I have the following data:
dataf <- read.table(text = "index,group,taxa1,taxa2,taxa3,total
s1,g1,2,5,3,10
s2,g1,3,4,3,10
s3,g2,1,2,7,10
s4,g2,0,4,6,10", header = T, sep = ",")
I'm trying to make a stacked bar plot of the frequences of the data so that it counts across the row (not down a column) for each index (s1,s2,s3,s4) and then for each group (g1,g2) of each taxa. I'm only able to figure out how to graph the species of one taxa but not all three stacked on each other.
Here are some examples of what I'm trying to make:
These were made on google sheets so they don't look like ggplot but it would be easier to make in r with ggplot2 because the real data set is larger.
You would need to reshape the data.
Here is my solution (broken down by plot)
For first plot
library(tidyverse)
##For first plot
prepare_data_1 <- dataf %>% select(index, taxa1:taxa3) %>%
gather(taxa,value, -index) %>%
mutate(index = str_trim(index)) %>%
group_by(index) %>% mutate(prop = value/sum(value))
##Plot 1
prepare_data_1 %>%
ggplot(aes(x = index, y = prop, fill = fct_rev(taxa))) + geom_col()
For second plot
##For second plot
prepare_data_2 <- dataf %>% select(group, taxa1:taxa3) %>%
gather(taxa,value, -group) %>%
mutate(group = str_trim(group)) %>%
group_by(group) %>% mutate(prop = value/sum(value))
##Plot 2
prepare_data_2 %>%
ggplot(aes(x = group, y = prop, fill = fct_rev(taxa))) + geom_col()
##You need to reshape data before doing that.
dfm = melt(dataf, id.vars=c("index","group"),
measure.vars=c("taxa1","taxa2","taxa3"),
variable.name="variable", value.name="values")
ggplot(dfm, aes(x = index, y = values, group = variable)) +
geom_col(aes(fill=variable)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.25)) +
geom_text(aes(label = values), position = position_stack(vjust = .5), size = 3) + theme_gray()
Related
I'm trying to mix stacked and unstacked bars in one graph.
Here is sample code:
Indicators <- c("Z","X","C","NO C","NA C","LOW C","HIGH C","D","E","F","G","H","LOW H","MED H","HIGH H",
"I","LOW I","MED I","HIGH I","J","LOW J","MED J","HIGH J")
Class_1 <- c(0.44,0.01,"",0.6,0.13,0.11,0.16,0.08,0.01,0.14,0.21,"",0.55,
0.23,0.22,"",0.4,0.31,0.29,"",0.41,0.23,0.36)
data <- data.frame(Indicators,Class_1)
How can I make a graph with both unstacked and stacked bars? I really appreciate all the help there is! Thanks!
Basically you want a stacked barchart even if for some categories there is nothing to stack. To this end you have to do some data wrangling and cleaning like getting rid of the empty value rows and splitting the Indicators column in two:
library(dplyr)
library(ggplot2)
library(tidyr)
data1 <- data |>
filter(Class_1 != "") |>
mutate(Class_1 = as.numeric(Class_1)) |>
separate(Indicators, into = c("level", "which"), fill = "left") |>
replace_na(list(level = "Total"))
ggplot(data1, aes(which, Class_1, fill = level)) +
geom_col()
Update II:
library(tidyverse)
my_pattern1 <- c("No|Non|Low-risk|High-risk|Low|Medium|High")
data %>%
filter(Class_1 != "") %>%
mutate(x = case_when(str_detect(Indicators, 'CSA|childhood sexual abuse') ~ "Childhood sexual abuse",
str_detect(Indicators, 'discrimination') ~ "discrimination"),
y = str_extract(Indicators, my_pattern1)) %>%
mutate(z = str_remove_all(Indicators, my_pattern1)) %>%
mutate(y= ifelse(is.na(y), "Total", y)) %>%
ggplot(aes(x = z, y = as.numeric(Class_1), fill= y))+
geom_bar(stat = "identity", position = "stack")+
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1))
Update: (#camille is right. I now adapted my answer and removed the first one)
library(tidyverse)
data %>%
separate(Indicators, c("Indicators_1", "Indicators_2")) %>%
mutate(Indicators_3 = ifelse(is.na(Indicators_2), "Total", Indicators_1),
Indicators_2 = coalesce(Indicators_2, Indicators_1)) %>%
filter(Class_1 != "") %>%
ggplot(aes(x = Indicators_2, y = as.numeric(Class_1), fill= Indicators_3))+
geom_bar(stat = "identity", position = "stack")
With the following example, I get a plot where the areas are not stacked. I would like to stack them. This should be a partial stack, intensity starting at 0.5, then reaching 0.8 where stacked, then reaching 0.3 at the end.
I assume that the position argument does not work as the start and end date are not the same.
Am I missing an argument that could solve this issue? Or maybe another geom?
Do I have to subset the data into days, to get the desired output. If so, how can I acheive that?
Thanks in advance,
# Library
library(tidyverse)
library(lubridate)
# Data
df <- tibble(date_debut = as_date(c("2022-09-28", "2022-10-05")),
intensity = c(0.5, 0.3),
duration = days(c(14, 10)),
type = (c("a", "b")))
# Adjustment
df <- df %>%
mutate(date_fin = date_debut + duration) %>%
pivot_longer(cols = c(date_debut, date_fin),
names_to = "date_type",
values_to = "date")
# Plot
df %>%
ggplot(aes(x = date, y = intensity, fill = type))+
geom_area(position = "stack")
This is a tough data wrangling problem. The area plots only stack where the points in the two series have the same x values. The following will achieve that, though it's quite a profligate approach.
df %>%
mutate(interval = interval(date_debut, date_debut + duration)) %>%
group_by(type) %>%
summarize(time = seq(as.POSIXct(min(df$date_debut)),
as.POSIXct(max(df$date_debut + df$duration)), by = 'min'),
intensity = ifelse(time %within% interval, intensity, 0)) %>%
ggplot(aes(x = time, y = intensity, fill = type)) +
geom_area(position = position_stack())
Allan Cameron's answer inspired me to look further into complete.
The proposed answer was solving my question, so I accepted. However, it is indeed more complex than needed.
I solved it this way:
# Adjustment
df <- df %>%
mutate(date_fin = date_debut + duration) %>%
group_by(type) %>%
complete(date_debut = seq(min(date_debut), max(date_fin), by = "1 day")) %>%
fill(intensity) %>%
select(date_debut, intensity, type)
ggplot(df, aes(x = date_debut, y = intensity, fill = type)) +
geom_area()+
scale_x_date(date_labels = "%d",
date_breaks = "1 day")
To avoid the weird empty space, it is fine for me to use geom_col (the question was about geom_area, so no worries).
ggplot(df, aes(x = date_debut, y = intensity, fill = type, colour = type)) +
geom_col(width = 0.95)+
scale_x_date(date_labels = "%d",
date_breaks = "1 day")
How can I change the "x-axis starting value" from the diverging bar chart below (extracted from here), so that the vertical axis is set at 25 instead of 0. And therefore the bars are drawn from 25 and not 0.
For instance, I want this chart:
To look like this:
EDIT
It it not the label I want to change, it is how the data is plotted. My apologies if I wasn't clear. See example below:
Another example to make it clear:
You can provide computed labels to an (x-)scale via scale_x_continuous(labels = function (x) x + 25).
If you also want to change the data, you’ll first need to offset the x-values by the equivalent amount (in the opposite direction):
Example:
df = tibble(Color = c('red', 'green', 'blue'), Divergence = c(5, 10, -5))
offset = 2
df %>%
mutate(Divergence = Divergence - offset) %>%
ggplot() +
aes(x = Divergence, y = Color) +
geom_col() +
scale_x_continuous(labels = function (x) x + offset)
I'm still not 100% clear on your intended outcome but you can "shift" your data by adding/subtracting 25 from each value, e.g.
Original plot:
library(tidyverse)
library(gapminder)
set.seed(123)
gapminder_subset <- gapminder %>%
pivot_longer(-c(country, continent, year)) %>%
filter(year == "1997" | year == "2007") %>%
select(-continent) %>%
filter(name == "gdpPercap") %>%
pivot_wider(names_from = year) %>%
select(-name) %>%
mutate(gdp_change = ((`2007` - `1997`) / `1997`) * 100) %>%
sample_n(15)
ggplot(data = gapminder_subset,
aes(x = country, y = gdp_change)) +
geom_bar(stat = "identity") +
coord_flip()
subtract 25:
library(tidyverse)
library(gapminder)
set.seed(123)
gapminder_subset <- gapminder %>%
pivot_longer(-c(country, continent, year)) %>%
filter(year == "1997" | year == "2007") %>%
select(-continent) %>%
filter(name == "gdpPercap") %>%
pivot_wider(names_from = year) %>%
select(-name) %>%
mutate(gdp_change = ((`2007` - `1997`) / `1997`) * 100) %>%
sample_n(15)
ggplot(data = gapminder_subset,
aes(x = country, y = gdp_change)) +
geom_bar(stat = "identity") +
coord_flip()
If you combine that with my original relabelling I think that's the solution:
ggplot(data = gapminder_subset,
aes(x = country, y = gdp_change - 25)) +
geom_bar(stat = "identity") +
coord_flip() +
scale_y_continuous(breaks = c(-25, 0, 25, 50),
labels = c(0, 25, 50, 75))
The answers that existed at the time that I'm writing this are suggesting to change the data or to change the label. Here, I'm proposing to change neither the data nor the labels, and instead just change where the starting position of a bar is.
First, for reproducibility, I took #jared_mamrot's approach for the data subset.
library(gapminder)
library(tidyverse)
set.seed(123)
gapminder_subset <- gapminder %>%
pivot_longer(-c(country, continent, year)) %>%
filter(year == "1997" | year == "2007") %>%
select(-continent) %>%
filter(name == "gdpPercap") %>%
pivot_wider(names_from = year) %>%
select(-name) %>%
mutate(gdp_change = ((`2007` - `1997`) / `1997`) * 100) %>%
sample_n(15)
Then, you can set xmin = after_scale(25). You'll get a warning that xmin doesn't exists, but it does exist after the bars are reparameterised to rectangles in the ggplot2 internals (which is after the x-scale has seen the data to determine limits). This effectively changes the position where bars start.
ggplot(gapminder_subset,
aes(gdp_change, country)) +
geom_col(aes(xmin = after_scale(25)))
#> Warning: Ignoring unknown aesthetics: xmin
Created on 2021-06-28 by the reprex package (v1.0.0)
I have a dataset with two variables: 1) ID, 2) Infection Status (Binary:1/0).
I would like to use ggplot2 to
Create a stacked percentage bar graph with the various ID on the verticle-axis (arranged alphabetically with A starting on top), and the percent on the horizontal-axis. I can't seem to get a code that will automatically sort the ID alphabetically as my original dataset has quite a number of categories and will be difficult to arrange them manually.
I also hope to have the infected category (1) to be red and towards the left of the blue non-infected category (0). Is it also possible to change the sub-heading of the legend box from "Non_infected" to "Non-infected"?
I hope that the displayed ID in the plot will include the count of the number of times the ID appeared in the dataset. E.g. "A (n=6)", "B (n=3)"
My sample code is as follow:
ID <- c("A","A","A","A","A","A",
"B","B","B",
"C","C","C","C","C","C","C",
"D","D","D","D","D","D","D","D","D")
Infection <- sample(c(1, 0), size = length(ID), replace = T)
df <- data.frame(ID, Infection)
library(ggplot2)
library(dplyr)
library(reshape2)
df.plot <- df %>%
group_by(ID) %>%
summarize(Infected = sum(Infection)/n(),
Non_Infected = 1-Infected)
df.plot %>%
melt() %>%
ggplot(aes(x = ID, y = value, fill = variable)) + geom_bar(stat = "identity", position = "stack") +
xlab("ID") +
ylab("Percent Infection") +
scale_fill_discrete(guide = guide_legend(title = "Infection Status")) +
coord_flip()
Right now I managed to get this output:
I hope to get this:
Thank you so much!
First, we need to add a count to your original data.frame.
df.plot <- df %>%
group_by(ID) %>%
summarize(Infected = sum(Infection)/n(),
Non_Infected = 1-Infected,
count = n())
Then, we augment our ID column, turn the Infection Status into a factor variable, use forcats::fct_rev to reverse the ID ordering, and use scale_fill_manual to control your legend.
df.plot %>%
mutate(ID = paste0(ID, " (n=", count, ")")) %>%
select(-count) %>%
melt() %>%
mutate(variable = factor(variable, levels = c("Non_Infected", "Infected"))) %>%
ggplot(aes(x = forcats::fct_rev(ID), y = value, fill = variable)) +
geom_bar(stat = "identity", position = "stack") +
xlab("ID") +
ylab("Percent Infection") +
scale_fill_manual("Infection Status",
values = c("Infected" = "#F8766D", "Non_Infected" = "#00BFC4"),
labels = c("Non-Infected", "Infected"))+
coord_flip()
I have some time series data plotted using ggplot. I'd like the legend, which appears to the right of the plot, to be in the same order as the line on the most recent date/value on the plot's x-axis. I tried using the case_when function, but I'm obviously using it wrong. Here is an example.
df <- tibble(
x = runif(100),
y = runif(100),
z = runif(100),
year = sample(seq(1900, 2010, 10), 100, T)
) %>%
gather(variable, value,-year) %>%
group_by(year, variable) %>%
summarise(mean = mean(value))
df %>%
ggplot(aes(year, mean, color = variable)) +
geom_line()
## does not work
df %>%
mutate(variable = fct_reorder(variable, case_when(mean ~ year == 2010)))
ggplot(aes(year, mean, color = variable)) +
geom_line()
We may add one extra line
ungroup() %>% mutate(variable = fct_reorder(variable, mean, tail, n = 1, .desc = TRUE))
before plotting, or use
df %>%
mutate(variable = fct_reorder(variable, mean, tail, n = 1, .desc = TRUE)) %>%
ggplot(aes(year, mean, color = variable)) +
geom_line()
In this way we look at the last values of mean and reorder variable accordingly.
There's another way without adding a new column using fct_reorder2():
library(tidyverse)
df %>%
ggplot(aes(year, mean, color = fct_reorder2(variable, year, mean))) +
geom_line() +
labs(color = "variable")
Although it's not recommendable in your case, to order the legend based on the first (earliest) values in your plot you can set
df %>%
ggplot(aes(year, mean, color = fct_reorder2(variable, year, mean, .fun = first2))) +
geom_line() +
labs(color = "variable")
The default is .fun = last2 (see also https://forcats.tidyverse.org/reference/fct_reorder.html)