gf_linerange  R Documentation 
Various 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,
linewidth,
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,
linewidth,
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,
linewidth,
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()
)
object 
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. 
gformula 
A formula with shape 
data 
The data to be displayed in this layer. There are three options: If A A 
... 
Additional arguments. Typically these are
(a) ggplot2 aesthetics to be set with 
alpha 
Opacity (0 = invisible, 1 = opaque). 
color 
A color or a formula used for mapping color. 
group 
Used for grouping. 
linetype 
A linetype (numeric or "dashed", "dotted", etc.) or a formula used for mapping linetype. 
linewidth 
A numerical line width or a formula used for mapping linewidth. 
xlab 
Label for xaxis. See also 
ylab 
Label for yaxis. See also 
title, subtitle, caption 
Title, subtitle, and caption for the plot.
See also 
geom 
The geometric object to use to display the data, either as a

stat 
The statistical transformation to use on the data for this
layer, either as a 
position 
Position adjustment, either as a string naming the adjustment
(e.g. 
show.legend 
logical. Should this layer be included in the legends?

show.help 
If 
inherit 
A logical indicating whether default attributes are inherited. 
environment 
An environment in which to look for variables not found in 
size 
size aesthetic for points ( 
fatten 
A multiplicative factor used to increase the size of the
middle bar in 
fun.ymin, fun.y, fun.ymax 

fun.args 
Optional additional arguments passed on to the functions. 
ggplot2::geom_linerange()
ggplot2::geom_pointrange()
ggplot2::geom_pointrange()
, ggplot2::stat_summary()
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 = "midtemp")
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)
}
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, linewidth = 1.3)
# You can supply individual functions to summarise the value at
# each x:
p > gf_summary(fun.y = "median", color = "red", size = 3, geom = "point")
p >
gf_summary(fun.y = "mean", color = "red", size = 3, geom = "point") >
gf_summary(fun.y = mean, geom = "line")
p >
gf_summary(fun.y = mean, fun.ymin = min, fun.ymax = max, color = "red")
## Not run:
p >
gf_summary(fun.ymin = min, fun.ymax = max, color = "red", geom = "linerange")
## End(Not run)
gf_bar(~ cut, data = diamonds)
gf_col(price ~ cut, data = diamonds, stat = "summary_bin", fun.y = "mean")
# 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
p > gf_lims(y = c(15, 30))
# Instead use coord_cartesian()
p > gf_refine(coord_cartesian(ylim = c(15, 30)))
# A set of useful summary functions is provided from the Hmisc package.
## Not run:
p < gf_jitter(mpg ~ cyl, data = mtcars, width = 0.15, height = 0); p
p > gf_summary(fun.data = mean_cl_boot, color = "red")
p > gf_summary(fun.data = mean_cl_boot, color = "red", geom = "crossbar")
p > gf_summary(fun.data = mean_sdl, group = ~ cyl, color = "red",
geom = "crossbar", width = 0.3)
p > gf_summary(group = ~ cyl, color = "red", geom = "crossbar", width = 0.3,
fun.data = mean_sdl, fun.args = list(mult = 1))
p > gf_summary(fun.data = median_hilow, group = ~ cyl, color = "red",
geom = "crossbar", width = 0.3)
## End(Not run)
# 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"))
}
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