R: موضوعات R ggplot2 والتصميم: دليل شامل لـ `theme`

library(ggplot2)
library(hrbrthemes)  # Professional Theme Packages

p <- ggplot(sales, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 3) +
  scale_color_brewer(palette = "Set1") +
  labs(
    title = "2024 Q1-Q4 Sales Trends",
    subtitle = "Data Source: Sales System | Chart: Analyst Team",
    caption = "Unit: 10,000 yuan",
    x = "Quarter", y = "Sales (10,000 yuan)",
    color = "City"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "gray50"),
    legend.position = "top",
    panel.grid.minor = element_blank()
  )

ggsave("professional_report.png", p, width = 10, height = 6, dpi = 300)

p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()

p + theme_gray()       # Default
p + theme_minimal()    # Minimalism
p + theme_bw()         # Black and White
p + theme_classic()    # Classic
p + theme_light()      # Light-colored
p + theme_dark()       # Dark
p + theme_void()       # Blank

# Set the font size globally
p + theme_minimal(base_size = 14)

# Set the Global Font
p + theme_minimal(base_family = "SimHei")  # Chinese

theme(
  plot.title = element_text(
    size = 16,        # Font size
    face = "bold",    # Bold/italics (plain/bold/italic/bold.italic)
    color = "blue",   # Color
    family = "SimHei",# Font
    hjust = 0.5,      # Horizontal Alignment (0=left, 1=right, 0.5=center)
    vjust = 0.5       # Vertical Alignment
  )
)

theme(
  panel.grid.major = element_line(
    color = "gray80",
    linewidth = 0.5,
    linetype = "dashed"   # solid/dashed/dotted
  ),
  panel.grid.minor = element_blank(),  # Hide Secondary Grid
  axis.line = element_line(color = "black", linewidth = 0.8)
)

theme(
  panel.background = element_rect(
    fill = "lightyellow",
    color = "black",
    linewidth = 0.5
  ),
  plot.background = element_rect(fill = "white")
)

theme(
  panel.grid = element_blank(),         # Hide All Grids
  axis.ticks = element_blank(),         # Hide Scale
  legend.position = "none"              # Hide Legend (Note: No element_blank)
)

p + theme(
  plot.title = element_text(size = 18, face = "bold"),
  plot.subtitle = element_text(size = 12, color = "gray50"),
  axis.title = element_text(size = 12),
  axis.text = element_text(size = 10),
  legend.title = element_text(size = 11),
  legend.text = element_text(size = 10)
)

p + theme(
  panel.grid.major = element_line(color = "gray90"),
  panel.grid.minor = element_blank(),  # Hide Secondary Grid (More concise)
  panel.border = element_rect(color = "black", fill = NA, linewidth = 0.5)
)

# 4 every corner
p + theme(legend.position = "top")
p + theme(legend.position = "bottom")
p + theme(legend.position = "left")
p + theme(legend.position = "right")

# Hide
p + theme(legend.position = "none")

# Coordinate Positioning (In the figure)
p + theme(legend.position = c(0.8, 0.2))  # 80% x, 20% y

p + theme(
  axis.line = element_line(color = "black", linewidth = 0.8),
  axis.ticks = element_line(color = "black"),
  axis.title.x = element_text(margin = margin(t = 10)),  # X Move the axis title down
  axis.title.y = element_text(margin = margin(r = 10))   # Y Shift the axis title to the left
)

p + labs(
  title = "Main Title",
  subtitle = "Subtitle",
  caption = "Data Sources",
  x = "X Axis",
  y = "Y Axis",
  color = "Color Mapping",  # Corresponding aes(color)
  fill = "Fill Mapping",   # Corresponding aes(fill)
  shape = "Shape Mapping",
  size = "Size Mapping"
)

# For use in formulas quote() or expression
p + labs(
  x = quote(x[i]),
  y = expression(paste("Concentration (", mu, "g/mL)"))
)

# 1. Specify manually
p + scale_color_manual(values = c("red", "blue", "green", "orange"))

# 2. ColorBrewer Color Palette
p + scale_color_brewer(palette = "Set1")    # Classic 9 colors
p + scale_color_brewer(palette = "Set2")    # Gentle 8 colors
p + scale_color_brewer(palette = "Dark2")   # Dark 8 colors
p + scale_color_brewer(palette = "Pastel1") # Light-colored 9 colors

# 3. viridis (Color-blind-friendly, Recommended)
library(viridis)
p + scale_color_viridis_d()  # Discrete
p + scale_color_viridis_c()  # Consecutive

# Gradient
p + scale_color_gradient(low = "blue", high = "red")
p + scale_color_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0)
p + scale_color_gradientn(colours = rainbow(7))

# 4 Class Classification
p + scale_color_brewer(palette = "Set1")
# 8 Class Classification
p + scale_color_brewer(palette = "Set2")
# Color-blind-friendly
p + scale_color_viridis_d()
# Academic/Serious
p + scale_color_manual(values = c("#1f77b4", "#ff7f0e", "#2ca02c"))

install.packages("ggthemes")
library(ggthemes)

p + theme_economist()    # The Economist
p + theme_wsj()           # The Wall Street Journal
p + theme_fivethirtyeight()  # FiveThirtyEight
p + theme_hc()            # Highcharts
p + theme_tufte()         # Tufte Minimalism
p + theme_stata()         # Stata
p + theme_solarized()     # Solarized
p + theme_excel()         # Excel Style

install.packages("hrbrthemes")
library(hrbrthemes)

p + theme_ipsum()         # ipsum Typographic Style
p + theme_ft_rc()         # The Financial Times

# Custom Themes
my_theme <- theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    panel.grid.minor = element_blank(),
    legend.position = "bottom"
  )

# Global Use
ggplot(df, aes(x, y)) + geom_point() + my_theme

ggsave(
  filename,            # File Name
  plot = last_plot(),   # Graph Object (Default: Last Image)
  width = 8,            # Width
  height = 6,           # High
  units = "in",         # Unit in/cm/mm
  dpi = 300             # Resolution
)

# PNG Printing Standard (300 dpi)
ggsave("report.png", width = 8, height = 6, dpi = 300)

# High-Definition Screen (150 dpi)
ggsave("screen.png", width = 1920, height = 1080, units = "px", dpi = 150)

# PDF For a thesis (Vector)
ggsave("paper.pdf", width = 8, height = 6)

# Chinese Title (Avoid garbled characters)
ggsave("Chinese.png", width = 8, height = 6, dpi = 300)

# Vector Graphics (Unlimited Clear Zoom)
ggsave("vector.svg", width = 8, height = 6)
ggsave("vector.pdf", width = 8, height = 6)

# ============================================
# 4 City Sales: Publication-Quality Charts
# Features: Complete Customization from Default to Professional Level
# ============================================

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

# 1. Prepare data
sales_wide <- tibble(
  city = c("Beijing", "Shanghai", "Guangzhou", "Shenzhen"),
  Q1 = c(1000, 1500, 800, 1200),
  Q2 = c(1200, 1800, 900, 1400),
  Q3 = c(1100, 1700, 1000, 1300),
  Q4 = c(1500, 2000, 1200, 1600)
)

sales_long <- sales_wide |>
  pivot_longer(cols = -city, names_to = "quarter", values_to = "sales") |>
  mutate(
    total = sum(sales),
    pct = round(sales / total * 100, 1)
  )

# 2. Custom Themes
my_theme <- theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16, hjust = 0,
                              margin = margin(b = 5)),
    plot.subtitle = element_text(color = "gray40", size = 11,
                                 margin = margin(b = 15)),
    plot.caption = element_text(color = "gray50", size = 9,
                                hjust = 1, margin = margin(t = 10)),
    axis.title = element_text(size = 11),
    axis.text = element_text(size = 10, color = "gray30"),
    panel.grid.major = element_line(color = "gray90", linewidth = 0.3),
    panel.grid.minor = element_blank(),
    legend.position = "top",
    legend.title = element_text(size = 11),
    legend.text = element_text(size = 10),
    plot.background = element_rect(fill = "white", color = NA)
  )

# 3. Plot 1: Line Trend (Professional Edition)
p1 <- ggplot(sales_long, aes(x = quarter, y = sales, color = city, group = city)) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 3.5, fill = "white", shape = 21, stroke = 1.5) +
  scale_color_brewer(palette = "Set1", name = "City") +
  scale_y_continuous(labels = scales::comma, expand = expansion(mult = 0.1)) +
  labs(
    title = "2024 Annual Quarterly Sales Trends",
    subtitle = "4 First-tier cities 4 Quarterly Sales Data",
    caption = "Data Source: Sales System | Chart: Analyst Team",
    x = NULL, y = "Sales (10,000 yuan)"
  ) +
  my_theme

# 4. Plot 2: Bar Chart Comparison (Professional Edition)
total_sales <- sales_wide |>
  mutate(total = Q1 + Q2 + Q3 + Q4) |>
  arrange(desc(total)) |>
  mutate(city = factor(city, levels = city))

p2 <- ggplot(total_sales, aes(x = city, y = total, fill = city)) +
  geom_col(width = 0.7) +
  geom_text(aes(label = scales::comma(total)), vjust = -0.5, size = 4) +
  scale_fill_brewer(palette = "Set1", guide = "none") +
  scale_y_continuous(labels = scales::comma,
                     expand = expansion(mult = c(0, 0.15))) +
  labs(
    title = "Annual Total Sales by City",
    subtitle = "Sort by total sales in descending order",
    x = NULL, y = "Total Sales (10,000 yuan)"
  ) +
  my_theme

# 5. Plot 3: Stacked Columns (Professional Edition)
sales_long_ordered <- sales_long |>
  left_join(total_sales |> select(city, total), by = "city") |>
  mutate(city = factor(city, levels = total_sales$city))

p3 <- ggplot(sales_long_ordered, aes(x = city, y = sales, fill = quarter)) +
  geom_col(position = "stack", width = 0.7) +
  scale_fill_brewer(palette = "YlGnBu", name = "Quarter") +
  labs(
    title = "Quarterly Sales Breakdown by City",
    subtitle = "Stacked bar chart showing quarterly contributions",
    x = NULL, y = "Sales (10,000 yuan)"
  ) +
  my_theme

# 6. Multi-Image Collage
combined <- (p1 / (p2 + p3)) +
  plot_annotation(
    title = "2024 4-City Comprehensive Sales Analysis",
    theme = theme(plot.title = element_text(face = "bold", size = 18, hjust = 0.5))
  )

print(combined)

# 7. Save Publication-Quality Images
ggsave("professional_report.png", combined,
       width = 14, height = 12, dpi = 300)
ggsave("professional_report.pdf", combined,
       width = 14, height = 12)
ggsave("professional_report.svg", combined,
       width = 14, height = 12)

cat("\n=== Publishing-quality charts have been saved ===\n")
cat("  - professional_report.png (PNG 300dpi)\n")
cat("  - professional_report.pdf (PDF Vector)\n")
cat("  - professional_report.svg (SVG Vector)\n")