How do I draw the sum value of each class (in my case: a=450, b=150, c=290, d=90) above the stacked bar in ggplot2？ Here is my code：
#Data
hp=read.csv(textConnection(
"class,year,amount
a,99,100
a,100,200
a,101,150
b,100,50
b,101,100
c,102,70
c,102,80
c,103,90
c,104,50
d,102,90"))
hp$year=as.factor(hp$year)
#Plotting
p=ggplot(data=hp)
p+geom_bar(binwidth=0.5,stat="identity")+
aes(x=reorder(class,-value,sum),y=value,label=value,fill=year)+
theme()
You can do this by creating a dataset of per-class totals (this can be done multiple ways but I prefer dplyr):
library(dplyr)
totals <- hp %>%
group_by(class) %>%
summarize(total = sum(value))
Then adding a geom_text layer to your plot, using totals as the dataset:
p + geom_bar(binwidth = 0.5, stat="identity") +
aes(x = reorder(class, -value, sum), y = value, label = value, fill = year) +
theme() +
geom_text(aes(class, total, label = total, fill = NULL), data = totals)
You can make the text higher or lower than the top of the bars using the vjust argument, or just by adding some value to total:
p + geom_bar(binwidth = 0.5, stat = "identity") +
aes(x = reorder(class, -value, sum), y = value, label = value, fill = year) +
theme() +
geom_text(aes(class, total + 20, label = total, fill = NULL), data = totals)
You can use the built-in summary functionality of ggplot2 directly:
ggplot(hp, aes(reorder(class, -amount, sum), amount, fill = year)) +
geom_col() +
geom_text(
aes(label = after_stat(y), group = class),
stat = 'summary', fun = sum, vjust = -1
)
Related
How can I set the break of the x-axis and drop every second factor? And also how can I adjust hover of ggplotly from factor(Year) to Year?
data <- data.frame (Year = c("2017","2017","2017","2016","2016","2016","2015","2015","2015","2018" ,"2018" ,"2018"),
condition = c("normal","stress","Nitrogen" ,"normal","stress", "Nitrogen","normal","stress","Nitrogen","normal","stress","Nitrogen"),
value = c(22.221268, 1.598309 ,20.560815 ,17.337966,20.440174 , 9.074674, 11.739466, 1.905651, 32.270223, 14.271606 ,12.375446, 17.470793))
library(tidyverse)
data %>%
group_by(Year) %>%
mutate(value = value / sum(value)) %>%
ggplot(aes(fill=condition, y=value, x=factor(Year))) +
geom_col(position="fill", width = 1, color = "white") +
geom_text(aes(label = scales::percent(value, accuracy = 0.1)),
position = position_fill(vjust = 0.50),
color = "white") +
scale_y_continuous(labels = scales::percent) +
scale_fill_brewer(palette = "Set1")
How to show every other value on a discrete axis is a duplicate of this question. Using my answer from there, we can define the every_nth function. As for the factor(Year) tooltip label, the easiest way to avoid that is to convert the column to factor before plotting, so the aesthetic mapping is simply x = Year.
every_nth = function(n) {
return(function(x) {x[c(TRUE, rep(FALSE, n - 1))]})
}
data %>%
group_by(Year) %>%
mutate(
value = value / sum(value),
Year = factor(Year) ## put this in mutate() before plotting
) %>%
ggplot(aes(fill = condition, y = value, x = Year)) +
geom_col(position = "fill", width = 1, color = "white") +
geom_text(aes(label = scales::percent(value, accuracy = 0.1)),
position = position_fill(vjust = 0.50),
color = "white") +
scale_y_continuous(labels = scales::percent) +
scale_x_discrete(breaks = every_nth(2)) +
scale_fill_brewer(palette = "Set1") -> p
ggplotly(p)
I am plotting a distribution of two variables on a single histogram. I am interested in highlighting each distribution's mean value on that graph through a doted line or something similar (but hopefully something that matches the color present already in the aes section of the code).
How would I do that?
This is my code so far.
hist_plot <- ggplot(data, aes(x= value, fill= type, color = type)) +
geom_histogram(position="identity", alpha=0.2) +
labs( x = "Value", y = "Count", fill = "Type", title = "Title") +
guides(color = FALSE)
Also, is there any way to show the count of n for each type on this graph?
i've made some reproducible code that might help you with your problem.
library(tidyverse)
# Generate some random data
df <- data.frame(value = c(runif(50, 0.5, 1), runif(50, 1, 1.5)),
type = c(rep("type1", 50), rep("type2", 50)))
# Calculate means from df
stats <- df %>% group_by(type) %>% summarise(mean = mean(value),
n = n())
# Make the ggplot
ggplot(df, aes(x= value, fill= type, color = type)) +
geom_histogram(position="identity", alpha=0.2) +
labs(x = "Value", y = "Count", fill = "Type", title = "Title") +
guides(color = FALSE) +
geom_vline(data = stats, aes(xintercept = mean, color = type), size = 2) +
geom_text(data = stats, aes(x = mean, y = max(df$value), label = n),
size = 10,
color = "black")
If things go as intended, you'll end up something akin to the following plot.
histogram with means
I'm tryng to add label to a grouped bar plot in r.
However I'm using percentege in the y axis, and I want the label to be count.
I've tried to use the geom_text() function, but I don't how exacly the parameters i need to use.
newdf3 %>%
dplyr::count(key, value) %>%
dplyr::group_by(key) %>%
dplyr::mutate(p = n / sum(n)) %>%
ggplot() +
geom_bar(
mapping = aes(x = key, y = p, fill = value),
stat = "identity",
position = position_dodge()
) +
scale_y_continuous(labels = scales::percent_format(),limits=c(0,1))+
labs(x = "", y = "%",title="")+
scale_fill_manual(values = c('Before' = "deepskyblue", 'During' = "indianred1", 'After' = "green2", '?'= "mediumorchid3"),
drop = FALSE, name="")
Here is an exemple of how I need it:
here's a sample of data I'm using:
key value
A Before
A After
A During
B Before
B Before
C After
D During
...
I also wanted to keep the bars with no value (label = 0).
Can someone help me with this?
Here is MWE of how to add count labels to a simple bar chart. See below for the case when these are grouped.
library(datasets)
library(tidyverse)
data <- chickwts %>%
group_by(feed) %>%
count %>%
ungroup %>%
mutate(p = n / sum(n))
ggplot(data, aes(x = feed, y = p, fill = feed)) +
geom_bar(stat = "identity") +
geom_text(stat = "identity",
aes(label = n), vjust = -1)
You should be able to do the same thing on your data.
EDIT: StupidWolf points out in the comments that the original example has grouped data. Adding position = position_dodge(0.9) in geom_text deals with this.
Again, no access to the original data, but here's a different MWE using mtcars showing this:
library(datasets)
library(tidyverse)
data <- mtcars %>%
as_tibble %>%
transmute(gear = as_factor(gear),
carb = as_factor(carb),
cyl = cyl) %>%
group_by(gear, carb) %>%
count
ggplot(data, aes(x = gear, y = n, fill = carb)) +
geom_bar(stat = "identity",
position = "dodge") +
geom_text(aes(label = n),
stat = "identity",
vjust = -1,
position = position_dodge(0.9))
I would like to draw a line (or making points) on top of my stacked bar_plots. As I have no real data points I can refer to (only the spereated values and not the sum of them) I don't know how I can add such line. The Code produce this plot:
I want to add this black line(my real data are not linear):
library(tidyverse)
##Create some fake data
data3 <- tibble(
year = 1991:2020,
One = c(31:60),
Two = c(21:50),
Three = c(11:40)
)
##Gather the variables to create a long dataset
new_data3 <- data3 %>%
gather(model, value, -year)
##plot the data
ggplot(new_data3, aes(x = year, y = value, fill=model)) +
geom_bar(stat = "identity",position = "stack")
You can use stat_summary and sum for the summary function:
ggplot(new_data3, aes(year, value)) +
geom_col(aes(fill = model)) +
stat_summary(geom = "line", fun.y = sum, group = 1, size = 2)
Result:
You could get sum by year and plot it with new geom_line
library(dplyr)
library(ggplot2)
newdata4 <- new_data3 %>%
group_by(year) %>%
summarise(total = sum(value))
ggplot(new_data3, aes(x = year, y = value, fill=model)) +
geom_bar(stat = "identity",position = "stack") +
geom_line(aes(year, total, fill = ""), data = newdata4, size = 2)
I'm trying to plot a basic bar chart per group.
As values are pretty big, I want to show for each bar (i.e. group) the % of each group within the bar.
I managed to show percentage of the total, but this is not what I'm expecting : in each bar, I would like that the sum of % equal 100%.
Is there an easy way to do it without changing the dataframe ?
(DF <- data.frame( year = rep(2015:2017, each = 4),
Grp = c("Grp1", "Grp2", "Grp3", "Grp4"),
Value = trunc(rnorm(12, 2000000, 100000))) )
ggplot(DF) +
geom_bar(aes(x = year, y = Value, fill = Grp),
stat = "identity",
position = position_stack()) +
geom_text(aes(x = year, y = Value, group = Grp,
label = percent(Value/sum(Value))) ,
position = position_stack(vjust = .5))
You can create a new variable for percentile by year:
library(dplyr)
library(ggplot2)
library(scales)
DF <- DF %>% group_by(year) %>% mutate(ValuePer=(Value/sum(Value))) %>% ungroup()
ggplot(DF, aes(year, ValuePer, fill = Grp)) +
geom_bar(stat = "identity", position = "fill") +
geom_text(aes(label = percent(ValuePer)),
position = position_fill())+
scale_y_continuous(labels = percent_format())
Use position = "fill" to turn scale into proportions and scale_y_continuous(labels = percent_format()) to turn this scale into percent.
DF <- data.frame( year = rep(2015:2017, each = 4),
Grp = c("Grp1", "Grp2", "Grp3", "Grp4"),
Value = trunc(rnorm(12, 2000000, 100000)))
library(ggplot2)
library(scales)
ggplot(DF, aes(year, Value, fill = Grp)) +
geom_bar(stat = "identity", position = "fill") +
geom_text(aes(label = percent(Value / sum(Value))),
position = position_fill()) +
scale_y_continuous(labels = percent_format())
OK gathering all your tricks, I finally get this :
I need to adjust my DF, what I wanted to avoid, but it remains simple so it works
library(dplyr)
library(ggplot2)
library(scales)
DF <- DF %>% group_by(year) %>% mutate(ValuePer=(Value/sum(Value))) %>% ungroup()
ggplot(DF, aes(year, Value, fill = Grp)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(aes(label = percent(ValuePer)),
position = position_stack()) +
scale_y_continuous(labels = unit_format("M", 1e-6) )
I would use a single geom_text for each bar while filtering data by year (bar) using dplyr. Check if is that what you need:
(DF <- data.frame( year = rep(2015:2017, each = 4),
Grp = c("Grp1", "Grp2", "Grp3", "Grp4"),
Value = trunc(rnorm(12, 2000000, 100000))) )
library(dplyr)
ggplot(DF) +
geom_bar(aes(x = year, y = Value, fill = Grp),
stat = "identity",
position = position_stack()) +
geom_text(data = DF %>% filter(year == 2015),
aes(x = year, y = Value,
label = scales::percent(Value/sum(Value))) ,
position = position_stack(vjust = .5)) +
geom_text(data = DF %>% filter(year == 2016),
aes(x = year, y = Value,
label = scales::percent(Value/sum(Value))) ,
position = position_stack(vjust = .5)) +
geom_text(data = DF %>% filter(year == 2017),
aes(x = year, y = Value,
label = scales::percent(Value/sum(Value))) ,
position = position_stack(vjust = .5))
Argument group is not necessary here. There may be more elegant solutions but that is the one I could think about. Tell me if this is the output you were waiting for:
Maybe creating a new column doing the right computation. I could not figure out how the computation could be done right inside aes(), the way you did you just computed the overall %, the Value should be grouped by year instead.
At least you got yourself the actually value by the Y axis and the Year grouped % inside bars. I would advise changing this labels by stacking something like this:
scale_y_continuous(breaks = seq(0,8*10^6,10^6),
labels = c(0, paste(seq(1,8,1),'M')))
Resulting this:
You can adapt to your context.