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changes.mady.by.user Alex Westerberg

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Note
titlePrerequisites

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.


Tip
titleHelp

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. 

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Code Block
languagexml
themeRDark
Aggregated (dimDS("DST")) [==============================================================] 100% / 0s
$`dimensions of DDST in study1`
[1] 2163   11

$`dimensions of DDST in study2`
[1] 3088   11

$`dimensions of DDST in combined studies`
[1] 5251   11

...

  • Up to here, the dimensions of the assigned data frame D DST have been found using the ds.dim command in which type='both' is the default argument.
  • Now use the type='combine' argument in the ds.dim function to identify the number of individuals (5251) and variables (11) pooled across all studies:
Code Block
xml
xml
ds.dim(x='DDST', type='combine', datasources = connections)

...

Code Block
themeRDark
  Aggregated (dimDS("DST")) [==============================================================] 100% / 0s
$`dimensions of DDST in combined studies`
[1] 5251   11

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Code Block
languagexml
ds.colnames(x='DDST', datasources = connections)

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  • 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$LABDST$LAB_HDL'.
Code Block
languagexml
ds.class(x='DST$LAB_HDL', datasources = connections)

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Code Block
languagexml
ds.quantileMean(x='D$LABDST$LAB_HDL', datasources = connections)

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