ggbreak

https://github.com/yulab-smu/ggbreak
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ggbreak is an R package that provides scale functions for setting axis breaks in ggplot2...
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### Install ggbreak Package from CRAN

Source: https://github.com/yulab-smu/ggbreak/blob/master/README.md

This code snippet shows how to install the stable version of the ggbreak package directly from the Comprehensive R Archive Network (CRAN). This is the recommended method for most users.

```r
install.packages("ggbreak")
```

--------------------------------

### Install ggbreak Package from GitHub

Source: https://github.com/yulab-smu/ggbreak/blob/master/README.md

This code snippet demonstrates how to install the development version of the ggbreak package from its GitHub repository using the 'remotes' package. This is useful for users who want the latest features or bug fixes.

```r
remotes::install_github("YuLab-SMU/ggbreak")
```

--------------------------------

### ggbreak with Date and Transformed Scales (R)

Source: https://context7.com/yulab-smu/ggbreak/llms.txt

Illustrates ggbreak's automatic handling of date axes and transformed scales (log, sqrt, reverse). Includes examples of breaking a date axis and applying breaks to a reversed y-axis. Dependencies: ggplot2, ggbreak.

```r
library(ggplot2)
library(ggbreak)

# Date axis with breaks
economics_subset <- subset(economics,
                           date >= as.Date("1980-01-01") &
                           date <= as.Date("2015-01-01"))

p <- ggplot(economics_subset, aes(x = date, y = unemploy)) +
     geom_line(color = "darkblue", size = 1)

# Break date axis (specify dates as Date objects)
p + scale_x_break(c(as.Date("1990-01-01"), as.Date("2000-01-01")),
                  scales = 0.5)

# Reverse scale with breaks
set.seed(2019-01-19)
d <- data.frame(x = 1:20, y = c(rnorm(5) + 4, rnorm(5) + 20,
                                 rnorm(5) + 5, rnorm(5) + 22))
p <- ggplot(d, aes(x, y)) + geom_col() + scale_y_reverse()
p + scale_y_break(c(7, 17))
```

--------------------------------

### ggbreak with ggplot2 Features (R)

Source: https://context7.com/yulab-smu/ggbreak/llms.txt

Demonstrates using ggbreak's scale_y_break with standard ggplot2 features like faceting, coord_flip, custom themes, and labels. Includes an example of error handling when break ranges are outside the data limits. Dependencies: ggplot2, ggbreak.

```r
library(ggplot2)
library(ggbreak)

set.seed(2019-01-19)
d <- data.frame(
  x = 1:20,
  y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22),
  group = rep(c("A", "B"), each = 10)
)

# With faceting
p <- ggplot(d, aes(x, y)) +
     geom_col() +
     facet_wrap(~group)
p + scale_y_break(c(7, 17))

# With coord_flip
p <- ggplot(d, aes(x, y)) + geom_col()
p + scale_y_break(c(7, 17)) + coord_flip()

# With custom theme and labels
p + scale_y_break(c(7, 17), scales = 1.5) +
    labs(title = "Data with Outliers",
         subtitle = "Using axis breaks for clarity",
         x = "Sample Index",
         y = "Measurement Value") +
    theme_minimal() +
    theme(plot.title = element_text(face = "bold", size = 16))

# Error handling - breaks outside range produce error
tryCatch({
  p + scale_y_cut(breaks = c(7, 30), which = 1, scales = 2)
}, error = function(e) {
  message("Error caught: ", e$message)
})
```

--------------------------------

### ggbreak with patchwork for Multi-Panel Layouts (R)

Source: https://context7.com/yulab-smu/ggbreak/llms.txt

Shows how to compose ggbreak plots with other ggplot objects using the patchwork package for creating complex multi-panel layouts. Compares a plot with and without an axis break, and demonstrates a more intricate layout. Dependencies: ggplot2, ggbreak, patchwork.

```r
library(ggplot2)
library(ggbreak)
library(patchwork)

set.seed(2019-01-19)
d <- data.frame(
  x = 1:20,
  y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22)
)

# Create broken axis plot
p1 <- ggplot(d, aes(x, y)) +
      geom_col(fill = "steelblue") +
      scale_y_break(c(7, 17)) +
      labs(title = "With Axis Break")

# Create standard plot for comparison
p2 <- ggplot(d, aes(x, y)) +
      geom_col(fill = "coral") +
      labs(title = "Without Axis Break")

# Combine with patchwork
p1 + p2

# Complex layout
p3 <- ggplot(d, aes(x, y)) + geom_point(size = 3)
(p1 | p2) / p3
```

--------------------------------

### Divide X-Axis into Custom Segments with scale_x_cut

Source: https://context7.com/yulab-smu/ggbreak/llms.txt

Divides the x-axis at specified points, allowing customizable relative widths for each resulting segment. This is useful for visualizing datasets with distinct, widely separated x-ranges or groups.

```r
library(ggplot2)
library(ggbreak)

# Dataset with distinct x-ranges
d <- data.frame(
  x = c(1:10, 20:30, 100:110),
  y = c(rnorm(10), rnorm(11, 5), rnorm(11, 10)),
  group = rep(c("A", "B", "C"), c(10, 11, 11))
)

p <- ggplot(d, aes(x, y, color = group)) +
     geom_point(size = 3) +
     geom_line()

# Cut x-axis to show three distinct ranges
p + scale_x_cut(breaks = c(15, 35), which = 2, scales = 0.5, space = 0.1)

```

--------------------------------

### Wrap Long X-Axis Across Multiple Rows with scale_wrap

Source: https://context7.com/yulab-smu/ggbreak/llms.txt

Wraps a plot with a long x-axis across multiple rows, simplifying the visualization of time series or sequential data. It creates equal-width segments, making the plot more compact and readable.

```r
library(ggplot2)
library(ggbreak)

# Long time series
p <- ggplot(economics, aes(x = date, y = unemploy, colour = uempmed)) +
     geom_line()

# Wrap into 4 rows
p + scale_wrap(n = 4)

# Wrap into 6 rows for more compact display
p + scale_wrap(n = 6)

# Works with categorical x-axis
d <- data.frame(
  category = paste0("Item_", 1:50),
  value = rnorm(50, 100, 15)
)
p2 <- ggplot(d, aes(x = category, y = value)) +
      geom_col() +
      theme(axis.text.x = element_text(angle = 45, hjust = 1))
p2 + scale_wrap(n = 5)

```

--------------------------------

### Create X-Axis Break Point with scale_x_break

Source: https://context7.com/yulab-smu/ggbreak/llms.txt

Sets one or more break points on the x-axis to create discontinuous axis ranges, enabling horizontal data compression. This function is useful for time series data with significant gaps or for compressing dense x-axis values.

```r
library(ggplot2)
library(ggbreak)

# Time series data with gaps
set.seed(123)
d <- data.frame(
  time = c(1:10, 50:60),
  value = c(rnorm(10, 5, 1), rnorm(11, 15, 2))
)

p <- ggplot(d, aes(time, value)) + geom_line() + geom_point()

# Break x-axis to compress the gap
p + scale_x_break(c(15, 45), scales = 0.5)

# Multiple x-axis breaks with free scaling
p + scale_x_break(c(12, 48), scales = "free", space = 0.2)

```

--------------------------------

### Divide Y-Axis into Custom Segments with scale_y_cut

Source: https://context7.com/yulab-smu/ggbreak/llms.txt

Divides the y-axis at specified break points, allowing independent relative scaling for each segment. This provides fine-grained control over the proportions of different data ranges within the plot. The `which` argument selects specific segments, and `scales` adjusts their relative sizes.

```r
library(ggplot2)
library(ggbreak)

set.seed(2019-01-19)
d <- data.frame(
  x = 1:20,
  y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22)
)

p <- ggplot(d, aes(x, y)) + geom_col()

# Cut axis with custom scaling for specific segments
# which=c(1,3) targets first and third segments
# scales=c(3, 0.5) makes first segment 3x and third 0.5x relative size
p + scale_y_cut(breaks = c(7, 18), which = c(1, 3), scales = c(3, 0.5))

# Single cut point with equal segments
p + scale_y_cut(breaks = 10, expand = FALSE, space = 0.15)

```

--------------------------------

### Create Y-Axis Break Point with scale_y_break

Source: https://context7.com/yulab-smu/ggbreak/llms.txt

Sets one or more break points on the y-axis to create discontinuous axis ranges. This is useful for datasets with outliers or large gaps between value clusters. It supports custom scales and tick labels for different break segments.

```r
library(ggplot2)
library(ggbreak)

# Create dataset with outliers
set.seed(2019-01-19)
d <- data.frame(
  x = 1:20,
  y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22)
)

# Basic y-axis break
p <- ggplot(d, aes(x, y)) + geom_col()
p + scale_y_break(c(7, 17))

# Multiple breaks with custom scales
p + scale_y_break(c(7, 17), scales = "fixed") +
    scale_y_break(c(10, 15), scales = 1.5)

# Custom tick labels for break segments
p + scale_y_break(c(7, 17),
                  scales = c(0.5, 2),
                  ticklabels = list(c(4, 5, 6), c(20, 21, 22)))

```
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