### Creating a Quantile-Quantile Plot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_qq() function to generate a quantile-quantile plot.

```r
tinyplot(data, type = type_qq())
```

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

### Install tinyplot from R-universe

Source: https://github.com/grantmcdermott/tinyplot/blob/main/README.md

Installs the latest development version of the tinyplot package from R-universe.

```r
install.packages("tinyplot", repos = "https://grantmcdermott.r-universe.dev")
```

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

### Install tinyplot from CRAN

Source: https://github.com/grantmcdermott/tinyplot/blob/main/README.md

Installs the stable version of the tinyplot package from CRAN.

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

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

### Plotting an Arbitrary Function

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_function() to plot the output of an arbitrary function.

```r
tinyplot(data, type = type_function(fun = sin, from = 0, to = pi))
```

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

### Fitting a Spline Model

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_spline() function to fit and visualize a spline model.

```r
tinyplot(data, type = type_spline())
```

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

### Fitting a Linear Model

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_lm() function to fit and visualize a linear model.

```r
tinyplot(data, type = type_lm())
```

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

### Fitting a Generalized Linear Model

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_glm() function to fit and visualize a generalized linear model.

```r
tinyplot(data, type = type_glm())
```

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

### Adding a Spineplot Visualization

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_spineplot() function to create spine plots or spinograms, useful for visualizing factor variables.

```r
tinyplot(data, type = type_spineplot())
```

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

### Adding a Rug Plot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_rug() function to add a rug plot to an existing visualization.

```r
tinyplot(data, type = type_rug())
```

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

### Fitting a LOESS Model

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_loess() function to fit and visualize a LOESS (Locally Estimated Scatterplot Smoothing) model.

```r
tinyplot(data, type = type_loess())
```

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

### Generating Ridge Plots (Joy Plots)

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_ridge() function to create ridge plots, also known as Joy plots.

```r
tinyplot(data, type = type_ridge())
```

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

### Summarizing Y values along unique X values

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_summary() function to summarize y values based on unique x values.

```r
tinyplot(data, type = type_summary())
```

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

### Adding a Line with Intercept and Slope

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_abline() function to add a line defined by an intercept and slope.

```r
tinyplot(data, type = type_abline(intercept = 1, slope = 2))
```

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

### Adding Text Annotations

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_text() function to add text annotations to a plot.

```r
tinyplot(data, type = type_text())
```

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

### Adding a Vertical Line

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_vline() function to add a vertical line to a plot.

```r
tinyplot(data, type = type_vline(x = 3))
```

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

### Adding a Horizontal Line

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Example of using the type_hline() function to add a horizontal line to a plot.

```r
tinyplot(data, type = type_hline(y = 5))
```

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

### Load Development Version

Source: https://github.com/grantmcdermott/tinyplot/blob/main/.CLAUDE.md

Use pkgload to load the package during interactive development to ensure local changes are tested.

```r
pkgload::load_all()

# Then test interactively, e.g.
plt(Sepal.Length ~ Petal.Length | Species, data = iris)
```

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

### Execute Tests via Makefile and R

Source: https://github.com/grantmcdermott/tinyplot/blob/main/.CLAUDE.md

Commands to run the test suite using either the Makefile or R console.

```bash
# Via Makefile
make testall                                          # Run all tests
make testone testfile="inst/tinytest/test-legend.R"   # Run single test file

# Via R
tinytest::run_test_dir("inst/tinytest")
tinytest::run_test_file("inst/tinytest/test-legend.R")
```

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

### Handle Resize with recordGraphics

Source: https://github.com/grantmcdermott/tinyplot/blob/main/.CLAUDE.md

Wrap coordinate-dependent calculations in recordGraphics to ensure correct re-rendering on device resize.

```r
recordGraphics(
  tinylegend(legend_env),
  list = list(legend_env = legend_env),
  env = getNamespace("tinyplot")
)
```

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

### Makefile Development Commands

Source: https://github.com/grantmcdermott/tinyplot/blob/main/.CLAUDE.md

Common development tasks available through the project Makefile.

```bash
make help        # Show all available commands
make document    # Generate documentation (devtools::document)
make check       # Full R CMD check
make install     # Install package locally
make website     # Build documentation website (altdoc)
```

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

### Create basic plots with tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/README.md

Demonstrates atomic and formula-based syntax for generating plots using the iris dataset.

```r
with(iris, tinyplot(x = Petal.Length, y = Sepal.Length, by = Species)) # atomic
tinyplot(Sepal.Length ~ Petal.Length | Species, data = iris)             # formula
```

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

### Migrate from plot2 to tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Demonstrates the syntax change required after the package rename from plot2 to tinyplot.

```r
library(plot2)
plot2(Sepal.Length ~ Petal.Length | Species, iris)
```

```r
library(tinyplot)
tinyplot(Sepal.Length ~ Petal.Length | Species, iris)

# Or, use the equivalent shorthand `plt` alias
plt(Sepal.Length ~ Petal.Length | Species, iris)
```

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

### Coding Conventions

Source: https://github.com/grantmcdermott/tinyplot/blob/main/.CLAUDE.md

Standard practices for assignment, function definition, and element access within the package.

```r
# Use = not <-
x = 5

# Use function() not \() — package requires R >= 4.0.0 compatibility
fn = function(x) x^2

# Prefer [[ over $ for element access (no partial matching, works with variables)
legend_args[["title"]]
settings[["datapoints"]]
# NOT: legend_args$title, settings$datapoints
```

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

### Specify Bounds in Formula Interface

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Compare the legacy with() approach to the updated formula interface for specifying ymin and ymax.

```r
with(dat, tinyplot(x = x, y = y, by = by ymin = lwr, ymax = upr))
```

```r
tinyplot(y ~ x | by, dat, ymin = lwr, ymax = upr)
```

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

### Applying a Pre-defined Theme in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Shows how to apply a pre-defined theme, such as 'clean', to subsequent plots using the tinytheme() function. Themes affect all plots until explicitly reset.

```r
tinytheme("clean")
```

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

### Apply built-in themes

Source: https://github.com/grantmcdermott/tinyplot/blob/main/README.md

Sets a persistent theme for plots using tinytheme().

```r
tinytheme("clean2")

plt(Sepal.Length ~ Petal.Length | Species, data = iris)
plt_add(type = "lm")
```

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

### Resetting to Default Theme in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Demonstrates how to reset plot aesthetics to the default settings by calling tinytheme() without any arguments.

```r
tinytheme()
```

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

### Using String vs. Functional Type Arguments in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Demonstrates the interchangeability of string-based and functional type arguments for plot types. The functional approach offers more flexibility for customization.

```r
tinyplot(Nile, type = "hist")
```

```r
tinyplot(Nile, type = type_hist())
```

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

### Create Histograms

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates histograms for distribution visualization using type_histogram(). Supports frequency/density toggles and faceting.

```r
# Basic histogram
tinyplot(Nile, type = "histogram")

# Customize number of breaks
tinyplot(Nile, type = type_histogram(breaks = 30))

# Density instead of frequency
tinyplot(Nile, type = type_histogram(breaks = 30, freq = FALSE))

# Grouped histogram
tinyplot(~Petal.Width | Species, type = "histogram", data = iris)

# Faceted histogram with free scales
tinyplot(
  ~Petal.Width,
  facet = ~Species,
  facet.args = list(free = TRUE),
  type = type_histogram(free.breaks = TRUE),
  data = iris
)
```

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

### Apply and View Plot Themes

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Sets persistent visual themes for plots using `tinytheme()`. Themes control styling like fonts, margins, and color palettes. You can also apply themes ephemerally to a single plot.

```r
# View current defaults
tinyplot(lat ~ long | depth, data = quakes)

# Apply a theme (persistent)
tinytheme("clean2")
tinyplot(lat ~ long | depth, data = quakes,
  main = "Earthquakes off Fiji",
  sub = "Clean theme with dynamic margins")

tinytheme("dark")
tinyplot(Sepal.Length ~ Petal.Length | Species, data = iris)

tinytheme("minimal")
tinyplot(mpg ~ wt | cyl, data = mtcars)

# Customize theme settings
tinytheme("bw", fg = "darkblue", font.main = 2, family = "Palatino")
tinyplot(mpg ~ wt, data = mtcars, main = "Custom Theme")

# Reset to default
tinytheme()

# Ephemeral theme (applies only to single plot)
tinyplot(0:10, theme = "clean", main = "Ephemeral theme")
tinyplot(10:0, main = "Back to default")
```

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

### Use shorthand plt() alias and add layers

Source: https://github.com/grantmcdermott/tinyplot/blob/main/README.md

Utilizes the plt() shorthand and adds a linear model layer using plt_add().

```r
plt(
  Sepal.Length ~ Petal.Length | Species, 
  data = iris,
  palette = "dark", pch = 16,
  grid = TRUE, frame = FALSE
)
plt_add(type = "lm")
```

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

### Query and Set Graphical Parameters

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Manages graphical parameters using `tpar()`, extending base R's `par()`. Allows querying and setting parameters like axis label orientation, point styles, and facet backgrounds.

```r
# Query current parameters
tpar("las", "pch", "facet.bg", "grid")

# Set parameters (returns previous values)
op = tpar(
  las = 1,            # horizontal axis labels
  pch = 16,           # filled circles
  facet.bg = "grey90",
  facet.cex = 1.2,
  grid = TRUE
)

tinyplot(mpg ~ wt, data = mtcars, facet = ~am)

# Reset to original
tpar(op)
```

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

### Create Density Plots

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Plots kernel density estimates using type_density(). Allows for grouping and custom bandwidth selection.

```r
# Basic density plot
tinyplot(~Sepal.Length, data = iris, type = "density")

# Grouped density
tinyplot(~Sepal.Length | Species, data = iris, type = "density")

# Filled densities
tinyplot(~Sepal.Length | Species, data = iris, type = "density", fill = "by")

# Custom bandwidth method
tinyplot(
  ~Sepal.Length | Species, data = iris,
  type = type_density(bw = "SJ", joint.bw = "none"),
  main = "Individual bandwidths per group"
)
```

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

### Create Ridge Plots

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates ridge plots for visualizing distributions across groups using type_ridge(). Recommended to use with tinytheme("ridge").

```r
aq = transform(
  airquality,
  Month = factor(month.abb[Month], levels = month.abb[5:9])
)

# Basic ridge plot (recommend using ridge theme)
tinytheme("ridge")
tinyplot(Month ~ Temp, data = aq, type = "ridge")

# Color by y groups
tinyplot(Month ~ Temp | Month, data = aq, type = "ridge")

# Gradient coloring along x-axis
tinyplot(Month ~ Temp | Temp, data = aq, type = "ridge")

# Manual gradient without legend
tinyplot(Month ~ Temp, data = aq, type = type_ridge(gradient = TRUE))

# Customize scaling and appearance
tinyplot(
  Month ~ Temp,
  data = aq,
  type = type_ridge(scale = 1.2, col = "white", gradient = "viridis")
)

tinytheme()  # reset theme
```

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

### Create QQ Plot

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates a Quantile-Quantile (QQ) plot to compare the quantiles of a sample distribution to the quantiles of a theoretical distribution. Useful for assessing normality or other distribution assumptions.

```r
tinyplot(~Sepal.Length | Species, data = iris, type = "qq")
```

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

### Load tinyplot library

Source: https://github.com/grantmcdermott/tinyplot/blob/main/README.md

Loads the tinyplot package into the current R session, making its functions available for use.

```r
library(tinyplot)
```

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

### Control grid appearance with tpar()

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

The `tpar()` function has gained `grid.col`, `grid.lty`, and `grid.lwd` arguments for fine-grained control over the default panel grid when `tinyplot(..., grid = TRUE)` is used.

```R
tpar(grid.col = "grey", grid.lty = 2, grid.lwd = 0.5)
```

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

### Generate grouped density plots

Source: https://github.com/grantmcdermott/tinyplot/blob/main/README.md

Creates a density plot grouped by species with custom titles and fill aesthetics.

```r
plt(
  ~ Petal.Length | Species,
  data = iris,
  type = "density",
  fill = "by",
  main = "Distribution of petal lengths",
  sub = "Grouped by species"
)
```

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

### Create Ribbon Plot with Ymin/Ymax

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates a ribbon plot using specified x, ymin, and ymax values. Use this to visualize ranges or confidence intervals.

```r
tinyplot(x = x, ymin = y - 1, ymax = y + 1, type = "ribbon")
```

```r
tinyplot(x = x, y = y, ymin = y - 1, ymax = y + 1, type = "ribbon")
```

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

### Use tinyplot() for grouped/faceted density plots

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Grouped and faceted density plots are no longer directly supported via `tinyplot.density()`. Instead, use `tinyplot(..., type = "density")` or `tinyplot(..., type = type_density())` on raw data, passing grouping or facet arguments as needed.

```R
tinyplot(..., type = "density")
```

```R
tinyplot(..., type = type_density())
```

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

### Creating Scatter Plots with type_points()

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates scatter plots, which is the default type for numeric data.

```r
# Basic scatter plot
tinyplot(Sepal.Width ~ Sepal.Length, data = iris, type = "p")

# Grouped scatter with filled points
tinyplot(
  Sepal.Width ~ Sepal.Length | Species,
  data = iris,
  type = "p",
  pch = 19,
  alpha = 0.7
)
```

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

### Create Violin Plots

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates violin plots as an alternative to boxplots using type_violin(). Supports orientation flipping and grouping.

```r
# Basic violin plot
tinyplot(count ~ spray, data = InsectSprays, type = "violin")

# Horizontal orientation
tinyplot(count ~ spray, data = InsectSprays, type = "violin", flip = TRUE)

# Grouped violin with fill
tinyplot(len ~ dose | supp, data = ToothGrowth, type = "violin", fill = 0.2)

# Customized violin
tinyplot(
  count ~ spray,
  data = InsectSprays,
  type = type_violin(trim = TRUE, width = 0.8, joint.bw = "none")
)
```

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

### Control ribbon transparency with type_ribbon()

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

The `ribbon.alpha` argument in `tinyplot()` is deprecated. Use the `alpha` argument within `type_ribbon()` (or equivalents) for controlling ribbon transparency.

```R
tinyplot(..., type = type_ribbon(alpha = 0.5))
```

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

### Creating Line Plots with type_lines()

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates line plots, typically used for time series or sequential data.

```r
# Basic line plot
tinyplot(AirPassengers, type = "l")

# Grouped line plot
tinyplot(
  Temp ~ Day | Month,
  data = airquality,
  type = "l",
  lwd = 2
)

# Both points and lines
tinyplot(Temp ~ Day | Month, data = airquality, type = "b")
```

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

### Main Plotting with tinyplot() and plt()

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

The primary function for creating various plot types with support for formulas, grouping, and faceting. The plt() function serves as a shorthand alias.

```r
library(tinyplot)

# Basic scatterplot (drop-in replacement for plot)
tinyplot(0:10)

# Grouped scatterplot with automatic legend using formula syntax
# The | operator specifies the grouping variable
tinyplot(Sepal.Length ~ Petal.Length | Species, data = iris)

# Equivalent using atomic method with `by` argument
with(iris, tinyplot(x = Petal.Length, y = Sepal.Length, by = Species))

# Shorthand alias with customization
plt(
  Sepal.Length ~ Petal.Length | Species,
  data = iris,
  pch = 16,           # filled points
  cex = 2,            # larger size
  alpha = 0.5,        # transparency
  palette = "tableau" # color palette
)

# Use "by" keyword to vary aesthetics by group
tinyplot(
  Temp ~ Day | Month,
  data = airquality,
  pch = "by",   # different point characters per group
  lty = "by",   # different line types per group
  cex = "by"    # different sizes per group
)

# Bubble plot: pass vector to cex for size mapping
tinyplot(
  Temp ~ Day | Month,
  data = airquality,
  pch = 21,
  cex = airquality$Wind,  # map point size to Wind variable
  bg = 0.3,               # filled with transparency
  col = "black"
)

# Faceted plot
tinyplot(
  Temp ~ Day,
  facet = ~Month,
  data = airquality,
  type = "area",
  main = "Temperatures by month"
)

# Facets with grouping combined
aq = transform(airquality, Summer = Month %in% c(6, 7, 8))
tinyplot(
  Temp ~ Day | Summer,
  facet = ~Month,
  facet.args = list(nrow = 1),  # single row layout
  data = aq,
  type = "area",
  palette = "dark2",
  frame = FALSE
)

# Two-sided formula for fixed grid layout (rows ~ columns)
tinyplot(
  Temp ~ Day,
  facet = am ~ vs,
  data = mtcars
)

# Add common elements to each facet using `draw`
tinyplot(
  Temp ~ Day,
  facet = ~Month,
  data = airquality,
  draw = abline(h = 75, lty = 2, col = "hotpink")
)

# Save plot directly to file
tinyplot(
  Sepal.Length ~ Petal.Length | Species,
  data = iris,
  file = "myplot.png",
  width = 8,
  height = 6
)
```

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

### Manage Plot Hooks for Themes

Source: https://github.com/grantmcdermott/tinyplot/blob/main/.CLAUDE.md

Preserve and restore plot hooks when applying custom themes to ensure correct rendering.

```r
oldhook = getHook("before.plot.new")
setHook("before.plot.new", function() par(new = TRUE), action = "append")
plot.new()
setHook("before.plot.new", oldhook, action = "replace")
```

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

### Independent axis scaling for facets with tinyplot()

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

The `facet.args` argument now includes a `free` sub-argument to independently scale axis limits for individual facets. This provides more control over faceted plot appearance.

```R
tinyplot(..., facet.args = list(free = TRUE))
```

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

### Create Point Range Plot

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates a point range plot, displaying a central estimate with upper and lower bounds. Useful for showing uncertainty in estimates.

```r
mod = lm(mpg ~ wt * factor(am), mtcars)
coefs = data.frame(
  term = names(coef(mod)),
  est = coef(mod),
  lwr = confint(mod)[,1],
  upr = confint(mod)[,2]
)

# Point range
tinyplot(est ~ term, ymin = lwr, ymax = upr, data = coefs, type = "pointrange")
```

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

### Customize Axis Breaks and Labels

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use yaxb to define manual break points and yaxl to apply formatting functions to axis tick labels.

```r
tinyplot((0:10)/10, yaxb = c(.17, .33, .5, .67, .83), yaxl = "%")
```

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

### Create Summary Statistics Plot

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates a plot displaying summary statistics (e.g., mean) for a numeric variable grouped by a factor variable. Use `type_summary` with a specified function.

```r
tinyplot(Temp ~ Month, data = airquality, type = type_summary(fun = mean))
```

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

### Create Function Plot

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates a plot of a specified mathematical function within given x-axis limits. Use `type_function` with `fun` and `args` arguments.

```r
tinyplot(type = type_function(fun = dnorm, args = list(mean = 0, sd = 1)),
         xlim = c(-4, 4))
```

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

### Create Bar Plots

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates bar plots for categorical data using type_barplot(). Supports stacking, side-by-side grouping, and aggregation.

```r
# Frequency table (one-sided formula)
tinyplot(~cyl, data = mtcars, type = "barplot")

# Stacked grouped bars (default)
tinyplot(~cyl | vs, data = mtcars, type = "barplot")

# Side-by-side grouped bars
tinyplot(~cyl | vs, data = mtcars, type = "barplot", beside = TRUE, fill = 0.2)

# Aggregated values (y ~ x)
tinyplot(extra ~ ID | group, facet = "by", data = sleep,
  type = "barplot", fill = 0.6)

# Reorder categories
tinyplot(~cyl, data = mtcars, type = "barplot", xlevels = c("8", "6", "4"))

# Horizontal bars
tinyplot(~cyl | vs, data = mtcars, type = "barplot", flip = TRUE)
```

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

### Create Box-and-Whisker Plots

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates boxplots using the type_boxplot() function or the 'boxplot' type string. Supports grouping and customization of box aesthetics.

```r
# Basic boxplot (automatic when x is factor)
tinyplot(count ~ spray, data = InsectSprays)

# Explicit type specification
tinyplot(count ~ spray, data = InsectSprays, type = "boxplot")

# Grouped boxplot
tinyplot(len ~ dose | supp, data = ToothGrowth, type = "boxplot")

# Customized boxplot using type_boxplot()
tinyplot(
  len ~ dose | supp,
  data = ToothGrowth,
  lty = 1,
  type = type_boxplot(boxwex = 0.3, staplewex = 0, outline = FALSE, notch = TRUE)
)

# Horizontal boxplot
tinyplot(count ~ spray, data = InsectSprays, type = "boxplot", flip = TRUE)
```

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

### Create Dodged Grouped Point Range Plot

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates dodged point range plots for grouped data, allowing comparison across categories while maintaining clarity for overlapping estimates. `fixed.dodge = TRUE` ensures consistent spacing.

```r
tinyplot(
  estimate ~ term | model,
  ymin = conf.low, ymax = conf.high,
  data = models_df,
  type = type_pointrange(dodge = 0.1, fixed.dodge = TRUE)
)
tinytheme()
```

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

### Create Ribbon and Area Plots

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Creates filled regions between y-values or from zero using type_ribbon() or type_area().

```r
x = 1:100 / 10
y = sin(x)
```

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

### Create faceted scatterplots

Source: https://github.com/grantmcdermott/tinyplot/blob/main/README.md

Generates a faceted scatterplot with a continuous gradient legend.

```r
plt(
  Sepal.Length ~ Petal.Length | Sepal.Length, data = iris,
  facet = ~Species, pch = 19,
  main = "Faceted flowers", sub = "Brought to you by tinyplot"
)
```

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

### Flip plot orientation with tinyplot()

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use the `flip` argument to easily swap the x and y axes orientation in any plot type. This is useful for adapting plots to different display needs.

```R
tinyplot(~Sepal.Length | Species, data = iris, type = "density", flip = TRUE)
```

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

### Format axis labels with tinylabel

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Uses convenience keywords or custom functions to format axis tick labels and breaks.

```r
# Convenience keywords for xaxl/yaxl
tinyplot(1:10 / 10, yaxl = "%")           # percent: "10%", "20%", ...
tinyplot(1e4:1e5, yaxl = ",")             # comma: "10,000", "20,000", ...
tinyplot(1:10 * 1000, yaxl = "$")         # dollar: "$1,000", "$2,000", ...

# Log-scale with log labels
tinyplot(x = 10^(0:10), y = 0:10, type = "b",
         log = "x", xaxl = "log")

# Custom break points + labels
tinyplot(x = 10^(0:10), y = 0:10, type = "b",
         log = "x", xaxl = "log", xaxb = 10^c(1, 3, 5, 7, 9))

# Custom formatting function
dat = data.frame(
  date = seq(as.Date("2000/1/1"), by = "month", length.out = 12),
  value = cumsum(rnorm(12))
)
tinyplot(value ~ date, data = dat,
         xaxl = function(x) format(x, "%b %Y"))

# Direct use of tinylabel
tinylabel(1e4, ",")     # "10,000"
tinylabel(0.25, "%")    # "25%"
tinylabel(1e6, "$")     # "$1,000,000"
```

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

### Create Error Bars for Coefficients

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates error bars to visualize estimates and their confidence intervals, commonly used for regression coefficients. Requires data with term, estimate, lower, and upper bounds.

```r
mod = lm(mpg ~ wt * factor(am), mtcars)
coefs = data.frame(
  term = names(coef(mod)),
  est = coef(mod),
  lwr = confint(mod)[,1],
  upr = confint(mod)[,2]
)

# Error bars
tinyplot(est ~ term, ymin = lwr, ymax = upr, data = coefs, type = "errorbar")
```

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

### Customize plot legends

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Demonstrates positioning, disabling, and detailed configuration of legends in tinyplot.

```r
# Default: legend outside on right
tinyplot(Temp ~ Day | Month, data = airquality, type = "l")

# Position keywords (add "!" for outside placement)
tinyplot(Temp ~ Day | Month, data = airquality, type = "l",
         legend = "topleft")     # inside
tinyplot(Temp ~ Day | Month, data = airquality, type = "l",
         legend = "bottom!")     # outside bottom

# Turn off legend
tinyplot(Temp ~ Day | Month, data = airquality, type = "l",
         legend = FALSE)

# Detailed customization via legend()
tinyplot(
  Temp ~ Day | Month,
  data = airquality,
  type = "l",
  legend = legend(
    "bottom!",
    title = "Month of Year",
    bty = "o",
    horiz = TRUE
  )
)

# Custom legend labels via labeller
tinyplot(
  Temp ~ Day | Month,
  data = airquality,
  legend = list(labeller = toupper)
)
```

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

### Create Horizontal Error Bars with Reference Line

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates horizontal error bars and adds a vertical reference line. Use `flip = TRUE` for horizontal orientation and `tinyplot_add` for reference lines.

```r
mod = lm(mpg ~ wt * factor(am), mtcars)
coefs = data.frame(
  term = names(coef(mod)),
  est = coef(mod),
  lwr = confint(mod)[,1],
  upr = confint(mod)[,2]
)

# Horizontal orientation with reference line
tinytheme("classic")
tinyplot(est ~ term, ymin = lwr, ymax = upr, data = coefs,
         type = "errorbar", flip = TRUE)
tinyplot_add(type = "vline", lty = 2)
```

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

### Customizing Plot Type with Functional Arguments

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Illustrates the recommended method for passing type-specific arguments by embedding them within the functional type call, such as type_hist(breaks = 30). This is preferred over passing arguments at the top level of tinyplot().

```r
tinyplot(Nile, type = type_hist(breaks = 30))
```

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

### Reset plot theme

Source: https://github.com/grantmcdermott/tinyplot/blob/main/README.md

Resets the current theme to default settings.

```r
# reset the theme 
tinytheme()
```

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

### Faceting with Complex Formulas in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Handles complex facet arrangements, including interactions and multivariate formulas, ensuring all panels are plotted correctly even if some combinations are missing.

```R
tinyplot(mpg ~ wt, data = mtcars, facet = am + vs ~ gear)
```

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

### Fit Linear Models

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Plots linear regression lines with confidence intervals using type_lm().

```r
# Basic linear fit with confidence interval
tinyplot(Sepal.Width ~ Petal.Width, data = iris, type = "lm")

# Grouped fits (demonstrates Simpson's paradox)
tinyplot(Sepal.Width ~ Petal.Width | Species, data = iris, type = "lm")
tinyplot_add(type = "p")  # add points

# Adjust confidence level
tinyplot(Sepal.Width ~ Petal.Width, data = iris, type = type_lm(level = 0.8))

# Without confidence interval
tinyplot(Sepal.Width ~ Petal.Width, data = iris, type = type_lm(se = FALSE))
```

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

### Create Spineplot for Factor Variables

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates a spineplot, which displays the conditional distribution of a numeric variable across categories of one or more factor variables. Useful for exploring relationships between categorical and continuous data.

```r
tinyplot(Survived ~ Class | Sex, data = as.data.frame(Titanic), type = "spineplot")
```

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

### Add custom drawing functions with tinyplot()

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

The `draw` argument allows passing arbitrary drawing functions that are evaluated before the main plotting elements. This is useful for adding common annotations like text or threshold lines across facets.

```R
tinyplot(..., draw = list(function(p) { p + geom_vline(xintercept = 0) }))
```

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

### Customize legend settings

Source: https://github.com/grantmcdermott/tinyplot/blob/main/.CLAUDE.md

Use this snippet within a type's data function to set default legend arguments if they are not already defined.

```r
settings$legend_args[["pch"]] = settings$legend_args[["pch"]] %||% 22
settings$legend_args[["pt.cex"]] = settings$legend_args[["pt.cex"]] %||% 3.5
```

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

### Plotting numeric ~ character variables

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Plots of the form `plt(numeric ~ character)` now work correctly by automatically coercing the character variable to a factor.

```R
plt(numeric_var ~ character_var)
```

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

### Fit LOESS Smoothing

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Performs local polynomial regression fitting using type_loess().

```r
# Basic loess fit
tinyplot(Sepal.Width ~ Petal.Width, data = iris, type = "loess")

# Grouped loess with points
tinyplot(Sepal.Width ~ Petal.Width | Species, data = iris, type = "p")
plt_add(type = "loess")
```

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

### Create Dodged Ribbons for Overlapping Groups

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates dodged ribbon plots to visualize ranges for different groups that might overlap. Requires data in a data frame format with explicit range columns.

```r
dat = data.frame(
  x = rep(c("Before", "After"), each = 2),
  grp = rep(c("A", "B"), 2),
  y = c(10, 10.5, 15, 15.3),
  lwr = c(8, 8.5, 13, 13.3),
  upr = c(12, 12.5, 17, 17.3)
)
tinyplot(
  y ~ x | grp, data = dat,
  ymin = lwr, ymax = upr,
  type = type_ribbon(dodge = 0.1)
)
```

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

### Fine-grained control for faceted histograms

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

The `type_histogram()` function now includes `free.breaks` and `drop.zeros` arguments for fine-grained control over free axis scaling in faceted histograms.

```R
type_histogram(free.breaks = TRUE, drop.zeros = FALSE)
```

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

### Plotting Logarithmic Transformation of X vs X in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Avoids triggering an inadvertent legend when plotting a function transformation of x against x itself, such as `log(x) ~ x`.

```R
tinyplot(log(x) ~ x)
```

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

### Layering Plot Elements with tinyplot_add()

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Adds additional layers to an existing tinyplot, inheriting arguments from the previous call.

```r
# Create base scatter plot
tinyplot(Sepal.Width ~ Sepal.Length | Species, facet = ~Species, data = iris)

# Add linear model fit layer
tinyplot_add(type = "lm")

# Equivalent shorthand
plt_add(type = "lm")

# Add multiple layers sequentially
tinyplot(Sepal.Width ~ Petal.Width | Species, data = iris, type = "p")
plt_add(type = "lm")        # add regression lines
plt_add(type = "loess")     # add loess smoothing

# Example with density plots comparing bandwidth methods
tinyplot(~Sepal.Length | Species, data = iris,
    ylim = c(0, 1.25), type = "density")
tinyplot_add(joint.bw = "full", lty = 2)
tinyplot_add(joint.bw = "none", lty = 3)
legend("topright", c("Mean", "Full", "None"), lty = 1:3, bty = "n")
```

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

### Customize joint bandwidth for density plots

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

For grouped density plots, the joint smoothing bandwidth calculation has changed. Use the `type_density(joint.bw = <option>)` argument to customize this bandwidth.

```R
type_density(joint.bw = "obs_mean")
```

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

### Retrieve Saved Graphical Parameters

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Retrieves graphical parameters saved before or after the most recent `tinyplot` call using `get_saved_par()`. Useful for restoring settings after plot generation.

```r
# Create plot that modifies margins
tinyplot(Sepal.Length ~ Petal.Length | Species, iris)

# Get original parameters
sp = get_saved_par("before")
```

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

### Control X and Y Axes Separately in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use `xaxt` and `yaxt` arguments to control the appearance of the x and y axes independently. Accepts keywords like 'standard', 'none', 'ticks', 'labels', or 'axis'.

```R
plt(0:10, xaxt = "l", yaxt = "t")
```

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

### Plotting with factor y-axis

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Plots where 'y' is a factor now work automatically by dispatching to the `type_spineplot()` type.

```R
tinyplot(factor_var ~ numeric_var, type = type_spineplot())
```

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

### Layer plots with tinyplot_add()

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use the `tinyplot_add()` function (or its alias `plt_add()`) for easy layering of plots without repeating arguments. This simplifies combining multiple plot elements.

```R
plt_add(p, p2)
```

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

### Create Jittered Points Plot

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates a jittered points plot, which adds random noise to point positions to reduce overplotting. Useful for visualizing discrete or categorical data with many overlapping points.

```r
tinyplot(count ~ spray, data = InsectSprays, type = "jitter")
```

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

### Add Rug Plot to Density Plot

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Adds a rug plot (small vertical lines indicating data points) to an existing density plot. Useful for showing the distribution of data along the axis.

```r
tinyplot(~Sepal.Length | Species, data = iris, type = "density")
plt_add(type = "rug")
```

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

### Add elements to plot

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Iterates through species levels to add regression lines to an existing plot.

```r
for (s in levels(iris$Species)) {
  abline(lm(Sepal.Length ~ Petal.Length, iris, subset = Species == s),
         col = which(levels(iris$Species) == s))
}

# Restore original parameters
tpar(sp)
```

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

### Create Area Plot

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Generates an area plot, filling the space between a baseline (0 or y) and the specified y values. Suitable for time series or cumulative data.

```r
tinyplot(x, y, type = "area")
```

```r
tinyplot(AirPassengers, type = "area")
```

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

### Empty Plot Type in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use `type = "n"` to create an empty plot. The `empty` argument can be used with any plot type to achieve a similar effect.

```R
type = "n"
```

```R
empty
```

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

### Define Plot Type

Source: https://github.com/grantmcdermott/tinyplot/blob/main/.CLAUDE.md

Construct a tinyplot_type S3 object by defining draw and data functions.

```r
type_boxplot = function(range = 1.5, ...) {
  out = list(
    draw = draw_boxplot(range = range, ...),  # closure: does actual plotting per group
    data = data_boxplot(...),                  # closure: preprocesses data, injects into settings
    name = "boxplot"                           # string identifier
  )
  class(out) = "tinyplot_type"
  return(out)
}
```

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

### Boxplot Type in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Enable `plt(numeric ~ factor)` support by setting `type = "boxplot"`. This automatically generates a boxplot when a numeric variable is plotted against a factor.

```R
type = "boxplot"
```

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

### Add Abline with Intercept and Slope

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Adds a line defined by an intercept and slope to a plot using `type_abline`. Useful for visualizing linear models or specific linear relationships.

```r
tinyplot(mpg ~ wt, data = mtcars)
plt_add(type = type_abline(a = 37, b = -5), col = "blue")
```

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

### Add Horizontal and Vertical Reference Lines

Source: https://context7.com/grantmcdermott/tinyplot/llms.txt

Adds horizontal and vertical reference lines to a plot. Use `type_hline` and `type_vline` with `plt_add` to specify line properties.

```r
tinyplot(mpg ~ wt, data = mtcars)
plt_add(type = type_hline(y = mean(mtcars$mpg)), lty = 2)
plt_add(type = type_vline(x = mean(mtcars$wt)), lty = 2, col = "red")
```

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

### Polypath Plot Type in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use `type = "polypath"` to create plots with polypaths.

```R
type = "polypath"
```

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

### Jitter Plot Type in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use `type = "jitter"` or its alias `type = "j"` to create jitter plots.

```R
type = "jitter"
```

```R
type = "j"
```

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

### Rectangle Plot Type in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use `type = "rect"` to create plots with rectangles.

```R
type = "rect"
```

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

### Boxplot Width Argument in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

When using `type = "boxplot"`, the `boxwidth` argument controls the width of the boxes. This is equivalent to the `width` argument in base R's `boxplot()` function.

```R
tinyplot(..., type = "boxplot", boxwidth = <num>)
```

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

### Segments Plot Type in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use `type = "segments"` to create plots with segments.

```R
type = "segments"
```

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

### Histogram Plot Type in Tinyplot

Source: https://github.com/grantmcdermott/tinyplot/blob/main/NEWS.md

Use `type = "histogram"` or its alias `type = "hist"` to generate histograms.

```R
type = "histogram"
```

```R
type = "hist"
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.