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Copy file name to clipboardExpand all lines: 02-omop-walkthrough-critical-care.md
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This document is an introductory walkthrough, demonstrating how to read into R some OMOP data from a series of csv files, join on concept names and explore and visualise the data.
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In the plot above bars are coloured by `gender_concept_id` which is the OMOP ID for gender, but we don't actually know which is which. We will look at resolving that by retrieving OMOP concept names in the next section.
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Note that we use `left_join` here because we only want to join on `person` information for rows occurring in the `measurement` table which is the left hand argument of the join. Also note that in this example we end up with one row per patient because the synthetic `measurement` table only has one row per patient. Usually we would expect multiple measurements per patient that would result in multiple rows per patient in the joined table.
# Starter guide for working with [SAFEHR-data](https://www.uclhospitals.brc.nihr.ac.uk/core-themes/safehr)
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SAFEHR (Secure or Anonymised Framework for Electronic Health Records) is a collaborative cross UCLH-UCL team supporting data access and extraction for research.
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This repository hosts a growing collection of documentation for working with UCLH clinical data.
[01-uclh-clinical-data-omop-intro](01-uclh-clinical-data-omop-intro.md) | intro to clinical data & OMOP
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[02-uclh-omop-walkthrough-critical-care](02-uclh-omop-walkthrough-critical-care.md) | reading in UCLH synthetic data in OMOP format & first exploration
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[01-clinical-data-omop-intro](01-clinical-data-omop-intro.md) | intro to clinical data & OMOP
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[02-omop-walkthrough-critical-care](02-omop-walkthrough-critical-care.md) | reading in UCLH synthetic data in OMOP format & first exploration
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