I made an upset plot using the ggupset package and added a break to the y axis with scale_y_break from the ggbreakpackage.
However, when I add scale_y_break, the combination matrix under the bar plot disappears.
Is there a way to combine the combination matrix of the plot made without scale_y_break with the bar plot portion of a plot made with scale_y_break? I can't seem to be able to access the grobs of these plots or use any other workaround. If anyone could help, I would greatly appreciate it!
Example with scale_x_upset and scale_y_break:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)+ scale_y_break(breaks = c(750,1000))
I would like to combine the barplot portion of the plot created with:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)+ scale_y_break(breaks = c(750,1000))
with the combination matrix portion of the plot made with:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)
Thanks!
I am trying to create a cumulative distribution function as a plotly object using ggplot2 and converting it using ggplotly, but the tails for 0 and 1 get lost in the conversion. How can I get the lines to extend to 0 and 1 using plotly in R?
This is the code I'm using:
gg <- ggplot(data=some_data, aes(x = x_var, color = grouping_var)) +
stat_ecdf()
ggplotly(gg)
This is the ggplot I get
But when I turn it into a plotly object the tails disappear and this is what I get
Edit: Editing to incorporate Quinten's request for recreation data:
new_data <- iris %>%
arrange(Petal.Length)
gg <- ggplot(data = new_data,
aes(x = Petal.Length,
color = Species)) +
stat_ecdf()
ggplotly(gg)
I think the problem here is the way that plotly handles the infinite x axis values produced as default by stat_ecdf. Although this can be turned off with stat_ecdf(pad = FALSE), this just leaves your initial ggplot without the endpoints too.
We can replicate your problem, as you suggested in the comments, with the iris data set:
library(ggplot2)
new_data <- iris %>%
arrange(Petal.Length)
gg <- ggplot(data = new_data, aes( x = Petal.Length, color = Species )) +
stat_ecdf()
In native ggplot, everything looks OK:
gg
But in plotly we miss the 0% and 100% lines because they stretch off to minus and positive infinity and are therefore dropped:
ggplotly(gg)
The way round this is to make the ecdf ourselves over a fixed range (say, 0 to 7). This requires a little data manipulation:
xmin <- 0
xmax <- 7
gg2 <- new_data %>%
group_by(Species) %>%
summarise(y = sapply(seq(xmin, xmax, 0.1), function(x) ecdf(Petal.Length)(x)),
Petal.Length = seq(xmin, xmax, 0.1)) %>%
ggplot(aes(Petal.Length, y, color = Species)) +
geom_step()
Now our ggplot looks like this:
gg2
And the plotly version remains faithful to this:
ggplotly(gg2)
average
Young 0.01921875
Cohoused Young 0.07111951
Old 0.06057224
Cohoused Old 0.12102273
I am using the above data frame to create a histogram or bar and my code is as follows:
C <-ggplot(data=c,aes(x=average))
C + geom_bar()
but the plot is attached here.
I would like the bar heights to reflect my data on the y axis instead of where the bar is placed on the x axis, but I don't know what my problem is in the code.
We can create a column with rownames_to_column
library(dplyr)
library(tibble)
library(ggplot2)
c %>%
rownames_to_column('rn') %>%
ggplot(aes(x = rn, y = average)) +
geom_col()
Or create a column directly in base R
c$rn <- row.names(c)
ggplot(c, aes(x = rn, y = average)) +
geom_col()
Or as #user20650 suggested
ggplot(data=c,aes(x=rownames(c) , y=average))
NOTE: It is better not to name objects with function names (c is a function)
In base R, with barplot, we can directly get the plots
barplot(as.matrix(c))
I’m totally new to ggplot, relatively fresh with R and want to make a smashing ”before-and-after” scatterplot with connecting lines to illustrate the movement in percentages of different subgroups before and after a special training initiative. I’ve tried some options, but have yet to:
show each individual observation separately (now same values are overlapping)
connect the related before and after measures (x=0 and X=1) with lines to more clearly illustrate the direction of variation
subset the data along class and id using shape and colors
How can I best create a scatter plot using ggplot (or other) fulfilling the above demands?
Main alternative: geom_point()
Here is some sample data and example code using genom_point
x <- c(0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1) # 0=before, 1=after
y <- c(45,30,10,40,10,NA,30,80,80,NA,95,NA,90,NA,90,70,10,80,98,95) # percentage of ”feelings of peace"
class <- c(0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1) # 0=multiple days 1=one day
id <- c(1,1,2,3,4,4,4,4,5,6,1,1,2,3,4,4,4,4,5,6) # id = per individual
df <- data.frame(x,y,class,id)
ggplot(df, aes(x=x, y=y), fill=id, shape=class) + geom_point()
Alternative: scale_size()
I have explored stat_sum() to summarize the frequencies of overlapping observations, but then not being able to subset using colors and shapes due to overlap.
ggplot(df, aes(x=x, y=y)) +
stat_sum()
Alternative: geom_dotplot()
I have also explored geom_dotplot() to clarify the overlapping observations that arise from using genom_point() as I do in the example below, however I have yet to understand how to combine the before and after measures into the same plot.
df1 <- df[1:10,] # data before
df2 <- df[11:20,] # data after
p1 <- ggplot(df1, aes(x=x, y=y)) +
geom_dotplot(binaxis = "y", stackdir = "center",stackratio=2,
binwidth=(1/0.3))
p2 <- ggplot(df2, aes(x=x, y=y)) +
geom_dotplot(binaxis = "y", stackdir = "center",stackratio=2,
binwidth=(1/0.3))
grid.arrange(p1,p2, nrow=1) # GridExtra package
Or maybe it is better to summarize data by x, id, class as mean/median of y, filter out ids producing NAs (e.g. ids 3 and 6), and connect the points by lines? So in case if you don't really need to show variability for some ids (which could be true if the plot only illustrates tendencies) you can do it this way:
library(ggplot)
library(dplyr)
#library(ggthemes)
df <- df %>%
group_by(x, id, class) %>%
summarize(y = median(y, na.rm = T)) %>%
ungroup() %>%
mutate(
id = factor(id),
x = factor(x, labels = c("before", "after")),
class = factor(class, labels = c("one day", "multiple days")),
) %>%
group_by(id) %>%
mutate(nas = any(is.na(y))) %>%
ungroup() %>%
filter(!nas) %>%
select(-nas)
ggplot(df, aes(x = x, y = y, col = id, group = id)) +
geom_point(aes(shape = class)) +
geom_line(show.legend = F) +
#theme_few() +
#theme(legend.position = "none") +
ylab("Feelings of peace, %") +
xlab("")
Here's one possible solution for you.
First - to get the color and shapes determined by variables, you need to put these into the aes function. I turned several into factors, so the labs function fixes the labels so they don't appear as "factor(x)" but just "x".
To address multiple points, one solution is to use geom_smooth with method = "lm". This plots the regression line, instead of connecting all the dots.
The option se = FALSE prevents confidence intervals from being plotted - I don't think they add a lot to your plot, but play with it.
Connecting the dots is done by geom_line - feel free to try that as well.
Within geom_point, the option position = position_jitter(width = .1) adds random noise to the x-axis so points do not overlap.
ggplot(df, aes(x=factor(x), y=y, color=factor(id), shape=factor(class), group = id)) +
geom_point(position = position_jitter(width = .1)) +
geom_smooth(method = 'lm', se = FALSE) +
labs(
x = "x",
color = "ID",
shape = 'Class'
)
I want to gradient fill a violin plot based on the density of points in the bins (blue for highest density and red for lowest).
I have generated a plot using the following commands but failed to color it based on density (in this case the width of the violin. I also would like to generate box plots with similar coloring).
library("ggplot2")
data(diamonds)
ggplot(diamonds, aes(x=cut,y=carat)) + geom_violin()
to change the colour of the violin plot you use fill = variable, like this:
ggplot(diamonds, aes(x=cut,y=carat)) + geom_violin(aes(fill=cut))
same goes for boxplot
ggplot(diamonds, aes(x=cut,y=carat)) + geom_boxplot(aes(fill=cut))
but whatever value you have has to have the same value for each cut, that is, if you wanted to use for example mean depth/cut as the color variable you would have to code it.
with dplyr group your diamonds by cut and with summarize get the mean depth (or any other variable)
library(dplyr)
diamonds_group <- group_by(diamonds, cut)
diamonds_group <- summarize(diamonds_group, Mean_Price = mean(price))
Then I used diamonds2 as a copy of diamonds to then manipulate the dataset
diamonds2 <- diamonds
I merge both dataframes to get the Mean_Depth as a variable in diamonds2
diamonds2 <- merge(diamonds2, diamonds_group)
And now I can plot it with mean depth as a color variable
ggplot(diamonds2, aes(x=cut,y=carat)) + geom_boxplot(aes(fill=Mean_Price)) + scale_fill_gradient2(midpoint = mean(diamonds2$price))
Just answered this for another thread, but believe it's possibly more appropriate for this thread. You can create a pseudo-fill by drawing many segments. You can get those directly from the underlying data in the ggplot_built object.
If you want an additional polygon outline ("border"), you'd need to create this from the x/y coordinates. Below one option.
library(tidyverse)
p <- ggplot(diamonds, aes(x=cut,y=carat)) + geom_violin()
mywidth <- .35 # bit of trial and error
# all you need for the gradient fill
vl_fill <- data.frame(ggplot_build(p)$data) %>%
mutate(xnew = x- mywidth*violinwidth, xend = x+ mywidth*violinwidth)
# the outline is a bit more convoluted, as the order matters
vl_poly <- vl_fill %>%
select(xnew, xend, y, group) %>%
pivot_longer(-c(y, group), names_to = "oldx", values_to = "x") %>%
arrange(y) %>%
split(., .$oldx) %>%
map(., function(x) {
if(all(x$oldx == "xnew")) x <- arrange(x, desc(y))
x
}) %>%
bind_rows()
ggplot() +
geom_polygon(data = vl_poly, aes(x, y, group = group),
color= "black", size = 1, fill = NA) +
geom_segment(data = vl_fill, aes(x = xnew, xend = xend, y = y, yend = y,
color = violinwidth))
Created on 2021-04-14 by the reprex package (v1.0.0)