In rfhb/ctrdata: Retrieve and Analyze Clinical Trials in Public Registers
knitr::opts_chunk$set(eval = FALSE)
Get started
Attach package ctrdata
library(ctrdata)
citation("ctrdata")
Remember to respect the registers' terms and conditions (see ctrOpenSearchPagesInBrowser(copyright = TRUE)). Please cite this package in any publication as follows: Ralf Herold (2024). ctrdata: Retrieve and Analyze Clinical Trials in Public Registers. R package version 1.16.0. https://cran.r-project.org/package=ctrdata
Open register's advanced search page in browser
These functions open the browser, where the user can start searching for trials of interest.
# Please review and respect register copyrights:
ctrOpenSearchPagesInBrowser(
copyright = TRUE
)
# Open browser with example search:
ctrOpenSearchPagesInBrowser(
url = "cancer&age=under-18",
register = "EUCTR"
)
Adjust search parameters and execute search in browser
Refine the search until the trials of interest are listed in the browser. The total number of trials that can be retrieved with package ctrdata is intentionally limited to queries with at most 10,000 result records.
Copy address from browser address bar to clipboard
Use functions or keyboard shortcuts according to the operating system. See here for our automation to copy the URLs of a user's queries in any of the supported clinical trial registers.
Get address from clipboard
The next steps are executed in the R environment:
q <- "https://www.clinicaltrialsregister.eu/ctr-search/search?query=cancer&age=under-18&status=completed&phase=phase-one"
clipr::write_clip(q)
q <- ctrGetQueryUrl()
# * Using clipboard content as register query URL: https://www.clinicaltrialsregister.eu/ctr-search/search?query=cancer&age=under-18&status=completed&phase=phase-one
# * Found search query from EUCTR: query=cancer&age=under-18&status=completed&phase=phase-one
q
# query-term query-register
# 1 query=cancer&age=under-18&status=completed&phase=phase-one EUCTR
# To check, this opens a browser with the query
ctrOpenSearchPagesInBrowser(url = q)
Retrieve protocol-related information, transform, save to database, check
# Count number of trial records
ctrLoadQueryIntoDb(
queryterm = q,
only.count = TRUE
)$n
# * Found search query from EUCTR: query=cancer&age=under-18&status=completed&phase=phase-one
# * Checking trials in EUCTR...
# Retrieved overview, multiple records of 103 trial(s) from 6 page(s) to be downloaded (estimate: 10 MB)
# [1] 103
# Connect to a database and chose a collection (table)
db <- nodbi::src_sqlite(
dbname = "sqlite_file.sql",
collection = "test"
)
# Retrieve records, download into database
ctrLoadQueryIntoDb(
queryterm = q,
con = db
)
# * Checking trials in EUCTR...
# Retrieved overview, multiple records of 103 trial(s) from 6 page(s) to be downloaded (estimate: 10 MB)
# (1/3) Downloading trials...
# Note: register server cannot compress data, transfer takes longer (estimate: 100 s)
# Download status: 6 done; 0 in progress. Total size: 8.73 Mb (100%)... done!
# (2/3) Converting to NDJSON (estimate: 2 s)...
# (3/3) Importing records into database...
# = Imported or updated 414 records on 103 trial(s)
# No history found in expected format.
# Updated history ("meta-info" in "test")
# $n
# [1] 414
# Show which queries have been downloaded into database
dbQueryHistory(con = db)
# query-timestamp query-register query-records query-term
# 1 2024-05-12 17:56:11 EUCTR 414 query=cancer&age=under-18&status=completed&phase=phase-one
With a file-base SQLite database, this takes about 20 seconds for about 300 records, with most of the time needed for internet-retrieval with is slow from this register. Speed is higher with other registers, with using MongoDB and with memory-based SQLite.
Repeat and update a previous query
Instead of "last", an integer number can be specified for querytoupdate that corresponds to the number when using dbQueryHistory().
ctrLoadQueryIntoDb(
querytoupdate = "last",
con = db
)
Depending on the register, an update (differential update) is possible or the original query is executed fully again.
Retrieve results
For EUCTR, result-related trial information has to be requested to be retrieved, because it will take longer to download and store. For CTGOV, any results are always included in the retrieval. Note that trial documents, including any results reports, can be downloaded by specifying parameter documents.path, see help(ctrLoadQueryIntoDb).
ctrLoadQueryIntoDb(
queryterm = q,
euctrresults = TRUE,
con = db
)
# * Checking trials in EUCTR...
# Retrieved overview, multiple records of 103 trial(s) from 6 page(s) to be downloaded (estimate: 10 MB)
# (1/3) Downloading trials...
# Note: register server cannot compress data, transfer takes longer (estimate: 100 s)
# (2/3) Converting to NDJSON (estimate: 2 s)...
# (3/3) Importing records into database...
# = Imported or updated 414 records on 103 trial(s)
# * Checking results if available from EUCTR for 103 trials:
# (1/4) Downloading results...
# Download status: 103 done; 0 in progress. Total size: 59.71 Mb (100%)... done!
# Download status: 27 done; 0 in progress. Total size: 108.69 Kb (100%)... done!
# - extracting results (. = data, F = file[s] and data, x = none):
# . . . . . . F . . . . F . . . . . . . F . . . . . . . . . . . . . . . . . . . . . . . . F . F F . F . .
# F . . . F F . . . . . F . . . . . . F . . . F .
# (2/4) Converting to NDJSON (estimate: 8 s)...
# (3/4) Importing results into database (may take some time)...
# (4/4) Results history: not retrieved (euctrresultshistory = FALSE)
# = Imported or updated results for 76 trials
# Updated history ("meta-info" in "test")
# $n
# [1] 414
With a file-base SQLite database, this takes about 4 minutes for about 300 records, with most of the time needed for merging result- and protocol-related information in SQLite; this is much faster with MongoDB and PostgreSQL.
The download or presence of results is not recorded in dbQueryHistory() because the availability of results increases over time.
Add information from another register
The same collection can be used to store (and analyse) trial information from different registers. Example:
ctrLoadQueryIntoDb(
queryterm = "https://classic.clinicaltrials.gov/ct2/results?cond=neuroblastoma&recrs=e&age=0&intr=Drug",
con = db
)
# * Appears specific for CTGOV Classic website
# * Found search query from CTGOV: cond=neuroblastoma&recrs=e&age=0&intr=Drug
# * Checking trials using CTGOV Classic website...
# Retrieved overview, records of 226 trial(s) are to be downloaded (estimate: 1.8 MB)
# (1/3) Downloading trial file...
# Download status: 1 done; 0 in progress. Total size: 1.59 Mb (100%)... done!
# (2/3) Converting to NDJSON (estimate: 10 s)...
# (3/3) Importing records into database...
# = Imported or updated 226 trial(s)
# Updated history ("meta-info" in "test")
# $n
# [1] 226
With a file-base SQLite database, this takes about 10 seconds for about 200 records.
Note that in this example, a warning message may be issued from importing an NDJSON file with trial records. The warning arises from the high level of complexity of some of the XML content of some of the trial records. The issue can be resolved by increasing in the operating system the stack size available to R, see: https://github.com/rfhb/ctrdata/issues/22
Add records from CTIS register into the same collection
As of May 2024, more than 750 trials are publicly accessible in CTIS, which has to be used since January 2023 for new clinical trials in the European Union (EU). Queries in the CTIS search interface can be automatically copied to the clipboard so that a user can paste them into queryterm, see here.
# Retrieve trials from another register:
ctrLoadQueryIntoDb(
queryterm = "ageGroupCode=2",
register = "CTIS",
con = db
)
# * Found search query from CTIS: ageGroupCode=2
# * Checking trials in CTIS...
# (1/5) Downloading trials list . found 78 trials
# (2/5) Downloading and processing part I and parts II... (estimate: 10 Mb)
# Download status: 78 done; 0 in progress. Total size: 16.26 Mb (100%)... done!
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# (3/5) Downloading and processing additional data:
# publicevents, summary, layperson, csr, cm, inspections, publicevaluation (estimate: 7 Mb)
# Download status: 160 done; 0 in progress. Total size: 4.26 Mb (100%)... done!
# 78
# (4/5) Importing records into database...
# (5/5) Updating with additional data: . . .
# = Imported 78, updated 78 / 78 / 1 record(s) on 78 trial(s)
# Updated history ("meta-info" in "test")
# $n
# [1] 78
Add personal annotations
When downloading trial information, the user can specify an annotation to all records that are downloaded. By default, annotations are accumulated if trial records are loaded again or updated; alternatively, annotations can be replaced.
Annotations are useful for analyses, for example to specially identify subsets of records in the database.
ctrLoadQueryIntoDb(
queryterm = "https://classic.clinicaltrials.gov/ct2/results?cond=neuroblastoma&recrs=e&age=0&intr=Drug&cntry=DE",
annotation.text = "site_DE ",
annotation.mode = "append",
con = db
)
# * Appears specific for CTGOV Classic website
# * Found search query from CTGOV: cond=neuroblastoma&recrs=e&age=0&intr=Drug&cntry=DE
# * Checking trials using CTGOV Classic website...
# Retrieved overview, records of 15 trial(s) are to be downloaded (estimate: 0.12 MB)
# (1/3) Downloading trial file...
# Download status: 1 done; 0 in progress. Total size: 86.91 Kb (100%)... done!
# (2/3) Converting to NDJSON (estimate: 0.8 s)...
# (3/3) Importing records into database...
# = Imported or updated 15 trial(s)
# = Annotated retrieved records (15 records)
# Updated history ("meta-info" in "test")
# $n
# [1] 15
Add information using trial identifiers
When identifiers of clinical trials of interest are already known, this example shows how they can be processed to import the trial information into a database collection. This involves constructing a query that combines the identifiers and then iterating over the sets of identifiers. Note to combine identifiers using "+OR+" into the queryterm, and that register has to be specified.
# ids of trials of interest
ctIds <- c(
"NCT00001209", "NCT00001436", "NCT00187109", "NCT01516567", "NCT01471782", "NCT03042429",
"NCT00357084", "NCT00357500", "NCT00365755", "NCT00407433", "NCT00410657", "NCT00436657",
"NCT00436852", "NCT00445965", "NCT00450307", "NCT00450827", "NCT00471679", "NCT00486564",
"NCT00492167", "NCT00499616", "NCT00503724", "NCT00509353", "NCT00520936", "NCT00536601",
"NCT00567567", "NCT00578864", "NCT00601003", "NCT00644696", "NCT00646230", "NCT00659984",
"NCT00716976", "NCT00743496", "NCT00793845", "NCT00806182", "NCT00831844", "NCT00867568",
"NCT00877110", "NCT00885326", "NCT00918320", "NCT00923351", "NCT00939770", "NCT00960739"
)
# split into sets of each 25 trial ids
# (larger sets e.g. 50 may still work)
idSets <- split(ctIds, ceiling(seq_along(ctIds) / 25))
# variable to collect import results
result <- NULL
# iterate over sets of trial ids
for (idSet in idSets) {
# import
setResult <- ctrLoadQueryIntoDb(
# for CTGOV Classic, EUCTR, ISRCTN use:
# queryterm = paste0(idSet, collapse = "+OR+"),
# for CTGOV2 use:
queryterm = paste0("term=", paste0(idSet, collapse = " ")),
# specify register that holds the information
register = "CTGOV2",
con = db
)
# append results
result <- c(result, list(setResult))
}
# inspect results
as.data.frame(do.call(rbind, result))[, c("n", "failed")]
# n failed
# 1 25 NULL
# 2 17 NULL
Note that from CTIS, trial information identified by trial identifiers can be retrieved only one-by-one, repeating queries for each trial such as https://euclinicaltrials.eu/app/#/search?number=2023-503994-39-00.
Find synonyms of active substance names
Not all registers automatically expand search terms to include alternative terms, such as codes and other names of active substances. To obtain a character vector of synonyms for any active substance name, use:
ctrFindActiveSubstanceSynonyms(
activesubstance = "imatinib"
)
# [1] "imatinib" "gleevec" "sti 571" "glivec" "CGP 57148" "st1571"
These names can then be used in queries in any register.
# cleanup
unlink("sqlite_file.sql")
Using a MongoDB database
This example works with a free service here. Note that the user name and password need to be encoded. The format of the connection string is documented at https://docs.mongodb.com/manual/reference/connection-string/.
For recommended databases, see vignette Install R package ctrdata.
# Specify base uri for remote MongoDB server,
# as part of the encoded connection string
db <- nodbi::src_mongo(
# Note: this provides read-only access
url = "mongodb+srv://DWbJ7Wh:bdTHh5cS@cluster0-b9wpw.mongodb.net",
db = "dbperm",
collection = "dbperm")
# Since the above access is read-only,
# just obtain fields of interest:
dbGetFieldsIntoDf(
fields = c("a2_eudract_number",
"e71_human_pharmacology_phase_i"),
con = db)
# _id a2_eudract_number e71_human_pharmacology_phase_i
# 1 2010-024264-18-3RD 2010-024264-18 TRUE
# 2 2010-024264-18-AT 2010-024264-18 TRUE
# 3 2010-024264-18-DE 2010-024264-18 TRUE
# 4 2010-024264-18-GB 2010-024264-18 TRUE
# 5 2010-024264-18-IT 2010-024264-18 TRUE
# 6 2010-024264-18-NL 2010-024264-18 TRUE
rfhb/ctrdata documentation built on May 15, 2024, 3:35 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set(eval = FALSE)
Get started
Attach package ctrdata
library(ctrdata) citation("ctrdata")
Remember to respect the registers' terms and conditions (see ctrOpenSearchPagesInBrowser(copyright = TRUE)). Please cite this package in any publication as follows: Ralf Herold (2024). ctrdata: Retrieve and Analyze Clinical Trials in Public Registers. R package version 1.16.0. https://cran.r-project.org/package=ctrdata
Open register's advanced search page in browser
These functions open the browser, where the user can start searching for trials of interest.
# Please review and respect register copyrights: ctrOpenSearchPagesInBrowser( copyright = TRUE ) # Open browser with example search: ctrOpenSearchPagesInBrowser( url = "cancer&age=under-18", register = "EUCTR" )
Adjust search parameters and execute search in browser
Refine the search until the trials of interest are listed in the browser. The total number of trials that can be retrieved with package ctrdata is intentionally limited to queries with at most 10,000 result records.
Copy address from browser address bar to clipboard
Use functions or keyboard shortcuts according to the operating system. See here for our automation to copy the URLs of a user's queries in any of the supported clinical trial registers.
Get address from clipboard
The next steps are executed in the R environment:
q <- "https://www.clinicaltrialsregister.eu/ctr-search/search?query=cancer&age=under-18&status=completed&phase=phase-one" clipr::write_clip(q)
q <- ctrGetQueryUrl() # * Using clipboard content as register query URL: https://www.clinicaltrialsregister.eu/ctr-search/search?query=cancer&age=under-18&status=completed&phase=phase-one # * Found search query from EUCTR: query=cancer&age=under-18&status=completed&phase=phase-one q # query-term query-register # 1 query=cancer&age=under-18&status=completed&phase=phase-one EUCTR # To check, this opens a browser with the query ctrOpenSearchPagesInBrowser(url = q)
Retrieve protocol-related information, transform, save to database, check
# Count number of trial records ctrLoadQueryIntoDb( queryterm = q, only.count = TRUE )$n # * Found search query from EUCTR: query=cancer&age=under-18&status=completed&phase=phase-one # * Checking trials in EUCTR... # Retrieved overview, multiple records of 103 trial(s) from 6 page(s) to be downloaded (estimate: 10 MB) # [1] 103 # Connect to a database and chose a collection (table) db <- nodbi::src_sqlite( dbname = "sqlite_file.sql", collection = "test" ) # Retrieve records, download into database ctrLoadQueryIntoDb( queryterm = q, con = db ) # * Checking trials in EUCTR... # Retrieved overview, multiple records of 103 trial(s) from 6 page(s) to be downloaded (estimate: 10 MB) # (1/3) Downloading trials... # Note: register server cannot compress data, transfer takes longer (estimate: 100 s) # Download status: 6 done; 0 in progress. Total size: 8.73 Mb (100%)... done! # (2/3) Converting to NDJSON (estimate: 2 s)... # (3/3) Importing records into database... # = Imported or updated 414 records on 103 trial(s) # No history found in expected format. # Updated history ("meta-info" in "test") # $n # [1] 414 # Show which queries have been downloaded into database dbQueryHistory(con = db) # query-timestamp query-register query-records query-term # 1 2024-05-12 17:56:11 EUCTR 414 query=cancer&age=under-18&status=completed&phase=phase-one
With a file-base SQLite database, this takes about 20 seconds for about 300 records, with most of the time needed for internet-retrieval with is slow from this register. Speed is higher with other registers, with using MongoDB and with memory-based SQLite.
Repeat and update a previous query
Instead of "last", an integer number can be specified for querytoupdate that corresponds to the number when using dbQueryHistory().
ctrLoadQueryIntoDb( querytoupdate = "last", con = db )
Depending on the register, an update (differential update) is possible or the original query is executed fully again.
Retrieve results
For EUCTR, result-related trial information has to be requested to be retrieved, because it will take longer to download and store. For CTGOV, any results are always included in the retrieval. Note that trial documents, including any results reports, can be downloaded by specifying parameter documents.path, see help(ctrLoadQueryIntoDb).
ctrLoadQueryIntoDb( queryterm = q, euctrresults = TRUE, con = db ) # * Checking trials in EUCTR... # Retrieved overview, multiple records of 103 trial(s) from 6 page(s) to be downloaded (estimate: 10 MB) # (1/3) Downloading trials... # Note: register server cannot compress data, transfer takes longer (estimate: 100 s) # (2/3) Converting to NDJSON (estimate: 2 s)... # (3/3) Importing records into database... # = Imported or updated 414 records on 103 trial(s) # * Checking results if available from EUCTR for 103 trials: # (1/4) Downloading results... # Download status: 103 done; 0 in progress. Total size: 59.71 Mb (100%)... done! # Download status: 27 done; 0 in progress. Total size: 108.69 Kb (100%)... done! # - extracting results (. = data, F = file[s] and data, x = none): # . . . . . . F . . . . F . . . . . . . F . . . . . . . . . . . . . . . . . . . . . . . . F . F F . F . . # F . . . F F . . . . . F . . . . . . F . . . F . # (2/4) Converting to NDJSON (estimate: 8 s)... # (3/4) Importing results into database (may take some time)... # (4/4) Results history: not retrieved (euctrresultshistory = FALSE) # = Imported or updated results for 76 trials # Updated history ("meta-info" in "test") # $n # [1] 414
With a file-base SQLite database, this takes about 4 minutes for about 300 records, with most of the time needed for merging result- and protocol-related information in SQLite; this is much faster with MongoDB and PostgreSQL.
The download or presence of results is not recorded in dbQueryHistory() because the availability of results increases over time.
Add information from another register
The same collection can be used to store (and analyse) trial information from different registers. Example:
ctrLoadQueryIntoDb( queryterm = "https://classic.clinicaltrials.gov/ct2/results?cond=neuroblastoma&recrs=e&age=0&intr=Drug", con = db ) # * Appears specific for CTGOV Classic website # * Found search query from CTGOV: cond=neuroblastoma&recrs=e&age=0&intr=Drug # * Checking trials using CTGOV Classic website... # Retrieved overview, records of 226 trial(s) are to be downloaded (estimate: 1.8 MB) # (1/3) Downloading trial file... # Download status: 1 done; 0 in progress. Total size: 1.59 Mb (100%)... done! # (2/3) Converting to NDJSON (estimate: 10 s)... # (3/3) Importing records into database... # = Imported or updated 226 trial(s) # Updated history ("meta-info" in "test") # $n # [1] 226
With a file-base SQLite database, this takes about 10 seconds for about 200 records.
Note that in this example, a warning message may be issued from importing an NDJSON file with trial records. The warning arises from the high level of complexity of some of the XML content of some of the trial records. The issue can be resolved by increasing in the operating system the stack size available to R, see: https://github.com/rfhb/ctrdata/issues/22
Add records from CTIS register into the same collection
As of May 2024, more than 750 trials are publicly accessible in CTIS, which has to be used since January 2023 for new clinical trials in the European Union (EU). Queries in the CTIS search interface can be automatically copied to the clipboard so that a user can paste them into queryterm, see here.
# Retrieve trials from another register: ctrLoadQueryIntoDb( queryterm = "ageGroupCode=2", register = "CTIS", con = db ) # * Found search query from CTIS: ageGroupCode=2 # * Checking trials in CTIS... # (1/5) Downloading trials list . found 78 trials # (2/5) Downloading and processing part I and parts II... (estimate: 10 Mb) # Download status: 78 done; 0 in progress. Total size: 16.26 Mb (100%)... done! # . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . # (3/5) Downloading and processing additional data: # publicevents, summary, layperson, csr, cm, inspections, publicevaluation (estimate: 7 Mb) # Download status: 160 done; 0 in progress. Total size: 4.26 Mb (100%)... done! # 78 # (4/5) Importing records into database... # (5/5) Updating with additional data: . . . # = Imported 78, updated 78 / 78 / 1 record(s) on 78 trial(s) # Updated history ("meta-info" in "test") # $n # [1] 78
Add personal annotations
When downloading trial information, the user can specify an annotation to all records that are downloaded. By default, annotations are accumulated if trial records are loaded again or updated; alternatively, annotations can be replaced.
Annotations are useful for analyses, for example to specially identify subsets of records in the database.
ctrLoadQueryIntoDb( queryterm = "https://classic.clinicaltrials.gov/ct2/results?cond=neuroblastoma&recrs=e&age=0&intr=Drug&cntry=DE", annotation.text = "site_DE ", annotation.mode = "append", con = db ) # * Appears specific for CTGOV Classic website # * Found search query from CTGOV: cond=neuroblastoma&recrs=e&age=0&intr=Drug&cntry=DE # * Checking trials using CTGOV Classic website... # Retrieved overview, records of 15 trial(s) are to be downloaded (estimate: 0.12 MB) # (1/3) Downloading trial file... # Download status: 1 done; 0 in progress. Total size: 86.91 Kb (100%)... done! # (2/3) Converting to NDJSON (estimate: 0.8 s)... # (3/3) Importing records into database... # = Imported or updated 15 trial(s) # = Annotated retrieved records (15 records) # Updated history ("meta-info" in "test") # $n # [1] 15
Add information using trial identifiers
When identifiers of clinical trials of interest are already known, this example shows how they can be processed to import the trial information into a database collection. This involves constructing a query that combines the identifiers and then iterating over the sets of identifiers. Note to combine identifiers using "+OR+" into the queryterm, and that register has to be specified.
# ids of trials of interest ctIds <- c( "NCT00001209", "NCT00001436", "NCT00187109", "NCT01516567", "NCT01471782", "NCT03042429", "NCT00357084", "NCT00357500", "NCT00365755", "NCT00407433", "NCT00410657", "NCT00436657", "NCT00436852", "NCT00445965", "NCT00450307", "NCT00450827", "NCT00471679", "NCT00486564", "NCT00492167", "NCT00499616", "NCT00503724", "NCT00509353", "NCT00520936", "NCT00536601", "NCT00567567", "NCT00578864", "NCT00601003", "NCT00644696", "NCT00646230", "NCT00659984", "NCT00716976", "NCT00743496", "NCT00793845", "NCT00806182", "NCT00831844", "NCT00867568", "NCT00877110", "NCT00885326", "NCT00918320", "NCT00923351", "NCT00939770", "NCT00960739" ) # split into sets of each 25 trial ids # (larger sets e.g. 50 may still work) idSets <- split(ctIds, ceiling(seq_along(ctIds) / 25)) # variable to collect import results result <- NULL # iterate over sets of trial ids for (idSet in idSets) { # import setResult <- ctrLoadQueryIntoDb( # for CTGOV Classic, EUCTR, ISRCTN use: # queryterm = paste0(idSet, collapse = "+OR+"), # for CTGOV2 use: queryterm = paste0("term=", paste0(idSet, collapse = " ")), # specify register that holds the information register = "CTGOV2", con = db ) # append results result <- c(result, list(setResult)) } # inspect results as.data.frame(do.call(rbind, result))[, c("n", "failed")] # n failed # 1 25 NULL # 2 17 NULL
Note that from CTIS, trial information identified by trial identifiers can be retrieved only one-by-one, repeating queries for each trial such as https://euclinicaltrials.eu/app/#/search?number=2023-503994-39-00.
Find synonyms of active substance names
Not all registers automatically expand search terms to include alternative terms, such as codes and other names of active substances. To obtain a character vector of synonyms for any active substance name, use:
ctrFindActiveSubstanceSynonyms( activesubstance = "imatinib" ) # [1] "imatinib" "gleevec" "sti 571" "glivec" "CGP 57148" "st1571"
These names can then be used in queries in any register.
# cleanup unlink("sqlite_file.sql")
Using a MongoDB database
This example works with a free service here. Note that the user name and password need to be encoded. The format of the connection string is documented at https://docs.mongodb.com/manual/reference/connection-string/.
For recommended databases, see vignette Install R package ctrdata.
# Specify base uri for remote MongoDB server, # as part of the encoded connection string db <- nodbi::src_mongo( # Note: this provides read-only access url = "mongodb+srv://DWbJ7Wh:bdTHh5cS@cluster0-b9wpw.mongodb.net", db = "dbperm", collection = "dbperm") # Since the above access is read-only, # just obtain fields of interest: dbGetFieldsIntoDf( fields = c("a2_eudract_number", "e71_human_pharmacology_phase_i"), con = db) # _id a2_eudract_number e71_human_pharmacology_phase_i # 1 2010-024264-18-3RD 2010-024264-18 TRUE # 2 2010-024264-18-AT 2010-024264-18 TRUE # 3 2010-024264-18-DE 2010-024264-18 TRUE # 4 2010-024264-18-GB 2010-024264-18 TRUE # 5 2010-024264-18-IT 2010-024264-18 TRUE # 6 2010-024264-18-NL 2010-024264-18 TRUE
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.