R: R ggplot2 المتقدم: تراكب الطبقات وأنظمة التجزئة

# Use facet_wrap() to split "Products" into 4 small plots
ggplot(sales, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_line(linewidth = 1) +
  geom_point(size = 3) +
  facet_wrap(~ product) +  # By Product Category
  labs(title = "4 City × 4 Products Sales Trends")

# A Single Painting 4 Post a picture,Automatic Page Break!

ggplot(df, aes(x, y)) +       # Data + Aesthetics
  geom_point() +                # Geometric Layer 1:Scatter points
  geom_smooth() +               # Geometric Layer 2:Trendline
  stat_summary() +              # Statistical Layer
  scale_y_log10() +             # Coordinate Transformation
  facet_wrap(~ group) +         # Sub-section
  theme_minimal()               # Topic

# Cone of Inadmissibility:1 Post a picture
ggplot(sales, aes(x, y)) + geom_point()

# After splitting:4 Zhang Xiaotu
ggplot(sales, aes(x, y)) + geom_point() + facet_wrap(~ category)

# Split by city(4 subplots)
ggplot(sales_long, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_line() +
  facet_wrap(~ city) +
  labs(title = "Quarterly Sales by City")

# Control the Layout of Sub-Faces:nrow / ncol
facet_wrap(~ city, nrow = 2)   # 2 row
facet_wrap(~ city, ncol = 4)   # 4 col

# Split by city(row) and product(col) Two-Dimensional Section
facet_grid(rows = vars(city), cols = vars(product))

# Abbreviation
facet_grid(city ~ product)

# Formula Syntax(Old Version)
facet_grid(. ~ category)        # Press only category Split Plane
facet_grid(category ~ .)        # Press only category Row-Column Plane

# Free Zoom(Independent coordinates for each subgraph)
facet_wrap(~ city, scales = "free")  # Freedom x/y
facet_wrap(~ city, scales = "free_x")  # Freedom Alone x
facet_wrap(~ city, scales = "free_y")  # Freedom Alone y

# Add a tag
facet_wrap(~ city, labeller = label_both)  # Display"city: Beijing"

# Control the number of subgraphs per row
facet_wrap(~ city, nrow = 2)

# Automatically Add Mean Points + Error bar
ggplot(df, aes(x = group, y = value)) +
  geom_boxplot() +
  stat_summary(fun = mean, geom = "point", color = "red", size = 3)

ggplot(df, aes(x = group, y = value, color = group)) +
  geom_point(alpha = 0.3) +  # Raw Data
  stat_summary(fun = mean, geom = "point", size = 4, color = "red") +
  stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.2)

# Auto-add LOESS Trendline
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth()

# Linear Regression
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth(method = "lm")

# Close Confidence Interval
geom_smooth(method = "lm", se = FALSE)

# Multiple lines by component
ggplot(sales_long, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_point() +
  geom_line() +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.5)

# Horizontal Bar Chart
ggplot(df, aes(x = city, y = sales)) +
  geom_col() +
  coord_flip()  # x/y Swap

# Pie Chart
ggplot(df, aes(x = 1, y = sales, fill = city)) +
  geom_col() +
  coord_polar(theta = "y")

# Rose Illustration
ggplot(df, aes(x = city, y = sales, fill = city)) +
  geom_col() +
  coord_polar()

# Scatter Plot X/Y In the same proportion
ggplot(mpg, aes(cty, hwy)) +
  geom_point() +
  coord_fixed(ratio = 1)  # 1:1 Ratio

# log Transformation
ggplot(df, aes(x, y)) + geom_point() + scale_y_log10()

# Square Root Transformation
ggplot(df, aes(x, y)) + geom_point() + scale_y_sqrt()

# Add labels next to the data points
ggplot(df, aes(x, y)) +
  geom_point() +
  geom_text(aes(label = name), vjust = -0.5)

# Add a circular label
ggplot(df, aes(x, y)) +
  geom_point() +
  geom_label(aes(label = name))

# Mark Key Points
ggplot(df, aes(x, y)) +
  geom_point() +
  annotate("text", x = 5, y = 8, label = "Key Points", color = "red") +
  annotate("rect", xmin = 4, xmax = 6, ymin = 7, ymax = 9,
           alpha = 0.2, fill = "yellow")

library(ggrepel)

# "Auto-Skip" tag
ggplot(df, aes(x, y, label = name)) +
  geom_point() +
  geom_text_repel()  # Non-overlapping tags

# Installation
install.packages("patchwork")
library(patchwork)

# 4 Image Collage 1
(p1 + p2) / (p3 + p4)  # 2 row 2 col

# Complex Layout
p1 + p2 + p3 + p4 + plot_layout(ncol = 2, heights = c(2, 1))

# Add a title
(p1 + p2) / (p3 + p4) + plot_annotation(title = "Comprehensive Analysis")

library(cowplot)
plot_grid(p1, p2, p3, p4, ncol = 2, labels = c("A", "B", "C", "D"))

# ============================================
# 4 City × 4 Products Multidimensional Sales Analysis
# Features:Sub-section + Trendline + Notes + Multi-Image Collage
# ============================================

library(ggplot2)
library(dplyr)
library(tidyr)
library(patchwork)

# 1. Prepare data
sales <- expand_grid(
  city = c("Beijing", "Shanghai", "Guangzhou", "Shenzhen"),
  quarter = c("Q1", "Q2", "Q3", "Q4"),
  product = c("Cell Phone", "Computer", "Tablet", "Headphones")
) |>
  mutate(sales = round(runif(64, 500, 3000)))

cat("=== Data Volume:", nrow(sales), "row ===\n")
print(head(sales, 5))

# 2. Plot 1:4 Overall Urban Trends(Basics)
p1 <- ggplot(sales, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_line(linewidth = 1) +
  geom_point(size = 2) +
  labs(title = "4 Overall Sales Trends by City", color = "City") +
  theme_minimal() +
  theme(legend.position = "bottom")

# 3. Plot 2:By City(facet_wrap)
p2 <- ggplot(sales, aes(x = quarter, y = sales, color = product, group = product)) +
  geom_line(linewidth = 1) +
  geom_point(size = 2) +
  facet_wrap(~ city, nrow = 2) +
  labs(title = "Product Sales by City", color = "Products") +
  theme_minimal() +
  theme(legend.position = "bottom",
        axis.text.x = element_text(angle = 45, hjust = 1))

# 4. Plot 3:By City(By Product Stack)
p3 <- ggplot(sales, aes(x = quarter, y = sales, fill = product)) +
  geom_col(position = "stack") +
  facet_wrap(~ city, nrow = 2) +
  labs(title = "Product Bundle Sales by City", fill = "Products") +
  scale_fill_brewer(palette = "Set2") +
  theme_minimal() +
  theme(legend.position = "bottom")

# 5. Plot 4:Trendline + Original Point
p4 <- ggplot(sales, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_point(alpha = 0.3) +
  geom_smooth(method = "lm", se = TRUE) +
  labs(title = "Linear Trendline + Confidence Interval") +
  theme_minimal() +
  theme(legend.position = "bottom")

# 6. Plot 5:Box-and-Whisker Plots
p5 <- ggplot(sales, aes(x = product, y = sales, fill = product)) +
  geom_boxplot() +
  labs(title = "Sales Breakdown by Product") +
  theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, hjust = 1))

# 7. Plot 6:Heat Map(geom_tile)
sales_summary <- sales |>
  group_by(city, product) |>
  summarise(total = sum(sales), .groups = "drop")

p6 <- ggplot(sales_summary, aes(x = city, y = product, fill = total)) +
  geom_tile() +
  geom_text(aes(label = total), color = "white") +
  labs(title = "City × Products Total Sales Heat Map", fill = "Total Sales") +
  scale_fill_gradient(low = "lightblue", high = "darkred") +
  theme_minimal()

# 8. Multi-Image Collage(patchwork)
combined <- (p1 + p2) / (p3 + p4) / (p5 + p6) +
  plot_annotation(
    title = "4 City × 4 Products Comprehensive Sales Analysis",
    subtitle = "Data Simulation | ggplot2 Photo Gallery"
  )

print(combined)

# 9. Save as high resolution PNG
ggsave("multi_dimension_analysis.png", combined,
       width = 16, height = 18, dpi = 300)
cat("\n=== The composite image has been saved:multi_dimension_analysis.png ===\n")