extract.RdThis is the workhorse function that transcribes data from EMAP OPS from OMOP CDM 5.3.1 to a standard rectangular table with 1 column per dataitem and 1 row per time per patient.
extract( connection, target_schema, visit_occurrence_ids = NULL, concept_names = NULL, relabel = NULL, coalesce_rows = dplyr::first, chunk_size = 5000, cadance = 1, dttmstamp = FALSE )
| connection | a EMAP database connection |
|---|---|
| target_schema | the target database schema |
| visit_occurrence_ids | an integer vector of episode_ids or NULL. If NULL (the default) then all visits are extracted. |
| concept_names | a vector of OMOP concept_ids to be extracted |
| relabel | a character vector of names you want to relabel OMOP codes
as, or NULL (the default) if you do not want to relabel. Given in the same
order as |
| coalesce_rows | a vector of summary functions that you want to summarise
data that is contributed higher than your set cadance. Given in the same
order as |
| chunk_size | a chunking parameter to help speed up the function and manage memory constaints. The defaults work well for most desktop computers. |
| cadance | a numerical scalar >= 0. Describes the base time unit to build each row, in divisions of an hour. For example: 1 = 1 hour, 0.5 = 30 mins, 2 = 2 hourly. If cadance = 0, then the pricise datetime will be used to generate the time column. This is likely to generate a large table, so use cautiously. |
| timestamp | logical scalar. Default FALSE. If TRUE the `time` column will present as a timestamp instead of time from admission. In this instance the `cadance` argument controls the roundings of the timestamp with 2 options: 1 = round to nearest hour, 2 = do not round. |
sparse tibble with hourly cadance as rows, and unique OMOP concepts as columns.
The time unit is user definable, and set by the "cadance" argument. The default behaviour is to produce a table with 1 row per hour per patient. If there are duplicates/conflicts (e.g more than 1 event for a given hour), then the default behaviour is that only the first result for that hour is returned. One can override this behvaiour by supplying a vector of summary functions directly to the 'coalesce_rows' argument. This could also include any custom function written by the end user, so long as it takes a vector of length n, and returns a vector of length 1, of the original data type.
Many events inside EMAP occur on a greater than hourly basis. Depending upon the chosen analysis, you may which to increase the cadance. 0.5 for example will produce a table with 1 row per 30 minutes per patient. Counter to this, 24 would produce 1 row per 24 hours.
Choose what variables you want to pull out wisely. This function is quite efficient considering what it needs to do, but it can take a very long time if extracting lots of data, and doing so repeatedly. It is a strong recomendation that you run your extraction on a small subset of patients first and check that you are happy with the result, before moving to a larger extraction.
The current implementation is focussed on in-patients only. And as such, all dataitems are referenced to the visit_start_datetime of the visit_occurrence. Thus, observations and measurements recorded outside the boudaries of the visit_occurrence are automatically removed. This is - at this stage - intensional behaviour.