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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. |
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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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6: Modelling
Quick reminder for logging in:
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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. Start R/RStudioLoad Packages Code Block | | xml | xml | #load
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#load libraries
library(DSI)
library(DSOpal)
library(dsBaseClient)
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Build your login dataframe
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builder <- DSI::newDSLoginBuilder() builder <- DSI::newDSLoginBuilder() builder$append(server = "study1server1", url = "httphttps://192opal-demo.168.56.100:8080/", user = "administrator", password = "datashield_test&", table = "CNSIM.CNSIM1obiba.org/", user = "dsuser", password = "P@ssw0rd", driver = "OpalDriver", options='list(ssl_verifyhost=0, ssl_verifypeer=0)') builder$append(server = "study2server2", url = "httphttps://192.168.56.101:8080/", opal-demo.obiba.org/", user = "administratordsuser", password = "datashield_test&P@ssw0rd", table driver = "CNSIM.CNSIM2OpalDriver", driver = "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 = "D""DST", table = c("CNSIM.CNSIM1","CNSIM.CNSIM2")) |
- Command to logout:
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DSI::datashield.logout(connections) |
...
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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 $validity.message [1] "Data in all studies were valid" |
...
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ds.table(rvar='D$DISDST$DIS_DIAB', cvar='D$GENDERDST$GENDER', datasources = connections, report.chisq.tests = TRUE) |
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6: Modelling
Tip |
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Also remember you can:
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