Warning: under active development 2023-09, breaking changes possible
omopcept provides access to a subset of OMOP conCEPTs (without
the cons!) and flexible tidyverse compatible R functions for querying.
It includes concise named copies of functions designed for interactive
use e.g. oid() and onames() to search concept ids and names
respectively. For example the line below can be used to return all ~
1000 OMOP ids for SNOMED codes for clinical drugs starting with A.
onames("^a",d="DRUG",v="SNOMED",cc="Clinical Drug")
Install the development version of omopcept with:
# install.packages("remotes")
remotes::install_github("andysouth/omopcept")On initial use omopcept downloads a parquet file containing the concepts to a local package cache where it can be accessed in future sessions. On use it opens the file (with omop_concept()) but doesn’t read in the data to save time. The arrow R package allows parquet files to be opened and queried in dplyr pipelines without having to read in the data. e.g. the code below will return just the top rows of the concept table.
library(omopcept)
omop_concept() |>
head() |>
dplyr::collect()| full name | quick interactive name | action |
|---|---|---|
omop_names() |
onames() |
search concepts by parts of names |
omop_id() |
oid() |
search for a concept_id |
omop_join_name() |
ojoin() |
join an omop name column onto a table with an id column |
omop_join_name_all() |
ojoinall() |
join omop names columns onto all id columns in a table |
omop_ancestors() |
oance() |
return ancestors of a concept |
omop_descendants() |
odesc() |
return descendants of a concept |
omop_relations() |
orels() |
return (immediate) relations of a concept including the nature of the relationship e.g. ‘Is a’ |
omop_concept() |
oc() |
return reference to concept table (for use in dplyr pipelines) |
omop_concept_ancestor() |
oca() |
return reference to concept ancestor table |
omop_concept_relationship() |
ocr() |
return reference to concept relationship table |
The OMOP Common Data Model is an open standard for health data. “[It is] designed to standardize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence”.
OMOP is maintained by OHDSI (pronounced “Odyssey”). “The Observational
Health Data Sciences and Informatics program is a multi-stakeholder,
interdisciplinary collaborative that strives to improve medical decision
making and bring better health outcomes to patients around the world.”
OMOP concepts can be searched and downloaded from Athena – the OHDSI
vocabularies repository. This package
provides R tools to interact with the concepts in a more reproducible
way.
OMOP vocab data downloaded from Athena includes a tables called CONCEPT.csv, CONCEPT_ANCESTOR.csv and CONCEPT_RELATIONSHIP.csv that we saved in parquet format for use in this package.
omopcept downloads a selection of vocabularies and stores them locally the first time you use it (in the recommended data location for R packages). The download does not need to be repeated until you update the package.
We plan to allow the location of the concept files to be specified so that in a future version users could point it to their own downloaded files.
| fields | about | query_arguments |
|---|---|---|
| concept_id | unique id | c_ids |
| concept_name | descriptive name | pattern |
| domain_id | e.g. drug, measurement | d_ids |
| vocabulary_id | e.g. LOINC, SNOMED | v_ids |
| concept_class_id | e.g. Clinical Observation, Organism | cc_ids |
| standard_concept | standard or not | standard |
| concept_code | source code | |
| valid_start_date | ||
| valid_end_date | ||
| invalid_reason |
omop_names("chemotherapy", v_ids="LOINC")
#> # A tibble: 71 × 10
#> conce…¹ conce…² domai…³ vocab…⁴ conce…⁵ stand…⁶ conce…⁷ valid_st…⁸ valid_en…⁹
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <date> <date>
#> 1 3010410 Chemot… Observ… LOINC Clinic… S 11486-8 1970-01-01 2099-12-31
#> 2 3002377 Chemot… Measur… LOINC Clinic… S 21881-8 1970-01-01 2099-12-31
#> 3 3011998 Date 1… Observ… LOINC Clinic… S 21927-9 1970-01-01 2099-12-31
#> 4 3003037 Chemot… Measur… LOINC Clinic… S 21946-9 1970-01-01 2099-12-31
#> 5 3000897 Reason… Measur… LOINC Clinic… S 21951-9 1970-01-01 2099-12-31
#> 6 3014397 Chemot… Measur… LOINC Clinic… S 21967-5 1970-01-01 2099-12-31
#> 7 3027104 Chemot… Measur… LOINC Clinic… S 22041-8 1970-01-01 2099-12-31
#> 8 3037369 2nd co… Measur… LOINC Clinic… S 42045-5 2005-08-05 2099-12-31
#> 9 3032293 3rd co… Measur… LOINC Clinic… S 42051-3 2005-08-05 2099-12-31
#> 10 3028808 4th co… Measur… LOINC Clinic… S 42057-0 2005-08-05 2099-12-31
#> # … with 61 more rows, 1 more variable: invalid_reason <chr>, and abbreviated
#> # variable names ¹concept_id, ²concept_name, ³domain_id, ⁴vocabulary_id,
#> # ⁵concept_class_id, ⁶standard_concept, ⁷concept_code, ⁸valid_start_date,
#> # ⁹valid_end_date
omop_names("chemotherapy", v_ids=c("LOINC","SNOMED"), d_ids=c("Observation","Procedure"))
#> # A tibble: 297 × 10
#> conce…¹ conce…² domai…³ vocab…⁴ conce…⁵ stand…⁶ conce…⁷ valid_st…⁸ valid_en…⁹
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <date> <date>
#> 1 3.01e6 Chemot… Observ… LOINC Clinic… S 11486-8 1970-01-01 2099-12-31
#> 2 3.01e6 Date 1… Observ… LOINC Clinic… S 21927-9 1970-01-01 2099-12-31
#> 3 3.05e6 Chemot… Observ… LOINC Survey S 45841-4 1970-01-01 2099-12-31
#> 4 4.08e7 Chemot… Observ… LOINC Survey S 54992-3 2009-01-26 2099-12-31
#> 5 4.08e7 Chemot… Observ… LOINC Survey S 54993-1 2009-01-26 2099-12-31
#> 6 4.08e7 Type o… Observ… LOINC Clinic… S 63938-5 2011-03-15 2099-12-31
#> 7 4.08e7 Cancer… Observ… LOINC Clinic… S 66178-5 2011-06-02 2099-12-31
#> 8 4.08e7 Have y… Observ… LOINC Clinic… S 67446-5 2011-07-11 2099-12-31
#> 9 4.08e7 Histor… Observ… LOINC Clinic… S 67469-7 2011-07-12 2099-12-31
#> 10 3.63e7 Chemot… Observ… LOINC Clinic… S 88060-9 2018-06-15 2099-12-31
#> # … with 287 more rows, 1 more variable: invalid_reason <chr>, and abbreviated
#> # variable names ¹concept_id, ²concept_name, ³domain_id, ⁴vocabulary_id,
#> # ⁵concept_class_id, ⁶standard_concept, ⁷concept_code, ⁸valid_start_date,
#> # ⁹valid_end_dateHelps to interpret OMOP data.
data.frame(concept_id=(c(3571338L,4002075L))) |>
omop_join_name()
#> concept_id concept_name
#> 1 3571338 Problem behaviour
#> 2 4002075 BLUE LOTION
data.frame(drug_concept_id=(c(4000794L,4002592L))) |>
omop_join_name(namestart="drug")
#> drug_concept_id drug_concept_name
#> 1 4000794 BUZZ OFF
#> 2 4002592 DEXAMETHASONE INJECTIONdata.frame(concept_id=(c(3571338L,3655355L)),
drug_concept_id=(c(4000794L,35628998L))) |>
omop_join_name_all()
#> concept_id drug_concept_id concept_name drug_concept_name
#> 1 3571338 4000794 Problem behaviour BUZZ OFF
#> 2 3655355 35628998 Erectile dysfunction ViagraThe vocabularies are a default download from Athena with a few extra vocabs added. Later we may offer option to add other vocabularies.
library(dplyr)
library(ggplot2)
library(forcats)
concept_summary <-
omop_concept() |>
count(vocabulary_id, sort=TRUE) |>
collect()
ggplot(concept_summary,aes(y=reorder(vocabulary_id,n),x=n,col=vocabulary_id)) +
geom_point() +
labs(y = "vocabulary_id") +
guides(col="none") +
theme_minimal()