The purpose of this vignette is to provide details on how FHIR documents are transformed to tables 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.
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"We perform a first stage of transformation with process_fhir_bundle:
## BiocFHIR FHIR.bundle instance.
## resource types are:
## AllergyIntolerance CarePlan ... Patient ProcedureEach processed bundle is a collection of data.frame instances, formed by splitting the input “entry” element by “resourceType”. These data.frames are mostly filled with NA missing values, but some columns have been ingested as lists. Executive decisions are made in the package regarding which columns are likely to hold useful information. Thus we have
## [1] 127 11A list of vectors of field names serves as the basis for filtering JSON elements into records for tabulation.
## $Condition
## [1] "id" "onsetDateTime" "code" "subject"
##
## $AllergyIntolerance
## [1] "id" "onsetDateTime" "code" "patient"
## [5] "category"
##
## $CarePlan
## [1] "id" "activity" "subject" "category"
##
## $Claim
## [1] "id" "provider" "patient" "billablePeriod"
## [5] "insurance" "created"
##
## $Encounter
## [1] "id" "type" "subject" "period"
## [5] "serviceProvider" "class"
##
## $MedicationRequest
## [1] "id" "subject"
## [3] "status" "requester"
## [5] "medicationCodeableConcept"
##
## $Observation
## [1] "id" "subject" "code"
## [4] "valueQuantity" "category" "effectiveDateTime"
## [7] "issued" "component"
##
## $Procedure
## [1] "id" "subject" "status" "performedPeriod"
## [5] "code"
##
## $Patient
## [1] "id" "identifier" "name"
## [4] "telecom" "gender" "birthDate"
## [7] "address" "maritalStatus" "multipleBirthBoolean"
## [10] "communication" "active"
##
## $Immunization
## [1] "id" "patient" "vaccineCode"
## [4] "occurrenceDateTime"Because each observation on Blood Pressure includes a “component” element with two elements (for systolic and diastolic blood pressure readings), special code is required to map the metadata for the Blood Pressure observations to the specific values for each component.
The process_* functions in BiocFHIR address various
resource types. As of version 0.0.15 we have
## [1] "process_AllergyIntolerance" "process_CarePlan"
## [3] "process_Claim" "process_Condition"
## [5] "process_Encounter" "process_Immunization"
## [7] "process_MedicationRequest" "process_Observation"
## [9] "process_Patient" "process_Procedure"There is no guarantee that any given bundle with have resources among all these types.
Bundles are not guaranteed to have any specific resources. To assemble all information on conditions recorded in the Synthea sample, we must program defensively. We obtain the indices of bundles possessing a “Condition” resource, and then combine the resulting tables, which are designed to have a common set of columns.
data("allin", package="BiocFHIR")
hascond = sapply(allin, function(x)length(x$Condition)>0)
oo = do.call(rbind, lapply(allin[hascond], function(x)process_Condition(x$Condition)))
dim(oo)## [1] 406 5## [1] 49The most commonly reported conditions in the sample are:
##
## Prediabetes Body mass index 30+ - obesity (finding)
## 16 17
## Normal pregnancy Acute bronchitis (disorder)
## 22 27
## Acute viral pharyngitis (disorder) Viral sinusitis (disorder)
## 30 63## 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