From 6ca5bc18cf97f18d416cd9d18ba2f87e0327bfec Mon Sep 17 00:00:00 2001 From: xihang-chen Date: Fri, 29 Nov 2024 10:40:47 +0000 Subject: [PATCH] Update a01_Introduction.Rmd --- vignettes/a01_Introduction.Rmd | 7 ------- 1 file changed, 7 deletions(-) diff --git a/vignettes/a01_Introduction.Rmd b/vignettes/a01_Introduction.Rmd index 967d058..b9c93c8 100644 --- a/vignettes/a01_Introduction.Rmd +++ b/vignettes/a01_Introduction.Rmd @@ -68,12 +68,6 @@ cdm <- emptyCdmReference(cdmName = "mock") |> con <- dbConnect(duckdb::duckdb()) cdm <- copyCdmTo(con = con, cdm = cdm, schema = "main", overwrite = T) - -cdm$index_cohort |> - dplyr::glimpse() - -cdm$marker_cohort |> - dplyr::glimpse() ``` Once we have established a connection to the database, we can use the `generateSequenceCohortSet()` function to find the intersection of the two cohorts. This function will provide us with the individuals who appear in both cohorts, which will be named **intersect** - another cohort in the cdm reference. @@ -100,7 +94,6 @@ Once we have the intersect cohort, you are able to explore the temporal symmetry ```{r message= FALSE, warning=FALSE} result <- summariseTemporalSymmetry(cohort = cdm$intersect, timescale = "year") -result |> dplyr::glimpse() plotTemporalSymmetry(result = result) ```