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

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  error = TRUE,  # Continue even if there are errors
  warning = TRUE  # Show warnings
)
```

```{r setup}
library(MedDataSets)

library(ggplot2)

library(dplyr)

```


# Introduction

The `MedDataSets` package provides an **extensive collection of datasets related to medicine, diseases, treatments, drugs, and public health**. It covers topics such as pharmacokinetics, drug effectiveness, vaccine trials, survival rates, infectious disease outbreaks, and medical treatments.

This package is a valuable resource for researchers, analysts, and healthcare professionals interested in performing in-depth analyses of medical and public health data in R. The datasets include information on various health conditions like AIDS, cancer, bacterial infections, COVID-19, and data on pharmaceuticals and vaccines.All datasets included in the `MedDataSets` package have been sourced from the R ecosystem and other R packages, and the content has not been modified in any way.


## Dataset Suffixes

To help identify the type and structure of each dataset, **a suffix is added to the end of the dataset name. The suffixes indicate the format and type of the dataset**, such as:

- `df`: A standard data frame
- `tbl_df`: A tibble data frame
- `matrix`: A matrix object
- `ts`: A time series object

This makes it easier to work with different datasets by quickly identifying their structure. For example:

- `ToothGrowth_df`: The Effect of Vitamin C on Tooth Growth in Guinea Pigs.
- `transplant_tbl_df`: Heart Transplant Data.
- `VADeaths_matrix`: Death Rates in Virginia (1940).
- `mdeaths_ts`: Monthly Deaths from Lung Diseases in the UK.


## Visualizing Data with ggplot2

To demonstrate the use of datasets in `MedDataSets`, we'll create some visualizations using the ggplot2 package.

### Visualization of Tooth Growth

```{r tooth,eval=TRUE, message=FALSE, warning=FALSE}
# Example: Visualizing tooth growth data
ggplot(ToothGrowth_df, aes(x = dose, y = len, color = supp)) +
  geom_point(size = 3, alpha = 0.7) +
  labs(title = "Tooth Growth by Supplement Type and Dose",
       x = "Dose",
       y = "Tooth Length",
       color = "Supplement Type") +
  theme_minimal()

```


### Visualization of Transplant Data

```{r transplant,eval=TRUE, message=FALSE, warning=FALSE}
ggplot(transplant_tbl_df, aes(x = outcome)) +
  geom_bar(fill = "steelblue", alpha = 0.8) +
  labs(title = "Transplant Outcomes",
       x = "Outcome",
       y = "Count") +
  theme_minimal()

```

### Visualization of mdeaths -  Monthly Deaths from Lung Diseases in the UK

```{r mdeath_ts,eval=TRUE, message=FALSE, warning=FALSE}
# Crear un gráfico de serie de tiempo utilizando ggplot2
# Convertir 'mdeaths_ts' en un data frame
mdeaths_df <- data.frame(
  month = time(mdeaths_ts),  # Extrae las fechas de la serie de tiempo
  deaths = as.numeric(mdeaths_ts)  # Convierte la serie de tiempo a numérico
)

# Crear gráfico
ggplot(mdeaths_df, aes(x = month, y = deaths)) +
  geom_line(color = "blue", size = 1) +
  labs(title = "Muertes Masculinas Respiratorias Mensuales (1974-1980)",
       x = "Mes",
       y = "Número de Muertes") +
  theme_minimal() +
  scale_x_continuous(breaks = seq(1974, 1980, by = 1), 
                     labels = seq(1974, 1980, by = 1)) +
  geom_point(color = "red", size = 1.5, alpha = 0.5)  # Añadir puntos para cada mes


```


## Conclusion
The `MedDataSets` package provides a wealth of **datasets that are essential for analyzing various medical and health-related topics. By using suffixes to identify the dataset types and leveraging ggplot2 for visualizations, users can easily explore the data and extract meaningful insights. For more details on the available datasets, please refer to the package documentation**.

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