Unfortunately, I think this is a tough item to reproduce, but I think the question should be simple enough to answer with a visual...
I'd like to build a legend for three specific dimensions in geom_point.
Any Hockey Fans Out There?
I'd like to build a legend for the dimensions that have colors on this chart. They are three different players I'd like to highlight, the rest of the points on the plot being general noise, but necessary for a visual.
In my opinion, here a legend would be more appealing than labels.
I know this is kind of ridiculous without being able to reproduce, but I hope the question is general enough (though I couldn't find an answer that satisfied what I was looking for) that it can be easily solved.
Happy to field questions.
Thanks!
Solved... the code looks like this:
library(ggplot2)
Offense <- read.csv("Offense1.csv")
plot <- ggplot(Offense[Offense$Gm>20,], aes(CF.Rel, SCF.Rel)) + geom_point() +
geom_point(data=Offense[Offense$Name == "Eric.Staal",], aes(colour="Eric Staal"), size=4) +
geom_point(data=Offense[Offense$Name == "Rick.Nash",], aes(colour="Rick Nash"), size=4) +
geom_point(data=Offense[Offense$Name == "Tanner.Glass",], aes(colour="Tanner Glass"), size=4)
plot <- plot + labs(title = "Driving Offense",
x = "Relative Corsi For %",
y= "Relative Scoring Chances For %")
plot <- plot + scale_colour_discrete(name="Player")
plot
They key here was to make the aesthetic color the dimension you want to include in the legend.
Related
I have recently been playing around with various plot types using fictitious data to get my head around how I could display various pieces of information. One plot type that is gaining popularity is the so called individual differences dot plot which shows the change in each subjects score pre-post. The plot is fairly easy to produce, but my issue is that when I go to change the labels using either the labs or xlab ylab functions in ggplot, the plot itself becomes messed up. Below I have attached the fictitious data, the code used and the results.
Data
df<- data.frame(Participant<- c(rep(1:10,2)), Score<- c(rnorm(20,100,5)), Session<- c(1,1,1,1,1,1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2))
colnames(df) <- c("Participant", "Score", "Session")
Code for plot
p<- ggplot(df, aes(x=df$Session, y=df$Score, colour=df$Participant))+ geom_point()+
geom_line(group=df$Participant)+
theme_classic()
Plot
Individual difference plot
My dilemma is that anytime I try to change the label names, the plot messes up as per below.
Problem
p + xlab("Session") + ylab("Score")
Plot after relabelling
The same thing happens if I try the labs function i.e, p + labs(x= "Session", y= "Score"). You can see that the labels themselves do actually change, but for some reason this messes up the actual plot. Does any have any ideas as to what could be going wrong here?
The issue appears to be the grouping is undone when the label functions are called. Instead, issue the grouping as an aesthetic mapping:
library(dplyr); library(ggplot)
df %>% mutate(across(c(Session,Participant),factor)) -> df
p <- ggplot(df, aes(x=Session, y=Score, colour=Participant))+ geom_point()+
geom_line(aes(group=Participant))+
theme_classic()
p + xlab("Session") + ylab("Score")
I suspect this is probably a bug.
I'm hoping to get some help on making the following histogram looks as nice and understandable as possible. I am plotting the salaries of Immigrant versus US Born workers. I am wondering
1. How would you modify colors, axis intervals, etc. to make the graph more clear/appealing?
2. How could I add a key to indicate purple is for US born workers, and pink is for foreign born?
3. How can I add two different lines to indicate the median of each group? And a corresponding label for each?
My current code is set up as this:
ggplot(NHIS1,aes(x=adj_SALARY, y=..density..)) +
geom_histogram(data=subset(NHIS1,IMMIGRANT=='0'), alpha=.5,binwidth=800, fill="purple",position="identity") + xlim(4430.4,50000) +
geom_vline(xintercept=median(NHIS1$adj_SALARY), col="black", linetype="dashed") +
geom_histogram(data=subset(NHIS1,IMMIGRANT=='1'), alpha=.5,binwidth=800,fill="red") + xlim(4430.4,50000)
geom_vline(xintercept=median(NHIS1$adj_SALARY), col="black", linetype="dashed")
And my final histogram at the moment appears as this:
If you have two variables, one for income , one for immigrant status, you do not need to plot two histograms but one will suffice if you specify the grouping. Also, I'd suggest you also use density lines, which help smooth over the histogram's bumps:
Assuming this is roughly like your data:
df <- data.frame(income = sample(1000:5000, 1000),
born = sample(c("US", "Foreign"), 1000, replace = T))
Then a crude way to plot one histogram as well as density lines for the two groups would be this:
ggplot(df, aes(x=income, color=born, fill=born)) +
geom_histogram(aes(y=..density..), alpha=0.5, binwidth=100,
position="identity") +
geom_density(alpha=.2)
This question has been asked before: overlaying-histograms-with-ggplot2-in-r discusses several options with many examples. You should definitely take a look at it.
Another option to compare the distributions could be violin plots using geom_violin(). I see violin plots as the better option when you need to compare distributions because they give you more flexibility and are still clearer. But that may be just me. Refer to the examples in the manual.
my question is very simple, but I have failed to solve it after many attempts. I just want to print some facets of a facetted plot (made with facet_wrap in ggplot2), and remove the ones I am no interested in.
I have facet_wrap with ggplot2, as follows:
#anomalies linear trends
an.trends <- ggplot()+
geom_smooth(method="lm", data=tndvilong.anomalies, aes(x=year, y=NDVIan, colour=TenureZone,
group=TenureZone))+
scale_color_manual(values=miscol) +
ggtitle("anomalies' trends")
#anomalies linear trends by VEG
an.trendsVEG <- an.trends + facet_wrap(~VEG,ncol=2)
print(an.trendsVEG)
And I get the plot as I expected (you can see it in te link below):
anomalies' trends by VEG
The question is: how do I get printed only the facest I am interested on?
I only want to print "CenKal_ShWoodl", "HlShl_ShDens", "NKal_ShWoodl", and "ThShl_ShDens"
Thanks
I suggest the easiest way to do that is to simply give ggplot() an appropriate subset. In this case:
facets <- c("CenKal_ShWoodl", "HlShl_ShDens", "NKal_ShWoodl", "ThShl_ShDens")
an.trends.sub <- ggplot(tndvilong.anomalies[tndvilong.anomalies$VEG %in% facets,])+
geom_smooth(method="lm" aes(x=year, y=NDVIan, colour=TenureZone,
group=TenureZone))+
scale_color_manual(values=miscol) +
ggtitle("anomalies' trends") +
facet_wrap(~VEG,ncol=2)
Obviously without your data I can't be sure this will give you what you want, but based on your description, it should work. I find that with ggplot, it is generally best to pass it the data you want plotted, rather than finding ways of changing the plot itself.
From what I can find on stackoverflow, (such as this answer to using two scale colour gradients on one ggplot) this may not (yet) be possible with ggplot2.
I want to create a bubbleplot with two size aesthetics, one always larger than the other. The idea is to show the proportion as well as the absolute values. Now I could colour the points by the proportion but I prefer multi-bubbles. In Excel this is relatively simple. (http://i.stack.imgur.com/v5LsF.png) Is there a way to replicate this in ggplot2 (or base)?
Here's an option. Mapping size in two geom_point layers should work. It's a bit of a pain getting the sizes right for bubblecharts in ggplot though.
p <- ggplot(mtcars, aes(mpg, wt)) + geom_point(aes(size = disp), shape = 1) +
geom_point(aes(size = hp/(2*disp))) + scale_size_continuous(range = c(15,30))
To get it looking most like your exapmle, add theme_bw():
P <- p + theme_bw()
The scale_size_continuous() is where you have to just fiddle around till you're happy - at least in my experience. If someone has a better idea there I'd love to hear it.
I am making a graph in ggplot2 consisting of a set of datapoints plotted as points, with the lines predicted by a fitted model overlaid. The general idea of the graph looks something like this:
names <- c(1,1,1,2,2,2,3,3,3)
xvals <- c(1:9)
yvals <- c(1,2,3,10,11,12,15,16,17)
pvals <- c(1.1,2.1,3.1,11,12,13,14,15,16)
ex_data <- data.frame(names,xvals,yvals,pvals)
ex_data$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))