Data Visualization in R with ggplot2

library(ggplot2)
library(palmerpenguins) # Sample dataset

# Basic template:
# ggplot(data = <DATA>) +
#   <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

# Scatterplot example
ggplot(data = penguins) +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g))

# Essential components:
# 1. Data (data frame)
# 2. Aesthetics (aes() - how vars map to visual properties)
# 3. Geometric objects (geoms - the shapes to draw)

# Scatterplot (numeric vs numeric)
ggplot(penguins) +
  geom_point(aes(x = bill_length_mm, y = bill_depth_mm))

# Bar plot (categorical counts)
ggplot(penguins) +
  geom_bar(aes(x = species))

# Histogram (numeric distribution)
ggplot(penguins) +
  geom_histogram(aes(x = body_mass_g), bins = 30)

# Boxplot (distribution by category)
ggplot(penguins) +
  geom_boxplot(aes(x = species, y = body_mass_g))

# Line plot (trends over time)
economics |> 
  ggplot(aes(x = date, y = unemploy)) +
  geom_line()

# Color by category
ggplot(penguins) +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g, color = species))

# Faceting (small multiples)
ggplot(penguins) +
  geom_point(aes(x = bill_length_mm, y = bill_depth_mm)) +
  facet_wrap(~species)

# Themes and labels
ggplot(penguins) +
  geom_bar(aes(x = island, fill = species)) +
  labs(title = "Penguins by Island",
       x = "Island",
       y = "Count") +
  theme_minimal() +
  theme(legend.position = "bottom")

# Manual scales
ggplot(penguins) +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  scale_color_manual(values = c("darkorange", "purple", "cyan4"))

# Smooth trend lines
ggplot(penguins) +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_smooth(aes(x = flipper_length_mm, y = body_mass_g))

# Bar plot with pre-computed stats
ggplot(penguins) +
  geom_bar(aes(x = species, y = after_stat(prop), group = 1))

# Density plots
ggplot(penguins) +
  geom_density(aes(x = body_mass_g, fill = species), alpha = 0.5)

# Quantile regression
ggplot(penguins) +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_quantile(aes(x = flipper_length_mm, y = body_mass_g))

# Combining geoms
ggplot(penguins) +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  geom_smooth(aes(x = flipper_length_mm, y = body_mass_g))

# Annotations
ggplot(penguins) +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g)) +
  annotate("text", x = 200, y = 4000, label = "Outliers") +
  annotate("rect", xmin = 220, xmax = 230, ymin = 3000, ymax = 4000, alpha = 0.2)

# Coordinate systems
ggplot(penguins) +
  geom_bar(aes(x = species, fill = species)) +
  coord_flip()

# Saving plots
ggsave("penguin_plot.png", width = 8, height = 6, dpi = 300)

# Patchwork - plot composition
library(patchwork)
p1 <- ggplot(penguins) + geom_point(aes(x = bill_length_mm, y = bill_depth_mm))
p2 <- ggplot(penguins) + geom_boxplot(aes(x = species, y = body_mass_g))
p1 + p2  # Side by side

# gganimate - animated plots
library(gganimate)
ggplot(penguins) +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  transition_states(year)

# ggrepel - non-overlapping labels
library(ggrepel)
ggplot(penguins) +
  geom_point(aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_text_repel(aes(x = flipper_length_mm, y = body_mass_g, label = island))