forked from tidyverse/ggplot2
-
Notifications
You must be signed in to change notification settings - Fork 0
/
_pkgdown.yml
247 lines (226 loc) · 6.93 KB
/
_pkgdown.yml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
url: https://ggplot2.tidyverse.org
template:
package: tidytemplate
development:
mode: auto
home:
strip_header: true
links:
- text: Learn more
href: http://r4ds.had.co.nz/data-visualisation.html
- text: Extensions
href: http://www.ggplot2-exts.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: "Layer: 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_")
- title: "Layer: 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 `stat()`.
contents:
- stat_ecdf
- stat_ellipse
- stat_function
- stat_identity
- stat_summary_2d
- stat_summary_bin
- stat_unique
- stat_sf_coordinates
- stat
- title: "Layer: 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_")
- title: "Layer: 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_colourbar()` or `guide_legend()`.
contents:
- guide_colourbar
- guide_legend
- 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
- title: "Facetting: 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
navbar:
structure:
right: [extensions, github]
components:
home: ~
news:
text: News
menu:
- text: "Release notes"
- 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/
- text: "------------------"
- text: "Change log"
href: news/index.html
extensions:
text: Extensions
href: http://www.ggplot2-exts.org/gallery/