Introduction

This is the third in a 6-part DataSHIELD tutorial series. Please see below for links to all prior and subsequent parts:

• Introduction and logging in

• Basic statistics and data manipulations

• Assign functions and tables

• Plotting graphs

• Subsetting

• Modelling

Quick reminder for logging in:

• 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. Load the following urls, waiting at least 1 minute after starting the training VMs.

• http://192.168.56.100:8080 
• http://192.168.56.101:8080 

Start R/RStudio

Load Packages

#load libraries
library(DSI)
library(DSOpal)
library(dsBaseClient)

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()

connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D")

• Command to logout:

DSI::datashield.logout(connections)

Sub-setting

In DataSHIELD there are currently 3 functions that allow us to generate subset data:

• ds.subsetByClass
• ds.dataFrameSubset
• ds.subset (WARNING: this function will be deprecated in the release of 6.1, all functionallity has been added to ds.dataFrameSubset which will become the one-stop replacement).

Sub-setting using ds.subsetByClass

• The ds.subsetByClass function generates subsets for each level of a categorical variable. If the input is a data frame it produces a subset of that data frame for each class of each categorical variable held in the data frame.
• Best practice is to state the categorical variable(s) to subset using the variables argument, and the name of the subset data using the subsets argument.
• The example subsets GENDER from our assigned data frame D , the subset data is named GenderTables :

ds.subsetByClass(x = 'D', subsets = "GenderTables", variables = 'GENDER', datasources = connections)

• The output of ds.subsetByClass is held in a list object stored server-side, as the subset data contain individual-level records. If no name is specified in the subsets argument, the default name "subClasses" is used.

In the previous example, the GENDER variable in assigned data frame D had females coded as 0 and males coded as 1. When GENDER was subset using the ds.subsetByClass  function, two subset tables were generated for each study dataset; one for females and one for males.

• The ds.names function obtains the names of these subset data:

ds.names('GenderTables', datasources = connections)

  Aggregated (exists("GenderTables")) [==================================================] 100% / 0s
  Aggregated (classDS("GenderTables")) [=================================================] 100% / 0s
  Aggregated (namesDS(GenderTables)) [===================================================] 100% / 0s
$study1
[1] "GENDER.level_0" "GENDER.level_1"

$study2
[1] "GENDER.level_0" "GENDER.level_1"

Sub-setting using ds.subset

The function ds.subset allows general sub-setting of different data types e.g. categorical, numeric, character, data frame, matrix. It is also possible to subset rows (the individual records). No output is returned to the client screen, the generated subsets are stored in the server-side R session.

• The example below uses the function ds.subset to subset the assigned data frame D by rows (individual records) that have no missing values (missing values are denoted with NA ) given by the argument completeCases=TRUE . The output subset is named "D_without_NA":

ds.subset(x='D', subset='D_without_NA', completeCases=TRUE, datasources = connections)

• The second example creates a subset of the assigned data frame D with BMI values ≥ 25 using the argument logicalOperator. The subset object is named BMI25plus using the subset argument and is not printed to client screen but is stored in the server-side R session:

ds.subset(x='D', subset='BMI25plus', logicalOperator='PM_BMI_CONTINUOUS>=', threshold=25, datasources = opals)

• To verify the subset above is correct (holds only observations with BMI ≥ 25) the function ds.quantileMean with the argument type='split' will confirm the BMI results for each study are ≥ 25.

ds.quantileMean('BMI25plus$PM_BMI_CONTINUOUS', type='split', datasources = opals)

$`dstesting-100`
     5%     10%     25%     50%     75%     90%     95%    Mean 
25.3500 25.7100 27.1500 29.2000 32.0600 34.6560 36.4980 29.9019 

$`dstesting-101`
      5%      10%      25%      50%      75%      90%      95%     Mean 
25.46900 25.91800 27.19000 29.27000 32.20500 34.76200 36.24300 29.92606 

• Also a histogram of the variable BMI of the new subset data frame could be created for each study separately:

ds.histogram('BMI25plus$PM_BMI_CONTINUOUS', datasources = opals)

Warning: dstesting-100: 2 invalid cells
Warning: dstesting-101: 1 invalid cells
[[1]]
$breaks
 [1] 23.93659 27.17016 30.40373 33.63731 36.87088 40.10445 43.33803 46.57160 49.80518 53.03875 56.27232
$counts
 [1] 365 511 331 150  49  15   0   0   0   0
$density
 [1] 0.079212771 0.110897880 0.071834047 0.032553194 0.010634043 0.003255319 0.000000000 0.000000000 0.000000000 0.000000000
$mids
 [1] 25.55337 28.78695 32.02052 35.25409 38.48767 41.72124 44.95482 48.18839 51.42196 54.65554
$xname
[1] "xvect"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"

[[2]]
$breaks
 [1] 23.93659 27.17016 30.40373 33.63731 36.87088 40.10445 43.33803 46.57160 49.80518 53.03875 56.27232
$counts
 [1] 506 750 476 229  62  11   4   0   0   0
$density
 [1] 0.0767450721 0.1137525773 0.0721949690 0.0347324536 0.0094035464 0.0016683711 0.0006066804 0.0000000000 0.0000000000 0.0000000000
$mids
 [1] 25.55337 28.78695 32.02052 35.25409 38.48767 41.72124 44.95482 48.18839 51.42196 54.65554
$xname
[1] "xvect"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram

Conclusion

The next part in this DataSHIELD tutorial series is: 

• Introduction and logging in

• Basic statistics and data manipulations

• Assign functions and tables 

• Plotting graphs

• Subsetting

• Modelling