---
title: "jpinfect: Notifiable Infectious Diseases in Japan"
author: "Tomonori Hoshi, Erina Ishigaki, Sathoshi Kaneko"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    smart: no
    toc: true
vignette: >
  %\VignetteIndexEntry{jpinfect: Notifiable Infectious Diseases in Japan}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup, include = FALSE}
library(jpinfect)
```

# Introduction

The `jpinfect` package provides tools for acquiring and processing notifiable infectious disease data in Japan. The package includes built-in datasets and functions to download, read and manipulate data from the Japan Institute for Health Security (JIHS). It also provides functions to merge datasets, transform data formats and check data sources.

This package is designed to assist researchers, epidemiologists, public health officials and developers in accessing, cleaning, and manipulating data for epidemiological analysis. The package is particularly useful for those working with infectious disease data in Japan, as it provides a streamlined process for obtaining and processing data from the JIHS.

### Dependencies

The `jpinfect` package depends on the following R packages:

-   `dplyr`: for data manipulation

-   `future`: for parallel processing

-   `future.apply`: for parallel processing

-   `httr`: for HTTP requests

-   `magrittr`: for piping

-   `readr`: for reading CSV files

-   `readxl`: for reading Excel files

-   `stats`: for statistical functions

-   `stringi`: for string manipulation

-   `stringr`: for string manipulation

-   `tidyr`: for data tidying

-   `tidyselect`: for data selection

-   `utils`: for utility functions

## Installation

The `jpinfect` package can be installed from either CRAN or GitHub using the [remotes](https://github.com/r-lib/remotes) package. Through the Github repository, the latest Provisional weekly Case Reports (bullet) data can be acquired, which may not be available on CRAN. To install the package, run the following command in your R console:

From CRAN:
```{r, message = FALSE, warning = FALSE, eval = FALSE}
install.packages("jpinfect")
```

From GitHub (for the latest version):
```{r, message = FALSE, warning = FALSE, eval = FALSE}
if(!require("remotes")) install.packages("remotes")
remotes::install_github("TomonoriHoshi/jpinfect")
```

Load the package after installation:

```{r load_package}
library(jpinfect)
```

## Usage

### Built-in Datasets

The `jpinfect` package includes three built-in datasets that can be used to start immediate data analysis. These datasets are:

-   `sex_prefecture`: Confirmed weekly case reports on the sex distribution of reported cases by prefecture from 1999 to 2023.

-   `place_prefecture`: Confirmed weekly case reports about the place of infection by prefecture between 2001 and 2023.

-   `bullet`: Provisional weekly case reported by prefecture from 2024 to the current latest reports.

These datasets are provided in a tidy format, making them easy to work with using the `dplyr` and `tidyr` packages.

```{r basic_usage}
data("sex_prefecture")
data("place_prefecture")
data("bullet")
```

#### Data Exploration

```{r data_exploration sex_prefecture}
str(sex_prefecture)
```

```{r data_exploration place_prefecture}
str(place_prefecture)
```

```{r data_exploration bullet}
str(bullet)
```

### Data Merging

The `jpinfect_merge` function helps to merge the datasets into one dataset if necessary, which enables users to start their data analysis instantly.

```{r merge}
# Load the built-in datasets
data("sex_prefecture")
data("place_prefecture")
data("bullet")

# Merge two datasets
confirmed_dataset <- jpinfect_merge(sex_prefecture, place_prefecture)

# Merge three datasets
bind_result <- jpinfect_merge(sex_prefecture, place_prefecture, bullet)
```

#### Data Exploration

```{r data_exploration_1, eval=FALSE}
# Check the structure of the merged dataset
head(confirmed_dataset)

head(bind_result)

```

### Data Transformation from Wider to Longer; Vice Versa

The `jpinfect_pivot` function enables users to seamlessly pivot datasets between wide and long formats. This functionality is particularly useful for reorganising data to suit analysis or visualisation needs.

```{r pivot}
# Convert from wide to long format
bullet_long <- jpinfect_pivot(bullet)

# Convert from long to wide format
bullet_wide <- jpinfect_pivot(bullet_long)
```

#### Data Exploration

```{r data_exploration_2}
# Check the structure of long format
head(bullet_long)

# Check the structure of wide format
head(bullet_wide)

```

## Building Datasets from Source

Although the build-in datasets are provided in this package, it is ideal for scientists, epidemiologists and public health officers to review whole data handling process from the upstream to downstream. For those who cares the precision of dataset, `jpinfect` provides the following functions to build the same datasets or even the latest bullet datasets sourced from the government-provided raw data.

### Data Source Checks

The sources of these datasets can be checked by using `jpinfect_url_confirmed` for confirmed case reports and `jpinfect_url_bullet` for provisional case reports, respectively.

```{r source_check, eval = FALSE}
# Check data source URL for sex and prefecture data
jpinfect_url_confirmed(year = 2021, type = "sex")

# Check data source URL for place of infection and prefecture data
jpinfect_url_confirmed(year = 2021, type = "place")
```

### Data Acquisition

The raw data can be downloaded using `jpinfect_get_confirmed` for confirmed case reports and `jpinfect_get_bullet` for provisional case reports, respectively. Confirmed weekly case data is organised into a single Microsoft Excel file for each year, while provisional data is provided as separate CSV files for each week. Since this function connect to the government website, it may take some time to download the data. To avoid excessive burden on the server, please kindly avoid downloading the files frequently. The downloaded files are saved under the *raw_data* folder or the specified directory.

```{r download, eval = FALSE}
# Download data for 2020 and 2021
jpinfect_get_confirmed(years = c(2020, 2021), type = "sex")

# Download English data for weeks 1 to 5 in 2025
jpinfect_get_bullet(year = 2025, week = 1:5, dest_dir = "raw_data")
```

### Data Import

The acquired raw data into your local computer can be imported into R using `jpinfect_read_confirmed` and `jpinfect_read_bullet`.

```{r read, eval = FALSE}
# Read a single file
dataset2021 <- jpinfect_read_confirmed(path = "2021_Syu_01_1.xlsx")

# Read all files in a directory
place_dataset <- jpinfect_read_confirmed(path = "raw_data", type = "place")

# Read provisional data
bullet <- jpinfect_read_bullet(directory = "raw_data")
```

## Important Notes

-   Data accuracy depends on the original source
-   Data is provided by the Japan Institute for Health Security (JIHS)
-   Usage is subject to the Government Standard Terms of Use (v1.0)
-   This library is independently developed and is not affiliated with any government entity

## Reporting Bugs

If you encounter any bugs or issues while using the `jpinfect` package, please report them on the GitHub Issues page. When reporting, please include the following information:

-   A clear description of the problem

-   Steps to reproduce the issue

-   Your R version and operating system

-   Relevant error messages

-   Example code to reproduce the problem
