---
title: "Summarise result"
output: 
  html_document:
    pandoc_args: [
      "--number-offset=1,0"
      ]
    number_sections: yes
    toc: yes
vignette: >
  %\VignetteIndexEntry{summarise}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Introduction

In previous vignettes we have seen how to add patient level demographics (age, sex, prior observation, ...) `r vignette("demographics", package = "PatientProfiles")` or intersections with cohorts `r vignette("cohort-intersect", package = "PatientProfiles")`, concepts and tables. 

Once we have added several columns to our table of interest we may want to summarise all this data into a `summarised_result` object using several different estimates.

### Variables type

We support different types of variables, variable type is assigned using `dplyr::type_sum`:

- Date: `date` or `dttm`.

- Numeric: `dbl` or `drtn`.

- Integer: `int` or `int64`.

- Categorical: `chr`, `fct` or `ord`.

- Logical: `lgl`.

### Estimates names

We can summarise this data using different estimates:

```{r, echo = FALSE, eval = TRUE}
PatientProfiles:::formats |>
  dplyr::rename(
    "Estimate name" = "estimate_name",
    "Description" = "estimate_description",
    "Estimate type" = "estimate_type"
  ) |>
  dplyr::group_by(.data$variable_type) |>
  gt::gt() |>
  gt::tab_style(
    style = gt::cell_fill(color = "#e1e1e1"),
    locations = gt::cells_row_groups()
  ) |>
  gt::tab_style(
    style = list(
      gt::cell_text(weight = "bold"),
      gt::cell_fill("#000"),
      gt::cell_text(color = "#fff")
    ),
    locations = gt::cells_column_labels()
  ) |>
  gt::tab_style(
    style = gt::cell_text(font = "consolas"),
    locations = gt::cells_body(columns = "Estimate name")
  )
```

## Summarise our first table

Lets get started creating our data that we are going to summarise:

```{r}
library(duckdb)
library(CDMConnector)
library(PatientProfiles)
library(dplyr)
library(CodelistGenerator)

requireEunomia()
cdm <- cdmFromCon(
  con = dbConnect(duckdb(), eunomiaDir()),
  cdmSchema = "main",
  writeSchema = "main"
)
cdm <- generateConceptCohortSet(
  cdm = cdm,
  conceptSet = list("sinusitis" = c(4294548, 4283893, 40481087, 257012)),
  limit = "first",
  name = "my_cohort"
)
cdm <- generateConceptCohortSet(
  cdm = cdm,
  conceptSet = getDrugIngredientCodes(cdm = cdm, name = c("morphine", "aspirin", "oxycodone")),
  name = "drugs"
)

x <- cdm$my_cohort |>
  # add demographics variables
  addDemographics() |>
  # add number of counts per ingredient before and after index date
  addCohortIntersectCount(
    targetCohortTable = "drugs",
    window = list("prior" = c(-Inf, -1), "future" = c(1, Inf)),
    nameStyle = "{window_name}_{cohort_name}"
  ) |>
  # add a flag regarding if they had a prior occurrence of pharyngitis
  addConceptIntersectFlag(
    conceptSet = list(pharyngitis = 4112343),
    window = c(-Inf, -1),
    nameStyle = "pharyngitis_before"
  ) |>
  # date fo the first visit for that individual
  addTableIntersectDate(
    tableName = "visit_occurrence",
    window = c(-Inf, Inf),
    nameStyle = "first_visit"
  ) |>
  # time till the next visit after sinusitis
  addTableIntersectDays(
    tableName = "visit_occurrence",
    window = c(1, Inf),
    nameStyle = "days_to_next_visit"
  )

x |>
  glimpse()
```

In this table (`x`) we have a cohort of first occurrences of sinusitis, and then we added: demographics; the counts of 3 ingredients, any time prior and any time after the index date; a flag indicating if they had pharyngitis before; date of the first visit; and, finally, time to next visit.

If we want to summarise the age stratified by sex we could use tidyverse functions like:

```{r}
x |>
  group_by(sex) |>
  summarise(mean_age = mean(age), sd_age = sd(age))
```

This would give us a first insight of the differences of age. But the output is not going to be in an standardised format.

In PatientProfiles we have built a function that:

- Allow you to get the standardised output.

- You have a wide range of estimates that you can get.

- You don't have to worry which of the functions are supported in the database side (e.g. not all dbms support quantile function).

For example we could get the same information like before using:

```{r}
x |>
  summariseResult(
    strata = "sex",
    variables = "age",
    estimates = c("mean", "sd"),
    counts = FALSE
  ) |>
  select(strata_name, strata_level, variable_name, estimate_value)
```

You can stratify the results also by "pharyngitis_before":
```{r}
x |>
  summariseResult(
    strata = list("sex", "pharyngitis_before"),
    variables = "age",
    estimates = c("mean", "sd"),
    counts = FALSE
  ) |>
  select(strata_name, strata_level, variable_name, estimate_value)
```

Note that the interaction term was not included, if we want to include it we have to specify it as follows:
```{r}
x |>
  summariseResult(
    strata = list("sex", "pharyngitis_before", c("sex", "pharyngitis_before")),
    variables = "age",
    estimates = c("mean", "sd"),
    counts = FALSE
  ) |>
  select(strata_name, strata_level, variable_name, estimate_value) |>
  print(n = Inf)
```

You can remove overall strata with the includeOverallStrata option:

```{r}
x |>
  summariseResult(
    includeOverallStrata = FALSE,
    strata = list("sex", "pharyngitis_before"),
    variables = "age",
    estimates = c("mean", "sd"),
    counts = FALSE
  ) |>
  select(strata_name, strata_level, variable_name, estimate_value) |>
  print(n = Inf)
```

The results model has two levels of grouping (group and strata), you can specify them independently:

```{r}
x |>
  addCohortName() |>
  summariseResult(
    group = "cohort_name",
    includeOverallGroup = FALSE,
    strata = list("sex", "pharyngitis_before"),
    includeOverallStrata = TRUE,
    variables = "age",
    estimates = c("mean", "sd"),
    counts = FALSE
  ) |>
  select(group_name, group_level, strata_name, strata_level, variable_name, estimate_value) |>
  print(n = Inf)
```

We can add or remove number subjects and records (if a person identifier is found) counts with the counts parameter:

```{r}
x |>
  summariseResult(
    variables = "age",
    estimates = c("mean", "sd"),
    counts = TRUE
  ) |>
  select(strata_name, strata_level, variable_name, estimate_value) |>
  print(n = Inf)
```

If you want to specify different groups of estimates per different groups of variables you can use lists:

```{r}
x |>
  summariseResult(
    strata = "pharyngitis_before",
    includeOverallStrata = FALSE,
    variables = list(c("age", "prior_observation"), "sex"),
    estimates = list(c("mean", "sd"), c("count", "percentage")),
    counts = FALSE
  ) |>
  select(strata_name, strata_level, variable_name, estimate_value) |>
  print(n = Inf)
```

An example of a complete analysis would be:

```{r}
drugs <- settings(cdm$drugs)$cohort_name
x |>
  addCohortName() |>
  summariseResult(
    group = "cohort_name",
    includeOverallGroup = FALSE,
    strata = list("pharyngitis_before"),
    includeOverallStrata = TRUE,
    variables = list(
      c(
        "age", "prior_observation", "future_observation", paste0("prior_", drugs),
        paste0("future_", drugs), "days_to_next_visit"
      ),
      c("sex", "pharyngitis_before"),
      c("first_visit", "cohort_start_date", "cohort_end_date")
    ),
    estimates = list(
      c("median", "q25", "q75"),
      c("count", "percentage"),
      c("median", "q25", "q75", "min", "max")
    ),
    counts = TRUE
  ) |>
  select(group_name, group_level, strata_name, strata_level, variable_name, estimate_value)
```
