Utilizing the example package code in ggpubr, the ggdotchart function does not create separate segments as is shown in the example, instead there is only a single segment, though the dots seem to be placed in the correct orientation. Does anyone have any tips on what the problem may be? I've thought it may be due to factors, tibbles vs. df, but I haven't been able to determine the problem.
Code:
df <- diamonds %>%
filter(color %in% c("J", "D")) %>%
group_by(cut, color) %>%
summarise(counts = n())
ggdotchart(df, x = "cut", y ="counts",
color = "color", palette = "jco", size = 3,
add = "segment",
add.params = list(color = "lightgray", size = 1.5),
position = position_dodge(0.3),
ggtheme = theme_pubclean()
)
With the expected output of:
But instead I am getting:
Here is a way to get your desired plot without ggpubr::ggdotchart. The issue seems to be that geom_segment does not allow dodging, as discussed here: R - ggplot dodging geom_lines and here: how to jitter/dodge geom_segments so they remain parallel?.
# your data
df <- diamonds %>%
filter(color %in% c("J", "D")) %>%
group_by(cut, color) %>%
summarise(counts = n())
The first step is to expand your data. We will need this when we call geom_line which allows for dodging. I took this idea from #Stibu's answer. We create a copy of df and change the counts column to be 0 in df2. Finally we use bind_rows to create a single data frame from df and df2.
df2 <- df
df2$counts <- 0
df_out <- purrr::bind_rows(df, df2)
df_out
Then I use ggplot to create / replicate your desired output.
ggplot(df_out, aes(x = cut, y = counts)) +
geom_line(
aes(col = color), # needed for dodging, we'll later change colors to "lightgrey"
position = position_dodge(width = 0.3),
show.legend = FALSE,
size = 1.5
) +
geom_point(
aes(fill = color),
data = subset(df_out, counts > 0),
col = "transparent",
shape = 21,
size = 3,
position = position_dodge(width = 0.3)
) +
scale_color_manual(values = c("lightgray", "lightgray")) + #change line colors
ggpubr::fill_palette(palette = "jco") +
ggpubr::theme_pubclean()
There is an extra "group" argument you need!
df <- diamonds %>%
dplyr::filter(color %in% c("J", "D")) %>%
dplyr::group_by(cut, color) %>%
dplyr::summarise(counts = n())
ggdotchart(df, x = "cut", y ="counts",
color = "color", group="color", # here it is
palette = "jco", size = 3,
add = "segment",
add.params = list(color = "lightgray", size = 1.5),
position = position_dodge(0.3),
ggtheme = theme_pubclean()
)
Related
I would like to plot densities by groups such that the alpha value decreases (more transparent) as the x axis value gets closer to 0.
Based on the data dataset, I generate the alpha column by rescaling the x axis values around 0.
I thought that adding the alpha inside the aes() would work but this throws and error.
library(tidyverse)
library(purrr)
library(scales)
set.seed(123)
data <- tibble(A = rnorm(100),
B = rnorm(100, mean = -0.7),
C = rnorm(100, mean = 1)) %>%
pivot_longer(cols = everything(),
names_to = "model") %>%
group_by(model) %>%
summarise(value = list(value)) %>%
mutate( xval = map(value, ~density(.x)$x),
yval = map(value, ~density(.x)$y)) %>%
select(-value) %>%
unnest(ends_with("val"))
#create alpha column
df <- data %>%
group_by(model) %>%
mutate(myalpha = abs(scale(xval, center = 0)), #scale to center around 0
myalpha2 = scales::rescale_mid(myalpha, mid = 0) #rescale 0-1, 0 for values around 0
) %>%
as_tibble()
df %>%
ggplot(aes(x = xval, y = yval,
fill = model, col = model))+
geom_line()+
geom_vline(xintercept = 0)+
geom_density(aes(alpha = myalpha2), #alpha white around 0
stat = "identity")+
scale_fill_manual(values = c("red", "pink", "orange"))+
scale_alpha_identity()
#> Error in `f()`:
#> ! Aesthetics can not vary with a ribbon
Created on 2022-09-11 by the reprex package (v2.0.1)
You cannot yet have a gradient fill in native ggplot (this includes gradients on the alpha channel). You can give the appearance of gradient fills using vertical line segments whose individual alpha values change along the x axis though.
Note that your alpha calculation isn't quite right here. myalpha2 has a minimum of 0.5 at the 0 point, as you can easily check with min(df$myalpha2).
To fix this, and implement the vertical line segment hack, you can do:
df %>%
mutate(myalpha2 = 2 * (as.vector(myalpha2) - 0.5)) %>%
ggplot(aes(x = xval, y = yval))+
geom_line()+
geom_vline(xintercept = 0)+
geom_segment(aes(alpha = myalpha2, xend = xval, yend = 0, color = model),
size = 1) +
scale_color_manual(values = c("red", "pink", "orange"))+
scale_alpha_identity()
TL;DR: with plot labels using geom_label etc., is it possible to use different data for the calculation of positions of using position_stack or similar functions, than for the display of the label itself? Or, less generally, is it possible to subset the label data after positions have been calculated?
I have some time series data for many different subjects. Observations took place at multiple time points, which are the same for each subject. I would like to plot this data as a stacked area plot, where the height of a subject's curve at each time point corresponds to the observed value for that subject at that time point. Crucially, I also need to add labels to identify each subject.
However, the trivial solution of adding one label at each observation makes the plot unreadable, so I would like to limit the displayed labels to the "most important" subjects (the ones that have the highest peak), as well as only display a label at the respective peak. This subsetting of the labels themselves is not a problem either, but I cannot figure out how to then position the (subset of) labels correctly so they match with the stacked area chart.
Here is some example code, which should work out of the box with tidyverse installed, to illustrate my issue. First, we generate some data which has the same structure as mine:
library(tidyverse)
set.seed(0)
# Generate some data
num_subjects = 50
num_timepoints = 10
labels = paste(sample(words, num_subjects), sample(fruit, num_subjects), sep = "_")
col_names = c("name", paste0("timepoint_", c(1:num_timepoints)))
df = bind_rows(map(labels,
~c(., cumsum(rnorm(num_timepoints))) %>%
set_names(col_names))) %>%
pivot_longer(starts_with("timepoint_"), names_to = "timepoint", names_prefix = "timepoint_") %>%
mutate(across(all_of(c("timepoint", "value")), as.numeric)) %>%
mutate(value = if_else(value < 0, 0, value)) %>%
group_by(name) %>% mutate(peak = max(value)) %>% ungroup()
Now, we can trivially make a simple stacked area plot without labels:
# Plot (without labels)
ggplot(df,
mapping = aes(x = factor(timepoint), y = value, group = name, fill = factor(peak))) +
geom_area(show.legend = FALSE, position = "stack", colour = "gray25") +
scale_fill_viridis_d()
Plot without labels (it appears that I currently cannot embed images, which is very unfortunate as they are extremely illustrative here...)
It is also not too hard to add non-specific labels to this data. They can easily be made to appear at the correct position — so the center of the label is at the middle of the area for each time point and subject — using position_stack:
# Plot (all labels, positions are correct but the plot is basically unreadable)
ggplot(df,
mapping = aes(x = factor(timepoint), y = value, group = name, fill = factor(peak))) +
geom_area(show.legend = FALSE, position = "stack", colour = "gray25") +
geom_label(mapping = aes(label = name), show.legend = FALSE, position = position_stack(vjust = 0.5)) +
scale_fill_viridis_d()
Plot with a label at each observation
However, as noted before, the labels almost entirely obscure the plot itself. So my approach would be to only show labels at the peaks, and only for the 10 subjects with the highest peaks:
# Plot (only show labels at the peak for the 10 highest peaks, readable but positions are wrong)
max_labels = 10 # how many labels to show
df_labels = df %>%
group_by(name) %>% slice_max(value, n = 1) %>% ungroup() %>%
slice_max(value, n = max_labels)
ggplot(df,
mapping = aes(x = factor(timepoint), y = value, group = name, fill = factor(peak))) +
geom_area(show.legend = FALSE, position = "stack", colour = "gray25") +
geom_label(data = df_labels, mapping = aes(label = name), show.legend = FALSE, position = position_stack(vjust = 0.5)) +
scale_fill_viridis_d()
Plot with only a subset of labels
This code also works fine, but it is apparent that the labels no longer show up at the correct positions, but are instead too low on the plot, especially for the subjects which would otherwise be higher up. (The only subject where the position is correct is work_eggplant.) This makes perfect sense, as the data used for calculation of position_stack are now only a subset of the original data, so the observations which would receive no labels are not considered when stacking. This can be illustrated by zeroing out all the observations which would not receive a label:
df_zeroed = anti_join(df %>% mutate(value = 0),
df_labels,
by = c("name", "timepoint")) %>% bind_rows(df_labels)
ggplot(df_zeroed,
mapping = aes(x = factor(timepoint), y = value, group = name, fill = factor(peak))) +
geom_area(show.legend = FALSE, position = "stack", colour = "gray25") +
geom_label(data = df_labels, mapping = aes(label = name), show.legend = FALSE, position = position_stack(vjust = 0.5)) +
scale_fill_viridis_d()
Plot with unlabeled observations zeroed out
So now my question is, how can this problem be solved? Is there a way to use the original data for the positioning, but the subset data for the actual display of the labels?
Maybe this is what you are looking for. To achieve the desired result you could
use the whole dataset for plotting the labels to get the right positions,
use an empty string "" for the non-desired labels ,
set the fill and color of non-desired labels to "transparent"
# Plot (only show labels at the peak for the 10 highest peaks, readable but positions are wrong)
max_labels = 10 # how many labels to show
df_labels = df %>%
group_by(name) %>%
slice_max(value, n = 1) %>%
ungroup() %>%
slice_max(value, n = max_labels) %>%
mutate(label = name)
df1 <- df %>%
left_join(df_labels) %>%
replace_na(list(label = ""))
#> Joining, by = c("name", "timepoint", "value", "peak")
ggplot(df1,
mapping = aes(x = factor(timepoint), y = value, group = name, fill = as.character(peak))) +
geom_area(show.legend = FALSE, position = "stack", colour = "gray25") +
geom_label(mapping = aes(
label = label,
fill = ifelse(label != "", as.character(peak), NA_character_),
color = ifelse(label != "", "black", NA_character_)),
show.legend = FALSE, position = position_stack(vjust = 0.5)) +
scale_fill_viridis_d(na.value = "transparent") +
scale_color_manual(values = c("black" = "black"), na.value = "transparent")
EDIT If you want the fill colors to correspond to the value of peak then
a simple solution would be to map peak on fill instead of factor(peak) and make use of fill = ifelse(label != "", peak, NA_real_) in geom_label. However, in that case you have to switch to a continuous fill scale.
as I guess that you had a good reason to make use of discrete scale an other option would be to make peak an orderd factor. This approach however is not that simple. To make this work I first reorder factor(peak) according to peak, add an additional NA level and make us of an auxilliary variable peak1 to fill the labels. However, as we have two different variables to be mapped on fill I would suggest to make use of a second fill scale using ggnewscale::new_scale_fill to achieve the desired result:
library(tidyverse)
set.seed(0)
#cumsum(rnorm(num_timepoints)) * 3
# Generate some data
num_subjects = 50
num_timepoints = 10
labels = paste(sample(words, num_subjects), sample(fruit, num_subjects), sep = "_")
col_names = c("name", paste0("timepoint_", c(1:num_timepoints)))
df = bind_rows(map(labels,
~c(., cumsum(rnorm(num_timepoints)) * 3) %>%
set_names(col_names))) %>%
pivot_longer(starts_with("timepoint_"), names_to = "timepoint", names_prefix = "timepoint_") %>%
mutate(across(all_of(c("timepoint", "value")), as.numeric)) %>%
mutate(value = if_else(value < 0, 0, value)) %>%
group_by(name) %>% mutate(peak = max(value)) %>% ungroup()
# Plot (only show labels at the peak for the 10 highest peaks, readable but positions are wrong)
max_labels = 10 # how many labels to show
df_labels = df %>%
group_by(name) %>%
slice_max(value, n = 1) %>%
ungroup() %>%
slice_max(value, n = max_labels) %>%
mutate(label = name)
df1 <- df %>%
left_join(df_labels) %>%
replace_na(list(label = ""))
#> Joining, by = c("name", "timepoint", "value", "peak")
df2 <- df1 %>%
mutate(
# Make ordered factor
peak = fct_reorder(factor(peak), peak),
# Add NA level to peak
peak = fct_expand(peak, NA),
# Auxilliary variable to set the fill to NA for non-desired labels
peak1 = if_else(label != "", peak, factor(NA)))
ggplot(df2, mapping = aes(x = factor(timepoint), y = value, group = name, fill = peak)) +
geom_area(show.legend = TRUE, position = "stack", colour = "gray25") +
scale_fill_viridis_d(na.value = "transparent") +
# Add a second fill scale
ggnewscale::new_scale_fill() +
geom_label(mapping = aes(
label = label,
fill = peak1,
color = ifelse(label != "", "black", NA_character_)),
show.legend = FALSE, position = position_stack(vjust = 0.5)) +
scale_fill_viridis_d(na.value = "transparent") +
scale_color_manual(values = c("black" = "black"), na.value = "transparent")
I try to plot labels above bars with the stat_summary function and a custom function that I wrote. There are three bars and each should be labeled with the letters a:c, respectively. However, instead of putting one label per bar, all three labels are placed on top of each other:
codes <- c ("a", "b", "c")
simple_y <- function(x) {
return (data.frame (y = mean (x) + 1, label = codes))
}
ggplot (iris, mapping = aes (x = Species, y = Sepal.Length)) +
geom_bar (stat = "summary", fun.y = "mean", fill = "blue", width = 0.7, colour = "black", size = 0.7) +
stat_summary (fun.data = simple_y, geom = "text", size = 10)
I do understand why this is not working: each time the simply_y-function is recycled, it sees the whole codes - vector. However, I have no clue how to tell R to separate the three labels. Is it possible to tell R to subsequently use the n_th element of an input-vector when recycling a function?
Does anybody have a good hint?
I would consider doing something like this:
labels <-
tibble(
Species = factor(c("setosa", "versicolor", "virginica")),
codes = c("a", "b", "c")
)
iris %>%
group_by(Species) %>%
summarize(Mean = mean(Sepal.Length)) %>%
ungroup() %>%
left_join(labels, by = "Species") %>%
ggplot(aes(x = Species, y = Mean)) +
geom_col(fill = "blue", width = 0.7, color = "black", size = 0.7) +
geom_text(aes(y = Mean + 0.3, label = codes), size = 6, show.legend = FALSE)
First, you can generate the data frame with means separately, avoiding the need for geom_bar and stat_summary. Then after joining the manual labels/codes to that summarized data frame, it's pretty straightforward to add them with geom_text.
The legend for my bar graph currently lists all the items in the graph in one long list. I would like to have the legend group itself by each column.
The number of columns is dynamic so the legend must be able to adjust accordingly.
library("phyloseq"); packageVersion("phyloseq")
library(ggplot2)
library(scales)
data("GlobalPatterns")
TopNOTUs <- names(sort(taxa_sums(GlobalPatterns), TRUE)[1:50])
gp.ch <- prune_species(TopNOTUs, GlobalPatterns)
gp.ch = subset_taxa(gp.ch, Genus != "NA")
mdf = psmelt(gp.ch)
# Create a ggplot similar to
library("ggplot2")
mdf$group <- paste0(mdf$Phylum, "-", mdf$Genus, sep = "")
colours <-ColourPalleteMulti(mdf, "Phylum", "Genus")
# Plot resultss
ggplot(mdf, aes(Phylum)) +
geom_bar(aes(fill = group), colour = "grey", position = "stack")
Right now the legend prints the items:
Actinobacteria-Bifidobacterium
Actinobacteria-Rothia
Bacteriodetes-Alistipes
Bacteriodetes-Bacteroides
...
I would like it to print:
Actinobacteria
-Bifidobacterium
-Rothia
Bacteriodetes
-Alistipes
-Bacteroides
...
This is hacky but might work for you. First, using mtcars dataset, I add dummy rows to the data representing the groupings, then assign a factor level to each of the groupings and component categories. Finally, I hack the alpha in the legend so that grouping headers have transparent colors and look hidden.
# Fake data sample
library(tidyverse)
cars_sample <- mtcars %>%
rownames_to_column(var = "name") %>%
mutate(make = word(name, end = 1),
model = word(name, start = 2, end = -1)) %>%
filter(make %in% c("Mazda", "Merc", "Hornet")) %>%
select(name, make, model, mpg, wt)
# Add rows for groups and make a factor for each group and each component
cars_sample_fct <- cars_sample %>%
bind_rows( cars_sample %>% count(make) %>% mutate(model = make, name = "")) %>%
arrange(make, name) %>%
mutate(name_fct = fct_inorder(if_else(name == "", make, paste0("- ", model))))
# Plot with transparent grouping legend labels
ggplot(cars_sample_fct, aes(wt, mpg, color = name_fct)) +
geom_point() +
scale_color_discrete(name = "Car") +
guides(color = guide_legend(
override.aes = list(size = 5,
alpha = cars_sample_fct$name != "")))
Does anyone know if there is a way to add variable labels to the ggparcoord function in GGally? I've tried numerous ways with geom_text, but nothing is yielding results.
To be more explicit, I am looking to pass the row.names(mtcars) through geom_text. The only way that I can distinguish the car is passing row.names(mtcars) through the groupColumn argument, but I don't like the way this looks.
Doesn't work:
mtcars$carName <- row.names(mtcars) # This becomes column 12
library(GGally)
# Attempt 1
ggparcoord(mtcars,
columns = c(12, 1, 6),
groupColumn = 1) +
geom_text(aes(label = carName))
# Attempt 2
ggparcoord(mtcars,
columns = c(12, 1, 6),
groupColumn = 1,
mapping = aes(label = carName))
Any ideas would be appreciated!
Solution 1: If you want to stick close to your original attempt, you can calculate the appropriate y coordinates for the car names, & add that as a separate data source. Use inherit.aes = FALSE so that this geom_text layer doesn't inherit anything from the ggplot object created using ggparcoord():
library(dplyr)
p1 <- ggparcoord(mtcars,
columns = c(12, 1, 6),
groupColumn = 1) +
geom_text(data = mtcars %>%
select(carName) %>%
mutate(x = 1,
y = scale(as.integer(factor(carName)))),
aes(x = x, y = y, label = carName),
hjust = 1.1,
inherit.aes = FALSE) +
# optional: remove "carName" from x-axis labels
scale_x_discrete(labels = function(x) c("", x[-1])) +
# also optional: hide legend, which doesn't really seem relevant here
theme(legend.position = "none")
p1
Solution 2: This alternative uses carName as the group column, & doesn't pass it as one of the parallel coordinate columns. (which I think this might be closer to the use cases intended by this function...) Specifying carName as the group column allows the car name values to be captured in the data slot of the ggplot object created by ggparcoord() this time, so our geom_text label can inherit it directly, & even filter only for rows corresponding to variable == "mpg" (or whatever the first of the parallel coordinate columns is named, in the actual use case). The y coordinates are not as evenly spread out as above, but geom_text_repel from the ggrepel package does a decent job at shifting overlapping text labels away from one another.
library(dplyr)
library(ggrepel)
p2 <- ggparcoord(mtcars,
columns = c(1, 6),
groupColumn = "carName") +
geom_text_repel(data = . %>%
filter(variable == "mpg"),
aes(x = variable, y = value, label = carName),
xlim = c(NA, 1)) + # limit repel region to the left of the 1st column
theme(legend.position = "none") # as before, hide legend since the labels
# are already in the plot
p2
Solution 3 / 4: You can actually plot the same with ggplot(), without relying on extensions that may do unexpected stuff behind the scenes:
library(dplyr)
library(tidyr)
library(ggrepel)
# similar output to solution 1
p3 <- mtcars %>%
select(carName, mpg, wt) %>%
mutate(carName.column = as.integer(factor(carName))) %>%
gather(variable, value, -carName) %>%
group_by(variable) %>%
mutate(value = scale(value)) %>%
ungroup() %>%
ggplot(aes(x = variable, y = value, label = carName, group = carName)) +
geom_line() +
geom_text(data = . %>% filter(variable == "carName.column"),
hjust = 1.1) +
scale_x_discrete(labels = function(x) c("", x[-1]))
p3
# similar output to solution 2
p4 <- mtcars %>%
select(carName, mpg, wt) %>%
gather(variable, value, -carName) %>%
group_by(variable) %>%
mutate(value = scale(value)) %>%
ungroup() %>%
ggplot(aes(x = variable, y = value, label = carName, group = carName)) +
geom_line() +
geom_text_repel(data = . %>% filter(variable == "mpg"),
xlim = c(NA, 1))
p4
Edit
You can add text labels on the right as well, for each of the above. Do note that the location for labels may not be nicely spaced out, since they are positioned according to wt's scaled values:
p1 +
geom_text(data = mtcars %>%
select(carName, wt) %>%
mutate(x = 3,
y = scale(wt)),
aes(x = x, y = y, label = carName),
hjust = -0.1,
inherit.aes = FALSE)
p2 +
geom_text_repel(data = . %>%
filter(variable == "wt"),
aes(x = variable, y = value, label = carName),
xlim = c(2, NA))
p3 +
geom_text(data = . %>% filter(variable == "wt"),
hjust = -0.1)
p4 +
geom_text_repel(data = . %>% filter(variable == "wt"),
xlim = c(2, NA))