---
title: "Palmer Penguins Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    theme: cosmo
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(palmerpenguins)
library(plotly)

# Remove missing values for cleaner visualizations
penguins_clean <- penguins |> 
  drop_na()
```

# Overview

## Row {data-height=150}

### Total Penguins

```{r}
valueBox(
  nrow(penguins_clean),
  icon = "fa-snowflake",
  color = "info"
)
```

### Species

```{r}
valueBox(
  n_distinct(penguins_clean$species),
  icon = "fa-list",
  color = "primary"
)
```

### Islands

```{r}
valueBox(
  n_distinct(penguins_clean$island),
  icon = "fa-map",
  color = "success"
)
```

### Average Body Mass

```{r}
avg_mass <- round(mean(penguins_clean$body_mass_g))
valueBox(
  paste(format(avg_mass, big.mark = ","), "g"),
  icon = "fa-weight",
  color = "warning"
)
```

## Row {data-height=425}

### Body Mass vs Flipper Length by Species

```{r}
p1 <- ggplot(penguins_clean, aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  geom_point(aes(text = paste("Species:", species,
                               "<br>Flipper:", flipper_length_mm, "mm",
                               "<br>Body Mass:", body_mass_g, "g",
                               "<br>Island:", island)), 
             size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    title = "Relationship between Flipper Length and Body Mass",
    x = "Flipper Length (mm)",
    y = "Body Mass (g)",
    color = "Species"
  ) +
  theme_minimal() +
  scale_color_manual(values = c("#FF6F00", "#C15BCB", "#007C91"))

ggplotly(p1, tooltip = "text")
```

### Bill Dimensions by Species

```{r}
p2 <- ggplot(penguins_clean, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point(aes(text = paste("Species:", species,
                               "<br>Bill Length:", bill_length_mm, "mm",
                               "<br>Bill Depth:", bill_depth_mm, "mm")),
             size = 2, alpha = 0.7) +
  labs(
    title = "Bill Dimensions Across Species",
    x = "Bill Length (mm)",
    y = "Bill Depth (mm)",
    color = "Species"
  ) +
  theme_minimal() +
  scale_color_manual(values = c("#FF6F00", "#C15BCB", "#007C91"))

ggplotly(p2, tooltip = "text")
```

## Row {data-height=425}

### Distribution of Body Mass

```{r}
p3 <- ggplot(penguins_clean, aes(x = body_mass_g, fill = species)) +
  geom_histogram(alpha = 0.6, position = "identity", bins = 30) +
  labs(
    title = "Body Mass Distribution by Species",
    x = "Body Mass (g)",
    y = "Count",
    fill = "Species"
  ) +
  theme_minimal() +
  scale_fill_manual(values = c("#FF6F00", "#C15BCB", "#007C91"))

ggplotly(p3)
```

### Penguins by Island and Species

```{r}
island_summary <- penguins_clean |> 
  count(island, species)

p4 <- ggplot(island_summary, aes(x = island, y = n, fill = species)) +
  geom_col(position = "dodge") +
  labs(
    title = "Penguin Counts by Island and Species",
    x = "Island",
    y = "Count",
    fill = "Species"
  ) +
  theme_minimal() +
  scale_fill_manual(values = c("#FF6F00", "#C15BCB", "#007C91"))

ggplotly(p4)
```

# By Species

## Row {.tabset}

### Adelie

```{r}
adelie_data <- penguins_clean |> filter(species == "Adelie")

p_adelie <- ggplot(adelie_data, aes(x = flipper_length_mm, y = body_mass_g, color = island)) +
  geom_point(size = 3, alpha = 0.7) +
  labs(
    title = "Adelie Penguins: Body Mass vs Flipper Length",
    x = "Flipper Length (mm)",
    y = "Body Mass (g)",
    color = "Island"
  ) +
  theme_minimal()

ggplotly(p_adelie)
```

### Chinstrap

```{r}
chinstrap_data <- penguins_clean |> filter(species == "Chinstrap")

p_chinstrap <- ggplot(chinstrap_data, aes(x = flipper_length_mm, y = body_mass_g, color = sex)) +
  geom_point(size = 3, alpha = 0.7) +
  labs(
    title = "Chinstrap Penguins: Body Mass vs Flipper Length",
    x = "Flipper Length (mm)",
    y = "Body Mass (g)",
    color = "Sex"
  ) +
  theme_minimal() +
  scale_color_manual(values = c("#E69F00", "#56B4E9"))

ggplotly(p_chinstrap)
```

### Gentoo

```{r}
gentoo_data <- penguins_clean |> filter(species == "Gentoo")

p_gentoo <- ggplot(gentoo_data, aes(x = flipper_length_mm, y = body_mass_g, color = sex)) +
  geom_point(size = 3, alpha = 0.7) +
  labs(
    title = "Gentoo Penguins: Body Mass vs Flipper Length",
    x = "Flipper Length (mm)",
    y = "Body Mass (g)",
    color = "Sex"
  ) +
  theme_minimal() +
  scale_color_manual(values = c("#E69F00", "#56B4E9"))

ggplotly(p_gentoo)
```

# Data Table

## Row

### Summary Statistics

```{r}
penguins_clean |> 
  group_by(species) |> 
  summarise(
    Count = n(),
    `Avg Body Mass (g)` = round(mean(body_mass_g)),
    `Avg Flipper Length (mm)` = round(mean(flipper_length_mm), 1),
    `Avg Bill Length (mm)` = round(mean(bill_length_mm), 1),
    `Avg Bill Depth (mm)` = round(mean(bill_depth_mm), 1)
  ) |> 
  knitr::kable()
```

### Dataset

```{r}
DT::datatable(
  penguins_clean,
  options = list(
    pageLength = 10,
    scrollX = TRUE
  ),
  filter = "top"
)
```