I'm struggling with the following issue:
I want to plot two histograms, but since the statistics of one of the two classes is much less than the other I need to add a second y-axis to allow a direct comparison of the values.
I report below the code I used at the moment and the result.
Thank you in advance!
ggplot(data,aes(x= x ,group=class,fill=class)) + geom_histogram(position="identity",
alpha=0.5, bins = 20)+ theme_bw()
Consider the following situation where you have 800 versus 200 observations:
library(ggplot2)
df <- data.frame(
x = rnorm(1000, rep(c(1, 2), c(800, 200))),
class = rep(c("A", "B"), c(800, 200))
)
ggplot(df, aes(x, fill = class)) +
geom_histogram(bins = 20, position = "identity", alpha = 0.5,
# Note that y = stat(count) is the default behaviour
mapping = aes(y = stat(count)))
You could scale the counts for each group to a maximum of 1 by using y = stat(ncount):
ggplot(df, aes(x, fill = class)) +
geom_histogram(bins = 20, position = "identity", alpha = 0.5,
mapping = aes(y = stat(ncount)))
Alternatively, you can set y = stat(density) to have the total area integrate to 1.
ggplot(df, aes(x, fill = class)) +
geom_histogram(bins = 20, position = "identity", alpha = 0.5,
mapping = aes(y = stat(density)))
Note that after ggplot 3.3.0 stat() probably will get replaced by after_stat().
How about comparing them side by side with facets?
ggplot(data,aes(x= x ,group=class,fill=class)) +
geom_histogram(position="identity",
alpha=0.5,
bins = 20) +
theme_bw() +
facet_wrap(~class, scales = "free_y")
Related
I'd like to draw bar plot like this but in dual Y axis
(https://i.stack.imgur.com/ldMx0.jpg)
the first three indexs range from 0 to 1,
so I want the left y-axis (corresponding to NSE, KGE, VE) to range from 0 to 1,
and the right y-axis (corresponding to PBIAS) to range from -15 to 5.
the following is my data and code:
library("ggplot2")
## data
data <- data.frame(
value=c(0.82,0.87,0.65,-3.39,0.75,0.82,0.63,1.14,0.85,0.87,0.67,-7.03),
sd=c(0.003,0.047,0.006,4.8,0.003,0.028,0.006,4.77,0.004,0.057,0.014,4.85),
index=c("NSE","KGE","VE","PBIAS","NSE","KGE","VE","PBIAS","NSE","KGE","VE","PBIAS"),
period=c("all","all","all","all","calibration","calibration","calibration","calibration","validation","validation","validation","validation")
)
## fix index sequence
data$index <- factor(data$index, levels = c('NSE','KGE','VE',"PBIAS"))
data$period <- factor(data$period, levels = c('all','calibration', 'validation'))
## bar plot
ggplot(data, aes(x=index, y=value, fill=period))+
geom_bar(position="dodge", stat="identity")+
geom_errorbar(aes(ymin=value-sd, ymax=value+sd),
position = position_dodge(0.9), width=0.2 ,alpha=0.5, size=1)+
theme_bw()
I try to scale and shift the second y-axis,
but PBIAS bar plot was removed because of out of scale limit as follow:
(https://i.stack.imgur.com/n6Jfm.jpg)
the following is my code with dual y axis:
## bar plot (scale and shift the second y-axis with slope/intercept in 20/-15)
ggplot(data, aes(x=index, y=value, fill=period))+
geom_bar(position="dodge", stat="identity")+
geom_errorbar(aes(ymin=value-sd, ymax=value+sd),
position = position_dodge(0.9), width=0.2 ,alpha=0.5, size=1)+
theme_bw()+
scale_y_continuous(limits = c(0,1), name = "value", sec.axis = sec_axis(~ 20*.- 15, name="value"))
Any advice for move bar_plot or other solution？
Taking a different approach, instead of using a dual axis one option would be to make two separate plots and glue them together using patchwork. IMHO that is much easier than fiddling around with the rescaling the data (that's the step you missed, i.e. if you want to have a secondary axis you also have to rescale the data) and makes it clearer that the indices are measured on a different scale:
library(ggplot2)
library(patchwork)
data$facet <- data$index %in% "PBIAS"
plot_fun <- function(.data) {
ggplot(.data, aes(x = index, y = value, fill = period)) +
geom_bar(position = "dodge", stat = "identity") +
geom_errorbar(aes(ymin = value - sd, ymax = value + sd),
position = position_dodge(0.9), width = 0.2, alpha = 0.5, size = 1
) +
theme_bw()
}
p1 <- subset(data, !facet) |> plot_fun() + scale_y_continuous(limits = c(0, 1))
p2 <- subset(data, facet) |> plot_fun() + scale_y_continuous(limits = c(-15, 15), position = "right")
p1 + p2 +
plot_layout(guides = "collect", width = c(3, 1))
A second but similar option would be to use ggh4x which via ggh4x::facetted_pos_scales allows to set the limits for facet panels individually. One drawback, the panels have the same width. (I failed in making this approach work with facet_grid and space="free")
library(ggplot2)
library(ggh4x)
data$facet <- data$index %in% "PBIAS"
ggplot(data, aes(x = index, y = value, fill = period)) +
geom_bar(position = "dodge", stat = "identity") +
geom_errorbar(aes(ymin = value - sd, ymax = value + sd),
position = position_dodge(0.9), width = 0.2, alpha = 0.5, size = 1
) +
facet_wrap(~facet, scales = "free") +
facetted_pos_scales(
y = list(
facet ~ scale_y_continuous(limits = c(-15, 15), position = "right"),
!facet ~ scale_y_continuous(limits = c(0, 1), position = "left")
)
) +
theme_bw() +
theme(strip.text.x = element_blank())
I want to know how to turn this plot:
Into this plot:
As you can see the panel and axis on the 2nd plot are limited to the data extent. I made the second graph using design software but want to know the code.
Ive already limited the x and y axis using
xlim and ylim but no difference.
Please see my code below, sorry its so messy, first time using r studio. Thanks!
ggplot() +
geom_errorbar(data = U1483_Coiling_B_M_Removed_R, mapping = aes(x = `Age (Ma) Linear Age Model`, ymin = `Lower interval*100`, ymax = `Upper interval*100`), width = 0.025, colour = 'grey') +
geom_line(data = U1483_Coiling_B_M_Removed_R, aes(x = `Age (Ma) Linear Age Model`, y = `Percent Dextral`)) +
geom_point(data = U1483_Coiling_B_M_Removed_R, aes(x = `Age (Ma) Linear Age Model`, y = `Percent Dextral`), colour = 'red') +
geom_point(data = U1483_Coiling_B_M_Removed_R, aes(x = `Age (Ma) Linear Age Model`, y = `Lab?`)) +
theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5)) +
theme(axis.text.y=element_text(angle=90, size=10, vjust=0.5)) +
theme_classic() +
theme(panel.background = element_rect(colour = 'black', size = 1)) +
xlim(0, 2.85) +
ylim(0, 100)
You can use expand when specifying axis scales, like so:
# Load library
library(ggplot2)
# Set RNG
set.seed(0)
# Create dummy data
df <- data.frame(x = seq(0, 3, by = 0.1))
df$y <- 100 - abs(rnorm(nrow(df), 0, 10))
# Plot results
# Original
ggplot(df, aes(x, y)) +
geom_line() +
geom_point(colour = "#FF3300", size = 5)
# With expand
ggplot(df, aes(x, y)) +
geom_line() +
geom_point(colour = "#FF3300", size = 5) +
scale_y_continuous(expand = c(0, 0))
based on some dummy data I created a histogram with desity plot
set.seed(1234)
wdata = data.frame(
sex = factor(rep(c("F", "M"), each=200)),
weight = c(rnorm(200, 55), rnorm(200, 58))
)
a <- ggplot(wdata, aes(x = weight))
a + geom_histogram(aes(y = ..density..,
# color = sex
),
colour="black",
fill="white",
position = "identity") +
geom_density(alpha = 0.2,
# aes(color = sex)
) +
scale_color_manual(values = c("#868686FF", "#EFC000FF"))
The histogram of weight shall be colored corresponding to sex, so I use aes(y = ..density.., color = sex) for geom_histogram():
a + geom_histogram(aes(y = ..density..,
color = sex
),
colour="black",
fill="white",
position = "identity") +
geom_density(alpha = 0.2,
# aes(color = sex)
) +
scale_color_manual(values = c("#868686FF", "#EFC000FF"))
As I want it to, the density plot stays the same (overall for both groups), but the histograms jump scale up (and seem to be treated individually now):
How do I prevent this from happening? I need individually colored histogram bars but a joint density plot for all coloring groups.
P.S.
Using aes(color = sex) for geom_density() gets everything back to original scales - but I don't want individual density plots (like below):
a + geom_histogram(aes(y = ..density..,
color = sex
),
colour="black",
fill="white",
position = "identity") +
geom_density(alpha = 0.2,
aes(color = sex)
) +
scale_color_manual(values = c("#868686FF", "#EFC000FF"))
EDIT:
As it has been suggested, dividing by the number of groups in geom_histogram()'s aesthetics with y = ..density../2 may approximate the solution. Nevertheless, this only works with symmetric distributions like in the first output below:
a + geom_histogram(aes(y = ..density../2,
color = sex
),
colour="black",
fill="white",
position = "identity") +
geom_density(alpha = 0.2,
) +
scale_color_manual(values = c("#868686FF", "#EFC000FF"))
which yields
Less symmetric distributions, however, may cause trouble using this approach. See those below, where for 5 groups, y = ..density../5 was used. First original, then manipulation (with position = "stack"):
Since the distribution is heavy on the left, dividing by 5 underestimates on the left and overestimates on the right.
EDIT 2: SOLUTION
As suggested by Andrew, the below (complete) code solves the problem:
library(ggplot2)
set.seed(1234)
wdata = data.frame(
sex = factor(rep(c("F", "M"), each = 200)),
weight = c(rnorm(200, 55), rnorm(200, 58))
)
binwidth <- 0.25
a <- ggplot(wdata,
aes(x = weight,
# Pass binwidth to aes() so it will be found in
# geom_histogram()'s aes() later
binwidth = binwidth))
# Basic plot w/o colouring according to 'sex'
a + geom_histogram(aes(y = ..density..),
binwidth = binwidth,
colour = "black",
fill = "white",
position = "stack") +
geom_density(alpha = 0.2) +
scale_color_manual(values = c("#868686FF", "#EFC000FF")) +
# Use fixed scale for sake of comparability
scale_x_continuous(limits = c(52, 61)) +
scale_y_continuous(limits = c(0, 0.25))
# Plot w/ colouring according to 'sex'
a + geom_histogram(aes(x = weight,
# binwidth will only be found if passed to
# ggplot()'s aes() (as above)
y = ..count.. / (sum(..count..) * binwidth),
color = sex),
binwidth = binwidth,
fill="white",
position = "stack") +
geom_density(alpha = 0.2) +
scale_color_manual(values = c("#868686FF", "#EFC000FF")) +
# Use fixed scale for sake of comparability
scale_x_continuous(limits = c(52, 61)) +
scale_y_continuous(limits = c(0, 0.25)) +
guides(color = FALSE)
Note:
binwidth = binwidth needed to be passed to ggplot()'s aes(), otherwise the pre-specified binwidth would not be found by geom_histogram()'s aes(). Further, position = "stack" is specified, so that both versions of the histogram are comparable. Plots for dummy data and the more complex distribution below:
Solved - Thanks for your help!
I don't think you can do it using y=..density.., but you can recreate the same thing like this...
binwidth <- 0.25 #easiest to set this manually so that you know what it is
a + geom_histogram(aes(y = ..count.. / (sum(..count..) * binwidth),
color = sex),
binwidth = binwidth,
fill="white",
position = "identity") +
geom_density(alpha = 0.2) +
scale_color_manual(values = c("#868686FF", "#EFC000FF"))
I have a ggplot2 linegraph with two lines featuring significant overlap. I'm trying to use position_jitterdodge() so that they are more visible, but I can't get the lines and points to both jitter in the same way. I'm trying to jitter the points and line horizontally only (as I don't want to suggest any change on the y-axis). Here is an MWE:
## Create data frames
dimension <- factor(c("A", "B", "C", "D"))
df <- data.frame("dimension" = rep(dimension, 2),
"value" = c(20, 21, 34, 32,
20, 21, 36, 29),
"Time" = c(rep("First", 4), rep("Second", 4)))
## Plot it
ggplot(data = df, aes(x = dimension, y = value,
shape = Time, linetype = Time, group = Time)) +
geom_line(position = position_jitterdodge(dodge.width = 0.45)) +
geom_point(position = position_jitterdodge(dodge.width = 0.45)) +
xlab("Dimension") + ylab("Value")
Which produces the ugly:
I've obviously got something fundamentally wrong here: What should I do to make the geom_point jitter follow the geom_line jitter?
Another option for horizontal only would be to specify position_dodge and pass this to the position argument for each geom.
pd <- position_dodge(0.4)
ggplot(data = df, aes(x = dimension, y = value,
shape = Time, linetype = Time, group = Time)) +
geom_line(position = pd) +
geom_point(position = pd) +
xlab("Dimension") + ylab("Value")
One solution is to manually jitter the points:
df$value_j <- jitter(df$value)
ggplot(df, aes(dimension, value_j, shape=Time, linetype=Time, group=Time)) +
geom_line() +
geom_point() +
labs(x="Dimension", y="Value")
The horizontal solution for your discrete X axis isn't as clean (it's clean under the covers when ggplot2 does it since it handles the axis and point transformations for you quite nicely) but it's doable:
df$dim_j <- jitter(as.numeric(factor(df$dimension)))
ggplot(df, aes(dim_j, value, shape=Time, linetype=Time, group=Time)) +
geom_line() +
geom_point() +
scale_x_continuous(labels=dimension) +
labs(x="Dimension", y="Value")
On July 2017, developpers of ggplot2 have added a seed argument on position_jitter function (https://github.com/tidyverse/ggplot2/pull/1996).
So, now (here: ggplot2 3.2.1) you can pass the argument seed to position_jitter in order to have the same jitter effect in geom_point and geom_line (see the official documentation: https://ggplot2.tidyverse.org/reference/position_jitter.html)
Note that this seed argument does not exist (yet) in geom_jitter.
ggplot(data = df, aes(x = dimension, y = value,
shape = Time, linetype = Time, group = Time)) +
geom_line(position = position_jitter(width = 0.25, seed = 123)) +
geom_point(position = position_jitter(width = 0.25, seed = 123)) +
xlab("Dimension") + ylab("Value")
Is there a method to overlay something analogous to a density curve when the vertical axis is frequency or relative frequency? (Not an actual density function, since the area need not integrate to 1.) The following question is similar:
ggplot2: histogram with normal curve, and the user self-answers with the idea to scale ..count.. inside of geom_density(). However this seems unusual.
The following code produces an overinflated "density" line.
df1 <- data.frame(v = rnorm(164, mean = 9, sd = 1.5))
b1 <- seq(4.5, 12, by = 0.1)
hist.1a <- ggplot(df1, aes(v)) +
stat_bin(aes(y = ..count..), color = "black", fill = "blue",
breaks = b1) +
geom_density(aes(y = ..count..))
hist.1a
#joran's response/comment got me thinking about what the appropriate scaling factor would be. For posterity's sake, here's the result.
When Vertical Axis is Frequency (aka Count)
Thus, the scaling factor for a vertical axis measured in bin counts is
In this case, with N = 164 and the bin width as 0.1, the aesthetic for y in the smoothed line should be:
y = ..density..*(164 * 0.1)
Thus the following code produces a "density" line scaled for a histogram measured in frequency (aka count).
df1 <- data.frame(v = rnorm(164, mean = 9, sd = 1.5))
b1 <- seq(4.5, 12, by = 0.1)
hist.1a <- ggplot(df1, aes(x = v)) +
geom_histogram(aes(y = ..count..), breaks = b1,
fill = "blue", color = "black") +
geom_density(aes(y = ..density..*(164*0.1)))
hist.1a
When Vertical Axis is Relative Frequency
Using the above, we could write
hist.1b <- ggplot(df1, aes(x = v)) +
geom_histogram(aes(y = ..count../164), breaks = b1,
fill = "blue", color = "black") +
geom_density(aes(y = ..density..*(0.1)))
hist.1b
When Vertical Axis is Density
hist.1c <- ggplot(df1, aes(x = v)) +
geom_histogram(aes(y = ..density..), breaks = b1,
fill = "blue", color = "black") +
geom_density(aes(y = ..density..))
hist.1c
Try this instead:
ggplot(df1,aes(x = v)) +
geom_histogram(aes(y = ..ncount..)) +
geom_density(aes(y = ..scaled..))
library(ggplot2)
smoothedHistogram <- function(dat, y, bins=30, xlabel = y, ...){
gg <- ggplot(dat, aes_string(y)) +
geom_histogram(bins=bins, center = 0.5, stat="bin",
fill = I("midnightblue"), color = "#E07102", alpha=0.8)
gg_build <- ggplot_build(gg)
area <- sum(with(gg_build[["data"]][[1]], y*(xmax - xmin)))
gg <- gg +
stat_density(aes(y=..density..*area),
color="#BCBD22", size=2, geom="line", ...)
gg$layers <- gg$layers[2:1]
gg + xlab(xlabel) +
theme_bw() + theme(axis.title = element_text(size = 16),
axis.text = element_text(size = 12))
}
dat <- data.frame(x = rnorm(10000))
smoothedHistogram(dat, "x")