ggplot(data = results, aes(x = inst, y = value, group = inst)) +
geom_boxplot() +
facet_wrap(~color) +
#geom_line(data = mean,
#mapping = aes(x = inst, y = average, group = 1))
theme_bw()
When I run the code above with the code line commented, it runs and gives the graph below but I want a joining mean lines on the boxplots based on its own color category for each group in facet wraps. Any ideas how can I do that?
Your code is generally correct (though you'll want to add color = color to the aes() specification in geom_line()), so I suspect your mean dataset isn't set up correctly. Do you have means grouped by both your x axis and faceting variable? Using ggplot2::mpg as an example:
library(dplyr) # >= v1.1.0
library(ggplot2)
mean_dat <- summarize(mpg, average = mean(hwy), .by = c(cyl, drv))
ggplot(mpg, aes(factor(cyl), hwy)) +
geom_boxplot() +
geom_line(
data = mean_dat,
aes(y = average, group = 1, color = drv),
linewidth = 1.5,
show.legend = FALSE
) +
facet_wrap(~drv) +
theme_bw()
Alternatively, you could use stat = "summary" and not have to create a means dataframe at all:
ggplot(mpg, aes(factor(cyl), hwy)) +
geom_boxplot() +
geom_line(
aes(group = 1, color = drv),
stat = "summary",
linewidth = 1.5,
show.legend = FALSE
) +
facet_wrap(~drv) +
theme_bw()
# same result as above
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 am interested in doing a plot showing percentages by group.
something like this:
data(iris)
ggplot(iris,
aes(x = Sepal.Length, group = factor(Species), fill = factor(Species))) +
geom_histogram(position = "fill")+theme_bw()
however, I would also like to plot a histogram showing the frequency distribution on top of this graph.
something like the plot below.
ggplot(iris,aes(x = Sepal.Length)) +
geom_histogram()+theme_bw()
Does anyone know how to do this?
Note I know how to do a frequency plot by group: ggplot(iris,aes(x = Sepal.Length, group = factor(Species), fill = factor(Species))) + geom_histogram()+theme_bw(). But this is not what I want. Rather I would like a small frequency distribution at the bottom of the percentage plot presented at the beginning.
Thank you very much
Something like this?
library(gridExtra)
p1 <- ggplot(iris,
aes(x = Sepal.Length,
group = factor(Species),
fill = factor(Species))) +
geom_histogram(position = "fill") +
theme_bw() +
theme(legend.position = "top")
p2 <- ggplot(iris,aes(x = Sepal.Length,
group = factor(Species),
fill = factor(Species))) +
geom_histogram() +
theme_bw() +
theme(legend.position = "none")
grid.arrange(p1, p2,
heights = c(4, 1.5))
Edit: So you are looking for this then? Note that in this case the absolute values of the smaller histogram become meaningless since they were scaled down to be ~25% of the vertical chart range.
ggplot() +
geom_histogram(data = iris,
aes(x = Sepal.Length,
group = factor(Species),
fill = factor(Species)),
position = "fill",
alpha = 1) +
geom_histogram(data = iris,
aes(x = Sepal.Length,
y = ..ncount.. / 4),
alpha = 0.5,
fill = 'black')
Sample data
data <- data.frame(Country = c("Mexico","USA","Canada","Chile"), Per = c(15.5,75.3,5.2,4.0))
I tried set position of labels.
ggplot(data =data) +
geom_bar(aes(x = "", y = Per, fill = Country), stat = "identity", width = 1) +
coord_polar("y", start = 0) +
theme_void()+
geom_text(aes(x = 1.2, y = cumsum(Per), label = Per))
But pie chart actually look like:
You have to sort the data before calculating the cumulative sum. Then, you can optimize label position, e.g. by subtracting half of Per:
library(tidyverse)
data %>%
arrange(-Per) %>%
mutate(Per_cumsum=cumsum(Per)) %>%
ggplot(aes(x=1, y=Per, fill=Country)) +
geom_col() +
geom_text(aes(x=1,y = Per_cumsum-Per/2, label=Per)) +
coord_polar("y", start=0) +
theme_void()
PS: geom_col uses stat_identity by default: it leaves the data as is.
Or simply use position_stack
data %>%
ggplot(aes(x=1, y=Per, fill=Country)) +
geom_col() +
geom_text(aes(label = Per), position = position_stack(vjust = 0.5))+
coord_polar(theta = "y") +
theme_void()
From the help:
# To place text in the middle of each bar in a stacked barplot, you
# need to set the vjust parameter of position_stack()
I created a plot like this;
library("ggplot2")
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = color, y = ..prop.., group = 2)) +
scale_y_continuous(labels=scales::percent) +
facet_grid(~cut)
Now I want to add a legend for the variable "color", also I want to change the colour of the bars. The graph is exactly how I want it to be, and if possible I don't want change the structure of the dataset, just add a legend and change colours.
I could not find example that fit for this "percentage"-style graphics.
ggplot(data = diamonds, aes(x = color, y = ..prop.., group = cut)) +
geom_bar(aes(fill = factor(..x.., labels = LETTERS[seq(from = 4, to = 10 )]))) +
labs(fill = "color") +
scale_y_continuous(labels = scales::percent) +
facet_grid(~ cut)