I have a swimlane plot which I want to order by a group variable. I was also wondering if it is possible to label the groups on the ggplot.
Here is the code to create the data set and plot the data
dataset <- data.frame(subject = c("1002", "1002", "1002", "1002", "10034","10034","10034","10034","10054","10054","10054","1003","1003","1003","1003"),
exdose = c(5,10,20,5,5,10,20,20,5,10,20,5,20,10,5),
p= c(1,2,3,4,1,2,3,4,1,2,3,1,2,3,4),
diff = c(3,3,9,7,3,3,4,5,3,5,6,3,5,6,7),
group =c("grp1","grp1","grp1","grp1","grp2","grp2","grp2","grp2","grp1","grp1","grp1","grp2","grp2","grp2","grp2")
)
ggplot(dataset, aes(x = diff + 1, y = subject, group = p)) +
geom_col(aes(fill = as.factor(exdose)), position = position_stack(reverse = TRUE))
I want the y axis order by group and I want a label on the side to label the groups if possible
you can see from the plot it is ordered by subject number but I want it ordered by group and some indicator of group.
I tried reorder but I was unsuccessful in getting the desired plot.
As Stefan points out, facets are probably the way to go here, but you can use them with subtle theme tweaks to make it look as though you have just added a grouping variable on the y axis:
library(tidyverse)
dataset %>%
mutate(group = factor(group),
subject = reorder(subject, as.numeric(group)),
exdose = factor(exdose)) %>%
ggplot(aes(x = diff + 1, y = subject, group = p)) +
geom_col(aes(fill = exdose), color = "gray50",
position = position_stack(reverse = TRUE)) +
scale_y_discrete(expand = c(0.1, 0.4)) +
scale_fill_brewer(palette = "Set2") +
facet_grid(group ~ ., scales = "free_y", switch = "y") +
theme_minimal(base_size = 16) +
theme(strip.background = element_rect(color = "gray"),
strip.text = element_text(face = 2),
panel.spacing.y = unit(0, "mm"),
panel.background = element_rect(fill = "#f9f8f6", color = NA))
Related
With ggplot2, I can create a violin plot with overlapping points, and paired points can be connected using geom_line().
library(datasets)
library(ggplot2)
library(dplyr)
iris_edit <- iris %>% group_by(Species) %>%
mutate(paired = seq(1:length(Species))) %>%
filter(Species %in% c("setosa","versicolor"))
ggplot(data = iris_edit,
mapping = aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
geom_line(mapping = aes(group = paired),
position = position_dodge(0.1),
alpha = 0.3) +
geom_point(mapping = aes(fill = Species, group = paired),
size = 1.5, shape = 21,
position = position_dodge(0.1)) +
theme_classic() +
theme(legend.position = "none",
axis.text.x = element_text(size = 15),
axis.title.y = element_text(size = 15),
axis.title.x = element_blank(),
axis.text.y = element_text(size = 10))
The see package includes the geom_violindot() function to plot a halved violin plot alongside its constituent points. I've found this function helpful when plotting a large number of points so that the violin is not obscured.
library(see)
ggplot(data = iris_edit,
mapping = aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violindot(dots_size = 0.8,
position_dots = position_dodge(0.1)) +
theme_classic() +
theme(legend.position = "none",
axis.text.x = element_text(size = 15),
axis.title.y = element_text(size = 15),
axis.title.x = element_blank(),
axis.text.y = element_text(size = 10))
Now, I would like to add geom_line() to geom_violindot() in order to connect paired points, as in the first image. Ideally, I would like the points to be inside and the violins to be outside so that the lines do not intersect the violins. geom_violindot() includes the flip argument, which takes a numeric vector specifying the geoms to be flipped.
ggplot(data = iris_edit,
mapping = aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violindot(dots_size = 0.8,
position_dots = position_dodge(0.1),
flip = c(1)) +
geom_line(mapping = aes(group = paired),
alpha = 0.3,
position = position_dodge(0.1)) +
theme_classic() +
theme(legend.position = "none",
axis.text.x = element_text(size = 15),
axis.title.y = element_text(size = 15),
axis.title.x = element_blank(),
axis.text.y = element_text(size = 10))
As you can see, invoking flip inverts the violin half, but not the corresponding points. The see documentation does not seem to address this.
Questions
How can you create a geom_violindot() plot with paired points, such that the points and the lines connecting them are "sandwiched" in between the violin halves? I suspect there is a solution that uses David Robinson's GeomFlatViolin function, though I haven't been able to figure it out.
In the last figure, note that the lines are askew relative to the points they connect. What position adjustment function should be supplied to the position_dots and position arguments so that the points and lines are properly aligned?
Not sure about using geom_violindot with see package. But you could use a combo of geom_half_violon and geom_half_dotplot with gghalves package and subsetting the data to specify the orientation:
library(gghalves)
ggplot(data = iris_edit[iris_edit$Species == "setosa",],
mapping = aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_half_violin(side = "l") +
geom_half_dotplot(stackdir = "up") +
geom_half_violin(data = iris_edit[iris_edit$Species == "versicolor",],
aes(x = Species, y = Sepal.Length, fill = Species), side = "r")+
geom_half_dotplot(data = iris_edit[iris_edit$Species == "versicolor",],
aes(x = Species, y = Sepal.Length, fill = Species),stackdir = "down") +
geom_line(data = iris_edit, mapping = aes(group = paired),
alpha = 0.3)
As a note, the lines in the pairing won't properly align because the dotplot is binning each observation then lengthing out the dotline-- the paired lines only correspond to x-value as defined in aes, not where the dot is in the line.
As per comment - this is not a direct answer to your question, but I believe that you might not get the most convincing visualisation when using the "slope graph" optic. This becomes quickly convoluted (so many dots/ lines overlapping) and the message gets lost.
To show change between paired observations (treatment 1 versus treatment 2), you can also (and I think: better) use a scatter plot. You can show each observation and the change becomes immediately clear. To make it more intuitive, you can add a line of equality.
I don't think you need to show the estimated distribution (left plot), but if you want to show this, you could make use of a two-dimensional density estimation, with geom_density2d (right plot)
library(tidyverse)
## patchwork only for demo purpose
library(patchwork)
iris_edit <- iris %>% group_by(Species) %>%
## use seq_along instead
mutate(paired = seq_along(Species)) %>%
filter(Species %in% c("setosa","versicolor")) %>%
## some more modificiations
select(paired, Species, Sepal.Length) %>%
pivot_wider(names_from = Species, values_from = Sepal.Length)
lims <- c(0, 10)
p1 <-
ggplot(data = iris_edit, aes(setosa, versicolor)) +
geom_abline(intercept = 0, slope = 1, lty = 2) +
geom_point(alpha = .7, stroke = 0, size = 2) +
cowplot::theme_minimal_grid() +
coord_equal(xlim = lims, ylim = lims) +
labs(x = "Treatment 1", y = "Treatment 2")
p2 <-
ggplot(data = iris_edit, aes(setosa, versicolor)) +
geom_abline(intercept = 0, slope = 1, lty = 2) +
geom_density2d(color = "Grey") +
geom_point(alpha = .7, stroke = 0, size = 2) +
cowplot::theme_minimal_grid() +
coord_equal(xlim = lims, ylim = lims) +
labs(x = "Treatment 1", y = "Treatment 2")
p1+ p2
Created on 2021-12-18 by the reprex package (v2.0.1)
I am trying to do a chart like this one:
The idea is to plot 3 amounts, in this mixed stacked bar chart we have a dataframe which has one row for a negative value and two rows for the positive value, however i need to stack the negative with the first positive bar, i also need 3 colors. The code I have so far is as follows: (the dataframe already has the desired shape):
df3 <- read.table(
text =
"region group metric somevalue
blue T1 epsilon 63
blue T2 epsilon -40
red T1 epsilon 100
blue T1 kappa 19
blue T2 kappa -30
red T1 kappa 75
blue T1 zulu 50
blue T2 zulu -18
red T1 zulu 68", header=TRUE)
p2 <- ggplot(df3, aes(x = metric, y = somevalue, fill=region))+
geom_col(aes(fill = group), width = 0.7) + geom_bar(position = 'dodge', stat='identity')
p2
please help me out, if you think the dataframe has to be modified please let me know. thanks
Stacking and dodging is always a bit tricky. In your case this could be achieved like so:
Convert region to a factor. (This makes sure that step 3 works)
Split your dataset in two for negative and positive values.
Fill up the datasets using tidy::complete so that each dataset contains "all" combinations of metric, region and group. (This makes sure that the dodging works
Use two geom_col layers to plot the positive and negative values using position="dodge". I added na.rm = TRUE to remove the missing values we added via complete.
library(ggplot2)
library(dplyr)
library(tidyr)
df3$region <- factor(df3$region)
df3_neg <- filter(df3, somevalue < 0) %>%
tidyr::complete(region, group, metric)
df3_pos<- filter(df3, somevalue > 0) %>%
tidyr::complete(region, group, metric)
p2 <- ggplot(df3, aes(somevalue, metric)) +
geom_col(aes(alpha = group, fill=region), data = df3_pos, position = "dodge", na.rm = TRUE) +
geom_col(aes(alpha = group, fill=region), data = df3_neg, position = "dodge", na.rm = TRUE) +
scale_fill_identity() +
scale_alpha_manual(values = c(T2 = .6, T1 = 1)) +
guides(alpha = FALSE)
p2
EDIT Adding annotations could be achieved the same way, e.g. my code below uses two geom_text to add the values next to the bar where I make use of position_dodge2(.9) so that the labels align nicely with the bars:
p2 <- ggplot(df3, aes(somevalue, metric)) +
geom_col(aes(alpha = group, fill=region), data = df3_pos, position = "dodge", na.rm = TRUE) +
geom_col(aes(alpha = group, fill=region), data = df3_neg, position = "dodge", na.rm = TRUE) +
geom_text(aes(x = somevalue + 1, label = somevalue), data = df3_pos, position = position_dodge2(width = .9), hjust = 0, na.rm = TRUE) +
geom_text(aes(x = somevalue - 1, label = somevalue), data = df3_neg, , position = position_dodge2(width = .9), hjust = 1, na.rm = TRUE) +
scale_fill_identity() +
scale_alpha_manual(values = c(T2 = .6, T1 = 1)) +
guides(alpha = FALSE)
p2
EDIT2 Adding a table is indeed a different thing. In that case I would go for patchwork which means making plots to mimic the table layout. To make the dodging work or to make sure that the table rows align with the bars you have make a plot for each table column. The basic approach may look like so:
library(patchwork)
# 1. Make a dataframe with all combinations of region and metric using expand_grid
d_table <- expand_grid(region = unique(df3$region), metric = unique(df3$metric))
# 2. Add columns with the table content
d_table$column1 <- LETTERS[1:6]
d_table$column2 <- letters[1:6]
# 3. Make a plot for each column of the table
p_column1 <- ggplot(d_table, aes(y = metric, x = 1, label = column1)) +
geom_text(aes(group = region), position = position_dodge2(width = .9), na.rm = TRUE) +
scale_x_continuous(position = "top", breaks = 1, labels = "column1") +
labs(y = NULL, x = "") +
theme(axis.text.y = element_blank(),
axis.text.x.bottom = element_blank(),
axis.ticks = element_blank(),
plot.margin = unit(rep(0, 4), "pt"),
panel.background = element_rect(fill = NA))
p_column2 <- ggplot(d_table, aes(y = metric, x = 1, label = column2)) +
geom_text(aes(group = region), position = position_dodge2(width = .9), na.rm = TRUE) +
scale_x_continuous(position = "top", breaks = 1, labels = "column2") +
labs(y = NULL, x = "") +
theme(axis.text.y = element_blank(),
axis.text.x.bottom = element_blank(),
axis.ticks = element_blank(),
plot.margin = unit(rep(0, 4), "pt"),
panel.background = element_rect(fill = NA))
# 4. Add the table columns via patchwork
p2 + p_column1 + p_column2 + plot_layout(widths = c(1, .1, .1))
I am using the windrose function posted here: Wind rose with ggplot (R)?
I need to have the percents on the figure showing on the individual lines (rather than on the left side), but so far I have not been able to figure out how. (see figure below for depiction of goal)
Here is the code that makes the figure:
p.windrose <- ggplot(data = data,
aes(x = dir.binned,y = (..count..)/sum(..count..),
fill = spd.binned)) +
geom_bar()+
scale_y_continuous(breaks = ybreaks.prct,labels=percent)+
ylab("")+
scale_x_discrete(drop = FALSE,
labels = waiver()) +
xlab("")+
coord_polar(start = -((dirres/2)/360) * 2*pi) +
scale_fill_manual(name = "Wind Speed (m/s)",
values = spd.colors,
drop = FALSE)+
theme_bw(base_size = 12, base_family = "Helvetica")
I marked up the figure I have so far with what I am trying to do! It'd be neat if the labels either auto-picked the location with the least wind in that direction, or if it had a tag for the placement so that it could be changed.
I tried using geom_text, but I get an error saying that "aesthetics must be valid data columns".
Thanks for your help!
One of the things you could do is to make an extra data.frame that you use for the labels. Since the data isn't available from your question, I'll illustrate with mock data below:
library(ggplot2)
# Mock data
df <- data.frame(
x = 1:360,
y = runif(360, 0, 0.20)
)
labels <- data.frame(
x = 90,
y = scales::extended_breaks()(range(df$y))
)
ggplot(data = df,
aes(x = as.factor(x), y = y)) +
geom_point() +
geom_text(data = labels,
aes(label = scales::percent(y, 1))) +
scale_x_discrete(breaks = seq(0, 1, length.out = 9) * 360) +
coord_polar() +
theme(axis.ticks.y = element_blank(), # Disables default y-axis
axis.text.y = element_blank())
#teunbrand answer got me very close! I wanted to add the code I used to get everything just right in case anyone in the future has a similar problem.
# Create the labels:
x_location <- pi # x location of the labels
# Get the percentage
T_data <- data %>%
dplyr::group_by(dir.binned) %>%
dplyr::summarise(count= n()) %>%
dplyr::mutate(y = count/sum(count))
labels <- data.frame(x = x_location,
y = scales::extended_breaks()(range(T_data$y)))
# Create figure
p.windrose <- ggplot() +
geom_bar(data = data,
aes(x = dir.binned, y = (..count..)/sum(..count..),
fill = spd.binned))+
geom_text(data = labels,
aes(x=x, y=y, label = scales::percent(y, 1))) +
scale_y_continuous(breaks = waiver(),labels=NULL)+
scale_x_discrete(drop = FALSE,
labels = waiver()) +
ylab("")+xlab("")+
coord_polar(start = -((dirres/2)/360) * 2*pi) +
scale_fill_manual(name = "Wind Speed (m/s)",
values = spd.colors,
drop = FALSE)+
theme_bw(base_size = 12, base_family = "Helvetica") +
theme(axis.ticks.y = element_blank(), # Disables default y-axis
axis.text.y = element_blank())
I have a geom_jitter plot showing Variables between 2 samples, I would like to include the Group-variable parameters on the left of the plot, setting a separation by lines like in the figure below. Thus, Variables are organised by Group.
Here is a reproducible example:
data<- tibble::tibble(
Variable = c("A","B","C","D","E", "F"),
Group = c("Asia","Asia","Europe","Europe","Africa","America"),
sample1 = c(0.38,0.22,0.18,0.12,0.1,0),
sample2 = c(0.23,0.2,0,0.12,0.11,0.15))
library(reshape2)
data2<- melt(data,
id.vars=c("Variable", "Group"),
measure.vars=c("sample1", "sample2"),
variable.name="Sample",
value.name="value")
data22[is.na(data22)] <- 0
library(ggplot2)
ggplot(data2, aes(x = Sample, y = Variable, label=NA)) +
geom_point(aes(size = value, colour = value)) +
geom_text(hjust = 1, size = 2) +
# scale_size(range = c(1,3)) +
theme_bw()+
scale_color_gradient(low = "lightblue", high = "darkblue")
Here is the current output I have:
And this is the format I would like:
To get a polished version of the plot most similar to your ideal plot, you can use facet_grid() plus some theme() customization.
ggplot(data2, aes(x = Sample, y = Variable, label=NA)) +
geom_point(aes(size = value, colour = value)) +
geom_text(hjust = 1, size = 2) +
# scale_size(range = c(1,3)) +
theme_bw()+
scale_color_gradient(low = "lightblue", high = "darkblue") +
facet_grid(Group~., scales = "free", switch = "y") +
theme(strip.placement = "outside",
strip.text.y = element_text(angle = 180),
panel.spacing = unit(0, "cm"))
I intend to make a dot plot somewhat like this:
But there's some issue with the code:
df = data.frame(x=runif(100))
df %>%
ggplot(aes(x )) +
geom_dotplot(binwidth =0.01, aes(fill = ..count..), stackdir = "centerwhole",dotsize=2, stackgroups = T, binpositions = "all")
how to choose bin width to avoid dots overlapping, bins wrapping itself in 2 columns or dots get truncated at the top and bottom?
And why is the y axis showing decimal points instead of count? And how to color the dots by x value? I tried fill = x and no color is shown.
The overlap is caused by the dotsize > 1; as #Jimbuo said, the decimal values on the y axis is due to the internals of this geom; for the fill and color you can use the ..x.. computed variable:
Computed variables
x center of each bin, if binaxis is "x"
df = data.frame(x=runif(1000))
library(dplyr)
library(ggplot2)
df %>%
ggplot(aes(x, fill = ..x.., color = ..x..)) +
geom_dotplot(method = 'histodot',
binwidth = 0.01,
stackdir = "down",
stackgroups = T,
binpositions = "all") +
scale_fill_gradientn('', colours = c('#5185FB', '#9BCFFD', '#DFDFDF', '#FF0000'), labels = c(0, 1), breaks = c(0,1), guide = guide_legend('')) +
scale_color_gradientn(colours = c('#5185FB', '#9BCFFD', '#DFDFDF', '#FF0000'), labels = c(0, 1), breaks = c(0,1), guide = guide_legend('')) +
scale_y_continuous() +
scale_x_continuous('', position = 'top') +
# coord_equal(ratio = .25) +
theme_classic() +
theme(axis.line = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
aspect.ratio = .25,
legend.position = 'bottom',
legend.direction = 'vertical'
)
Created on 2018-05-18 by the reprex package (v0.2.0).
First from the help of ?geom_dotplot
When binning along the x axis and stacking along the y axis, the
numbers on y axis are not meaningful, due to technical limitations of
ggplot2. You can hide the y axis, as in one of the examples, or
manually scale it to match the number of dots.
Thus you can try following. Note, the coloring is not completly fitting the x axis.
library(tidyverse)
df %>%
ggplot(aes(x)) +
geom_dotplot(stackdir = "down",dotsize=0.8,
fill = colorRampPalette(c("blue", "white", "red"))(100)) +
scale_y_continuous(labels = c(0,10), breaks = c(0,-0.4)) +
scale_x_continuous(position = "top") +
theme_classic()
For the correct coloring, you have to calculate the bins by yourself using e.g. .bincode:
df %>%
mutate(gr=with(.,.bincode(x ,breaks = seq(0,1,1/30)))) %>%
mutate(gr2=factor(gr,levels = 1:30, labels = colorRampPalette(c("blue", "white", "red"))(30))) %>%
arrange(x) %>%
{ggplot(data=.,aes(x)) +
geom_dotplot(stackdir = "down",dotsize=0.8,
fill = .$gr2) +
scale_y_continuous(labels = c(0,10), breaks = c(0,-0.4)) +
scale_x_continuous(position = "top") +
theme_classic()}