  • The dimensions of the assigned data frame D can be found using the ds.dim command in which type='split' is the default argument:
opals <- datashield.login(logins=logindata,assign=TRUE)
ds.dim(x='D')

The output of the command is shown below. It shows that in study1 there are 2163 individuals with 11 variables and in study2 there are 3088 individuals with 11 variables:

> 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!
Assigning 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


> ds.dim(x='D')
$study1
[1] 2163   11

$study2
[1] 3088   11
  • Use the type='combine' argument in the ds.dim function to identify the number of individuals (5251) and variables (11) pooled across all studies:
ds.dim('D', type='combine')
#$pooled.dimension
#[1] 5251   11
  • To check the variables in each study are identical (as is required for pooled data analysis), use the ds.colnames function on the assigned data frame D :
ds.colnames(x='D')
#$study1
# [1] "LAB_TSC"            "LAB_TRIG"           "LAB_HDL"            "LAB_GLUC_ADJUSTED"  "PM_BMI_CONTINUOUS"  "DIS_CVA"
# [7] "MEDI_LPD"           "DIS_DIAB"           "DIS_AMI"            "GENDER"             "PM_BMI_CATEGORICAL"

#$study2
# [1] "LAB_TSC"            "LAB_TRIG"           "LAB_HDL"            "LAB_GLUC_ADJUSTED"  "PM_BMI_CONTINUOUS"  "DIS_CVA"
# [7] "MEDI_LPD"           "DIS_DIAB"           "DIS_AMI"            "GENDER"             "PM_BMI_CATEGORICAL"
  • Use the ds.class function to identify the class (type) of a variable - for example if it is an integer, character, factor etc. This will determine what analysis you can run using this variable class. The example below defines the class of the variable LAB_HDL held in the assigned data frame D, denoted by the argument x='D$LAB_HDL'
ds.class(x='D$LAB_HDL')
#$study1
#[1] "numeric"

#$study2
#[1] "numeric"

Continuing the Tutorial

You can now continue with the remainder of the DataSHIELD training tutorial from Descriptive Statistices: Quantiles and Mean as it is for the DataSHIELD Cloud training environment. 

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If you have not installed the DataSHIELD test environment you can login to our cloud Opal test servers using the alternative details below. This will require a good internet connection. Please note this service is not reliable and will be discontinued soon.

opals <- datashield.login(logins=login_remoteServer,assign=TRUE)

The login template for the cloud Opal test servers can be called using:

data(login_remoteServer)