_pkgdown.yml

url: https://ggplot2.tidyverse.org

template:
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        api_key: e1261f3df95d2d4704c626ff11a4b536
        index_name: tidyverse_ggplot2

development:
  mode: auto

authors:
  Winston Chang:
    href: https://github.com/wch
  Lionel Henry:
    href: https://github.com/lionel-
  Thomas Lin Pedersen:
    href: https://data-imaginist.com
  Claus Wilke:
    href: https://clauswilke.com
  Kara Woo:
    href: https://karawoo.com
  Hiroaki Yutani:
    href: https://yutani.rbind.io
  Dewey Dunnington:
    href: https://fishandwhistle.net

home:
  strip_header: true
  links:
  - text: Learn more
    href: https://r4ds.had.co.nz/data-visualisation.html
  - text: Extensions
    href: https://exts.ggplot2.tidyverse.org/gallery/

reference:
- title: Plot basics
  desc: >
    All ggplot2 plots begin with a call to `ggplot()`, supplying default data and
    aesthethic mappings, specified by `aes()`. You then add layers, scales,
    coords and facets with `+`. To save a plot to disk, use `ggsave()`.
  contents:
  - ggplot
  - aes
  - "`+.gg`"
  - ggsave
  - qplot

- title: Layers

- subtitle: Geoms
  desc: >
    A layer combines data, aesthetic mapping, a geom (geometric object),
    a stat (statistical transformation), and a position adjustment. Typically,
    you will create layers using a `geom_` function, overriding the default
    position and stat if needed.
  contents:
  - starts_with("geom_")

- subtitle: Stats
  desc: >
    A handful of layers are more easily specified with a `stat_` function,
    drawing attention to the statistical transformation rather than the visual
    appearance. The computed variables can be mapped using `after_stat()`.
  contents:
  - stat_ecdf
  - stat_ellipse
  - stat_function
  - stat_identity
  - stat_summary_2d
  - stat_summary_bin
  - stat_unique
  - stat_sf_coordinates
  - after_stat

- subtitle: Position adjustment
  desc: >
    All layers have a position adjustment that resolves overlapping geoms.
    Override the default by using the `position` argument to the `geom_` or
    `stat_` function.
  contents:
  - starts_with("position_")

- subtitle: Annotations
  desc: >
    Annotations are a special type of layer that don't inherit global settings
    from the plot. They are used to add fixed reference data to plots.
  contents:
  - geom_abline
  - annotate
  - annotation_custom
  - annotation_logticks
  - annotation_map
  - annotation_raster
  - borders

- title: Aesthetics
  desc: >
    The following help topics give a broad overview of some of the ways
    you can use each aesthetic.
  contents:
  - aes_colour_fill_alpha
  - aes_group_order
  - aes_linetype_size_shape
  - aes_position

- title: Scales
  desc: >
    Scales control the details of how data values are translated to visual
    properties. Override the default scales to tweak details like the axis
    labels or legend keys, or to use a completely different translation from
    data to aesthetic. `labs()` and `lims()` are convenient helpers for the
    most common adjustments to the labels and limits.
  contents:
  - labs
  - lims
  - expand_limits
  - expansion
  - starts_with("scale_")

- title: "Guides: axes and legends"
  desc: >
    The guides (the axes and legends) help readers interpret your plots.
    Guides are mostly controlled via the scale (e.g. with the `limits`,
    `breaks`, and `labels` arguments), but sometimes you will need additional
    control over guide appearance. Use `guides()` or the `guide` argument to
    individual scales along with `guide_*()` functions.
  contents:
  - draw_key
  - guide_colourbar
  - guide_legend
  - guide_axis
  - guide_bins
  - guide_coloursteps
  - guide_none
  - guides
  - sec_axis

- title: Facetting
  desc: >
    Facetting generates small multiples, each displaying a different
    subset of the data. Facets are an alternative to aesthetics for
    displaying additional discrete variables.
  contents:
  - facet_grid
  - facet_wrap
  - vars

- subtitle: Labels
  desc: >
    These functions provide a flexible toolkit for controlling the display
    of the "strip" labels on facets.
  contents:
  - labeller
  - labellers
  - label_value
  - label_bquote

- title: Coordinate systems
  desc: >
    The coordinate system determines how the `x` and `y` aesthetics combine
    to position elements in the plot. The default coordinate system is
    Cartesian (`coord_cartesian()`), which can be tweaked with `coord_map()`,
    `coord_fixed()`, `coord_flip()`, and `coord_trans()`, or completely
    replaced with `coord_polar()`.
  contents:
  - coord_cartesian
  - coord_fixed
  - coord_flip
  - coord_map
  - coord_polar
  - coord_trans

- title: Themes
  desc: >
    Themes control the display of all non-data elements of the plot. You
    can override all settings with a complete theme like `theme_bw()`, or
    choose to tweak individual settings by using `theme()` and the `element_`
    functions. Use `theme_set()` to modify the active theme, affecting all
    future plots.
  contents:
  - theme
  - theme_bw
  - theme_update
  - element_line
  - margin

- title: Programming with ggplot2
  desc: >
    These functions provides tools to help you program with ggplot2,
    creating functions and for-loops that generate plots for you.
  contents:
  - aes_
  - print.ggplot

- title: Extending ggplot2
  desc: >
    To create your own geoms, stats, scales, and facets, you'll need to learn
    a bit about the object oriented system that ggplot2 uses. Start by
    reading `vignette("extending-ggplot2")` then consult these functions
    for more details.
  contents:
  - ggproto
  - print.ggproto

- title: Vector helpers
  desc: >
    ggplot2 also provides a handful of helpers that are useful for creating
    visualisations.
  contents:
  - cut_interval
  - hmisc
  - mean_se
  - resolution

- title: Data
  desc: >
    ggplot2 comes with a selection of built-in datasets that are used in
    examples to illustrate various visualisation challenges.
  contents:
  - diamonds
  - economics
  - faithfuld
  - midwest
  - mpg
  - msleep
  - presidential
  - seals
  - txhousing
  - luv_colours

- title: Autoplot and fortify
  desc: >
    `autoplot()` is an extension mechanism for ggplot2: it provides a way
    for package authors to add methods that work like the base `plot()`
    function, generating useful default plots with little user interaction.
    `fortify()` turns objects into tidy data frames: it has largely been
    superceded by the [broom package](https://github.com/tidyverse/broom).
  contents:
  - autoplot
  - autolayer
  - fortify
  - map_data

news:
  releases:
  - text: "Version 3.3.0"
    href: https://www.tidyverse.org/blog/2020/03/ggplot2-3-3-0/
  - text: "Version 3.2.0"
    href: https://www.tidyverse.org/articles/2019/06/ggplot2-3-2-0/
  - text: "Version 3.1.0"
    href: https://www.tidyverse.org/articles/2018/10/ggplot2-3-1-0/
  - text: "Version 3.0.0"
    href: https://www.tidyverse.org/articles/2018/07/ggplot2-3-0-0/
  - text: "Version 2.2.0"
    href: https://blog.rstudio.com/2016/11/14/ggplot2-2-2-0/
  - text: "Version 2.1.0"
    href: https://blog.rstudio.com/2016/03/03/ggplot2-2-1-0/
  - text: "Version 2.0.0"
    href: https://blog.rstudio.com/2015/12/21/ggplot2-2-0-0/
  - text: "Version 1.0.0"
    href: https://blog.rstudio.com/2015/01/09/ggplot2-updates/

navbar:
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    right: [extensions, github]
  components:
    home: ~
    extensions:
      text: Extensions
      href: https://exts.ggplot2.tidyverse.org/gallery/