How can I add the sample ID (row number) as labels to each point in this LDA plot using ggplot2?
Thanks
Script:
require(MASS)
require(ggplot2)
data(iris)
irisLda <- lda(iris[,-5],iris[,5])
irisLda <- lda(Species~.,data=iris)
plot(irisLda)
irisProjection <- cbind(scale(as.matrix(iris[,-5]),scale=FALSE) %*% irisLda$scaling,iris[,5,drop=FALSE])
p <- ggplot(data=irisProjection,aes(x=LD1,y=LD2,col=Species))
p + geom_point()
You simply need to use geom_text:
irisProjection$row_num = 1:nrow(irisProjection)
p <- ggplot(data=irisProjection, aes(x=LD1,y=LD2,col=Species)) +
geom_point() + geom_text(aes(label = row_num))
print(p)
Maybe you need to play around a bit with hjust and vjust, which are part of geom_text. You also might want to have a look at the directlabels package for smart label placement.
Related
When I plot densities with ggplot, it seems to be very wrong around the limits. I see that geom_density and other functions allow specifying various density kernels, but none of them seem to fix the issue.
How do you correctly plot densities around the limits with ggplot?
As an example, let's plot the Chi-square distribution with 2 degrees of freedom. Using the builtin probability densities:
library(ggplot2)
u = seq(0, 2, by=0.01)
v = dchisq(u, df=2)
df = data.frame(x=u, p=v)
p = ggplot(df) +
geom_line(aes(x=x, y=p), size=1) +
theme_classic() +
coord_cartesian(xlim=c(0, 2), ylim=c(0, 0.5))
show(p)
We get the expected plot:
Now let's try simulating it and plotting the empirical distribution:
library(ggplot2)
u = rchisq(10000, df=2)
df = data.frame(x=u)
p = ggplot(df) +
geom_density(aes(x=x)) +
theme_classic() +
coord_cartesian(xlim=c(0, 2))
show(p)
We get an incorrect plot:
We can try to visualize the actual distribution:
library(ggplot2, dplyr, tidyr)
u = rchisq(10000, df=2)
df = data.frame(x=u)
p = ggplot(df) +
geom_point(aes(x=x, y=0.5), position=position_jitter(height=0.2), shape='.', alpha=1) +
theme_classic() +
coord_cartesian(xlim=c(0, 2), ylim=c(0, 1))
show(p)
And it seems to look correct, contrary to the density plot:
It seems like the problem has to do with kernels, and geom_density does allow using different kernels. But they don't really correct the limit problem. For example, the code above with triangular looks about the same:
Here's an idea of what I'm expecting to see (of course, I want a density, not a histogram):
library(ggplot2)
u = rchisq(10000, df=2)
df = data.frame(x=u)
p = ggplot(df) +
geom_histogram(aes(x=x), center=0.1, binwidth=0.2, fill='white', color='black') +
theme_classic() +
coord_cartesian(xlim=c(0, 2))
show(p)
The usual kernel density methods have trouble when there is a constraint such as in this case for a density with only support above zero. The usual recommendation for handling this has been to use the logspline package:
install.packages("logspline")
library(logspline)
png(); fit <- logspline(rchisq(10000, 3))
plot(fit) ; dev.off()
If this needed to be done in the ggplot2 environment there is a dlogspline function:
densdf <- data.frame( y=dlogspline(seq(0,12,length=1000), fit),
x=seq(0,12,length=1000))
ggplot(densdf, aes(y=y,x=x))+geom_line()
Perhaps you were insisting on one with 2 degrees of freedom?
I'm trying to identify the densest region in the plot. And I do this using stat_ellipse() in ggplot2. But I can not get the information (sum total, order number of each point and so on) of the points inside of the ellipse.
Seldom see the discussion about this problem. Is this possible?
For example:
ggplot(faithful, aes(waiting, eruptions))+
geom_point()+
stat_ellipse()
Here is Roman's suggestion implemented. The help for stat_ellipse says it uses a modified version of car::ellipse, so therefore I chose to extract the ellipse points from the ggplot object. That way it should always be correct (also if you change options in stat_ellipse).
# Load packages
library(ggplot2)
library(sp)
# Build the plot first
p <- ggplot(faithful, aes(waiting, eruptions)) +
geom_point() +
stat_ellipse()
# Extract components
build <- ggplot_build(p)$data
points <- build[[1]]
ell <- build[[2]]
# Find which points are inside the ellipse, and add this to the data
dat <- data.frame(
points[1:2],
in.ell = as.logical(point.in.polygon(points$x, points$y, ell$x, ell$y))
)
# Plot the result
ggplot(dat, aes(x, y)) +
geom_point(aes(col = in.ell)) +
stat_ellipse()
I have scatterplots of 2D data from two categories. I want to add density lines for each dimension -- not outside the plot (cf. Scatterplot with marginal histograms in ggplot2) but right on the plotting surface. I can get this for the x-axis dimension, like this:
set.seed(123)
dim1 <- c(rnorm(100, mean=1), rnorm(100, mean=4))
dim2 <- rnorm(200, mean=1)
cat <- factor(c(rep("a", 100), rep("b", 100)))
mydf <- data.frame(cbind(dim2, dim1, cat))
ggplot(data=mydf, aes(x=dim1, y=dim2, colour=as.factor(cat))) +
geom_point() +
stat_density(aes(x=dim1, y=(-2+(..scaled..))),
position="identity", geom="line")
It looks like this:
But I want an analogous pair of density curves running vertically, showing the distribution of points in the y-dimension. I tried
stat_density(aes(y=dim2, x=0+(..scaled..))), position="identity", geom="line)
but receive the error "stat_density requires the following missing aesthetics: x".
Any ideas? thanks
You can get the densities of the dim2 variables. Then, flip the axes and store them in a new data.frame. After that it is simply plotting them on top of the other graph.
p <- ggplot(data=mydf, aes(x=dim1, y=dim2, colour=as.factor(cat))) +
geom_point() +
stat_density(aes(x=dim1, y=(-2+(..scaled..))),
position="identity", geom="line")
stuff <- ggplot_build(p)
xrange <- stuff[[2]]$ranges[[1]]$x.range # extract the x range, to make the new densities align with y-axis
## Get densities of dim2
ds <- do.call(rbind, lapply(unique(mydf$cat), function(lev) {
dens <- with(mydf, density(dim2[cat==lev]))
data.frame(x=dens$y+xrange[1], y=dens$x, cat=lev)
}))
p + geom_path(data=ds, aes(x=x, y=y, color=factor(cat)))
So far I can produce:
distrib_horiz <- stat_density(aes(x=dim1, y=(-2+(..scaled..))),
position="identity", geom="line")
ggplot(data=mydf, aes(x=dim1, y=dim2, colour=as.factor(cat))) +
geom_point() + distrib_horiz
And:
distrib_vert <- stat_density(data=mydf, aes(x=dim2, y=(-2+(..scaled..))),
position="identity", geom="line")
ggplot(data=mydf, aes(x=dim2, y=dim1, colour=as.factor(cat))) +
geom_point() + distrib_vert + coord_flip()
But combining them is proving tricky.
So far I have only a partial solution since I didn't manage to obtain a vertical stat_density line for each individual category, only for the total set. Maybe this can nevertheless help as a starting point for finding a better solution. My suggestion is to try with the ggMarginal() function from the ggExtra package.
p <- ggplot(data=mydf, aes(x=dim1, y=dim2, colour=as.factor(cat))) +
geom_point() + stat_density(aes(x=dim1, y=(-2+(..scaled..))),
position="identity", geom="line")
library(ggExtra)
ggMarginal(p,type = "density", margins = "y", size = 4)
This is what I obtain:
I know it's not perfect, but maybe it's a step in a helpful direction. At least I hope so. Looking forward to seeing other answers.
Suppose I have this plot:
ggplot(iris) + geom_point(aes(x=Sepal.Width, y=Sepal.Length, colour=Sepal.Length)) + scale_colour_gradient()
what is the correct way to discretize the color scale, like the plot shown below the accepted answer here (gradient breaks in a ggplot stat_bin2d plot)?
ggplot correctly recognizes discrete values and uses discrete scales for these, but my question is if you have continuous data and you want a discrete colour bar for it (with each square corresponding to a value, and squares colored in a gradient still), what is the best way to do it? Should the discretizing/binning happen outside of ggplot and get put in the dataframe as a separate discrete-valued column, or is there a way to do it within ggplot? an example of what I'm looking for is similar to the scale shown here:
except I'm plotting a scatter plot and not something like geom_tile/heatmap.
thanks.
The solution is slightly complicated, because you want a discrete scale. Otherwise you could probably simply use round.
library(ggplot2)
bincol <- function(x,low,medium,high) {
breaks <- function(x) pretty(range(x), n = nclass.Sturges(x), min.n = 1)
colfunc <- colorRampPalette(c(low, medium, high))
binned <- cut(x,breaks(x))
res <- colfunc(length(unique(binned)))[as.integer(binned)]
names(res) <- as.character(binned)
res
}
labels <- unique(names(bincol(iris$Sepal.Length,"blue","yellow","red")))
breaks <- unique(bincol(iris$Sepal.Length,"blue","yellow","red"))
breaks <- breaks[order(labels,decreasing = TRUE)]
labels <- labels[order(labels,decreasing = TRUE)]
ggplot(iris) +
geom_point(aes(x=Sepal.Width, y=Sepal.Length,
colour=bincol(Sepal.Length,"blue","yellow","red")), size=4) +
scale_color_identity("Sepal.Length", labels=labels,
breaks=breaks, guide="legend")
You could try the following, I have your example code modified appropriately below:
#I am not so great at R, so I'll just make a data frame this way
#I am convinced there are better ways. Oh well.
df<-data.frame()
for(x in 1:10){
for(y in 1:10){
newrow<-c(x,y,sample(1:1000,1))
df<-rbind(df,newrow)
}
}
colnames(df)<-c('X','Y','Val')
#This is the bit you want
p<- ggplot(df, aes(x=X,y=Y,fill=cut(Val, c(0,100,200,300,400,500,Inf))))
p<- p + geom_tile() + scale_fill_brewer(type="seq",palette = "YlGn")
p<- p + guides(fill=guide_legend(title="Legend!"))
#Tight borders
p<- p + scale_x_continuous(expand=c(0,0)) + scale_y_continuous(expand=c(0,0))
p
Note the strategic use of cut to discretize the data followed by the use of color brewer to make things pretty.
The result looks as follows.
Is there any way to plot the cumulative probability from a frequency table? I mean a "smooth" version of it, similar to the way geom_density() plots.
So far, I managed to plot the individually calculated probabilities as points joined by lines, but it doesn't look very good.
I generate some test data:
set.seed(1)
x <- sort(sample(1:100, 20))
p <- runif(x); p <- cumsum(p)/sum(p)
table <- data.frame(x=x, prob=p)
You can use geom_smooth from the ggplot2 package.
require("ggplot2")
qplot(x=x, y=p, data=table, aes(ymin=0, ymax=1)) + ylab("ecf") +
geom_smooth(se=F, stat="smooth", method="loess", fullrange=T, fill="lightgrey", size=1)
As an alternative, an easy way to specifiy smoothing by a parameter try DeconCdf from the decon package:
require("decon")
plot(DeconCdf(x, sig=1))
If you want to use ggplot, you first have to transform the Decon function object in a data.frame.
f <- DeconCdf(x, sig=1)
m <- ggplot(data=data.frame(x=f$x, p=f$y), aes(x=x, y=p, ymin=0, ymax=1)) + ylab("ecf")
m + geom_line(size=1)
Use the sig-Parameter as your smoothing parameter:
f <- DeconCdf(x, sig=0.3)
m <- ggplot(data=data.frame(x=f$x, p=f$y), aes(x=x, y=p, ymin=0, ymax=1)) + ylab("ecf")
m + geom_line(size=1)
This version plots a histogram with a smoothed line from geom_density:
# Generate some data:
set.seed(28986)
x2 <- rweibull(100, 1, 1/2)
# Plot the points:
library(ggplot2)
library(scales)
ggplot(data.frame(x=x2),aes(x=x, y=1-cumsum(..count..)/sum(..count..))) +
geom_histogram(aes(fill=..count..)) +
geom_density(fill=NA, color="black", adjust=1/2) +
scale_y_continuous("Percent of units\n(equal to or larger than x)",labels=percent) +
theme_grey(base_size=18)
Note that I've used 1 - "cumulative probability" due to individual preference (I think it looks better and I'm accustomed to dealing with "reliability" metrics), but obviously that's just a preference that you could ignore by removing the 1- part in the aes.