speed up docs

Author: edward-burn

README.md

@@ -93,15 +93,15 @@ hip_fx_codes <- getCandidateCodes(cdm, "hip fracture")
 #> Search completed. Finishing up.
 #> ✔ 1 candidate concept identified
 #> 
-#> Time taken: 0 minutes and 1 seconds
+#> Time taken: 0 minutes and 0 seconds
 forearm_fx_codes <- getCandidateCodes(cdm, "forearm fracture")
 #> Limiting to domains of interest
 #> Getting concepts to include
 #> Adding descendants
 #> Search completed. Finishing up.
 #> ✔ 1 candidate concept identified
 #> 
-#> Time taken: 0 minutes and 1 seconds
+#> Time taken: 0 minutes and 0 seconds
 
 fx_codes <- newCodelist(list("hip_fracture" = hip_fx_codes$concept_id,
                              "forearm_fracture"= forearm_fx_codes$concept_id))

vignettes/a06_concatanate_cohorts.Rmd

@@ -45,12 +45,11 @@ cdm <- CDMConnector::cdmFromCon(con, cdmSchema = "main",
                     writeSchema = "main", writePrefix = "my_study_")
 ```
 
-Let's start by creating two drug cohorts, one for users of diclofenac and another for users of acetaminophen.
+Let's start by creating a cohort of users of acetaminophen
 
 ```{r}
 cdm$medications <- conceptCohort(cdm = cdm, 
-                                 conceptSet = list("diclofenac" = 1124300,
-                                                   "acetaminophen" = 1127433), 
+                                 conceptSet = list("acetaminophen" = 1127433), 
                                  name = "medications")
 cohortCount(cdm$medications)
 ```
@@ -59,8 +58,8 @@ We can merge cohort records using the `collapseCohorts()` function in the Cohort
 Let's first define a new cohort where records within 1095 days (~ 3 years) of each other will be merged.
 
 ```{r}
-cdm$medications_collapsed <- collapseCohorts(
-  cohort = cdm$medications,
+cdm$medications_collapsed <- cdm$medications |> 
+  collapseCohorts(
   gap = 1095,
   name = "medications_collapsed"
 )
@@ -80,14 +79,6 @@ Now let's look at how the cohorts have been changed.
 
 ```{r}
 summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed)
-plotCohortAttrition(summary_attrition, cohortId = 1)
+tableCohortAttrition(summary_attrition)
 ```
 
-The flow chart above illustrates the changes to cohort 1 (users of acetaminophen) when entries within 3 years of each other are merged. We see that collapsing the cohort has led to 1,390 fewer records.
-
-```{r}
-summary_attrition <- summariseCohortAttrition(cdm$medications_collapsed)
-plotCohortAttrition(summary_attrition, cohortId = 2)
-```
-
-The flow chart above illustrates the changes to cohort 2 (users of diclofenac) when entries within 3 years of each other are merged. Since this cohort only has one record per individual the function collapseCohorts() had no impact on the final number of records.

vignettes/a08_split_cohorts.Rmd

@@ -44,6 +44,7 @@ cdm$medications <- conceptCohort(cdm = cdm,
                                                    "acetaminophen" = 1127433), 
                                  name = "medications")
 cohortCount(cdm$medications)
+settings(cdm$medications)
 ```
 
 We can stratify cohorts based on specified columns using the function `stratifyCohorts()`. In this example, let's stratify the medications cohort by age and sex.
@@ -52,30 +53,9 @@ We can stratify cohorts based on specified columns using the function `stratifyC
 cdm$stratified <- cdm$medications |>
   addAge(ageGroup = list("Child" = c(0,17), "18 to 65" = c(18,64), "65 and Over" = c(65, Inf))) |>
   addSex(name = "stratified") |>
-  stratifyCohorts(strata = list("sex", "age_group", c("sex", "age_group")), name = "stratified")
+  stratifyCohorts(strata = list("sex", "age_group"), name = "stratified")
 
+cohortCount(cdm$stratified)
 settings(cdm$stratified)
 ```
 
-The age and sex columns are added using functions from the package `PatientProfiles`. The 'stratified' table includes 22 cohorts, representing various combinations of sex and age groups.
-
-We can also split cohorts for specified years using the function `yearCohorts()`.
-
-```{r, include=FALSE, warning=FALSE}
-con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomiaDir())
-cdm <- cdmFromCon(con, cdmSchema = "main", 
-                    writeSchema = c(prefix = "my_study_", schema = "main"))
-
-cdm$medications <- conceptCohort(cdm = cdm, 
-                                 conceptSet = list("diclofenac" = 1124300,
-                                                   "acetaminophen" = 1127433), 
-                                 name = "medications")
-```
-
-```{r}
-cdm$years <- cdm$medications |>
-  yearCohorts(years = 2005:2010, name = "years")
-
-settings(cdm$years)
-```
-The 'years' table includes 12 cohorts, with each cohort representing a specific drug and year.

vignettes/a09_combine_cohorts.Rmd

@@ -7,19 +7,6 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-```
-
-```{r setup}
-library(CohortConstructor)
-library(CohortCharacteristics)
-library(ggplot2)
-```
-
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
@@ -37,6 +24,12 @@ if (!dir.exists(Sys.getenv("EUNOMIA_DATA_FOLDER"))){ dir.create(Sys.getenv("EUNO
 }
 ```
 
+```{r setup}
+library(CohortConstructor)
+library(CohortCharacteristics)
+library(ggplot2)
+```
+
 For this example we'll use the Eunomia synthetic data from the CDMConnector package.
 
 ```{r}