Visualization with ggplot2

## Learning Objectives - Understand ggplot2 grammar of graphics - Create basic plots - Customize aesthetics - Facet and layer plots - Save plots ## Introduction to ggplot2 ### What is ggplot2? ggplot2 is a system for creating graphics based on the grammar of graphics: - **Layered** - Plots built from components - **Declarative** - You specify what, not how - **Extensible** - Easy to customize ### Installation ```r install.packages("ggplot2") library(ggplot2) ``` ## Basic Concepts ### Components of a Plot 1. **Data** - The dataframe 2. **Aesthetics** - Mapping variables to visual properties 3. **Geoms** - Geometric objects (points, lines, bars) 4. **Stats** - Statistical transformations 5. **Scales** - How aesthetics map to values 6. **Facets** - Split into subplots 7. **Theme** - Visual styling ### Basic Template ```r ggplot(data = ) + (mapping = aes()) ``` ## Getting Started ### Simple Scatter Plot ```r # Create data df <- data.frame( x = c(1, 2, 3, 4, 5), y = c(2, 4, 3, 5, 4) ) # Basic scatter plot ggplot(data = df, aes(x = x, y = y)) + geom_point() ``` ### Same with Pipe ```r df %>% ggplot(aes(x = x, y = y)) + geom_point() ``` ## Aesthetic Mappings ### What are Aesthetics? Aesthetics connect data variables to visual properties: - x, y - position - color - point/line color - size - point size - shape - point shape - alpha - transparency ### Mapping Variables ```r df <- data.frame( x = 1:10, y = 1:10, group = rep(c("A", "B"), 5) ) # Color by group ggplot(df, aes(x = x, y = y, color = group)) + geom_point() # Size by value ggplot(df, aes(x = x, y = y, size = x)) + geom_point() # Shape by group ggplot(df, aes(x = x, y = y, shape = group)) + geom_point() ``` ### Setting vs Mapping ```r # Mapping: variable mapped to aesthetic ggplot(df, aes(x = x, y = y, color = group)) + geom_point() # Setting: fixed value ggplot(df, aes(x = x, y = y)) + geom_point(color = "blue") ``` ## Common Geoms ### geom_point() ```r # Scatter plot ggplot(df, aes(x = x, y = y)) + geom_point() # With all aesthetics ggplot(df, aes(x = x, y = y, size = x, color = group)) + geom_point(alpha = 0.7) # transparency ``` ### geom_line() ```r # Line plot ggplot(df, aes(x = x, y = y)) + geom_line() # Line + points ggplot(df, aes(x = x, y = y)) + geom_line() + geom_point() ``` ### geom_smooth() ```r # Add smoothed line ggplot(df, aes(x = x, y = y)) + geom_point() + geom_smooth() # Without confidence interval ggplot(df, aes(x = x, y = y)) + geom_point() + geom_smooth(se = FALSE) # Linear model ggplot(df, aes(x = x, y = y)) + geom_point() + geom_smooth(method = "lm") ``` ### geom_bar() ```r # Count of categorical variable df <- data.frame( category = c("A", "B", "C", "A", "B", "A") ) ggplot(df, aes(x = category)) + geom_bar() # From summarized data df <- data.frame( category = c("A", "B", "C"), count = c(30, 45, 25) ) ggplot(df, aes(x = category, y = count)) + geom_bar(stat = "identity") ``` ### geom_histogram() ```r # Histogram of continuous variable df <- data.frame(x = rnorm(1000)) ggplot(df, aes(x = x)) + geom_histogram() # Adjust bins ggplot(df, aes(x = x)) + geom_histogram(bins = 30) # Different fill ggplot(df, aes(x = x)) + geom_histogram(fill = "steelblue", color = "white") ``` ### geom_boxplot() ```r # Boxplot df <- data.frame( group = rep(c("A", "B"), each = 50), value = c(rnorm(50, 5, 1), rnorm(50, 7, 1)) ) ggplot(df, aes(x = group, y = value)) + geom_boxplot() # Horizontal ggplot(df, aes(x = group, y = value)) + geom_boxplot() + coord_flip() ``` ### geom_violin() ```r # Violin plot (distribution shape) ggplot(df, aes(x = group, y = value)) + geom_violin() ``` ### geom_density() ```r # Density plot ggplot(df, aes(x = value)) + geom_density() # Filled ggplot(df, aes(x = value, fill = group)) + geom_density(alpha = 0.5) # semi-transparent ``` ### geom_text() ```r # Add labels ggplot(df, aes(x = x, y = y, label = group)) + geom_text() ``` ### geom_label() ```r # Labels with background ggplot(df, aes(x = x, y = y, label = group)) + geom_label() ``` ## Scales ### Color Scales ```r # Manual colors ggplot(df, aes(x = x, y = y, color = group)) + geom_point() + scale_color_manual(values = c("red", "blue")) # Gradient ggplot(df, aes(x = x, y = y, color = value)) + geom_point() + scale_color_gradient(low = "blue", high = "red") # Gradient2 for diverging ggplot(df, aes(x = x, y = y, color = value)) + geom_point() + scale_color_gradient2(low = "blue", mid = "white", high = "red") ``` ### Size Scales ```r # Manual size ggplot(df, aes(x = x, y = y, size = value)) + geom_point() + scale_size(range = c(1, 10)) ``` ### Axis Scales ```r # Log scale ggplot(df, aes(x = x, y = y)) + geom_point() + scale_y_log10() # Manual breaks ggplot(df, aes(x = x, y = y)) + geom_point() + scale_y_continuous(breaks = seq(0, 100, 10)) ``` ## Labels and Titles ### labs() ```r ggplot(df, aes(x = x, y = y)) + geom_point() + labs( title = "My Title", subtitle = "Subtitle here", x = "X Axis Label", y = "Y Axis Label", color = "Legend Title", caption = "Data source: ..." ) ``` ### ggtitle() ```r ggplot(df, aes(x = x, y = y)) + geom_point() + ggtitle("Title") + xlab("X") + ylab("Y") ``` ## Themes ### Built-in Themes ```r ggplot(df, aes(x = x, y = y)) + geom_point() + theme_minimal() ggplot(df, aes(x = x, y = y)) + geom_point() + theme_bw() ggplot(df, aes(x = x, y = y)) + geom_point() + theme_classic() ggplot(df, aes(x = x, y = y)) + geom_point() + theme_dark() ``` ### Theme Customization ```r ggplot(df, aes(x = x, y = y)) + geom_point() + theme( panel.background = element_rect(fill = "white"), panel.grid.major = element_line(color = "gray90"), panel.grid.minor = element_line(color = "gray95"), text = element_text(family = "sans", size = 12), plot.title = element_text(hjust = 0.5, face = "bold") ) ``` ## Facets ### facet_wrap() ```r # Wrap by one variable df <- data.frame( x = 1:20, y = 1:20, group = rep(c("A", "B", "C", "D"), 5) ) ggplot(df, aes(x = x, y = y)) + geom_point() + facet_wrap(~group) # Multiple rows ggplot(df, aes(x = x, y = y)) + geom_point() + facet_wrap(~group, nrow = 2) ``` ### facet_grid() ```r # Grid by two variables df <- data.frame( x = 1:10, y = 1:10, group1 = rep(c("A", "B"), each = 10), group2 = rep(c("X", "Y"), 10) ) ggplot(df, aes(x = x, y = y)) + geom_point() + facet_grid(group1 ~ group2) ``` ## Statistical Transformations ### Built-in Stats ```r # Statistical summaries ggplot(df, aes(x = x, y = y)) + stat_summary() # Boxplot with notches ggplot(df, aes(x = group, y = value)) + geom_boxplot(notch = TRUE) ``` ## Position Adjustments ### Dodge, Stack, Fill ```r # Bar chart df <- data.frame( group = c("A", "A", "B", "B"), type = c("X", "Y", "X", "Y"), value = c(10, 20, 15, 25) ) # Side by side ggplot(df, aes(x = group, y = value, fill = type)) + geom_bar(position = "dodge", stat = "identity") # Stacked ggplot(df, aes(x = group, y = value, fill = type)) + geom_bar(position = "stack", stat = "identity") # Filled (proportions) ggplot(df, aes(x = group, y = value, fill = type)) + geom_bar(position = "fill", stat = "identity") ``` ## Coordinates ### coord_flip() ```r # Horizontal bars ggplot(df, aes(x = category, y = value)) + geom_bar(stat = "identity") + coord_flip() ``` ### coord_cartesian() ```r # Zoom without affecting data ggplot(df, aes(x = x, y = y)) + geom_point() + coord_cartesian(xlim = c(0, 50), ylim = c(0, 50)) ``` ## Saving Plots ### ggsave() ```r # Save last plot ggsave("my_plot.png") ggsave("my_plot.pdf") # Specific plot p <- ggplot(df, aes(x = x, y = y)) + geom_point() ggsave("my_plot.png", p) # Specify size and resolution ggsave("my_plot.png", width = 10, height = 8, dpi = 300) ``` ### Export Functions ```r # PNG png("plot.png", width = 800, height = 600) print(ggplot(df, aes(x = x, y = y)) + geom_point()) dev.off() # PDF pdf("plot.pdf", width = 10, height = 8) print(ggplot(df, aes(x = x, y = y)) + geom_point()) dev.off() ``` ## Extensions ### Common Extensions ```r # patchwork - combine plots install.packages("patchwork") library(patchwork) p1 <- ggplot(df, aes(x = x, y = y)) + geom_point() p2 <- ggplot(df, aes(x = x)) + geom_histogram() p1 + p2 # gganimate - animations install.packages("gganimate") library(gganimate) ``` ## Summary - ggplot2 uses layered grammar of graphics - Map data to aesthetics with `aes()` - Use `geom_*` functions for different plot types - Customize with `scale_*`, `theme()`, `labs()` - `facet_wrap()` and `facet_grid()` for subplots - `ggsave()` to save plots - Combine plots with patchwork or gridExtra