geom_axis {gggda} | R Documentation |
Axes through or offset from the origin
Description
geom_axis()
renders lines through or orthogonally translated
from the origin and the position of each case or variable.
Usage
geom_axis(
mapping = NULL,
data = NULL,
stat = "identity",
position = "identity",
axis_labels = TRUE,
axis_ticks = TRUE,
axis_text = TRUE,
by = NULL,
num = NULL,
tick_length = 0.025,
text_dodge = 0.03,
label_dodge = 0.03,
...,
axis.colour = NULL,
axis.color = NULL,
axis.alpha = NULL,
label.angle = 0,
label.colour = NULL,
label.color = NULL,
label.alpha = NULL,
tick.linewidth = 0.25,
tick.colour = NULL,
tick.color = NULL,
tick.alpha = NULL,
text.size = 2.6,
text.angle = 0,
text.hjust = 0.5,
text.vjust = 0.5,
text.family = NULL,
text.fontface = NULL,
text.colour = NULL,
text.color = NULL,
text.alpha = NULL,
parse = FALSE,
check_overlap = FALSE,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
Arguments
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data for this layer.
When using a
|
position |
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The
|
axis_labels , axis_ticks , axis_text |
Logical; whether to include labels, tick marks, and text value marks along the axes. |
by , num |
Intervals between elements or number of elements; specify only one. |
tick_length |
Numeric; the length of the tick marks, as a proportion of the minimum of the plot width and height. |
text_dodge |
Numeric; the orthogonal distance of tick mark text from the axis, as a proportion of the minimum of the plot width and height. |
label_dodge |
Numeric; the orthogonal distance of the axis label from the axis, as a proportion of the minimum of the plot width and height. |
... |
Additional arguments passed to |
axis.colour , axis.color , axis.alpha |
Default aesthetics for axes. Set to NULL to inherit from the data's aesthetics. |
label.angle , label.colour , label.color , label.alpha |
Default aesthetics for labels. Set to NULL to inherit from the data's aesthetics. |
tick.linewidth , tick.colour , tick.color , tick.alpha |
Default aesthetics for tick marks. Set to NULL to inherit from the data's aesthetics. |
text.size , text.angle , text.hjust , text.vjust , text.family , text.fontface , text.colour , text.color , text.alpha |
Default aesthetics for tick mark labels. Set to NULL to inherit from the data's aesthetics. |
parse |
If |
check_overlap |
If |
na.rm |
Passed to |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
Details
Axes are lines that track the values of linear variables across a plot. Multivariate scatterplots may include more axes than plotting dimensions, in which case the plot may display only a fraction of the total variation in the data.
Gower & Hand (1996) recommend using axes to represent numerical variables in biplots. Consequently, Gardner & le Roux (2002) refer to these as Gower biplots.
Axes positioned orthogonally at the origin are a ubiquitous feature of scatterplots and used both to recover variable values from case markers (prediction) and to position new case markers from variables (interpolation). When they are not orthogonal, these two uses conflict, so interpolative versus predictive axes must be used appropriately.
Value
Aesthetics
geom_axis()
understands the following aesthetics (required aesthetics are
in bold):
-
x
-
y
-
lower
-
upper
-
yintercept
orxintercept
orxend
andyend
-
linetype
-
linewidth
-
size
-
hjust
-
vjust
-
colour
-
alpha
-
label
-
family
-
fontface
-
center
,scale
-
group
References
Gower JC & Hand DJ (1996) Biplots. Chapman & Hall, ISBN: 0-412-71630-5.
Gardner S, le Roux N (2002) "Biplot Methodology for Discriminant Analysis Based upon Robust Methods and Principal Curves". Classification, Clustering, and Data Analysis: Recent Advances and Applications: 169–176. https://link.springer.com/chapter/10.1007/978-3-642-56181-8_18
See Also
Other geom layers:
geom_bagplot()
,
geom_isoline()
,
geom_lineranges()
,
geom_rule()
,
geom_text_radiate()
,
geom_vector()
Examples
# stack loss gradient
stackloss %>%
lm(formula = stack.loss ~ Air.Flow + Water.Temp + Acid.Conc.) %>%
coef() %>%
as.list() %>% as.data.frame() %>%
subset(select = c(Air.Flow, Water.Temp, Acid.Conc.)) ->
coef_data
# gradient axis with respect to two predictors
scale(stackloss, scale = FALSE) %>%
ggplot(aes(x = Acid.Conc., y = Air.Flow)) +
coord_square() +
geom_point(aes(size = stack.loss, alpha = sign(stack.loss))) +
scale_size_area() + scale_alpha_binned(breaks = c(-1, 0, 1)) +
geom_axis(data = coef_data)
# unlimited axes with window forcing
stackloss_centered <- scale(stackloss, scale = FALSE)
stackloss_centered %>%
ggplot(aes(x = Acid.Conc., y = Air.Flow)) +
coord_square() +
geom_point(aes(size = stack.loss, alpha = sign(stack.loss))) +
scale_size_area() + scale_alpha_binned(breaks = c(-1, 0, 1)) +
stat_rule(
geom = "axis", data = coef_data,
referent = stackloss_centered,
fun.lower = function(x) minpp(x, p = 1),
fun.upper = function(x) maxpp(x, p = 1),
fun.offset = function(x) minabspp(x, p = 1)
)
# NB: `geom_axis(stat = "rule")` would fail to pass positional aesthetics.
# eigen-decomposition of covariance matrix
ability.cov$cov %>%
cov2cor() %>%
eigen() %>% getElement("vectors") %>%
as.data.frame() %>%
transform(test = rownames(ability.cov$cov)) ->
ability_cor_eigen
# test axes in best-approximation space
ability_cor_eigen %>%
transform(E3 = ifelse(V3 > 0, "rise", "fall")) %>%
ggplot(aes(V1, V2, color = E3)) +
coord_square() +
geom_axis(aes(label = test), text.color = "black", text.alpha = .5) +
expand_limits(x = c(-1, 1), y = c(-1, 1))