Adds all the .txt files in a directory, with certain file names, to an SQLite database on the hard disk.
cprd_extract.RdAdd the raw data from more than one of the CPRD flatfiles to an SQLite database.
Usage
cprd_extract(
db,
filepath,
filetype = c("observation", "drugissue", "referral", "problem", "consultation",
"hes_primary", "death"),
nrows = -1,
select = NULL,
subset_patids = NULL,
use_set = FALSE,
extract_txt_func = NULL,
str_match = NULL,
tablename = NULL
)Arguments
- db
An open SQLite database connection created using RSQLite::dbConnect.
- filepath
Path to directory containing .txt files.
- filetype
Type of CPRD Aurum file (observation, drugissue, referral, problem, consultation, hes_primary, death)
- nrows
Number of rows to read in from .txt file.
- select
Vector of column names to select before adding to the SQLite database.
- subset_patids
Patient id's to subset the .txt file on before adding to the SQLite database.
- use_set
Reduce subset_patids to just those with a corresponding set value to the .txt file being read in. Can greatly improve computational efficiency when subset_patids is large. See vignette XXXX for more details.
- extract_txt_func
User-defined function to read the .txt file into R.
- str_match
Character vector to match on when searching for file names to add to the database.
- tablename
Name of table in SQLite database that the data will be added to.
Details
By default, will add files that contain filetype in the file name to a table named filetype in the SQLite database.
If str_match is specified, will add files that contain str_match in the file name to a table named str_match in the SQLite database.
In this case, filetype will still be used to choose which function reads in and formats the raw data, although this can be overwritten with
extract_txt_func. If argument tablename is specified, data will be added to a table called tablename in the SQLite database.
Currently, rcprd only deals with filetype = c("observation", "drugissue", "referral", "problem", "consultation", "hes_primary", "death") by default.
However, by using str_match and extract_txt_func, the user can manually search for files with any string in the file name, and read them in
and format using a user-defined function. This means the user is not restricted to only adding the pre-defined file types to the SQLite database.
If use_set = FALSE, then subset_patids should be a vector of patid's that the .txt files will be subsetted on before adding to the SQLite database.
If use_set = TRUE, then subset_patids should be a dataframe with two columns, patid and set, where set corresponds to the number in the file name
following the word 'set'. This functionality is provided to increase computational efficiency when subsetting to a cohort of patients which is very large (millions).
This can be a computationally expensive process as each flatfile being read in, must be cross matched with a large vector .
The CPRD flatfiles are split up into groups which can be identified from their naming convention. Patients from set 1, will have their data
in DrugIssue, Observation, etc, all with the same "set" suffix in the flatfile name. We can utilise this to speed up the process of subsetting
the data from the flatfiles to only those with patids in subset.patid. Instead we subset to those with patids in subset_patids, and with the
corresponding value of "set", which matches the suffix "set" in the CPRD flatfile file name. For example, patients in the Patient file which had
suffix "set1", will have their medical data in the Observation file with suffix "set1". When subsetting the Observation file to those in
subset_patids (our cohort), we only need to do so for patients who were also in the patient file with suffix "set1".
If the cohort of patients for which you want to subset the data to is very small, the computational gains from this argument are minor and it
can be ignored.
The function for reading in the .txt file will be chosen from a set of functions provided with rcprd, based on the filetype (filetype).
extract_txt_func does not need to be specified unless wanting to manually define the function for doing this. This may be beneficial if wanting to
change variable formats, or if the variables in the .txt files change in future releases of CPRD AURUM and rcprd has not been updated.
Examples
## Create connection to a temporary database
aurum_extract <- connect_database(file.path(tempdir(), "temp.sqlite"))
## Add observation data from all observation files in specified directory
cprd_extract(db = aurum_extract,
filepath = system.file("aurum_data", package = "rcprd"),
filetype = "observation")
#>
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#> Adding /home/runner/work/_temp/Library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_001.txt 2024-11-15 11:32:15.483492
#>
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|======================= | 33%
#> Adding /home/runner/work/_temp/Library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_002.txt 2024-11-15 11:32:15.496181
#>
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|=============================================== | 67%
#> Adding /home/runner/work/_temp/Library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_003.txt 2024-11-15 11:32:15.507304
#>
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## Query database
RSQLite::dbGetQuery(aurum_extract, 'SELECT * FROM observation', n = 3)
#> patid consid pracid obsid obsdate enterdate staffid parentobsid
#> 1 1 33 1 100 -15931 -994 79 95
#> 2 1 66 1 46 -13782 -15232 34 17
#> 3 1 41 1 53 -20002 8845 35 79
#> medcodeid value numunitid obstypeid numrangelow numrangehigh
#> 1 498521000006119 48 16 20 28 86
#> 2 401539014 22 1 2 27 8
#> 3 13483031000006114 17 78 13 87 41
#> probobsid
#> 1 54
#> 2 35
#> 3 74
## clean up
RSQLite::dbDisconnect(aurum_extract)
unlink(file.path(tempdir(), "temp.sqlite"))