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title: "Getting started with claudeplot"
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`claudeplot` brings the visual language of Anthropic and Claude to `ggplot2`:
a publication-ready theme, color palettes from Anthropic's brand and its
data-visualization style, color/fill scales, and palette helpers.

```{r setup}
library(ggplot2)
library(claudeplot)
```

## The theme

`theme_claude()` gives a light background, light horizontal grid lines, strong
axis lines, a bold Poppins title, and a serif Lora subtitle.

```{r theme}
ggplot(mtcars, aes(wt, mpg, color = factor(cyl))) +
  geom_point(size = 3) +
  scale_color_claude_d() +
  labs(
    title = "Fuel efficiency by weight",
    subtitle = "Heavier cars travel fewer miles per gallon",
    x = "Weight (1000 lbs)",
    y = "Miles per gallon",
    color = "Cylinders"
  ) +
  theme_claude()
```

You can control the grid (`"y"`, `"x"`, `"xy"`, `"none"`), toggle the axis
lines, and switch to Anthropic's warm off-white background:

```{r theme-variants}
ggplot(mpg, aes(displ, hwy)) +
  geom_point(color = claude_colors[["viz_blue"]]) +
  labs(title = "Engine size vs. highway mileage") +
  theme_claude(grid = "xy", background = "cloud")
```

## Color scales

Every scale comes in discrete (`_d`) and continuous (`_c`) forms, for both
`color`/`colour` and `fill`.

```{r bar}
df <- data.frame(
  model = c("Opus 4.6", "Opus 4.7", "Opus 4.8", "Fable 5"),
  score = c(58.6, 69.2, 80.3, 88.4)
)

ggplot(df, aes(model, score, fill = model)) +
  geom_col(width = 0.7) +
  geom_text(aes(label = score), vjust = -0.5, fontface = "bold") +
  scale_fill_claude_d() +
  scale_y_continuous(
    limits = c(0, 100),
    expand = expansion(mult = c(0, 0.05))
  ) +
  labs(
    title = "Agentic coding",
    subtitle = "SWE-Bench Pro (%)",
    x = NULL,
    y = NULL
  ) +
  theme_claude() +
  theme(legend.position = "none")
```

### Rounded bars

Anthropic's benchmark charts often use bars with softly rounded tops. The
[gground](https://github.com/dxsbiocc/gground/tree/main) package pairs nicely with
claudeplot: swap `geom_col()` for `geom_round_col()` for the same look.

```{r rounded, eval = requireNamespace("gground", quietly = TRUE)}
library(gground)

ggplot(df, aes(model, score, fill = model)) +
  geom_round_col(width = 0.7, radius = 0.3) +
  geom_text(aes(label = score), vjust = -0.5, fontface = "bold") +
  scale_fill_claude_d() +
  scale_y_continuous(
    limits = c(0, 100),
    expand = expansion(mult = c(0, 0.05))
  ) +
  labs(
    title = "Agentic coding",
    subtitle = "SWE-Bench Pro (%)",
    x = NULL,
    y = NULL
  ) +
  theme_claude() +
  theme(legend.position = "none")
```

Continuous scales interpolate the sequential and diverging palettes:

```{r continuous}
ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_raster() +
  scale_fill_claude_c(palette = "oranges") +
  labs(title = "Old Faithful eruptions") +
  theme_claude(grid = "none")
```

## Palettes

List the palettes, draw one, or draw them all:

```{r names}
claude_palette_names()
```

```{r show-one}
show_claude_palette("spectral", n = 9, type = "continuous")
```

```{r show-all, fig.height = 5}
show_claude_palettes()
```

The qualitative families are `claude` (the vivid benchmark palette), `brand`
(muted Anthropic accents), `warm`, `cool`, and `neutral`. Sequential families
are `oranges`, `blues`, `greens`, and `grays`; diverging families are
`orange_blue`, `green_orange`, and `spectral`.

## Fonts

claudeplot bundles Poppins and Lora and registers them with `systemfonts` on
load. They render on `ragg` and `svglite` devices; check availability with:

```{r fonts}
claude_font_status()
```

If a font is unavailable, `theme_claude()` falls back to generic `"sans"` and
`"serif"` families, so plots always render.