Using ggplot2 1.0.0, I followed the instructions in below post to figure out how to plot percentage bar plots across factors:
Sum percentages for each facet - respect "fill"
test <- data.frame(
test1 = sample(letters[1:2], 100, replace = TRUE),
test2 = sample(letters[3:8], 100, replace = TRUE)
)
library(ggplot2)
library(scales)
ggplot(test, aes(x= test2, group = test1)) +
geom_bar(aes(y = ..density.., fill = factor(..x..))) +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
However, I cannot seem to get a label for either the total count or the percentage above each of the bar plots when using geom_text.
What is the correct addition to the above code that also preserves the percentage y-axis?
Staying within ggplot, you might try
ggplot(test, aes(x= test2, group=test1)) +
geom_bar(aes(y = ..density.., fill = factor(..x..))) +
geom_text(aes( label = format(100*..density.., digits=2, drop0trailing=TRUE),
y= ..density.. ), stat= "bin", vjust = -.5) +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
For counts, change ..density.. to ..count.. in geom_bar and geom_text
UPDATE for ggplot 2.x
ggplot2 2.0 made many changes to ggplot including one that broke the original version of this code when it changed the default stat function used by geom_bar ggplot 2.0.0. Instead of calling stat_bin, as before, to bin the data, it now calls stat_count to count observations at each location. stat_count returns prop as the proportion of the counts at that location rather than density.
The code below has been modified to work with this new release of ggplot2. I've included two versions, both of which show the height of the bars as a percentage of counts. The first displays the proportion of the count above the bar as a percent while the second shows the count above the bar. I've also added labels for the y axis and legend.
library(ggplot2)
library(scales)
#
# Displays bar heights as percents with percentages above bars
#
ggplot(test, aes(x= test2, group=test1)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..),
y= ..prop.. ), stat= "count", vjust = -.5) +
labs(y = "Percent", fill="test2") +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
#
# Displays bar heights as percents with counts above bars
#
ggplot(test, aes(x= test2, group=test1)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes(label = ..count.., y= ..prop..), stat= "count", vjust = -.5) +
labs(y = "Percent", fill="test2") +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
The plot from the first version is shown below.
This is easier to do if you pre-summarize your data. For example:
library(ggplot2)
library(scales)
library(dplyr)
set.seed(25)
test <- data.frame(
test1 = sample(letters[1:2], 100, replace = TRUE),
test2 = sample(letters[3:8], 100, replace = TRUE)
)
# Summarize to get counts and percentages
test.pct = test %>% group_by(test1, test2) %>%
summarise(count=n()) %>%
mutate(pct=count/sum(count))
ggplot(test.pct, aes(x=test2, y=pct, colour=test2, fill=test2)) +
geom_bar(stat="identity") +
facet_grid(. ~ test1) +
scale_y_continuous(labels=percent, limits=c(0,0.27)) +
geom_text(data=test.pct, aes(label=paste0(round(pct*100,1),"%"),
y=pct+0.012), size=4)
(FYI, you can put the labels inside the bar as well, for example, by changing the last line of code to this: y=pct*0.5), size=4, colour="white"))
I've used all of your code and came up with this. First assign your ggplot to a variable i.e. p <- ggplot(...) + geom_bar(...) etc. Then you could do this. You don't need to summarize much since ggplot has a build function that gives you all of this already. I'll leave it to you for the formatting and such. Good luck.
dat <- ggplot_build(p)$data %>% ldply() %>% select(group,density) %>%
do(data.frame(xval = rep(1:6, times = 2),test1 = mapvalues(.$group, from = c(1,2), to = c("a","b")), density = .$density))
p + geom_text(data=dat, aes(x = xval, y = (density + .02), label = percent(density)), colour="black", size = 3)
Related
I'm currently trying to plot mean values of a variable pt for each combination of species/treatments in my experiments. This is the code I'm using:
ggplot(data = data, aes(x=treat, y=pt, fill=species)) +
geom_bar(position = "dodge", stat="identity") +
labs(x = "Treatment",
y = "Proportion of Beetles on Treated Side",
colour = "Species") +
theme(legend.position = "right")
As you can see, the plot seems to assume the mean of my 5N and 95E treatments are 1.00, which isn't correct. I have no idea where the problem could be here.
Took a stab at what you are asking using tidyverse and ggplot2 which is in tidyverse.
dat %>%
group_by(treat, species) %>%
summarise(mean_pt = mean(pt)) %>%
ungroup() %>%
ggplot(aes(x = treat, y = mean_pt, fill = species, group = species)) +
geom_bar(position = "dodge", stat = "identity")+
labs(x = "Treatment",
y = "Proportion of Beetles on Treated Side",
colour = "Species") +
theme(legend.position = "right") +
geom_text(aes(label = round(mean_pt, 3)), size = 3, hjust = 0.5, vjust = 3, position = position_dodge(width = 1))
dat is the actual dataset. and I calculated the mean_pt as that is what you are trying to plot. I also added a geom_text piece just so you can see what the results were and compare them to your thoughts.
From my understanding, this won't plot the means of your y variable by default. Have you calculated the means for each treatment? If not, I'd recommend adding a column to your dataframe that contains the mean. I'm sure there's an easier way to do this, but try:
data$means <- rep(NA, nrow(data))
for (x in 1:nrow(data)) {
#assuming "treat" column is column #1 in your data fram
data[x,ncol(data)] <- mean(which(data[,1]==data[x,1]))
}
Then try replacing
geom_bar(position = "dodge", stat="identity")
with
geom_col(position = "dodge")
If your y variable already contains means, simply switching geom_bar to geom_col as shown should work. Geom_bar with stat = "identity" will sum the values rather than return the mean.
I am trying to create a donut chart using ggplot2 with the following data (example).
library(ggplot2)
library(svglite)
library(scales)
# dataframe
Sex = c('Male', 'Female')
Number = c(125, 375)
df = data.frame(Sex, Number)
df
The code I used to generate donut chart is
ggplot(aes(x= Sex, y = Number, fill = Sex), data = df) +
geom_bar(stat = "identity") +
coord_polar("y") +
theme_void() +
theme (legend.position="top") + # legend position
geom_text(aes(label = percent(Number/sum(Number))), position = position_stack(vjust = 0.75), size = 3) +
ggtitle("Participants by Sex")
The above code generated the following chart. Some how not convinced with the chart.
For our purposes, the following chart would better communicate the message. How do I create a chart like this. Where am I doing wrong in my code? I have googled with out any success.
Thanks in advance for help.
They aren't in the same 'circle' because they have different x values. Imagine it as a normal plot first (i.e. without coord_polar("y")) and this will become clear. What you really want is them set at the same x value and then stacked. Here I set x to 2 because it then makes a nicely sized "donut".
donut <- ggplot(df, aes(x = 2, y = Number, fill = Sex)) +
geom_col(position = "stack", width = 1) +
geom_text(aes(label = percent(Number/sum(Number))), position = position_stack(vjust = 0.75), size = 3) +
xlim(0.5, 2.5) +
ggtitle("Participants by Sex")
donut
donut +
coord_polar("y") +
theme_void() +
theme(legend.position="top")
Below code works well and it labels the barplot correctly, However, if I try geom_text for a histogram I fail since geom_text requires a y-component and a histogram's y component is not part of the original data.
Label an "ordinary" bar plot (geom_bar(stat = "identity") works well:
ggplot(csub, aes(x = Year, y = Anomaly10y, fill = pos)) +
geom_bar(stat = "identity", position = "identity") +
geom_text(aes(label = Anomaly10y,vjust=1.5))
My Problem: How to get the correct y and label (indicated by ?) for geom_text, to put labels on top of the histogram bars
ggplot(csub,aes(x = Anomaly10y)) +
geom_histogram()
geom_text(aes(label = ?, vjust = 1.5))
geom_text requires x, y and labels. However, y and labels are not in the original data, but generated by the geom_histogram function. How can I extract the necessary data to position labels on a histogram?
geom_histogram() is just a fancy wrapper to stat_bin so you can all that yourself with the bars and text that you like. Here's an example
#sample data
set.seed(15)
csub<-data.frame(Anomaly10y = rpois(50,5))
And then we plot it with
ggplot(csub,aes(x=Anomaly10y)) +
stat_bin(binwidth=1) + ylim(c(0, 12)) +
stat_bin(binwidth=1, geom="text", aes(label=..count..), vjust=-1.5)
to get
Ok to make it aesthetically appealing here is the solution:
set.seed(15)
csub <- data.frame(Anomaly10y = rpois(50, 5))
Now Plot it
csub %>%
ggplot(aes(Anomaly10y)) +
geom_histogram(binwidth=1) +
stat_bin(binwidth=1, geom='text', color='white', aes(label=..count..),
position=position_stack(vjust = 0.5))
resultant plot will be
Using ggplot2 1.0.0, I followed the instructions in below post to figure out how to plot percentage bar plots across factors:
Sum percentages for each facet - respect "fill"
test <- data.frame(
test1 = sample(letters[1:2], 100, replace = TRUE),
test2 = sample(letters[3:8], 100, replace = TRUE)
)
library(ggplot2)
library(scales)
ggplot(test, aes(x= test2, group = test1)) +
geom_bar(aes(y = ..density.., fill = factor(..x..))) +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
However, I cannot seem to get a label for either the total count or the percentage above each of the bar plots when using geom_text.
What is the correct addition to the above code that also preserves the percentage y-axis?
Staying within ggplot, you might try
ggplot(test, aes(x= test2, group=test1)) +
geom_bar(aes(y = ..density.., fill = factor(..x..))) +
geom_text(aes( label = format(100*..density.., digits=2, drop0trailing=TRUE),
y= ..density.. ), stat= "bin", vjust = -.5) +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
For counts, change ..density.. to ..count.. in geom_bar and geom_text
UPDATE for ggplot 2.x
ggplot2 2.0 made many changes to ggplot including one that broke the original version of this code when it changed the default stat function used by geom_bar ggplot 2.0.0. Instead of calling stat_bin, as before, to bin the data, it now calls stat_count to count observations at each location. stat_count returns prop as the proportion of the counts at that location rather than density.
The code below has been modified to work with this new release of ggplot2. I've included two versions, both of which show the height of the bars as a percentage of counts. The first displays the proportion of the count above the bar as a percent while the second shows the count above the bar. I've also added labels for the y axis and legend.
library(ggplot2)
library(scales)
#
# Displays bar heights as percents with percentages above bars
#
ggplot(test, aes(x= test2, group=test1)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..),
y= ..prop.. ), stat= "count", vjust = -.5) +
labs(y = "Percent", fill="test2") +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
#
# Displays bar heights as percents with counts above bars
#
ggplot(test, aes(x= test2, group=test1)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes(label = ..count.., y= ..prop..), stat= "count", vjust = -.5) +
labs(y = "Percent", fill="test2") +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
The plot from the first version is shown below.
This is easier to do if you pre-summarize your data. For example:
library(ggplot2)
library(scales)
library(dplyr)
set.seed(25)
test <- data.frame(
test1 = sample(letters[1:2], 100, replace = TRUE),
test2 = sample(letters[3:8], 100, replace = TRUE)
)
# Summarize to get counts and percentages
test.pct = test %>% group_by(test1, test2) %>%
summarise(count=n()) %>%
mutate(pct=count/sum(count))
ggplot(test.pct, aes(x=test2, y=pct, colour=test2, fill=test2)) +
geom_bar(stat="identity") +
facet_grid(. ~ test1) +
scale_y_continuous(labels=percent, limits=c(0,0.27)) +
geom_text(data=test.pct, aes(label=paste0(round(pct*100,1),"%"),
y=pct+0.012), size=4)
(FYI, you can put the labels inside the bar as well, for example, by changing the last line of code to this: y=pct*0.5), size=4, colour="white"))
I've used all of your code and came up with this. First assign your ggplot to a variable i.e. p <- ggplot(...) + geom_bar(...) etc. Then you could do this. You don't need to summarize much since ggplot has a build function that gives you all of this already. I'll leave it to you for the formatting and such. Good luck.
dat <- ggplot_build(p)$data %>% ldply() %>% select(group,density) %>%
do(data.frame(xval = rep(1:6, times = 2),test1 = mapvalues(.$group, from = c(1,2), to = c("a","b")), density = .$density))
p + geom_text(data=dat, aes(x = xval, y = (density + .02), label = percent(density)), colour="black", size = 3)
Below code works well and it labels the barplot correctly, However, if I try geom_text for a histogram I fail since geom_text requires a y-component and a histogram's y component is not part of the original data.
Label an "ordinary" bar plot (geom_bar(stat = "identity") works well:
ggplot(csub, aes(x = Year, y = Anomaly10y, fill = pos)) +
geom_bar(stat = "identity", position = "identity") +
geom_text(aes(label = Anomaly10y,vjust=1.5))
My Problem: How to get the correct y and label (indicated by ?) for geom_text, to put labels on top of the histogram bars
ggplot(csub,aes(x = Anomaly10y)) +
geom_histogram()
geom_text(aes(label = ?, vjust = 1.5))
geom_text requires x, y and labels. However, y and labels are not in the original data, but generated by the geom_histogram function. How can I extract the necessary data to position labels on a histogram?
geom_histogram() is just a fancy wrapper to stat_bin so you can all that yourself with the bars and text that you like. Here's an example
#sample data
set.seed(15)
csub<-data.frame(Anomaly10y = rpois(50,5))
And then we plot it with
ggplot(csub,aes(x=Anomaly10y)) +
stat_bin(binwidth=1) + ylim(c(0, 12)) +
stat_bin(binwidth=1, geom="text", aes(label=..count..), vjust=-1.5)
to get
Ok to make it aesthetically appealing here is the solution:
set.seed(15)
csub <- data.frame(Anomaly10y = rpois(50, 5))
Now Plot it
csub %>%
ggplot(aes(Anomaly10y)) +
geom_histogram(binwidth=1) +
stat_bin(binwidth=1, geom='text', color='white', aes(label=..count..),
position=position_stack(vjust = 0.5))
resultant plot will be