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I have created a boxplot using the following code -
ggplot(xray50g, aes(x = Company, y = DefScore, label = Batch,
label2 = PercentPopAff, label3 = AvVertAff,
label4 = EggsPerLitreReceiving)) +
geom_boxplot() +
geom_point(aes(colour = Ploidy), size = 0.5) +
geom_jitter() +
# USE ENVSTATS PACKAGE TO INCLUDE SAMPLE SIZE
stat_n_text(size = 3) +
# INCLUDE MEAN VALUES
stat_summary(fun = mean, geom = "point", shape = 4, size = 2, color = "black") +
stat_summary(fun = mean, colour = "black", geom = "text", size = 3, show.legend = FALSE,
hjust = -0.35, vjust = -0.5, aes( label = round(..y.., digits = 2)))
I wanted to spread the data points out a little; however, when I use geom_jitter it seems to blur all the data points together and ruin the chart (see image).
Any help with this would be greatly appreciated.
You can use the width argument of geom_jitter to control how much the points are spread along the x-axis. I'd also recommend making the jittered points transparent (alpha argument) and to stop geom_boxplot from plotting the outliers with the outlier.shape argument (as those points also will be plotted by the jitter layer). Try the following:
ggplot(xray50g, aes(x = Company, y = DefScore, label = Batch,
label2 = PercentPopAff, label3 = AvVertAff,
label4 = EggsPerLitreReceiving)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha = 0.25, width = 0.1)
I'd like to insert median lines for factor levels into a violin plot in ggplot2. Here's some reproducible data:
set.seed(12)
FactorVar <- sample(LETTERS[1:5], 500, replace = T)
NumericVar <- abs(rnorm(500))
df <- data.frame(FactorVar, NumericVar)
To get the grouped medians I use tapply:
medians <- tapply(df$NumericVar, df$FactorVar, FUN = median)
And this is the code for the plot. As can be seen, I'm inserting each median line individually. That's cumbersome and uneconomical:
library(ggplot2)
g <-
ggplot(data = df,
aes(x = FactorVar, y = NumericVar, fill = FactorVar)) +
geom_violin(scale = "count", trim = F, adjust = 0.75) +
geom_point(aes(y = NumericVar),
position = position_jitter(width = .15), size = 0.9, alpha = 0.8) +
geom_hline(yintercept = mean(NumericVar), color = "blue", size = 0.8, linetype = 4) +
geom_segment(x = 0.5, xend = 1.5, y= medians[1], yend = medians[1], color = "red", linetype = 2) +
geom_segment(x = 1.5, xend = 2.5, y = medians[2], yend = medians[2], color = "red", linetype = 2) +
geom_segment(x = 2.5, xend = 3.5, y = medians[3], yend = medians[3], color = "red", linetype = 2) +
geom_segment(x = 3.5, xend = 4.5, y = medians[4], yend = medians[4], color = "red", linetype = 2) +
geom_segment(x = 4.5, xend = 5.5, y = medians[5], yend = medians[5], color = "red", linetype = 2) +
guides(fill = FALSE) +
guides(color = FALSE) +
coord_flip() +
theme_gray(); g
How can the median segments be inserted in a single command? Also, observe how the median line for factor A is thinner than the others? Why's that?
One method (that simplifies the +/- axis) would be to facet it. Before, though, we'll need to put the medians into a frame, preferably with the same grouping factors as the original.
mediansdf <- data.frame(FactorVar=names(medians), NumericVar=medians)
g <-
ggplot(data = df,
aes(x = FactorVar, y = NumericVar, fill = FactorVar)) +
geom_violin(scale = "count", trim = F, adjust = 0.75) +
geom_point(aes(y = NumericVar),
position = position_jitter(width = .15), size = 0.9, alpha = 0.8) +
geom_hline(yintercept = mean(NumericVar), color = "blue", size = 0.8, linetype = 4) +
guides(fill = FALSE) +
guides(color = FALSE) +
coord_flip() +
theme_gray() +
facet_grid(FactorVar~., scales="free") +
geom_segment(aes(x = 0.5, xend = 1.5, yend = NumericVar), color = "red", linetype = 2, data = mediansdf)
g
This example reused the y aesthetic, but since we have a different frame, we could easily use different names (and specify them within aes(...). One advantage to using the same variable names is (in my opinion) clearer declarative code.
Since the facet_grid adds the factor label on the right side, you likely could remove it from the axis. Note, if you do not use scales="free", then you'll see all factors in each facet, which is distracting and unnecessary.
The reason I am suggesting facets is that it makes the x and xend simple and relative to a single violin, so 0.5 to 1.5; otherwise, as you saw, there is some assumption on which is going with which integer placement.
Last, the appearance of thinner red lines for me was while looking at the raster plot window. If you save to vector-based format (e.g., PDF), the lines appear to be the same thickness.
I have data that looks like this:
example.df <- as.data.frame(matrix( c("height","fruit",0.2,0.4,0.7,
"height","veggies",0.3,0.6,0.8,
"height","exercise",0.1,0.2,0.5,
"bmi","fruit",0.2,0.4,0.6,
"bmi","veggies",0.1,0.5,0.7,
"bmi","exercise",0.4,0.7,0.8,
"IQ","fruit",0.4,0.5,0.6,
"IQ","veggies",0.3,0.5,0.7,
"IQ","exercise",0.1,0.4,0.6),
nrow=9, ncol=5, byrow = TRUE))
colnames(example.df) <- c("phenotype","predictor","corr1","corr2","corr3")
So basically three different correlations between 3x3 variables. I want to visualize the increase in correlations as follows:
ggplot(example.df, aes(x=phenotype, y=corr1, yend=corr3, colour = predictor)) +
geom_linerange(aes(x = phenotype,
ymin = corr1, ymax = corr3,
colour = predictor),
position = position_dodge(width = 0.5))+
geom_point(size = 3,
aes(x = phenotype, y = corr1, colour = predictor),
position = position_dodge(width = 0.5), shape=4)+
geom_point(size = 3,
aes(x = phenotype, y = corr2, colour = predictor),
position = position_dodge(width = 0.5), shape=18)+
geom_point(size = 3,
aes(x = phenotype, y = corr3, colour = predictor),
position = position_dodge(width = 0.5))+
labs(x=NULL, y=NULL,
title="Stackoverflow Example Plot")+
scale_colour_manual(name="", values=c("#4682B4", "#698B69", "#FF6347"))+
theme_minimal()
This gives me the following plot:
Problems:
Tthere is something wrong with the way the geom_point shapes are dodged with BMI and IQ. They should be all with on the line with the same colour, like with height.
How do I get an extra legend that can show what the circle, cross, and square represent? (i.e., the three different correlations shown on the line: cross = correlation 1, square = correlation 2, circle = correlation 3).
The legend now shows a line, circle, cross through each other, while just a line for the predictors (exercise, fruit, veggies) would suffice..
Sorry for the multiple issues, but adding the extra legend (problem #2) is the most important one, and I would be already very satisfied if that could be solved, the rest is bonus! :)
See if the following works for you? The main idea is to convert the data frame from wide to long format for the geom_point layer, and map correlation as a shape aesthetic:
example.df %>%
ggplot(aes(x = phenotype, color = predictor, group = predictor)) +
geom_linerange(aes(ymin = corr1, ymax = corr3),
position = position_dodge(width = 0.5)) +
geom_point(data = . %>% tidyr::gather(corr, value, -phenotype, -predictor),
aes(y = value, shape = corr),
size = 3,
position = position_dodge(width = 0.5)) +
scale_color_manual(values = c("#4682B4", "#698B69", "#FF6347")) +
scale_shape_manual(values = c(4, 18, 16),
labels = paste("correlation", 1:3)) +
labs(x = NULL, y = NULL, color = "", shape = "") +
theme_minimal()
Note: The colour legend is based on both geom_linerange and geom_point, hence the legend keys include both a line and a point shape. While it's possible to get rid of the second one, it does take some more convoluted code, and I don't think the plot would be much improved as a result...
I am trying to plot using ggplot and trying to set the transparency, size and shape for geom_point using a binary variable in my dataset.
For example, if binary_variable == 1 then set the size to 1, shape = triangle, transparency = 0.2, if binary_variable == 0 set the size to 0.5 etc.
I have been able to make the colour change as follows:
library(ggplot2)
df <- data.frame(variable1 = 1:5,
variable2 = 1:5,
binary = c(0,0,0,1,1))
ggplot(df, aes(x = variable1, y = variable2, colour = as.factor(binary))) +
geom_point(size = 2, alpha = 0.3) +
scale_colour_manual(values = c("grey", "black"), labels = c("cat1", "cat2")) +
theme_bw()
You can control shape, colour and aesthetics in the same way using the scale_X_manual functions. See the help page for all the different ways these can be controlled.
The key part to make this work though is to make sure that you added the variable you want to control to the aes part of the ggplot function.
Here is an example:
df$binary <- as.factor(df$binary)
ggplot(df, aes(x = variable1, y = variable2, colour = binary, shape = binary, alpha = binary)) +
geom_point(size = 2) +
scale_colour_manual(values = c("blue", "red")) +
scale_shape_manual(values=c(16,17)) +
scale_alpha_manual(values=c(1, 0.5)) +
theme_bw()
I'm trying to create a scatterplot where the points are jittered (geom_jitter), but I also want to create a black outline around each point. Currently I'm doing it by adding 2 geom_jitters, one for the fill and one for the outline:
beta <- paste("beta == ", "0.15")
ggplot(aes(x=xVar, y = yVar), data = data) +
geom_jitter(size=3, alpha=0.6, colour=my.cols[2]) +
theme_bw() +
geom_abline(intercept = 0.0, slope = 0.145950, size=1) +
geom_vline(xintercept = 0, linetype = "dashed") +
annotate("text", x = 2.5, y = 0.2, label=beta, parse=TRUE, size=5)+
xlim(-1.5,4) +
ylim(-2,2)+
geom_jitter(shape = 1,size = 3,colour = "black")
However, that results in something like this:
Because jitter randomly offsets the data, the 2 geom_jitters are not in line with each other. How do I ensure the outlines are in the same place as the fill points?
I've see threads about this (e.g. Is it possible to jitter two ggplot geoms in the same way?), but they're pretty old and not sure if anything new has been added to ggplot that would solve this issue
The code above works if, instead of using geom_jitter, I use the regular geom_point, but I have too many overlapping points for that to be useful
EDIT:
The solution in the posted answer works. However, it doesn't quite cooperate for some of my other graphs where I'm binning by some other variable and using that to plot different colours:
ggplot(aes(x=xVar, y = yVar, color=group), data = data) +
geom_jitter(size=3, alpha=0.6, shape=21, fill="skyblue") +
theme_bw() +
geom_vline(xintercept = 0, linetype = "dashed") +
scale_colour_brewer(name = "Title", direction = -1, palette = "Set1") +
xlim(-1.5,4) +
ylim(-2,2)
My group variable has 3 levels, and I want to colour each group level by a different colour in the brewer Set1 palette. The current solution just colours everything skyblue. What should I fill by to ensure I'm using the correct colour palette?
You don't actually have to use two layers; you can just use the fill aesthetic of a plotting character with a hole in it:
# some random data
set.seed(47)
df <- data.frame(x = rnorm(100), y = runif(100))
ggplot(aes(x = x, y = y), data = df) + geom_jitter(shape = 21, fill = 'skyblue')
The colour, size, and stroke aesthetics let you customize the exact look.
Edit:
For grouped data, set the fill aesthetic to the grouping variable, and use scale_fill_* functions to set color scales:
# more random data
set.seed(47)
df <- data.frame(x = runif(100), y = rnorm(100), group = sample(letters[1:3], 100, replace = TRUE))
ggplot(aes(x=x, y = y, fill=group), data = df) +
geom_jitter(size=3, alpha=0.6, shape=21) +
theme_bw() +
geom_vline(xintercept = 0, linetype = "dashed") +
scale_fill_brewer(name = "Title", direction = -1, palette = "Set1")