I have a question about using geom_segment in R ggplot2.
For example, I have three facets and two clusters of points(points which have the same y values) in each facets, how do I draw multiple vertical line segments for each clustering with geom_segment?
Like if my data is
x <- (1:24)
y <- (rep(1,2),2,rep(2,2),1,rep(3,2),4, rep(4,1),5,6, ..rep(8,2),7)
facets <-(1,2,3)
factors <-(1,2,3,4,5,6)
xmean <- ( (1+2+3)/3, (4+5+6)/3, ..., (22+23+24)/3)
Note: (1+2+3)/3 is the mean first cluster in the first facet and (4+5+6)/3 is the mean second cluster in the second facet and (7+8+9)/3 is the first cluster in the second facet.
My Code:
ggplot(,aes(x=as.numeric(x),y=as.numeric(y),color=factors)+geom_point(alpha=0.85,size=1.85)+facet_grid(~facets)
+geom_segment(what should I put here to draw this line in different factors?)
Desired result:
Please see the picture!
Please see the updated picture!
Thank you so much! Have a nice day :).
Maybe this is what you are looking for. Instead of working with vectors put your data in a dataframe. Doing so you could easily make an aggregated dataframe with the mean values per facet and cluster which makes it easy to the segments:
Note: Wasn't sure about the setup of your data. You talk about two clusters per facet but your data has 8. So I slightly changed the example data.
library(ggplot2)
library(dplyr)
df <- data.frame(
x = 1:24,
y = rep(1:6, each = 4),
facets = rep(1:3, each = 8)
)
df_sum <- df %>%
group_by(facets, y) %>%
summarise(x = mean(x))
#> `summarise()` has grouped output by 'facets'. You can override using the `.groups` argument.
ggplot(df, aes(x, y, color = factor(y))) +
geom_point(alpha = 0.85, size = 1.85) +
geom_segment(data = df_sum, aes(x = x, xend = x, y = y - .25, yend = y + .25), color = "black") +
facet_wrap(~facets)
Related
I have been working on plotting several lines according to different probability levels and am stuck adding labels to each line to represent the probability level.
Since each curve plotted has varying x and y coordinates, I cannot simply have a large data-frame on which to perform usual ggplot2 functions.
The end goal is to have each line with a label next to it according to the p-level.
What I have tried:
To access the data comfortably, I have created a list df with for example 5 elements, each element containing a nx2 data frame with column 1 the x-coordinates and column 2 the y-coordinates. To plot each curve, I create a for loop where at each iteration (i in 1:5) I extract the x and y coordinates from the list and add the p-level line to the plot by:
plot = plot +
geom_line(data=df[[i]],aes(x=x.coor, y=y.coor),color = vector_of_colors[i])
where vector_of_colors contains varying colors.
I have looked at using ggrepel and its geom_label_repel() or geom_text_repel() functions, but being unfamiliar with ggplot2 I could not get it to work. Below is a simplification of my code so that it may be reproducible. I could not include an image of the actual curves I am trying to add labels to since I do not have 10 reputation.
# CREATION OF DATA
plevel0.5 = cbind(c(0,1),c(0,1))
colnames(plevel0.5) = c("x","y")
plevel0.8 = cbind(c(0.5,3),c(0.5,1.5))
colnames(plevel0.8) = c("x","y")
data = list(data1 = line1,data2 = line2)
# CREATION OF PLOT
plot = ggplot()
for (i in 1:2) {
plot = plot + geom_line(data=data[[i]],mapping=aes(x=x,y=y))
}
Thank you in advance and let me know what needs to be clarified.
EDIT :
I have now attempted the following :
Using bind_rows(), I have created a single dataframe with columns x.coor and y.coor as well as a column called "groups" detailing the p-level of each coordinate.
This is what I have tried:
plot = ggplot(data) +
geom_line(aes(coors.x,coors.y,group=groups,color=groups)) +
geom_text_repel(aes(label=groups))
But it gives me the following error:
geom_text_repel requires the following missing aesthetics: x and y
I do not know how to specify x and y in the correct way since I thought it did this automatically. Any tips?
You approach is probably a bit to complicated. As far as I get it you could of course go on with one dataset and use the group aesthetic to get the same result you are trying to achieve with your for loop and multiple geom_line. To this end I use dplyr:.bind_rows to bind your datasets together. Whether ggrepel is needed depends on your real dataset. In my code below I simply use geom_text to add an label at the rightmost point of each line:
plevel0.5 <- data.frame(x = c(0, 1), y = c(0, 1))
plevel0.8 <- data.frame(x = c(0.5, 3), y = c(0.5, 1.5))
library(dplyr)
library(ggplot2)
data <- list(data1 = plevel0.5, data2 = plevel0.8) |>
bind_rows(.id = "id")
ggplot(data, aes(x = x, y = y, group = id)) +
geom_line(aes(color = id)) +
geom_text(data = ~ group_by(.x, id) |> filter(x %in% max(x)), aes(label = id), vjust = -.5, hjust = .5)
The grouping variable for creating a geom_violin() plot in ggplot2 is expected to be discrete for obvious reasons. However my discrete values are numbers, and I would like to show them on a continuous scale so that I can overlay a continuous function of those numbers on top of the violins. Toy example:
library(tidyverse)
df <- tibble(x = sample(c(1,2,5), size = 1000, replace = T),
y = rnorm(1000, mean = x))
ggplot(df) + geom_violin(aes(x=factor(x), y=y))
This works as you'd imagine: violins with their x axis values (equally spaced) labelled 1, 2, and 5, with their means at y=1,2,5 respectively. I want to overlay a continuous function such as y=x, passing through the means. Is that possible? Adding + scale_x_continuous() predictably gives Error: Discrete value supplied to continuous scale. A solution would presumably spread the violins horizontally by the numeric x values, i.e. three times the spacing between 2 and 5 as between 1 and 2, but that is not the only thing I'm trying to achieve - overlaying a continuous function is the key issue.
If this isn't possible, alternative visualisation suggestions are welcome. I know I could replace violins with a simple scatter plot to give a rough sense of density as a function of y for a given x.
The functionality to plot violin plots on a continuous scale is directly built into ggplot.
The key is to keep the original continuous variable (instead of transforming it into a factor variable) and specify how to group it within the aesthetic mapping of the geom_violin() object. The width of the groups can be modified with the cut_width argument, depending on the data at hand.
library(tidyverse)
df <- tibble(x = sample(c(1,2,5), size = 1000, replace = T),
y = rnorm(1000, mean = x))
ggplot(df, aes(x=x, y=y)) +
geom_violin(aes(group = cut_width(x, 1)), scale = "width") +
geom_smooth(method = 'lm')
By using this approach, all geoms for continuous data and their varying functionalities can be combined with the violin plots, e.g. we could easily replace the line with a loess curve and add a scatter plot of the points.
ggplot(df, aes(x=x, y=y)) +
geom_violin(aes(group = cut_width(x, 1)), scale = "width") +
geom_smooth(method = 'loess') +
geom_point()
More examples can be found in the ggplot helpfile for violin plots.
Try this. As you already guessed, spreading the violins by numeric values is the key to the solution. To this end I expand the df to include all x values in the interval min(x) to max(x) and use scale_x_discrete(drop = FALSE) so that all values are displayed.
Note: Thanks #ChrisW for the more general example of my approach.
library(tidyverse)
set.seed(42)
df <- tibble(x = sample(c(1,2,5), size = 1000, replace = T), y = rnorm(1000, mean = x^2))
# y = x^2
# add missing x values
x.range <- seq(from=min(df$x), to=max(df$x))
df <- df %>% right_join(tibble(x = x.range))
#> Joining, by = "x"
# Whatever the desired continuous function is:
df.fit <- tibble(x = x.range, y=x^2) %>%
mutate(x = factor(x))
ggplot() +
geom_violin(data=df, aes(x = factor(x, levels = 1:5), y=y)) +
geom_line(data=df.fit, aes(x, y, group=1), color = "red") +
scale_x_discrete(drop = FALSE)
#> Warning: Removed 2 rows containing non-finite values (stat_ydensity).
Created on 2020-06-11 by the reprex package (v0.3.0)
I try to connect jittered points between measurements from two different methods (measure) on an x-axis. These measurements are linked to one another by the probands (a), that can be separated into two main groups, patients (pat) and controls (ctr),
My df is like that:
set.seed(1)
df <- data.frame(a = rep(paste0("id", "_", 1:20), each = 2),
value = sample(1:10, 40, rep = TRUE),
measure = rep(c("a", "b"), 20), group = rep(c("pat", "ctr"), each = 2,10))
I tried
library(ggplot2)
ggplot(df,aes(measure, value, fill = group)) +
geom_point(position = position_jitterdodge(jitter.width = 0.1, jitter.height = 0.1,
dodge.width = 0.75), shape = 1) +
geom_line(aes(group = a), position = position_dodge(0.75))
Created on 2020-01-13 by the reprex package (v0.3.0)
I used the fill aesthetic in order to separate the jittered dots from both groups (pat and ctr). I realised that when I put the group = a aesthetics into the ggplot main call, then it doesn't separate as nicely, but seems to link better to the points.
My question: Is there a way to better connect the lines to the (jittered) points, but keeping the separation of the two main groups, ctr and pat?
Thanks a lot.
The big issue you are having is that you are dodging the points by only group but the lines are being dodged by a, as well.
To keep your lines with the axes as is, one option is to manually dodge your data. This takes advantage of factors being integers under the hood, moving one level of group to the right and the other to the left.
df = transform(df, dmeasure = ifelse(group == "ctr",
as.numeric(measure) - .25,
as.numeric(measure) + .25 ) )
You can then make a plot with measure as the x axis but then use the "dodged" variable as the x axis variable in geom_point and geom_line.
ggplot(df, aes(x = measure, y = value) ) +
geom_blank() +
geom_point( aes(x = dmeasure), shape = 1 ) +
geom_line( aes(group = a, x = dmeasure) )
If you also want jittering, that can also be added manually to both you x and y variables.
df = transform(df, dmeasure = ifelse(group == "ctr",
jitter(as.numeric(measure) - .25, .1),
jitter(as.numeric(measure) + .25, .1) ),
jvalue = jitter(value, amount = .1) )
ggplot(df, aes(x = measure, y = jvalue) ) +
geom_blank() +
geom_point( aes(x = dmeasure), shape = 1 ) +
geom_line( aes(group = a, x = dmeasure) )
This turned out to be an astonishingly common question and I'd like to add an answer/comment to myself with a suggestion of a - what I now think - much, much better visualisation:
The scatter plot.
I originally intended to show paired data and visually guide the eye between the two comparisons. The problem with this visualisation is evident: Every subject is visualised twice. This leads to a quite crowded graphic. Also, the two dimensions of the data (measurement before, and after) are forced into one dimension (y), and the connection by ID is awkwardly forced onto your x axis.
Plot 1: The scatter plot naturally represents the ID by only showing one point per subject, but showing both dimensions more naturally on x and y. The only step needed is to make your data wider (yes, this is also sometimes necessary, ggplot not always requires long data).
The box plot
Plot 2: As rightly pointed out by user AllanCameron, another option would be to plot the difference of the paired values directly, for example as a boxplot. This is a nice visualisation of the appropriate paired t-test where the mean of the differences is tested against 0. It will require the same data shaping to "wide format". I personally like to show the actual values as well (if there are not too many).
library(tidyr)
library(dplyr)
library(ggplot2)
## first reshape the data wider (one column for each measurement)
df %>%
pivot_wider(names_from = "measure", values_from = "value", names_prefix = "time_" ) %>%
## now use the new columns for your scatter plot
ggplot() +
geom_point(aes(time_a, time_b, color = group)) +
## you can add a line of equality to make it even more intuitive
geom_abline(intercept = 0, slope = 1, lty = 2, linewidth = .2) +
coord_equal()
Box plot to show differences of paired values
df %>%
pivot_wider(names_from = "measure", values_from = "value", names_prefix = "time_" ) %>%
ggplot(aes(x = "", y = time_a - time_b)) +
geom_boxplot() +
# optional, if you want to show the actual values
geom_point(position = position_jitter(width = .1))
I am trying to generate a (grouped) density plot labelled with sample sizes.
Sample data:
set.seed(100)
df <- data.frame(ab.class = c(rep("A", 200), rep("B", 200)),
val = c(rnorm(200, 0, 1), rnorm(200, 1, 1)))
The unlabelled density plot is generated and looks as follows:
ggplot(df, aes(x = val, group = ab.class)) +
geom_density(aes(fill = ab.class), alpha = 0.4)
What I want to do is add text labels somewhere near the peak of each density, showing the number of samples in each group. However, I cannot find the right combination of options to summarise the data in this way.
I tried to adapt the code suggested in this answer to a similar question on boxplots: https://stackoverflow.com/a/15720769/1836013
n_fun <- function(x){
return(data.frame(y = max(x), label = paste0("n = ",length(x))))
}
ggplot(df, aes(x = val, group = ab.class)) +
geom_density(aes(fill = ab.class), alpha = 0.4) +
stat_summary(geom = "text", fun.data = n_fun)
However, this fails with Error: stat_summary requires the following missing aesthetics: y.
I also tried adding y = ..density.. within aes() for each of the geom_density() and stat_summary() layers, and in the ggplot() object itself... none of which solved the problem.
I know this could be achieved by manually adding labels for each group, but I was hoping for a solution that generalises, and e.g. allows the label colour to be set via aes() to match the densities.
Where am I going wrong?
The y in the return of fun.data is not the aes. stat_summary complains that he cannot find y, which should be specificed in global settings at ggplot(df, aes(x = val, group = ab.class, y = or stat_summary(aes(y = if global setting of y is not available. The fun.data compute where to display point/text/... at each x based on y given in the data through aes. (I am not sure whether I have made this clear. Not a native English speaker).
Even if you have specified y through aes, you won't get desired results because stat_summary compute a y at each x.
However, you can add text to desired positions by geom_text or annotate:
# save the plot as p
p <- ggplot(df, aes(x = val, group = ab.class)) +
geom_density(aes(fill = ab.class), alpha = 0.4)
# build the data displayed on the plot.
p.data <- ggplot_build(p)$data[[1]]
# Note that column 'scaled' is used for plotting
# so we extract the max density row for each group
p.text <- lapply(split(p.data, f = p.data$group), function(df){
df[which.max(df$scaled), ]
})
p.text <- do.call(rbind, p.text) # we can also get p.text with dplyr.
# now add the text layer to the plot
p + annotate('text', x = p.text$x, y = p.text$y,
label = sprintf('n = %d', p.text$n), vjust = 0)
I have a large matrix mdat (1000 rows and 16 columns) contains first column as x variable and other columns as y variables. What I want to do is to make scatter plot in R having 15 figures on the same window. For example:
mdat <- matrix(c(1:50), nrow = 10, ncol=5)
In the above matrix, I have 10 rows and 5 columns. Is it possible that to use the first column as variable on x axes and other columns as variable on y axes, so that I have four different scatterplots on the same window? Keep in mind that I will not prefer par(mfrow=, because in that case I have to run each graph and then produce them on same window. What I need is a package so that I will give it just data and x, y varaibeles, and have graphs on same windows.
Is there some package available that can do this? I cannot find one.
Perhaps the simplest base R way is mfrow (or mfcol)
par(mfrow = c(2, 2)) ## the window will have 2 rows and 2 columns of plots
for (i in 2:ncol(mdat)) plot(mdat[, 1], mdat[, i])
See ?par for everything you might want to know about further adjustments.
Another good option in base R is layout (the help has some nice examples). To be fancy and pretty, you could use the ggplot2 package, but you'll need to reshape your data into a long format.
require(ggplot2)
require(reshape2)
molten <- melt(as.data.frame(mdat), id = "V1")
ggplot(molten, aes(x = V1, y = value)) +
facet_wrap(~ variable, nrow = 2) +
geom_point()
Alternatively with colors instead of facets:
ggplot(molten, aes(x = V1, y = value, color = variable)) +
geom_point()
#user4299 You can re-write shujaa's ggplot command in this form, using qplot which means 'quick plot' which is easier when starting out. Then instead of faceting, use variable to drive the color. So first command produces the same output as shujaa's answer, then the second command gives you all the lines on one plot with different colors and a legend.
qplot(data = molten, x = V1, y = value, facets = . ~ variable, geom = "point")
qplot(data = molten, x = V1, y = value, color = variable, geom = "point")
Maybe
library(lattice)
x = mdat[,1]; y = mdat[,-1]
df = data.frame(X = x, Y = as.vector(y),
Grp = factor(rep(seq_len(ncol(y)), each=length(x))))
xyplot(Y ~ X | Grp, df)