Hello I Have a plot showing a PCA reuslt, the issue is that I want to show how the points behave in the plot accoriding to two variables, let's say sample date and depth of sampling:
I use autoplot for this porpuse:
autoplot(PCA_results, data = data1, colour = 'sample_date' ,
shape = 'depth_m', main = "PCA for samples") + scale_shape_identity()
I had to use scale_shape_identity() due to the fact that the "depth_m" variable have two continuous values (5 meters and 20 meters) and was not acepted without scale_shape_identity()
the resulting plot is the following one:
So in addition to showing sample_date variable in the legend I want to display the depth one too, how can I do this ?
The description of the help menu of scale_shape_identity() appoints that "These scales will not produce a legend unless you also supply the breaks, labels, and type of guide you want." But I'm not sure how to provide the labels, breaks and type of guide.
Related
I have used the following code to generate a plot with ggplot:
I want the legend to show the runs 1-8 and only the volumes 12.5 and 25 why doesn't it show it?
And is it possible to show all the points in the plot even though there is an overlap? Because right now the plot only shows 4 of 8 points due to overlap.
OP. You've already been given a part of your answer. Here's a solution given your additional comment and some explanation.
For reference, you were looking to:
Change a continuous variable to a discrete/discontinuous one and have that reflected in the legend.
Show runs 1-8 labeled in the legend
Disconnect lines based on some criteria in your dataset.
First, I'm representing your data here again in a way that is reproducible (and takes away the extra characters so you can follow along directly with all the code):
library(ggplot2)
mydata <- data.frame(
`Run`=c(1:8),
"Time"=c(834, 834, 584, 584, 1184, 1184, 938, 938),
`Area`=c(55.308, 55.308, 79.847, 79.847, 81.236, 81.236, 96.842, 96.842),
`Volume`=c(12.5, 12.5, 12.5, 12.5, 25.0, 25.0, 25.0, 25.0)
)
Changing to a Discrete Variable
If you check the variable type for each column (type str(mydata)), you'll see that mydata$Run is an int and the rest of the columns are num. Each column is understood to be a number, which is treated as if it were a continuous variable. When it comes time to plot the data, ggplot2 understands this to mean that since it is reasonable that values can exist between these (they are continuous), any representation in the form of a legend should be able to show that. For this reason, you get a continuous color scale instead of a discrete one.
To force ggplot2 to give you a discrete scale, you must make your data discrete and indicate it is a factor. You can either set your variable as a factor before plotting (ex: mydata$Run <- as.factor(mydata$Run), or use code inline, referring to aes(size = factor(Run),... instead of just aes(size = Run,....
Using reference to factor(Run) inline in your ggplot calls has the effect of changing the name of the variable to be "factor(Run)" in your legend, so you will have to also add that to the labs() object call. In the end, the plot code looks like this:
ggplot(data = mydata, aes(x=Area, y=Time)) +
geom_point(aes(color =as.factor(Volume), size = Run)) +
geom_line() +
labs(
x = "Area", y = "Time",
# This has to be changed now
color='Volume'
) +
theme_bw()
Note in the above code I am also not referring to mydata$Run, but just Run. It is greatly preferable that you refer to just the name of the column when using ggplot2. It works either way, but much better in practice.
Disconnect Lines
The reason your lines are connected throughout the data is because there's no information given to the geom_line() object other than the aesthetics of x= and y=. If you want to have separate lines, much like having separate colors or shapes of points, you need to supply an aesthetic to use as a basis for that. Since the two lines are different based on the variable Volume in your dataset, you want to use that... but keep the same color for both. For this, we use the group= aesthetic. It tells ggplot2 we want to draw a line for each piece of data that is grouped by that aesthetic.
ggplot(data = mydata, aes(x=Area, y=Time)) +
geom_point(aes(color =as.factor(Volume), size = Run)) +
geom_line(aes(group=as.factor(Volume))) +
labs(
x = "Area", y = "Time", color='Volume'
) +
theme_bw()
Show Runs 1-8 Labeled in Legend
Here I'm reading a bit into what you exactly wanted to do in terms of "showing runs 1-8" in the legend. This could mean one of two things, and I'll assume you want both and show you how to do both.
Listing and showing sizes 1-8 in the legend.
To set the values you see in the scale (legend) for size, you can refer to the various scale_ functions for all types of aesthetics. In this case, recall that since mydata$Run is an int, it is treated as a continuous scale. ggplot2 doesn't know how to draw a continuous scale for size, so the legend itself shows discrete sizes of points. This means we don't need to change Run to a factor, but what we do need is to indicate specifically we want to show in the legend all breaks in the sequence from 1 to 8. You can do this using scale_size_continuous(breaks=...).
ggplot(data = mydata, aes(x=Area, y=Time)) +
geom_point(aes(color =as.factor(Volume), size = Run)) +
geom_line(aes(group=as.factor(Volume))) +
labs(
x = "Area", y = "Time", color='Volume'
) +
scale_size_continuous(breaks=c(1:8)) +
theme_bw()
Showing all of your runs as points.
The note about showing all runs might also mean you want to literally see each run represented as a discrete point in your plot. For this... well, they already are! ggplot2 is plotting each of your points from your data into the chart. Since some points share the same values of x= and y=, you are getting overplotting - the points are drawn over top of one another.
If you want to visually see each point represented here, one option could be to use geom_jitter() instead of geom_point(). It's not really great here, because it will look like your data has different x and y values, but it is an option if this is what you want to do. Note in the code below I'm also changing the shape of the point to be a hollow circle for better clarity, where the color= is the line around each point (here it's black), and the fill= aesthetic is instead used for Volume. You should get the idea though.
set.seed(1234) # using the same randomization seed ensures you have the same jitter
ggplot(data = mydata, aes(x=Area, y=Time)) +
geom_jitter(aes(fill =as.factor(Volume), size = Run), shape=21, color='black') +
geom_line(aes(group=as.factor(Volume))) +
labs(
x = "Area", y = "Time", fill='Volume'
) +
scale_size_continuous(breaks=c(1:8)) +
theme_bw()
I have 5 plots for 5 different groups. I want to indicate a statistically significant difference a specific time points. I used annotate() to place asterisks in individual plots above the time points. However, when I combine all the plots together to make one figure, the asterisks get pushed off the plots. It looks like it is a problem with the y scales not being fixed. I'm providing as much data as I feel comfortable with. The first bit of code is for one of the groups. The plots all look relatively similar for the 5 groups. The second bit is the data frame I am using to combine the plots. Pictures attached of one plot by itself, then all plots combined. There should be multiple asterisks on multiple plots
ggplot(data,aes(X,Y,group=Group,color=Group))+
theme_bw()+
theme(panel.grid.major=element_line(color="white",size=.1))+
theme(panel.grid.minor=element_line(color="white",size=.1))+
geom_point(stat="summary")+
geom_errorbar(stat="summary",fun.data=mean_se,width=0.25)+
geom_line(stat="summary")+
scale_color_manual(labels = c("C", "T"),values=c("black", "red"))+
theme(axis.title.y = element_text(vjust=2.5))+
annotate("text", x=5, y=3, label= "*",size=10)
grid.newpage()
grid.draw(rbind(ggplotGrob(plotanimal1),
ggplotGrob(plotanimal2),
ggplotGrob(plotanimal3),
ggplotGrob(plotanimal4),
ggplotGrob(plotanimal5)))
You can make the asterisks by using geom_point with shape = 42. That way, ggplot will automatically fix the y axis values itself. You need to set the aesthetics at the same values you would have with annotate. So instead of
annotate("text", x=5, y=3, label= "*",size=10)
You can do
geom_point(aes(x=5, y=3), shape = 42, size = 2)
Have you tried using the package patchwork to organize the plots? It typically works better than grid.draw
I am doing kmeans clustering on a png image and have been plotting it using grid::grid.raster(image). But I would like to put a legend which shows the intensity in a bar(from blue to red) marked with values, essentially indicating the intensity on the image. (image is an array where the third dimension equals 3 giving the red, green and blue channels.)
I thought of using grid.legend() but couldn't figure it out. I am hoping the community can help me out. Following is the image I have been using and after I perform kmeans clustering want a legend beside it that displays intensity on a continuous scale on a color bar.
Also I tried with ggplot2 and could plot the image but still couldn't plot the legend. I am providing the ggplot code for plotting the image. I can extract the RGB channels separately using ggplot2 also, so showing that also helps.
colassign <- rgb(Kmeans2#centers[clusters(Kmeans2),])
library(ggplot2)
ggplot(data = imgVEC, aes(x = x, y = y)) +
geom_point(colour = colassign) +
labs(title = paste("k-Means Clustering of", kClusters, "Colours")) +
xlab("x") +
ylab("y")
Did not find a way to use grid.raster() properly but found a way to do it by ggplot2 when plotting the RGB channels separately. Note: this only works for plotting the pannels separately, but this is what I needed. Following shows the code for green channel.
#RGB channels are respectively stored in columns 1,2,3.
#x-axis and y-axis values are stored in columns 4,5.
#original image is a nx5 matrix
ggplot(original_img[,c(3,4,5)], aes(x, y)) +
geom_point(aes(colour = segmented_img[,3])) +
scale_color_gradient2()+
# scale_color_distiller(palette="RdYlBu") can be used instead of scale_color_gradient2() to get color selections of choice using palette as argument.
I've tried using xyplot, symbols, and plot and am not getting exactly what I'm looking for. Basically, I have x and y data that I want to plot on a log scale. Each data point will be a solid filled circle, with the circle size dependent on a numerical variable (z), and the color based on a categorical value (w). Here are things I've tried:
radius <- sqrt(z / pi)
symbols(x, y, circles=radius, inches = 0.35)
Adding in a log scale and color completely threw it off. Basically trying to do something like this (http://flowingdata.com/2010/11/23/how-to-make-bubble-charts/) minus labels, with log scales and colored by a categorical variable (w).
I also tried xyplot and plot (using cex for size of points), but couldn't quite get what I was looking for either...can anyone point me in the right direction? Just starting off learning R and appreciate the help!
You can do something like this in ggplot:
ggplot(d)+
geom_point(aes(x=Expectancy,y=Fertility,size=Population,colour=Region),alpha=0.8)+
theme_bw()
To do it with log scales you would simply add :
+ scale_y_log10() + scale_x_log10()
I am making a graph in ggplot2 consisting of a set of datapoints plotted as points, with the lines predicted by a fitted model overlaid. The general idea of the graph looks something like this:
names <- c(1,1,1,2,2,2,3,3,3)
xvals <- c(1:9)
yvals <- c(1,2,3,10,11,12,15,16,17)
pvals <- c(1.1,2.1,3.1,11,12,13,14,15,16)
ex_data <- data.frame(names,xvals,yvals,pvals)
ex_data$names <- factor(ex_data$names)
graph <- ggplot(data=ex_data, aes(x=xvals, y=yvals, color=names))
print(graph + geom_point() + geom_line(aes(x=xvals, y=pvals)))
As you can see, both the lines and the points are colored by a categorical variable ('names' in this case). I would like the legend to contain 2 entries: a dot labeled 'Data', and a line labeled 'Fitted' (to denote that the dots are real data and the lines are fits). However, I cannot seem to get this to work. The (awesome) guide here is great for formatting, but doesn't deal with the actual entries, while I have tried the technique here to no avail, i.e.
print(graph + scale_colour_manual("", values=c("green", "blue", "red"))
+ scale_shape_manual("", values=c(19,NA,NA))
+ scale_linetype_manual("",values=c(0,1,1)))
The main trouble is that, in my actual data, there are >200 different categories for 'names,' while I only want the 2 entries I mentioned above in the legend. Doing this with my actual data just produces a meaningless legend that runs off the page, because the legend is trying to be a key for the colors (of which I have way too many).
I'd appreciate any help!
I think this is close to what you want:
ggplot(ex_data, aes(x=xvals, group=names)) +
geom_point(aes(y=yvals, shape='data', linetype='data')) +
geom_line(aes(y=pvals, shape='fitted', linetype='fitted')) +
scale_shape_manual('', values=c(19, NA)) +
scale_linetype_manual('', values=c(0, 1))
The idea is that you specify two aesthetics (linetype and shape) for both lines and points, even though it makes no sense, say, for a point to have a linetype aesthetic. Then you manually map these "nonsense" aesthetics to "null" values (NA and 0 in this case), using a manual scale.
This has been answered already, but based on feedback I got to another question (How can I fix this strange behavior of legend in ggplot2?) this tweak may be helpful to others and may save you headaches (sorry couldn't put as a comment to the previous answer):
ggplot(ex_data, aes(x=xvals, group=names)) +
geom_point(aes(y=yvals, shape='data', linetype='data')) +
geom_line(aes(y=pvals, shape='fitted', linetype='fitted')) +
scale_shape_manual('', values=c('data'=19, 'fitted'=NA)) +
scale_linetype_manual('', values=c('data'=0, 'fitted'=1))