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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 |
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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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The other parts in this DataSHIELD tutorial series are:
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.
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Start R/RStudioLoad Packages Code Block |
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| #load libraries
library(DSI)
library(DSOpal)
library(dsBaseClient)
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Build your login dataframe Code Block |
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language | xml |
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title | Build your login dataframe |
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| builder <- DSI::newDSLoginBuilder()
builder <- DSI::newDSLoginBuilder()
builder$append(server = " | study1 http192.168.56.100:8080/",
opal-demo.obiba.org/",
user = " | administratordatashield_test& table CNSIM.CNSIM1driver = "OpalDriver"options='list(ssl_verifyhost=0, ssl_verifypeer=0)')
builder$append(server = " | study2http192168.56.101:8080
administratordatashield_test& table = "CNSIM.CNSIM2", driver = "OpalDriver""OpalDriver", options='list(ssl_verifyhost=0, ssl_verifypeer=0)')
logindata <- builder$build()
logindata <- builder$build() |
connections <- DSI::datashield.login(logins = logindata, assign = TRUE)
DSI::datashield.assign.table(conns = connections, symbol = "DST", | "D"table = c("CNSIM.CNSIM1","CNSIM.CNSIM2")) |
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| DSI::datashield.logout(connections) |
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Descriptive statistics: assigning variables
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ds.log(x='D$LABDST$LAB_HDL', datasources = connections)
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Aggregated (exists("DDST")) [=============================================================] 100% / 0s
Aggregated (classDS("D$LABDST$LAB_HDL")) [====================================================] 100% / 1s
Assigned expr. (log.newobj <- log(D$LABDST$LAB_HDL,2.71828182845905)) [=======================] 100% / 0s
Aggregated (exists("log.newobj")) [====================================================] 100% / 0s |
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ds.log(x='D$LABDST$LAB_HDL', newobj='LAB_HDL_log', datasources = connections)
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ds.assign(toAssign='D$LABDST$LAB_HDL-1.562', newobj='LAB_HDL.c', datasources = connections) |
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ds.table(rvar="D$GENDERDST$GENDER")
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Aggregated (asFactorDS1("D$GENDERDST$GENDER")) [=================================================] 100% / 0s
Aggregated (tableDS(rvar.transmit = "D$GENDERDST$GENDER", cvar.transmit = NULL, stvar.transmit = NULL, ) ...
Data in all studies were valid
Study 1 : No errors reported from this study
Study 2 : No errors reported from this study
$output.list
$output.list$TABLE_rvar.by.study_row.props
study
D$GENDERDST$GENDER 1 2
0 0.4079193 0.5920807
1 0.4160839 0.5839161
$output.list$TABLE_rvar.by.study_col.props
study
D$GENDERDST$GENDER 1 2
0 0.5048544 0.5132772
1 0.4951456 0.4867228
$output.list$TABLE_rvar.by.study_counts
study
D$GENDERDST$GENDER 1 2
0 1092 1585
1 1071 1503
$output.list$TABLES.COMBINED_all.sources_proportions
D$GENDERDST$GENDER
0 1
0.51 0.49
$output.list$TABLES.COMBINED_all.sources_counts
D$GENDERDST$GENDER
0 1
2677 2574
$validity.message
[1] "Data in all studies were valid" |
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ds.table(rvar='D$DISDST$DIS_DIAB', cvar='D$GENDERDST$GENDER', datasources = connections) |
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Aggregated (asFactorDS1("D$DISDST$DIS_DIAB")) [===============================================] 100% / 0s
Aggregated (asFactorDS1("D$GENDERDST$GENDER")) [=================================================] 100% / 0s
Aggregated (tableDS(rvar.transmit = "D$DISDST$DIS_DIAB", cvar.transmit = "D$GENDERDST$GENDER", ) [======] 100% / 0s
Data in all studies were valid
Study 1 : No errors reported from this study
Study 2 : No errors reported from this study
$output.list
$output.list$TABLE.STUDY.1_row.props
D$GENDERDST$GENDER
D$DISDST$DIS_DIAB 0 1
0 0.502 0.498
1 0.700 0.300
$output.list$TABLE.STUDY.1_col.props
D$GENDERDST$GENDER
D$DISDST$DIS_DIAB 0 1
0 0.9810 0.9920
1 0.0192 0.0084
$output.list$TABLE.STUDY.2_row.props
D$GENDERDST$GENDER
D$DISDST$DIS_DIAB 0 1
0 0.511 0.489
1 0.660 0.340
$output.list$TABLE.STUDY.2_col.props
D$GENDERDST$GENDER
D$DISDST$DIS_DIAB 0 1
0 0.9800 0.9890
1 0.0196 0.0106
$output.list$TABLES.COMBINED_all.sources_row.props
D$GENDERDST$GENDER
D$DISDST$DIS_DIAB 0 1
0 0.507 0.493
1 0.675 0.325
$output.list$TABLES.COMBINED_all.sources_col.props
D$GENDERDST$GENDER
D$DISDST$DIS_DIAB 0 1
0 0.9810 0.99000
1 0.0194 0.00971
$output.list$TABLE_STUDY.1_counts
D$GENDERDST$GENDER
D$DISDST$DIS_DIAB 0 1
0 1071 1062
1 21 9
$output.list$TABLE_STUDY.2_counts
D$GENDERDST$GENDER
D$DISDST$DIS_DIAB 0 1
0 1554 1487
1 31 16
$output.list$TABLES.COMBINED_all.sources_counts
D$GENDERDST$GENDER
D$DISDST$DIS_DIAB 0 1
0 2625 2549
1 52 25
$validity.message
[1] "Data in all studies were valid" |
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The function can additionally compute a chi-squared test for homogeneity on (nc-1)*(nr-1) degrees of freedom (where nc is the number of columns and nr is the number of rows):
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ds.table(rvar='DST$DIS_DIAB', cvar='DST$GENDER', datasources = connections, report.chisq.tests = TRUE) |
Below code omits the first section of output which is an exact duplicate of above, only chisquare reports shown:
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The other parts in this DataSHIELD tutorial series are:
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Also remember you can: |
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