The purpose of this vignette is to provide details on how FHIR documents are handled in BiocFHIR.
This text uses R commands that will work for an R (version 4.2 or greater) in which BiocFHIR (version 0.0.14 or greater) has been installed. The source codes are always available at github and may be available for installation by other means.
We conclude this vignette with a very brief example of the use of consjson to interrogate the FHIR JSON documents directly.
In the “Upper level FHIR concepts” vignette, we used the following code to get a peek at the information structure in a single document representing a Bundle associated with a patient.
tfile = dir(system.file("json", package="BiocFHIR"), full=TRUE)
peek = jsonlite::fromJSON(tfile)
names(peek)## [1] "resourceType" "type" "entry"## [1] "Bundle"## [1] "fullUrl" "resource" "request"## [1] 72## [1] "data.frame"## [1] 301 72## [1] "resourceType" "id" "text" "extension" "identifier"
## [6] "name"Some of the complexity of working with FHIR JSON in R in this way can be seen in the following:
## [1] 6 10 11 12 13 14## coding text
## 1 http://s.... Self care## [[1]]
## system code display
## 1 http://snomed.info/sct 326051000000105 Self care## [1] "food"Elements of category can be data.frame or atomic.
This is a consequence of naive use of jsonlite::fromJSON.
When we reduce the transformations attempted by fromJSON,
empty fields are not propagated.
peek2 = jsonlite::fromJSON(dir(system.file("json", package="BiocFHIR"), full=TRUE), simplifyVector=FALSE)
lapply(peek2$entry[1:5], function(x) names(x[["resource"]]))## [[1]]
## [1] "resourceType" "id" "text"
## [4] "extension" "identifier" "name"
## [7] "telecom" "gender" "birthDate"
## [10] "deceasedDateTime" "address" "maritalStatus"
## [13] "multipleBirthBoolean" "communication"
##
## [[2]]
## [1] "resourceType" "id" "identifier" "active" "type"
## [6] "name" "telecom" "address"
##
## [[3]]
## [1] "resourceType" "id" "identifier" "active" "name"
## [6] "telecom" "address" "gender"
##
## [[4]]
## [1] "resourceType" "id" "status" "class"
## [5] "type" "subject" "participant" "period"
## [9] "serviceProvider"
##
## [[5]]
## [1] "resourceType" "id" "status"
## [4] "subject" "encounter" "period"
## [7] "participant" "managingOrganization"Because the JSON ingestion does not attempt to simplify table-like content, we have a list of lists with varying depths of nesting.
We can tabulate the resource types using
## rtyvec
## AllergyIntolerance CarePlan CareTeam
## 8 3 3
## Claim Condition DiagnosticReport
## 46 15 3
## Encounter ExplanationOfBenefit Immunization
## 37 37 10
## MedicationRequest Observation Organization
## 9 114 3
## Patient Practitioner Procedure
## 1 3 9Let’s use peek2 to extract
Conditions recorded on the patient.
## [1] 15## List of 3
## $ fullUrl : chr "urn:uuid:adc05f8e-0e90-4217-b1d7-fa41b8e33638"
## $ resource:List of 9
## ..$ resourceType : chr "Condition"
## ..$ id : chr "adc05f8e-0e90-4217-b1d7-fa41b8e33638"
## ..$ clinicalStatus :List of 1
## .. ..$ coding:List of 1
## .. .. ..$ :List of 2
## .. .. .. ..$ system: chr "http://terminology.hl7.org/CodeSystem/condition-clinical"
## .. .. .. ..$ code : chr "active"
## ..$ verificationStatus:List of 1
## .. ..$ coding:List of 1
## .. .. ..$ :List of 2
## .. .. .. ..$ system: chr "http://terminology.hl7.org/CodeSystem/condition-ver-status"
## .. .. .. ..$ code : chr "confirmed"
## ..$ code :List of 2
## .. ..$ coding:List of 1
## .. .. ..$ :List of 3
## .. .. .. ..$ system : chr "http://snomed.info/sct"
## .. .. .. ..$ code : chr "446096008"
## .. .. .. ..$ display: chr "Perennial allergic rhinitis"
## .. ..$ text : chr "Perennial allergic rhinitis"
## ..$ subject :List of 1
## .. ..$ reference: chr "urn:uuid:eedf9986-9cf8-4e90-bf68-12a6dd9a31c2"
## ..$ encounter :List of 1
## .. ..$ reference: chr "urn:uuid:9c95930d-83a4-4a89-9bbb-8e540e729233"
## ..$ onsetDateTime : chr "1973-06-29T07:25:09-04:00"
## ..$ recordedDate : chr "1973-06-29T07:25:09-04:00"
## $ request :List of 2
## ..$ method: chr "POST"
## ..$ url : chr "Condition"Digging out the data and metadata on the first condition recorded (Perennial allergic rhinitis), is somewhat complex using R. Direct operations on JSON with JMESPATH might be more effective, but we postpone this investigation.
In the process_fhir_bundle function of BiocFHIR
we allow jsonlite::fromJSON to conduct some
simplification of list structures amenable to representation
as tables (data.frames).
## BiocFHIR FHIR.bundle instance.
## resource types are:
## AllergyIntolerance CarePlan ... Patient ProcedureFor the reports of Conditions, we extract specific fields that are commonly used in the Synthea examples. Other bundle sets may use different fields.
## [1] 15 5The fields collected in process_Condition are specified
in FHIR_retention_schemas(). Eventually this will
need to become a user-specified element of ingestion and
transformation.
We’ve shown how we can operate on FHIR documents from the Synthea
project using specific schemas to select elements from lists
produced by parsing JSON. The FHIR specification is very
flexible, and the process_* methods defined here may
not work for FHIR documents from other sources.
The jsoncons library provides C++ code for parsing and filtering JSON, and the rjsoncons package is available to support JMESPATH queries.
In this example, we’ll take 4 Synthea FHIR documents and extract patient addresses to a data.frame.
z = make_test_json_set()
myl = lapply(z[1:4], jsonlite::fromJSON) # list that rconsjson will convert to JSON
library(rjsoncons)
tmp = jmespath(myl, "[*].entry[0].resource.address") |> jsonlite::fromJSON()
do.call(rbind,lapply(tmp, function(x) x[,-(1:2)]))## city state postalCode country
## 1 Malden Massachusetts 02148 US
## 2 Springfield Massachusetts 01013 US
## 3 Brewster Massachusetts 02631 US
## 4 Webster Massachusetts 01570 USThe JMESPATH query projects from all documents via the initial [*].
It then retrieves the address element from the resource element of
the first ([0]) entry. An overview of the hierarchical structure of
myl can be obtained using listviewer::jsonedit.
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rjsoncons_1.0.0 jsonlite_1.8.3 DT_0.26 BiocFHIR_1.0.0
## [5] BiocStyle_2.26.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 xfun_0.34 bslib_0.4.0
## [4] purrr_0.3.5 vctrs_0.5.0 generics_0.1.3
## [7] htmltools_0.5.3 stats4_4.2.1 yaml_2.3.6
## [10] utf8_1.2.2 rlang_1.0.6 jquerylib_0.1.4
## [13] later_1.3.0 pillar_1.8.1 glue_1.6.2
## [16] DBI_1.1.3 BiocGenerics_0.44.0 lifecycle_1.0.3
## [19] stringr_1.4.1 visNetwork_2.1.2 htmlwidgets_1.5.4
## [22] evaluate_0.17 knitr_1.40 fastmap_1.1.0
## [25] crosstalk_1.2.0 httpuv_1.6.6 fansi_1.0.3
## [28] Rcpp_1.0.9 xtable_1.8-4 promises_1.2.0.1
## [31] BiocManager_1.30.19 cachem_1.0.6 graph_1.76.0
## [34] mime_0.12 digest_0.6.30 stringi_1.7.8
## [37] bookdown_0.29 dplyr_1.0.10 shiny_1.7.3
## [40] cli_3.4.1 tools_4.2.1 magrittr_2.0.3
## [43] sass_0.4.2 tibble_3.1.8 tidyr_1.2.1
## [46] BiocBaseUtils_1.0.0 pkgconfig_2.0.3 ellipsis_0.3.2
## [49] assertthat_0.2.1 rmarkdown_2.17 R6_2.5.1
## [52] igraph_1.3.5 compiler_4.2.1