--- title: "Step 3. Visualise the sequence ratios" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{a04_Visualise_sequence_ratios} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( warning = FALSE, message = FALSE, collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5, eval = Sys.getenv("$RUNNER_OS") != "macOS" ) ``` ```{r, include = FALSE} if (Sys.getenv("EUNOMIA_DATA_FOLDER") == "") Sys.setenv("EUNOMIA_DATA_FOLDER" = tempdir()) if (!dir.exists(Sys.getenv("EUNOMIA_DATA_FOLDER"))) dir.create(Sys.getenv("EUNOMIA_DATA_FOLDER")) if (!CDMConnector::eunomia_is_available()) CDMConnector::downloadEunomiaData() ``` # Introduction In this vignette we will explore the functionality and arguments of a set of functions that will help us to understand and visualise the sequence ratio results. In particular, we will delve into the following functions: - `tableSequenceRatios()`: to generate a table summarising the results. - `plotSequenceRatios()`: to plot the sequence ratios. This function builds-up on previous functions, such as `generateSequenceCohortSet()` and `summariseSequenceRatios()` function (explained in detail in previous vignettes: **Step 1. Generate a sequence cohort** and **Step 2. Obtain the sequence ratios** respectively). Hence, we will pick up the explanation from where we left off in the previous vignette. ```{r message= FALSE, warning=FALSE, include=FALSE} # Load libraries library(CDMConnector) library(dplyr) library(DBI) library(CohortSymmetry) library(duckdb) library(DrugUtilisation) # Connect to the database db <- DBI::dbConnect(duckdb::duckdb(), dbdir = CDMConnector::eunomia_dir()) cdm <- cdm_from_con( con = db, cdm_schema = "main", write_schema = "main" ) # Generate cohorts cdm <- DrugUtilisation::generateIngredientCohortSet( cdm = cdm, name = "aspirin", ingredient = "aspirin") cdm <- DrugUtilisation::generateIngredientCohortSet( cdm = cdm, name = "acetaminophen", ingredient = "acetaminophen") # Generate a sequence cohort cdm <- generateSequenceCohortSet( cdm = cdm, indexTable = "aspirin", markerTable = "acetaminophen", name = "intersect", combinationWindow = c(0,Inf)) ``` Recall we had the table **intersect** in the cdm reference and that the results of sequence ratio could produced as follows (**Step 2. Obtain the sequence ratios**): ```{r message= FALSE, warning=FALSE} result <- summariseSequenceRatios(cohort = cdm$intersect) ``` # Table output of the sequence ratio results The function `tableSequenceRatios` inputs the result from `summariseSequenceRatios`, the default outputs a gt table. ```{r message= FALSE, warning=FALSE} tableSequenceRatios(result = result) ``` ## Modify `type` Instead of a gt table, the user may also want to put the sequence ratio results in a flex table format (the rest of the arguments that we saw for a gt table also applies here): ```{r message= FALSE, warning=FALSE} tableSequenceRatios(result = result, type = "flextable") ``` Or a tibble: ```{r message= FALSE, warning=FALSE} tableSequenceRatios(result = result, type = "tibble") ``` # Plot output of the sequence ratio results Similarly, we also have `plotSequenceRatios()` to visualise the results. ```{r message= FALSE, warning=FALSE} plotSequenceRatios(result = result) ``` By default, it plots both the adjusted sequence ratios (and its CIs) and crude sequence ratios (and its CIs). One may wish to only plot adjusted one like so (note since only adjusted is plotted, only one colour needs to be specified): ## Modify `onlyASR` and `colours` ```{r message= FALSE, warning=FALSE} plotSequenceRatios(result = result, onlyASR = T, colours = "black") ``` One could change the colour like so: ```{r message= FALSE, warning=FALSE} plotSequenceRatios(result = result, onlyASR = T, colours = "red") ``` ```{r message= FALSE, warning=FALSE, eval=FALSE} CDMConnector::cdmDisconnect(cdm = cdm) ```