So created a loadings plot via arrow style using ggplot command. In order to make things easier for graphing, I added a column into the dataframe of my rr.pr$rotation code with colours so that it graphs those arrows based on the colour I specified. The colours that match the arrows are important which is why I did it that way. I am having trouble now adding a legend as ggplot isn't adding a legend.
Is there a way to add one or do I have to do something to the dataframe?
I was thinking of adding the colours manually, but I am getting stuck.
Green represents Sulfated, Orange represents Sialyllated, and Brown represents Neutral. And I would like the legend to show that.
Here is the code:
Dataframe
rrload<-data.frame(rr.pr$rotation[c(2,15,17,24,52),c(1:5)])
rrload$class<-c('orange','springgreen3','bisque3','bisque3','bisque3')
rrload1<-rrload[,c(1:5)]
rrload1<-as.numeric(as.matrix(rrload1))
rrload1<-matrix(rrload1,nrow=5,ncol=5,byrow = F)
rrload[,c(1:5)]<-rrload1
Code for plotting it:
ggplot(rrload)+geom_segment(aes(xend=PC1,yend=PC2),x=0,y=0,arrow = arrowstyle2,color=rrload$class)+
geom_text(aes(x=PC1,y=PC2,label=row.names(rrload)),hjust=0,nudge_x = -0.05,vjust=1,nudge_y = 0.025,size=3.5,color='black')+xlim(-0.3,0.3)+ylim(-0.3,0.3)+theme_light()+
theme_minimal()+theme(legend.title = element_text("Class"),axis.text.x = element_text(colour = "black",size = 10),axis.text.y = element_text(colour = "black",size = 10),axis.title.x = element_text(colour = "black",size = 10),axis.title.y = element_text(colour = "black",size = 10),axis.ticks = element_line(color = "black"),panel.grid = element_blank(), panel.border = element_rect(colour = "black",fill = NA,size = 1))+geom_hline(yintercept = 0,linetype="dashed",color="gray69")+geom_vline(xintercept = 0,linetype="dashed",color="gray69")
This is the graph:
Loadings plot
Without access to your full data (your code is unable to recreate the dataframe, rrload properly), it's hard to help. I managed to estimate the numbers based on the plot you shared. Here's the dataframe I used - note the naming conventions for the columns:
d <- data.frame(
PC1=c(-0.2,-0.2,0.1,0.15,-0.08),
PC2=c(0.13,-0.1,0.2,0.1,-0.2300),
class=c('Neutral','Neutral','Neutral','Sulfated','Silylated'),
name=c('o53','o18','o25','o15','o2')
)
To prepare the data for plotting, I included d$name and d$class. d$class is similar to the column you had, although instead of the color, I'm using the actual name. d$name is the name that I'm using to plot your labels.
Here's the code I used and resulting plot. Explanation will come after:
library(ggrepel)
ggplot(d) + theme_classic() +
geom_vline(xintercept=0, linetype=2, color='gray60') +
geom_hline(yintercept=0, linetype=2, color='gray60') +
geom_segment(
aes(xend=PC1,yend=PC2, color=class), x=0,y=0,
arrow=arrow(type='closed', angle=20, length=unit(0.02,'npc'))
) +
geom_text_repel(
aes(x=PC1, y=PC2, label=name), force=6, min.segment.length = 10, seed=123
) +
ylim(-0.3,0.3) + xlim(-0.3,0.3) +
scale_color_manual(
name='Legend Title',
values=c('Neutral'='bisque3','Sulfated'='springgreen3','Silylated'='orange'))
ggplot2 will create a legend for certain aesthetics, but they must be placed within aes(). Once you do that, ggplot2 will create the legend and automatically assign colors. This means that if we want to create a legend for color=, you need to put it within aes(). The interesting part is that you can put it within aes() anywhere in the call, or just apply to specific geom/geoms. This allows a lot of flexibility in creating your plot. In this case, I only want to color the arrows, so you include color=class within the geom_segment() call. If you put it within the ggplot() call, it would color both the line segment as well as the text geom.
I'm also paying attention to the ordering. We want to make sure the background dotted lines for the central axis at 0,0 are "behind" everything, so they go first. Then the segments, and then the text geom.
The scale_color_manual() function is used to specify the colors for the different d$class values explicitly and the name of the legend. You can also just let ggplot2 find a palette by default, or you can specify via a palette (there are a ton of other methods to specify color). BTW - you can also specify the name of the legend via labs(color=....
Finally, I decided to use geom_text_repel() rather than geom_text(). Since the lines go out in every direction, the "nudge" values for each text item are not going to work going in the same direction. In other words, if you plot the text at x=PC1, y=PC2, it will overlap the arrowheads. You noticed this too and applied nudge_ values, which happens to work, but if your data was a bit different, it would not have worked. geom_text_repel from the ggrepel package can work to do this by kind of "pushing" the text away from your points.
Related
In ggplot2 , I want to plot boxplot+dotplot side by side as attached image. But the code can't work, anyone can help? this code from 'R graphic cookbook'. Thanks!
library(gcookbook)
library(tidyverse)
ggplot(heightweight,aes(x=sex,y=heightIn ))+
geom_boxplot(aes(x=as.numeric(sex)+0.2),group=sex)+
geom_dotplot(aes(x=as.numeric(sex)-0.2),group=sex,
binaxis = "y",stackdir = 'center',
binwidth = 0.5)
This is a very interesting question. OP is looking to dodge geoms along the x axis, which is not typically difficult to do. The difficulty here lies in that you are dodging the same data using different geoms.
What you can do is use a bit of clever formatting, mapping, and faceting to recreate an example of the type of plot OP shows. For this example solution, I am using the built-in dataset, iris. In the future, OP, please be sure to provide a reproducible example using a built-in dataset, your data, or a sample of your data.
Here's the basic plot showing a dotplot on top of a box plot below - I'll be trying to split the boxplot on the right and dotplot on the left.
ggplot(iris, aes(x=Species, y=Sepal.Width)) +
geom_boxplot(width=0.3) +
geom_dotplot(binaxis = 'y', binwidth=0.04, stackdir = "center")
Dodging is the act of splitting an aesthetic across a specific geom according to a value of a particular column in your data frame. Basically, it means you can have two boxplots next to one another, two points, etc - each one colored or represented differently according to a value for another column in your data. We cannot use dodging to move the boxplot alongside the dotplot because dodging only works across the same geom. You can have two boxplots next to one another for the same specified value of x... but not a boxplot and a dotplot.
The solution here is to draw our geoms individually - effectively "manually" doing the dodging. I can't specify a segment within a specific x value (like "x right" vs. "x left"), so the only way to make this work in my mind is to use faceting to create the actual x positions in the dataset, and the positional information for the dodging is going to be specified in the plot using the x axis. This means each value in x (in this example, each Species) will be kind of a mini plot - dotplot on the left and boxplot on the right.
Here's the code and result:
ggplot(iris, aes(x=positional, y=Sepal.Width)) +
geom_dotplot(aes(x = "1"), binaxis="y", binwidth=0.04, stackdir="center") +
geom_boxplot(aes(x = "2"), width=0.6) +
facet_wrap(~Species, strip.position = "bottom") +
theme(
# panel.spacing.x = unit(0, "npc"),
strip.background = element_blank(),
strip.text = element_text(size=12),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()
)
What's going on here? Well, you'll notice that I'm mapping x to a new "column" in the dataset called "positional". This column does not exist in the dataset, so I define it separately for geom_boxplot() and geom_dotplot(). You have to do this in aes(), since it's required for mapping, but if you map in aes() to a constant value, the plot will be created as if every observation is set at that value. This is useful, because this creates our dotplot on the left (where positional == "1" and our boxplot on the right (where positional == "2").
The rest of the code is just theme stuff and creating the facets. Note that I use strip.placement to move the facet labels to the bottom, then remove all the other axis elements so that our facet labels take the place as the new axis label.
Finally, you can either keep the spacing between the facets (I kind of like it), or you can also remove that by using another theme() element. Adding theme(panel.spacing = unit(0, "npc")) gives you:
thanks for your help.
I am making a simple boxplot with p value markers and a legend in r using ggplot() and aes(). This generates a legend with boxplots as markers- which is what I was trying to do- but the legend includes the letter 'a' inside the markers. screenshot of the legend here
I don't want the letter to show up in the legend- just the boxplot marker.
I've found several solutions to this problem on stack overflow that involve setting show.legend to False in geomtext(). However, I didn't use geomtext() when making the plot. I tried adding a line with geomtext(show.legend = False) in the call to make the boxplot. I tried adding show.legend = FALSE inside of aes() and inside theme() and theme_classic(), but those solutions did not work.
I gather that it's showing some kind of text legend as well as the boxplot marker- how do I remove that legend? Am I using geomtext() incorrectly or is there another way?
Here's the code for the boxplot-
p<-ggplot(data=df,aes(x = welldata,y =efficiency, color = yeardata))+
geom_boxplot(outlier.size = 0.8)+
scale_color_manual(values=c("cornflowerblue","goldenrod1"))+
labs(color="Year")+scale_x_discrete(name= "Distance From Creek (m)")+
scale_y_continuous(name="Fraction of Tidal Influence")+
ggtitle("Fraction of Tidal Influence vs Distance From Tidal Creek")+
theme_classic()+
theme(plot.title = element_text(hjust = 0.5),
legend.position = "bottom", legend.text=element_text(size=12))
p+stat_compare_means(aes(group=year),label = "p.signif")
df%>%group_by(year,well)%>% get_summary_stats(efficiency, type="mean_sd")
df%>%group_by(year,well)%>% get_summary_stats(efficiency, type="mean_sd")
I am working with data that I am turning into a tornado diagram that in the end I want to look something like this:
Currently my data looks like this:
Using facet_wrap I get a result like this but want it all on one graph to look like the sample at the start:
ggplot(data = df, aes(x = Person)) +
geom_crossbar(aes(y=Mean, ymin= Min, ymax = Max)) +
coord_flip() +
facet_wrap(~Letter, strip.position = "left", scales = "free_x") +
theme(panel.spacing = unit(0, "lines"), strip.background = element_blank(), strip.placement = "outside")
Is there any way to do this in ggplot?
Sorry I had to include the images as links!
How about this? See the code and result below with some explanations after.
# reorder levels in order of appearance
df$Person <- factor(df$Person, levels=unique(df$Person))
ggplot(df, aes(x=Letter)) +
geom_crossbar(
aes(y=Mean, ymin=Min, ymax=Max),
fill='dodgerblue2', width=0.8
) +
facet_grid(Person~., scales='free', space='free', switch = 'y') +
scale_y_continuous(expand=expansion(mult=c(0.3,0.3))) +
labs(x=NULL, y='Axis Label Here') +
theme_classic() +
theme(
strip.placement = 'outside',
strip.background = element_rect(color=NA, fill=NA),
strip.text.y=element_text(angle=90, size=12),
panel.spacing = unit(0,'pt'),
panel.background = element_rect(color='black')
) +
coord_flip()
Now, for some explanation, where I'll step through the code from top to bottom, calling out specific changes/adjustments as they come up.
Re-leveling Person Factor. The purpose here is to ensure that the order we see the listing of "Persons" matches the order in which they are listed in the dataset. You can list them in any order, of course, but the default for characters/strings is: If it is a factor, then the order = the ordering of the levels. If it is not a factor, the order = alphabetical.
Overall Adjustment for facets. Given that each df$Person has one or more df$Letters associated, and given your example plot, it seems that you actually want to have facets be df$Person, with each having an x aesthetic for df$Letter.
Facet_grid. I use facet_grid() instead of facet_wrap(), since it offers more control. If you use the . ~ facet or facet ~ . notation, it acts just like facet_wrap(), except it will not "wrap" around. There are three critical arguments that are only all available via facet_grid():
scales=. This will remove the extra space in each facet that is not used. Since not every df$Person has the same amount of df$Letter associated, this is very important.
space=. By default, the space occupied by each facet is kept constant. This means that if one name has 3 letters and another facet has only 1, the width of the bars in each facet will be smaller in the one with 3 vs the one bar in the facet with only 2. Setting space="free" allows for all widths to be constant: it's the facet size that is "free" to adjust to the bar - not the other way around.
switch=. This allows for the strip placement (facet label) to be on the opposite side. It doesn't place it outside though...
Expanding the y scale. This is purely aesthetic. I'm trying to match what you show, which has extra space around the bars.
Theme Elements. There's a decent amount going on here, but basically I'm putting the facet label outside (strip.placement) and removing the box that goes around it usually (strip.background). I also smoosh the facets together (panel.spacing) and decided it was easier to view when you drew some lines between the facets (panel.background).
Some other things would be purely aesthetic, but I think this gets you close to your desired result. If you want to include the information / text... that's a different matter.
I'm making many plots and want to set a default color for the data labels without having to pass the color argument to every geom_text call. I can do it for the plot titles and axes, but not the data labels.
# Example of how to set default color for other text elements
library(ggplot2)
theme_set(theme_bw() + theme(text = element_text(color = "red"),
axis.text = element_text(color = "red")))
ggplot(mtcars, aes(x = cyl, label = ..count..)) +
geom_bar() +
geom_text(stat = "count") +
labs(title = "title")
Unfortunately, I believe theme elements are only intended to apply to non-data-related elements of the plot, meaning the theme does not cover the text in geom_text. The default color "black" is hard-coded in the source of geom_text, so as far as I know, there's not a simple way to override it. (Though, if someone cares to correct me, excellent!)
However, one simple solution that may help streamline things is to create a wrapper function that will return a geom_text with all the defaults that you will be passing over and over. For example:
geom_text_wrap <- function(col="red", ...) {
geom_text(col=col, ...)
}
can be used in place of geom_text directly, and will, by default, create red text. So the following will create red text without you having to specify it directly in the plot creation.
ggplot(mtcars, aes(x = cyl, label = ..count..)) +
geom_bar() +
geom_text_wrap(stat="count") +
labs(title = "title")
Note: If you really are creating a ton of similar plots to the point that you are tiring of specifying repetitive arguments, you may consider writing a function that will create the complete graph objects programmatically. That will depend on your specific use-case.
I've only very recently started learning R. Now what I'm trying to do is to integrate two legends for the same plot. In other words, I want the default size legend to change color depending on it's size.
I have been Googling several solutions that apparently all don't seem to work, but again, I'm new to R so maybe I'm just doing something wrong.
My code:
ggplot(Caschool, aes(x=testscr, y=avginc), colour="green") +
geom_point(aes(size=enrltot, color=enrltot)) +
geom_smooth(colour="blue") +
labs(x="Test Score", y="Average Income", title="California Test Score Data", color="Number of Students\nPer District") +
theme(
panel.grid.minor = element_blank(),
panel.grid.major=element_line(colour="grey", size=0.4),
panel.background=element_rect(fill="beige"),
axis.line=element_line(size = 1.2, colour = "black"),
plot.title = element_text(size = rel(2))) +
scale_color_continuous(limits=c(0, 30000), breaks=seq(0,30000, by=2500)) +
guides(color= guide_legend(), size=guide_legend())
Apparently, I'm not allowed to post pictures, or I would have shown what this looks like so far.
ggplot2 can indeed combine size and colour legends into one, however, this only works, if they are compatible: they need to have exactly the same breaks, otherwise they can not be combined.
Let me make an example: Assume, you have values between 0 and 10 that you want to map on size and colour. You tell ggplo2 to use small points for values below 5 and large points for larger value. It will then plot a legend with a small and a large point, as expected. Now, you also want to add colour and you require points below 3 to be green and points above to be blue. ggplot2 will also draw a legend for this, but it is impossible to combine the two legends. The small point would have to be both, green and blue. The problem can be solved by using the same breaks for colour and size.
In your example, you manually change the breaks of the colour scale, but not those of the size scale. This results in incompatible legends that can not be combined.
I can not demonstrate this using your date, because I don't have it. So I will create an example with mtcars. The variant with incompatible legends is constructed as follows:
p <- ggplot(mtcars, aes(x=mpg, y=drat)) +
geom_point(aes(size=gear, color=gear)) +
scale_color_continuous(limits=c(2, 5), breaks=seq(2, 5, by=0.5)) +
guides(color= guide_legend(), size=guide_legend())
which gives the following plot:
If I now add the same breaks for size,
p + scale_size_continuous(limits=c(2, 5), breaks=seq(2, 5, by=0.5))
I get a plot with only one legend:
For your code, this means that you should add the following to your plot:
+ scale_size_continuous(limits=c(0, 30000), breaks=seq(0,30000, by=2500))
A little side remark: What do you intend by using colour = "green" in your call to ggplot? I don't see that this has any effect at all, because you set the colour again in both geoms that you use later. Maybe a relic from an older variant of the plot?