---
title: "Introduction to epocakir"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to epocakir}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.height = 4,
  fig.width = 7
)
```

## Introduction

The epocakir package makes clinical coding of patients with kidney disease using clinical practice guidelines easy.
The guidelines used are the evidence-based [KDIGO guidelines](https://kdigo.org/guidelines/).
This package covers acute kidney injury (AKI), anemia, and chronic liver disease(CKD).

## Features

- `aki_staging()`: Classification of AKI staging (`aki_stages`) with automatic selection of:

  - `aki_bCr()`: AKI based on baseline creatinine
  - `aki_SCr()`: AKI based on changes in serum creatinine
  - `aki_UO()`: AKI based on urine output

- `anemia()`: Classification of anemia

- Classification of albuminuria (`Albuminuria_stages`)

  - `Albuminuria_staging_ACR()`: Albuminuria based on Albumin excretion rate
  - `Albuminuria_staging_AER()`: Albuminuria based on Albumin-to-creatinine ratio

- `eGFR()`: Estimation of glomerular filtration rate with automatic selection of:

  - `eGFR_adult_SCr()`: eGFR based on the 2009 CKD-EPI creatinine equation
  - `eGFR_adult_SCysC()`: eGFR based on the 2012 CKD-EPI cystatin C equation
  - `eGFR_adult_SCr_SCysC()`: eGFR based on the 2012 CKD-EPI creatinine-cystatin C equation
  - `eGFR_child_SCr()`: eGFR based on the pediatric creatinine-based equation
  - `eGFR_child_SCr_BUN()`: eGFR based on the pediatric creatinine-BUN equation
  - `eGFR_child_SCysC()`: eGFR based on the pediatric cystatin C-based equation

- `GFR_staging()`: Staging of GFR (`GFR_stages`)
  
- Multiple utility functions including:

  - `conversion_factors`: Conversion factors used throughout the KDIGO guidelines
  - `as_metric()`: Conversion of a measured value into metric units
  - `dob2age()`: Calculation of age from a date of birth
  - `binary2factor()`: Conversion of binary data into factors based on a column name
  - `combine_date_time_cols()`: Combining separate date and time columns into a single date and time column
  - `combn_changes`: Generating changes between measurements

- Automatic conversion of units class objects

- Tidy output allowing seamless integration with functions from the tidyverse

- Tidyeval via programming with dplyr

- Comprehensive tests and coverage

## Examples

```{r pkgs, message = FALSE, warning=FALSE}
library(epocakir)
library(dplyr)
library(units)
```

### Clinical Data

Often clinical data must be cleansed and tidied before analysis can begin.
To assist in this, several utility functions have been included.
To explore these, consider a sample clinical dataset `clinical_obvs`:

```{r clinical_data}
# Example workflow: clinical_obvs <- read.csv("cohort.csv")
glimpse(clinical_obvs)

tidy_obvs <- clinical_obvs %>%
  combine_date_time_cols() %>%
  mutate(
    Age = dob2age(`Date of Birth`),
    Height = as_metric(height = set_units(as.numeric(Height), "cm"))
  ) %>%
  binary2factor(Male, Surgery)

glimpse(tidy_obvs)
```

Make sure to use `set_units()` from the `units` package to convert all measurements into unit objects for automatic unit conversion in epocakir.

### AKI Staging

Next consider the sample `aki_pt_data` dataset.
It is possible to use `aki_staging()` to automatically classify the presence and staging of AKI.
If a particular method is required, it is possible to classify AKI using `aki_bCr()`, `aki_SCr()` or `aki_UO().`

```{r aki}
# Example workflow: aki_pt_data <- read.csv("aki.csv")
head(aki_pt_data)

aki_staging(aki_pt_data,
  SCr = "SCr_", bCr = "bCr_", UO = "UO_",
  dttm = "dttm_", pt_id = "pt_id_"
)

aki_pt_data %>%
  mutate(aki = aki_staging(
    SCr = SCr_, bCr = bCr_, UO = UO_,
    dttm = dttm_, pt_id = pt_id_
  )) %>%
  select(pt_id_, SCr_:dttm_, aki)

aki_pt_data %>%
  mutate(aki = aki_SCr(
    SCr = SCr_, dttm = dttm_, pt_id = pt_id_
  )) %>%
  select(pt_id_, SCr_:dttm_, aki)
```

### Estimated Glomerular Filtration Rate

Similarly, `eGFR()` offers the ability to automatically select the appropriate formula to estimate the glomerular filtration rate.
If a particular formula is required, then `eGFR_adult_SCr`, `eGFR_adult_SCysC`, `eGFR_adult_SCr_SCysC`, `eGFR_child_SCr`, `eGFR_child_SCr_BUN`, or `eGFR_child_SCysC` can be used.

```{r eGFR}
# Example workflow: aki_pt_data <- read.csv("aki.csv")
head(eGFR_pt_data)

eGFR(eGFR_pt_data,
  SCr = "SCr_", SCysC = "SCysC_",
  Age = "Age_", height = "height_", BUN = "BUN_",
  male = "male_", black = "black_", pediatric = "pediatric_"
)

eGFR_pt_data %>%
  dplyr::mutate(eGFR = eGFR(
    SCr = SCr_, SCysC = SCysC_,
    Age = Age_, height = height_, BUN = BUN_,
    male = male_, black = black_, pediatric = pediatric_
  )) %>%
  select(SCr_:pediatric_, eGFR)

eGFR_pt_data %>%
  dplyr::mutate(eGFR = eGFR_adult_SCr(
    SCr = SCr_, Age = Age_, male = male_, black = black_
  )) %>%
  select(SCr_:pediatric_, eGFR)
```

## Reference

See <https://alwinw.github.io/epocakir/reference/index.html> for more usage details and package reference.

See <https://kdigo.org/guidelines/> for full KDIGO guidelines.