---
title: "NeuroDataSets: A Comprehensive Collection of Neuroscience and Brain-Related Datasets"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{NeuroDataSets: A Comprehensive Collection of Neuroscience and Brain-Related Datasets}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup}
library(NeuroDataSets)
library(dplyr)
library(ggplot2)
```

# Introduction

The `NeuroDataSets` package offers a rich and diverse collection of datasets focused on the brain, the nervous system, and neurological and psychiatric disorders. It includes data on conditions such as **Parkinson's disease, Alzheimer's disease, epilepsy, schizophrenia, gliomas, and mental health**.

The package contains a wide variety of data types, including clinical, experimental, neuroimaging, behavioral, cognitive, and simulated datasets. These datasets encompass structural and functional **brain data, neurotransmission metrics, gene expression profiles, cognitive performance assessments, and treatment outcomes**.

## All datasets within NeuroDataSets

```{r NeuroDataSets-datasets,echo = TRUE,message = FALSE,warning = FALSE,results = 'markup'}


view_datasets_NeuroDataSets()


```


## Dataset Suffixes

Each dataset in the `NeuroDataSets` package uses a `suffix` to denote the type of R object:

- `_df`: A data frame

- `_list`: A list

- `_tbl_df`: A tibble

- `_matrix`: A matrix

## Example Datasets

Below are selected example datasets included in the `NeuroDataSets` package:

- `subcortical_patterns_tbl_df`: Patterns of Subcortical Structures.

- `white_matter_patterns_tbl_df`: Expected Patterns of White Matter.

- `hippocampus_lesions_df`: Memory and the Hippocampus.


## Data Visualization with NeuroDataSets Data

###  Patterns of Subcortical Structures

```{r patterns-subcortical-plot, fig.width=6, fig.height=4, out.width="100%"}

# Convert the dataset to long format using only base R + dplyr

long_data <- subcortical_patterns_tbl_df %>%
  select(Subcortical, everything()) %>%
  as.data.frame() %>%
  reshape(
    varying = names(.)[-1],
    v.names = "Value",
    timevar = "Condition",
    times = names(.)[-1],
    direction = "long"
  ) %>%
  select(Subcortical, Condition, Value)

# Create a heatmap
ggplot(long_data, aes(x = Condition, y = Subcortical, fill = Value)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "lightblue", high = "darkred") +
  labs(
    title = "Subcortical Patterns by Condition",
    x = "Condition",
    y = "Subcortical Region",
    fill = "Value"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

```


### Expected Patterns of White Matter

```{r white-matter-plot, fig.width=6, fig.height=4.5, out.width="90%"}

# Compute mean values using updated anonymous function syntax
summary_data <- white_matter_patterns_tbl_df %>%
  select(-WM) %>%
  summarise(across(everything(), \(x) mean(x, na.rm = TRUE))) %>%
  as.data.frame()

# Reshape from wide to long format using base R
summary_data <- data.frame(
  Condition = names(summary_data),
  MeanValue = as.numeric(summary_data[1, ])
)

# Plot
ggplot(summary_data, aes(x = Condition, y = MeanValue, fill = Condition)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Average Value per Condition across White Matter Regions",
    x = "Condition",
    y = "Mean Value"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  guides(fill = "none")  # Optional

```


### Memory and the Hippocampus

```{r memory-hippocampus-plot, fig.width=6, fig.height=4.5, out.width="90%"}

# Lesion Size and Memory Score

ggplot(hippocampus_lesions_df, aes(x = lesion, y = memory)) +
  geom_point(color = "blue", size = 2) +
  labs(
    title = "Relationship Between Lesion Size and Memory Score",
    x = "Lesion Size",
    y = "Memory Score"
  ) +
  theme_minimal()


```

## Conclusion

The `NeuroDataSets` package offers a rich, curated collection of datasets focused on neuroscience and related disorders. It supports advanced statistical analysis, exploratory data science, and educational purposes by providing well-structured and documented datasets across a variety of neurological and neuropsychiatric conditions.

For detailed information and full documentation of each dataset, please refer to the reference manual and help files included within the package.

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