R/gf_functions.R, R/ggstance.R
gf_linerange.RdVarious ways of representing a vertical interval defined by x,
ymin and ymax. Each case draws a single graphical object.
gf_linerange(
object = NULL,
gformula = NULL,
data = NULL,
...,
alpha,
color,
group,
linetype,
size,
xlab,
ylab,
title,
subtitle,
caption,
geom = "linerange",
stat = "identity",
position = "identity",
show.legend = NA,
show.help = NULL,
inherit = TRUE,
environment = parent.frame()
)
gf_pointrange(
object = NULL,
gformula = NULL,
data = NULL,
...,
alpha,
color,
group,
linetype,
size,
fatten = 2,
xlab,
ylab,
title,
subtitle,
caption,
geom = "pointrange",
stat = "identity",
position = "identity",
show.legend = NA,
show.help = NULL,
inherit = TRUE,
environment = parent.frame()
)
gf_summary(
object = NULL,
gformula = NULL,
data = NULL,
...,
alpha,
color,
group,
linetype,
size,
fun.y = NULL,
fun.ymax = NULL,
fun.ymin = NULL,
fun.args = list(),
fatten = 2,
xlab,
ylab,
title,
subtitle,
caption,
geom = "pointrange",
stat = "summary",
position = "identity",
show.legend = NA,
show.help = NULL,
inherit = TRUE,
environment = parent.frame()
)
gf_linerangeh(
object = NULL,
gformula = NULL,
data = NULL,
...,
alpha,
color,
group,
linetype,
size,
xlab,
ylab,
title,
subtitle,
caption,
geom = "linerangeh",
stat = "identity",
position = "identity",
show.legend = NA,
show.help = NULL,
inherit = TRUE,
environment = parent.frame()
)
gf_pointrangeh(
object = NULL,
gformula = NULL,
data = NULL,
...,
alpha,
color,
group,
linetype,
size,
xlab,
ylab,
title,
subtitle,
caption,
geom = "pointrangeh",
stat = "identity",
position = "identity",
show.legend = NA,
show.help = NULL,
inherit = TRUE,
environment = parent.frame()
)When chaining, this holds an object produced in the earlier portions of the chain. Most users can safely ignore this argument. See details and examples.
A formula with shape ymin + ymax ~ x.
Faceting can be achieved by including | in the formula.
The data to be displayed in this layer. There are three options:
If NULL, the default, the data is inherited from the plot
data as specified in the call to ggplot().
A data.frame, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
fortify() for which variables will be created.
A function will be called with a single argument,
the plot data. The return value must be a data.frame, and
will be used as the layer data. A function can be created
from a formula (e.g. ~ head(.x, 10)).
Additional arguments. Typically these are
(a) ggplot2 aesthetics to be set with attribute = value,
(b) ggplot2 aesthetics to be mapped with attribute = ~ expression, or
(c) attributes of the layer as a whole, which are set with attribute = value.
Opacity (0 = invisible, 1 = opaque).
A color or a formula used for mapping color.
Used for grouping.
A linetype (numeric or "dashed", "dotted", etc.) or a formula used for mapping linetype.
A numeric size or a formula used for mapping size.
Label for x-axis. See also gf_labs().
Label for y-axis. See also gf_labs().
Title, sub-title, and caption for the plot.
See also gf_labs().
A character string naming the geom used to make the layer.
The statistical transformation to use on the data for this layer, as a string.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
logical. Should this layer be included in the legends?
NA, the default, includes if any aesthetics are mapped.
FALSE never includes, and TRUE always includes.
It can also be a named logical vector to finely select the aesthetics to
display.
If TRUE, display some minimal help.
A logical indicating whether default attributes are inherited.
An environment in which to look for variables not found in data.
A multiplicative factor used to increase the size of the
middle bar in geom_crossbar() and the middle point in
geom_pointrange().
Deprecated, use the versions specified above instead.
Optional additional arguments passed on to the functions.
gf_linerange()
#> gf_linerange() uses
#> * a formula with shape ymin + ymax ~ x or y ~ xmin + xmax.
#> * geom: linerange
#> * key attributes: alpha, color, group, linetype, size
#>
#> For more information, try ?gf_linerange
gf_ribbon(low_temp + high_temp ~ date,
data = mosaicData::Weather,
fill = ~city, alpha = 0.4
) %>%
gf_theme(theme = theme_minimal())
gf_linerange(
low_temp + high_temp ~ date | city ~ .,
data = mosaicData::Weather,
color = ~ ((low_temp + high_temp) / 2)
) %>%
gf_refine(scale_colour_gradientn(colors = rev(rainbow(5)))) %>%
gf_labs(color = "mid-temp")
gf_ribbon(low_temp + high_temp ~ date | city ~ ., data = mosaicData::Weather)
# Chaining in the data
mosaicData::Weather %>%
gf_ribbon(low_temp + high_temp ~ date, alpha = 0.4) %>%
gf_facet_grid(city ~ .)
if (require(mosaicData) && require(dplyr)) {
HELP2 <- HELPrct %>%
group_by(substance, sex) %>%
summarise(
age = NA,
mean.age = mean(age),
median.age = median(age),
max.age = max(age),
min.age = min(age),
sd.age = sd(age),
lo = mean.age - sd.age,
hi = mean.age + sd.age
)
gf_jitter(age ~ substance, data = HELPrct,
alpha = 0.5, width = 0.2, height = 0, color = "skyblue") %>%
gf_pointrange(mean.age + lo + hi ~ substance, data = HELP2) %>%
gf_facet_grid(~sex)
gf_jitter(age ~ substance, data = HELPrct,
alpha = 0.5, width = 0.2, height = 0, color = "skyblue") %>%
gf_errorbar(lo + hi ~ substance, data = HELP2, inherit = FALSE) %>%
gf_facet_grid(~sex)
# width is defined differently for gf_boxplot() and gf_jitter()
# * for gf_boxplot() it is the full width of the box.
# * for gf_jitter() it is half that -- the maximum amount added or subtracted.
gf_boxplot(age ~ substance, data = HELPrct, width = 0.4) %>%
gf_jitter(width = 0.4, height = 0, color = "skyblue", alpha = 0.5)
gf_boxplot(age ~ substance, data = HELPrct, width = 0.4) %>%
gf_jitter(width = 0.2, height = 0, color = "skyblue", alpha = 0.5)
}
#> `summarise()` has grouped output by 'substance'. You can override using the
#> `.groups` argument.
p <- gf_jitter(mpg ~ cyl, data = mtcars, height = 0, width = 0.15); p
p %>% gf_summary(fun.data = "mean_cl_boot", color = "red", size = 2)
#> Warning: Computation failed in `stat_summary()`:
# You can supply individual functions to summarise the value at
# each x:
p %>% gf_summary(fun.y = "median", color = "red", size = 2, geom = "point")
#> No summary function supplied, defaulting to `mean_se()`
p %>%
gf_summary(fun.y = "mean", color = "red", size = 2, geom = "point") %>%
gf_summary(fun.y = mean, geom = "line")
#> No summary function supplied, defaulting to `mean_se()`
#> No summary function supplied, defaulting to `mean_se()`
p %>%
gf_summary(fun.y = mean, fun.ymin = min, fun.ymax = max, color = "red")
#> No summary function supplied, defaulting to `mean_se()`
p %>%
gf_summary(fun.ymin = min, fun.ymax = max, color = "red", geom = "linerange")
#> No summary function supplied, defaulting to `mean_se()`
gf_bar(~ cut, data = diamonds)
gf_col(price ~ cut, data = diamonds, stat = "summary_bin", fun.y = "mean")
#> No summary function supplied, defaulting to `mean_se()`
#> No summary function supplied, defaulting to `mean_se()`
#> No summary function supplied, defaulting to `mean_se()`
#> No summary function supplied, defaulting to `mean_se()`
#> No summary function supplied, defaulting to `mean_se()`
# Don't use gf_lims() to zoom into a summary plot - this throws the
# data away
p <- gf_summary(mpg ~ cyl, data = mtcars, fun.y = "mean", geom = "point")
p
#> No summary function supplied, defaulting to `mean_se()`
p %>% gf_lims(y = c(15, 30))
#> Warning: Removed 9 rows containing non-finite values (stat_summary).
#> No summary function supplied, defaulting to `mean_se()`
# Instead use coord_cartesian()
p %>% gf_refine(coord_cartesian(ylim = c(15, 30)))
#> No summary function supplied, defaulting to `mean_se()`
# A set of useful summary functions is provided from the Hmisc package.
p <- gf_jitter(mpg ~ cyl, data = mtcars, width = 0.15, height = 0); p
p %>% gf_summary(fun.data = mean_cl_boot, color = "red")
#> Warning: Computation failed in `stat_summary()`:
p %>% gf_summary(fun.data = mean_cl_boot, color = "red", geom = "crossbar")
#> Warning: Computation failed in `stat_summary()`:
p %>% gf_summary(fun.data = mean_sdl, group = ~ cyl, color = "red",
geom = "crossbar", width = 0.3)
#> Warning: Computation failed in `stat_summary()`:
p %>% gf_summary(group = ~ cyl, color = "red", geom = "crossbar", width = 0.3,
fun.data = mean_sdl, fun.args = list(mult = 1))
#> Warning: Computation failed in `stat_summary()`:
p %>% gf_summary(fun.data = median_hilow, group = ~ cyl, color = "red",
geom = "crossbar", width = 0.3)
#> Warning: Computation failed in `stat_summary()`:
# An example with highly skewed distributions:
if (require("ggplot2movies")) {
set.seed(596)
Mov <- movies[sample(nrow(movies), 1000), ]
m2 <- gf_jitter(votes ~ factor(round(rating)), data = Mov, width = 0.15, height = 0, alpha = 0.3)
m2 <- m2 %>%
gf_summary(fun.data = "mean_cl_boot", geom = "crossbar",
colour = "red", width = 0.3) %>%
gf_labs(x = "rating")
m2
# Notice how the overplotting skews off visual perception of the mean
# supplementing the raw data with summary statistics is _very_ important
# Next, we'll look at votes on a log scale.
# Transforming the scale means the data are transformed
# first, after which statistics are computed:
m2 %>% gf_refine(scale_y_log10())
# Transforming the coordinate system occurs after the
# statistic has been computed. This means we're calculating the summary on the raw data
# and stretching the geoms onto the log scale. Compare the widths of the
# standard errors.
m2 %>% gf_refine(coord_trans(y="log10"))
}
#> Loading required package: ggplot2movies
#> Warning: Computation failed in `stat_summary()`:
gf_linerangeh(date ~ low_temp + high_temp | ~city,
data = mosaicData::Weather,
color = ~avg_temp
) %>%
gf_refine(scale_color_viridis_c(begin = 0.1, end = 0.9, option = "C"))
gf_linerange(date ~ low_temp + high_temp | ~city,
data = mosaicData::Weather,
color = ~avg_temp,
orientation = 'y'
) %>%
gf_refine(scale_color_viridis_c(begin = 0.1, end = 0.9, option = "C"))
gf_pointrangeh(date ~ avg_temp + low_temp + high_temp | ~city,
data = Weather,
color = ~avg_temp
) %>%
gf_refine(scale_color_viridis_c(begin = 0.1, end = 0.9, option = "C"))