import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

tips = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/tips.csv")

# Set style for better-looking plots
plt.style.use('seaborn-v0_8')
sns.set_palette("husl")

# basic bubble chart with matplotlib
plt.figure(figsize=(10, 6))
bubble_sizes = tips['size'] * 50  # Scale party size for visibility
plt.scatter(tips['total_bill'], tips['tip'], s=bubble_sizes, alpha=0.6, c='steelblue', edgecolors='white', linewidth=0.5)
plt.title('Bubble Chart: Tips vs Total Bill (Bubble Size = Party Size)')
plt.xlabel('Total Bill ($)')
plt.ylabel('Tip Amount ($)')
plt.grid(True, alpha=0.3)
plt.show()

# multi-dimensional bubble chart
plt.figure(figsize=(12, 8))
sns.scatterplot(data=tips, x='total_bill', y='tip', 
                hue='time', size='size', sizes=(100, 400),
                palette='viridis', alpha=0.7, edgecolor='white', linewidth=0.5)
plt.title('Multi-Dimensional Bubble Chart: Restaurant Tips Analysis')
plt.xlabel('Total Bill ($)')
plt.ylabel('Tip Amount ($)')
plt.legend(title='Time of Day', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

library(tidyverse)

# Load tips dataset
tips <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/tips.csv")

# Basic bubble chart
ggplot(tips, aes(x = total_bill, y = tip, size = size)) +
  geom_point(alpha = 0.7, color = "steelblue", stroke = 0.5) +
  scale_size_continuous(name = "Party Size", range = c(2, 12)) +
  labs(title = "Restaurant Tips Bubble Chart",
       subtitle = "Bubble size represents party size",
       x = "Total Bill ($)",
       y = "Tip Amount ($)") +
  theme_minimal()

# Bubble chart with color and size mapping
ggplot(tips, aes(x = total_bill, y = tip, size = size, color = tip)) +
  geom_point(alpha = 0.7, stroke = 0.5) +
  scale_color_gradient(low = "lightblue", high = "darkred", name = "Tip Amount") +
  scale_size_continuous(name = "Party Size", range = c(3, 15)) +
  labs(title = "Tips Bubble Chart with Color Gradient",
       x = "Total Bill ($)",
       y = "Tip Amount ($)") +
  theme_minimal()

# Calculate correlation coefficient
cor_value <- cor(tips$total_bill, tips$tip)
print(paste("Correlation coefficient:", round(cor_value, 4)))

# Bubble chart with time of day as color
ggplot(tips, aes(x = total_bill, y = tip, size = size, color = time)) +
  geom_point(alpha = 0.8, stroke = 0.5) +
  scale_color_viridis_d(name = "Time of Day") +
  scale_size_continuous(name = "Party Size", range = c(3, 15)) +
  labs(title = "Restaurant Tips Bubble Chart by Time of Day",
       x = "Total Bill ($)",
       y = "Tip Amount ($)") +
  theme_minimal()

# multi-dimensional bubble chart with facets
ggplot(tips, aes(x = total_bill, y = tip, size = size, color = day)) +
  geom_point(alpha = 0.7, stroke = 0.5) +
  facet_wrap(~time, labeller = labeller(time = c("Lunch" = "Lunch", "Dinner" = "Dinner"))) +
  scale_color_brewer(palette = "Set1", name = "Day") +
  scale_size_continuous(name = "Party Size", range = c(2, 12)) +
  labs(title = "Multi-Dimensional Bubble Chart Analysis",
       subtitle = "Faceted by time, colored by day, bubble size = party size",
       x = "Total Bill ($)",
       y = "Tip Amount ($)") +
  theme_minimal() +
  theme(legend.position = "bottom")