homework #12.Rmd

---
title: "Homework #12"
date: "`r Sys.Date()`"
output:
  prettydoc::html_pretty:
    theme: architect
    highlight: github
---


## Advanced ggplotting

```{r echo=FALSE, message=FALSE, warning=FALSE}
library(ggstatsplot)
library(palmerpenguins)
library(tidyverse)
library(patchwork)
library(plotly)

```
 
```{r echo=FALSE, message=FALSE, warning=FALSE}
data("penguins", package = "palmerpenguins")
penguins <- drop_na(penguins)
```

## Exploring histogram
```{r echo=FALSE, message=FALSE, warning=FALSE}

hist <- ggplot(data = penguins, aes(x = flipper_length_mm)) +
  geom_histogram(aes(fill = species), 
                 color = "black",            # Add black outline to bars
                 alpha = 0.5, 
                 position = "identity",
                 binwidth = 5) +            # Adjust bin width as needed
  scale_fill_manual(values = c("darkblue", "red", "cyan4")) +
  labs(x = "Flipper length (mm)",
       y = "Frequency",
       title = "Flipper") +
  theme(panel.background = element_rect(fill = "lightgray"),  # Add background color
        panel.grid.major = element_line(color = "white", size = 0.5),  # Add grid lines
        panel.grid.minor = element_line(color = "white", size = 0.25),
        plot.title = element_text(size = 18, face = "bold", color = "red"),
        legend.title=element_text(size=10, face = "italic", color = "blue"), 
        legend.text=element_text(size=6, color="darkorange"),
        axis.title = element_text(face = "bold", color="purple"))

hist
```

## Convert ggplot to interactive plot

```{r echo=FALSE, message=FALSE, warning=FALSE}
hist_interactive  <- ggplotly(hist)
hist_interactive

```


```{r echo=FALSE, message=FALSE, warning=FALSE}

# # Initial bin width
# initial_binwidth <- 5
# 
# # Function to create histogram
# create_histogram <- function(binwidth) {
#   ggplot(data = penguins, aes(x = flipper_length_mm)) +
#     geom_histogram(aes(fill = species), 
#                    color = "black",
#                    alpha = 0.5, 
#                    position = "identity",
#                    binwidth = binwidth) +
#     scale_fill_manual(values = c("darkblue", "red", "cyan4")) +
#     labs(x = "Flipper length (mm)",
#          y = "Frequency",
#          title = "Flipper ") +
#     theme(panel.background = element_rect(fill = "lightgray"),
#           panel.grid.major = element_line(color = "white", size = 0.5),
#           panel.grid.minor = element_line(color = "white", size = 0.25))
# }
# 
# # Create initial plot
# histogram <- create_histogram(initial_binwidth)
# 
# # Convert ggplot to plotly
# histogram_plotly <- ggplotly(histogram)
# 
# # Add slider for bin width
# histogram_plotly <- histogram_plotly %>%
#   layout(
#     updatemenus = list(
#       list(
#         active = 0,
#         buttons = list(
#           list(
#             label = "Bin Width",
#             method = "update",
#             args = list(list(visible = c(TRUE, FALSE)),
#                         list(title = "Bin Width",
#                              xaxis = list(title = "Flipper length (mm)"),
#                              yaxis = list(title = "Frequency")))),
#           list(
#             label = "5",
#             method = "update",
#             args = list(list(visible = c(TRUE, FALSE)),
#                         list(title = "Bin Width",
#                              xaxis = list(title = "Flipper length (mm)"),
#                              yaxis = list(title = "Frequency")),
#                         list(binwidth = 5))),
#           list(
#             label = "10",
#             method = "update",
#             args = list(list(visible = c(TRUE, FALSE)),
#                         list(title = "Bin Width",
#                              xaxis = list(title = "Flipper length (mm)"),
#                              yaxis = list(title = "Frequency")),
#                         list(binwidth = 10)))
#         )
#       )
#     )
#   )
# 
# # Show plot
# #histogram_plotly
```

## Exploring scatter plot


```{r echo=FALSE, message=FALSE, warning=FALSE}
point <- ggplot(data = penguins, 
                       aes(x = flipper_length_mm,
                           y = body_mass_g)) +
  geom_point(aes(color = species, 
                 shape = species),
             size = 6,
             alpha = 0.8) +
  geom_smooth(method = "lm", se = FALSE, color = "black", linetype = "dashed") +  # Add a trend line
  scale_color_manual(values = c("darkblue","red","cyan4")) +
  scale_shape_manual(values = c(16, 17, 15)) +  # Modify point shapes
  labs(title = "Penguin size",
       subtitle = "Flipper length and body mass",
       x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin species",
       shape = "Penguin species") +
      theme(plot.title = element_text(size = 18, face = "bold", color = "red"),
        legend.title=element_text(size=10, face = "italic", color = "blue"), 
        legend.text=element_text(size=6, color="darkgreen"),
        axis.title = element_text(face = "bold", color="orange"))

point
```


## Interactive plot

```{r echo=FALSE, message=FALSE, warning=FALSE}
ggplotly(point)

```

## Statistical plot

```{r echo=FALSE, message=FALSE, warning=FALSE}
stat_plot <- ggbetweenstats(
  data = penguins,
  x = species,
  y = bill_length_mm
)
stat_plot  <- stat_plot  + 
  # Add labels and title
  labs(
    x = "Penguins Species",
    y = "Bill Length",
    title = "Distribution of bill length across penguins species"
  ) + 
  # Customizations
  theme(
    # This is the new default font in the plot
    text = element_text(family = "Roboto", size = 8, color = "black"),
    plot.title = element_text(
      family = "Lobster Two", 
      size = 20,
      face = "bold",
      color = "#2a475e"
    ),
    # Statistical annotations below the main title
    plot.subtitle = element_text(
      family = "Roboto", 
      size = 15, 
      face = "bold",
      color="#1b2838"
    ),
    plot.title.position = "plot", # slightly different from default
    axis.text = element_text(size = 10, color = "black"),
    axis.title = element_text(size = 12)
  )
stat_plot 

#stat_plot  <- stat_plot   
#stat_plot + facet_wrap(~species, scales = "free_y")
```


# Multiple plots in ggplot2 with patchwork

## Exploring different layouts

```{r echo=FALSE, message=FALSE, warning=FALSE}
point + hist + stat_plot + plot_layout(ncol = 1, nrow = 3)
```


```{r echo=FALSE, message=FALSE, warning=FALSE}
hist | point
```


```{r echo=FALSE, message=FALSE, warning=FALSE}
hist / point
```