The goal of rcprd is to simplify the process of extracting and processing CPRD Aurum data into an ‘analysis-ready’ dataset which can be used for statistical analyses. This process is somewhat difficult in R, as the raw data is very large, provided in a large number of .txt files, which cannot all be read into the R workspace. rcprd utilises RSQLite to create SQLite databases which are stored on the hard disk. These are then queried to extract the required information for a cohort of interest. The processes follow closely that from the rEHR package.
For a detailed guide on how to use rcprd please see the user-guide vignette.
Installation
The package can be installed from CRAN as follows:
# install.packages("rcprd")You can install the development version of rcprd from GitHub with:
# install.packages("devtools")
# devtools::install_github("alexpate30/rcprd")Example
This is a basic example which shows you how to create a dataset containing age. All data provided with package and utilised in this example is simulated.
Load rcprd:
Create cohort based on patient files:
pat <- extract_cohort(filepath = system.file("aurum_data", package = "rcprd"))
str(pat)
#> 'data.frame': 12 obs. of 12 variables:
#> $ patid : chr "1" "2" "3" "4" ...
#> $ pracid : int 49 79 98 53 62 54 49 79 98 53 ...
#> $ usualgpstaffid: chr "6" "11" "43" "72" ...
#> $ gender : int 2 1 1 2 2 1 2 1 1 2 ...
#> $ yob : int 1984 1932 1930 1915 1916 1914 1984 1932 1930 1915 ...
#> $ mob : int NA NA NA NA NA NA NA NA NA NA ...
#> $ emis_ddate : Date, format: "1976-11-21" "1979-02-14" ...
#> $ regstartdate : Date, format: "1940-07-24" "1929-02-23" ...
#> $ patienttypeid : int 58 21 81 10 45 85 58 21 81 10 ...
#> $ regenddate : Date, format: "1996-08-25" "1945-03-19" ...
#> $ acceptable : int 1 0 1 0 0 1 1 0 1 0 ...
#> $ cprd_ddate : Date, format: "1935-03-17" "1932-02-05" ...Connect to an SQLite database (in this example, we create a temporary file):
aurum_extract <- connect_database(file.path(tempdir(), "temp.sqlite"))Read in medical data (from the observation files) and add to the SQLite database.
cprd_extract(db = aurum_extract,
filepath = system.file("aurum_data", package = "rcprd"),
filetype = "observation")
#> | | | 0%
#> Adding C:/Program Files/R/R-4.4.2/library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_001.txt 2024-11-14 15:20:22.632475
#> | |======================= | 33%
#> Adding C:/Program Files/R/R-4.4.2/library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_002.txt 2024-11-14 15:20:22.746196
#> | |=============================================== | 67%
#> Adding C:/Program Files/R/R-4.4.2/library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_003.txt 2024-11-14 15:20:22.83656
#> | |======================================================================| 100%Query the database for specific codes and store in an R object using the db_query function:
### Create codelist
codelist <- "187341000000114"
### Query for observations with this code
db_query(db_open = aurum_extract,
tab ="observation",
codelist_vector = codelist)
#> patid consid pracid obsid obsdate enterdate staffid parentobsid
#> <char> <char> <int> <char> <num> <num> <char> <char>
#> 1: 1 42 1 81 -5373 4302 85 35
#> 2: 2 56 1 77 -5769 -13828 24 4
#> 3: 6 40 1 41 -14727 -6929 98 80
#> medcodeid value numunitid obstypeid numrangelow numrangehigh probobsid
#> <char> <num> <int> <int> <num> <num> <char>
#> 1: 187341000000114 84 79 67 24 22 5
#> 2: 187341000000114 46 92 81 56 30 18
#> 3: 187341000000114 28 20 5 41 97 92Add an index date to the patient file, which we will extract variables relative to:
pat$fup_start <- as.Date("01/01/2020", format = "%d/%m/%Y")Extract a ‘history of’ type variable, which will be equal to 1 if an individual has a record with the specified medcodeid prior to the index date, and equal 0 otherwise.
ho <- extract_ho(pat,
codelist_vector = codelist,
indexdt = "fup_start",
db_open = aurum_extract,
tab = "observation",
return_output = TRUE)
str(ho)
#> 'data.frame': 12 obs. of 2 variables:
#> $ patid: chr "1" "2" "3" "4" ...
#> $ ho : int 1 1 0 0 0 1 0 0 0 0 ...Merge the patient file with the ‘history of’ variable to create an analysis-ready dataset:
### Recursive merge
analysis.ready.pat <- merge(pat[,c("patid", "fup_start", "gender")], ho, by.x = "patid", by.y = "patid", all.x = TRUE)
analysis.ready.pat
#> patid fup_start gender ho
#> 1 1 2020-01-01 2 1
#> 2 10 2020-01-01 2 0
#> 3 11 2020-01-01 2 0
#> 4 12 2020-01-01 1 0
#> 5 2 2020-01-01 1 1
#> 6 3 2020-01-01 1 0
#> 7 4 2020-01-01 2 0
#> 8 5 2020-01-01 2 0
#> 9 6 2020-01-01 1 1
#> 10 7 2020-01-01 2 0
#> 11 8 2020-01-01 1 0
#> 12 9 2020-01-01 1 0Currently functionality exists in rcprd to extract medical data from the observation file (including specific functions for extracting test data) and medication data from the drugissue file. Low level functions exist to allow the user to query the RSQLite database and write their own functions to define variables of interest. There are mid-level functions which allow users to extract variables of certain types (‘history of’, ‘time to event’, and ‘most recent test result’). There are then very high level functions which allow users to extract specific variables, such as body mass index, systolic blood pressure, smoking status, diabetes status, etc. There are all functions where decisions have been made over how to define variables. Be sure to check the code to make sure it matches with your definition. For example, extract_diabetes will return a categorical variable with three categories, Absent, type1 and type2. If an individual has a record for both type 1 and type 2 diabetes (according to the users code lists), extract_diabetes will assign the individual to the group type1.
Package maintainence
This parts of this package which create the SQLite database are somewhat dependent on the structure of the raw CPRD Aurum data. For example, the functions to read in the raw text files (e.g. extract_txt_obs) are hard coded to format variables with specific names in a certain way (e.g. convert obsdate from a character variable to a date variable). Over time, the structure of the CPRD Aurum data may change, which could impact the utility of this package. We will endeavor to keep rcprd updated with new releases of CPRD Aurum. However, where possible, we have tried to protect against this by giving the user flexible options as well as the defaults. For example, add_to_database defaults to using extract_txt_obs to read in the raw text data when filetype = "observation" is specified. However, there is also an option extract_txt_func, which allows the users to specify their own function to read in the text data, and will override the use of extract_txt_obs.
Despite this, there may have been breaking points we haven’t thought of, in which case please let us know.
Getting help
If you encounter a bug, please file an issue with a minimal reproducible example on GitHub.