I'd like to annotate my plot of bugs with the release date which is in another data.frame, but I'd like the colour of the vline to match that of corresponding original trace
# first create some dummy data
set.seed(123)
N <- 100
adf <- data.frame(version=sample(c('A','B','C'), N, replace=TRUE),
cs=as.POSIXct('2011-06-01 00:00') + rnorm(N, 20, 70)*86400)
# lets just shift things slightly, depending on version
adf$cs <- adf$cs + (as.integer(adf$version) - 1)*5e6
adf <- adf[order(adf$cs),]
library(plyr)
adf <- ddply(adf, .(version), function(bdf) { cbind(bdf, bugno=1:nrow(bdf)) } )
# now lets plot these bug curves by version
library(ggplot2)
q <- qplot(cs, bugno, data=adf, geom='line', colour=version,
xlab='', ylab='Number of Bugs')
print(q)
# however I'd like to annotate these plots by adding the
# dates of "release", with the colour matching that of release
# in the plot q, so no further annotation necessary (hopefully!)
g.res <- data.frame(version=c('A','B','C'),
releasedate=c(as.Date('2011-06-01'), as.Date('2011-10-01'),
as.Date('2012-01-01')))
# works... but only in blue...
q + geom_vline(data=g.res, aes(xintercept=as.POSIXct(releasedate)), col="blue")
I am aware of Axis breaks at noon each day of ggplot2 chart and How to get a vertical geom_vline to an x-axis of class date?
And since I've put all this work into the question I've just realised the answer... the colour must be part of the aes! I still don't have a proper understanding of how aes works, I'll have to read the book again! :-)
q + geom_vline(data=g.res, aes(xintercept=as.POSIXct(releasedate), col=version) )
Related
I'm superimposing two images in R. One image is a boxplot (using boxplot()), the other a scatterplot (using scatterplot()). I noticed a discrepancy in the scale along the x-axis. (A) is the boxplot scale. (B) is for the scatterplot.
What I've been trying to do is re-scale (B) to suit (A). I note there is a condition called xlim in scatterplot. Tried it, didn't work. I've also noted this example came up as I was typing out the question: Change Axis Label - R scatterplot.
Tried it, didn't work.
How can I modify the x-axis to change the scale from 1.0, 1.5, 2.0, 2.5, 3.0 to simply 1,2,3.
In Stata, I'm aware you can specify the x-axis range, and then indicate the step-ups between. For example, the range may be 0-100, and each measurable point would be set to 10. So you'd end up with 10, 20,....,100.
My R code, as it stands, looks something like this:
library(car)
boxplot(a,b,c)
par(new=T)
scatterplot(x, y, smooth=TRUE, boxplots=FALSE)
I've tried modifying scatterplot as such without any success:
scatterplot(x, y, smooth=TRUE, boxplots=FALSE, xlim=c(1,3))
As mentioned in comments use as.factor, then xaxis should align. Here is ggplot solution:
#dummy data
dat1 <- data.frame(group=as.factor(rep(1:3,4)),
var=c(runif(12)))
dat2 <- data.frame(x=as.factor(1:3),y=runif(3))
library(ggplot2)
library(grid)
library(gridExtra)
#plot points on top of boxplot
ggplot(dat1,aes(group,var)) +
geom_boxplot() +
geom_point(aes(x,y),dat2)
Plot as separate plots
gg_boxplot <-
ggplot(dat1,aes(group,var)) +
geom_boxplot()
gg_point <-
ggplot(dat2,aes(x,y)) +
geom_point()
grid.arrange(gg_boxplot,gg_point,
ncol=1,
main="Plotting is easier with ggplot")
EDIT
Using xlim as suggested by #RuthgerRighart
#dummy data - no factors
dat1 <- data.frame(group=rep(1:3,4),
var=c(runif(12)))
dat2 <- data.frame(x=1:3,y=runif(3))
par(mfrow=c(2,1))
boxplot(var~group,dat1,xlim=c(1,3))
plot(dat2$x,dat2$y,xlim=c(1,3))
I've seen many examples of a density plot but the density plot's y-axis is the probability. What I am looking for a is a line plot (like a density plot) but the y-axis should contain counts (like a histogram).
I can do this in excel where I manually make the bins and the frequencies and make a bar histogram and then I can change the chart type to a line - but can't find anything similar in R.
I've checked out both base and ggplot2; yet can't seem to find an answer. I understand that histograms are meant to be bars but I think representing them as a continuous line makes more visual sense.
Using default R graphics (i.e. without installing ggplot) you can do the following, which might also make what the density function does a bit clearer:
# Generate some data
data=rnorm(1000)
# Get the density estimate
dens=density(data)
# Plot y-values scaled by number of observations against x values
plot(dens$x,length(data)*dens$y,type="l",xlab="Value",ylab="Count estimate")
This is an old question, but I thought it might be helpful to post a solution that specifically addresses your question.
In ggplot2, you can plot a histogram and display the count with bars using:
ggplot(data) +
geom_histogram()
You can also plot a histogram and display the count with lines using a frequency polygon:
ggplot(data) +
geom_freqpoly()
For more info --
ggplot2 reference
To adapt the example on the ?stat_density help page:
m <- ggplot(movies, aes(x = rating))
# Standard density plot.
m + geom_density()
# Density plot with y-axis scaled to counts.
m + geom_density(aes(y = ..count..))
Although this is old, I thought the following might be useful.
Let's say you have a data set of 10,000 points, and you believe they belong to a certain distribution, and you would like to plot the histogram of the actual data and the line of the probability density of the ideal distribution on top of it.
noise <- 2
#
# the noise is tagged onto the end using runif
# just do demo issues w/real data and fitting
# the subtraction causes the data to have some
# negative values, which must be addressed in
# the fit later on
#
noisylognorm <- rlnorm(10000,
mean = 0.25,
sd = 1) +
(noise * runif(10000) - noise / 10)
#
# using package fitdistrplus
#
# subset is used to remove the negative values
# as the lognormal distribution needs positive only
#
fitlnorm <- fitdist(subset(noisylognorm,
noisylognorm > 0),
"lnorm")
fitlnorm_density <- density(rlnorm(10000,
mean = fitlnorm$estimate[1],
sd = fitlnorm$estimate[2]))
hist(subset(noisylognorm,
noisylognorm < 25),
breaks = seq(-1, 25, 0.5),
col = "lightblue",
xlim = c(0, 25),
xlab = "value",
ylab = "frequency",
main = paste0("Log Normal Distribution\n",
"noise = ", noise))
lines(fitlnorm_density$x,
10000 * fitlnorm_density$y * 0.5,
type="l",
col = "red")
Note the * 0.5 in the lines function. As far as I can tell, this is necessary to account for the width of the hist() bars.
There is a very simple and fast way for count data.
First let's generate some dummy count data:
my.count.data = rpois(n = 10000, lambda = 3)
And then the plotting command (assuming you have called library(magrittr)):
my.count.data %>% table %>% plot
What I really want to do is plot a histogram, with the y-axis on a log-scale. Obviously this i a problem with the ggplot2 geom_histogram, since the bottom os the bar is at zero, and the log of that gives you trouble.
My workaround is to use the freqpoly geom, and that more-or less does the job. The following code works just fine:
ggplot(zcoorddist) +
geom_freqpoly(aes(x=zcoord,y=..density..),binwidth = 0.001) +
scale_y_continuous(trans = 'log10')
The issue is that at the edges of my data, I get a couple of garish vertical lines that really thro you off visually when combining a bunch of these freqpoly curves in one plot. What I'd like to be able to do is use points at every vertex of the freqpoly curve, and no lines connecting them. Is there a way to to this easily?
The easiest way to get the desired plot is to just recast your data. Then you can use geom_point. Since you don't provide an example, I used the standard example for geom_histogram to show this:
# load packages
require(ggplot2)
require(reshape)
# get data
data(movies)
movies <- movies[, c("title", "rating")]
# here's the equivalent of your plot
ggplot(movies) + geom_freqpoly(aes(x=rating, y=..density..), binwidth=.001) +
scale_y_continuous(trans = 'log10')
# recast the data
df1 <- recast(movies, value~., measure.var="rating")
names(df1) <- c("rating", "number")
# alternative way to recast data
df2 <- as.data.frame(table(movies$rating))
names(df2) <- c("rating", "number")
df2$rating <- as.numeric(as.character(df$rating))
# plot
p <- ggplot(df1, aes(x=rating)) + scale_y_continuous(trans="log10", name="density")
# with lines
p + geom_linerange(aes(ymax=number, ymin=.9))
# only points
p + geom_point(aes(y=number))
What's the ggplot2 equivalent of "dotplot" histograms? With stacked points instead of bars? Similar to this solution in R:
Plot Histogram with Points Instead of Bars
Is it possible to do this in ggplot2? Ideally with the points shown as stacks and a faint line showing the smoothed line "fit" to these points (which would make a histogram shape.)
ggplot2 does dotplots Link to the manual.
Here is an example:
library(ggplot2)
set.seed(789); x <- data.frame(y = sample(1:20, 100, replace = TRUE))
ggplot(x, aes(y)) + geom_dotplot()
In order to make it behave like a simple dotplot, we should do this:
ggplot(x, aes(y)) + geom_dotplot(binwidth=1, method='histodot')
You should get this:
To address the density issue, you'll have to add another term, ylim(), so that your plot call will have the form ggplot() + geom_dotplot() + ylim()
More specifically, you'll write ylim(0, A), where A will be the number of stacked dots necessary to count 1.00 density. In the example above, the best you can do is see that 7.5 dots reach the 0.50 density mark. From there, you can infer that 15 dots will reach 1.00.
So your new call looks like this:
ggplot(x, aes(y)) + geom_dotplot(binwidth=1, method='histodot') + ylim(0, 15)
Which will give you this:
Usually, this kind of eyeball estimate will work for dotplots, but of course you can try other values to fine-tune your scale.
Notice how changing the ylim values doesn't affect how the data is displayed, it just changes the labels in the y-axis.
As #joran pointed out, we can use geom_dotplot
require(ggplot2)
ggplot(mtcars, aes(x = mpg)) + geom_dotplot()
Edit: (moved useful comments into the post):
The label "count" it's misleading because this is actually a density estimate may be you could suggest we changed this label to "density" by default. The ggplot implementation of dotplot follow the original one of Leland Wilkinson, so if you want to understand clearly how it works take a look at this paper.
An easy transformation to make the y axis actually be counts, i.e. "number of observations". From the help page it is written that:
When binning along the x axis and stacking along the y axis, the numbers on y axis are not meaningful, due to technical limitations of ggplot2. You can hide the y axis, as in one of the examples, or manually scale it to match the number of dots.
So you can use this code to hide y axis:
ggplot(mtcars, aes(x = mpg)) +
geom_dotplot(binwidth = 1.5) +
scale_y_continuous(name = "", breaks = NULL)
I introduce an exact approach using #Waldir Leoncio's latter method.
library(ggplot2); library(grid)
set.seed(789)
x <- data.frame(y = sample(1:20, 100, replace = TRUE))
g <- ggplot(x, aes(y)) + geom_dotplot(binwidth=0.8)
g # output to read parameter
### calculation of width and height of panel
grid.ls(view=TRUE, grob=FALSE)
real_width <- convertWidth(unit(1,'npc'), 'inch', TRUE)
real_height <- convertHeight(unit(1,'npc'), 'inch', TRUE)
### calculation of other values
width_coordinate_range <- diff(ggplot_build(g)$panel$ranges[[1]]$x.range)
real_binwidth <- real_width / width_coordinate_range * 0.8 # 0.8 is the argument binwidth
num_balls <- real_height / 1.1 / real_binwidth # the number of stacked balls. 1.1 is expanding value.
# num_balls is the value of A
g + ylim(0, num_balls)
Apologies : I don't have enough reputation to 'comment'.
I like cuttlefish44's "exact approach", but to make it work (with ggplot2 [2.2.1]) I had to change the following line from :
### calculation of other values
width_coordinate_range <- diff(ggplot_build(g)$panel$ranges[[1]]$x.range)
to
### calculation of other values
width_coordinate_range <- diff(ggplot_build(g)$layout$panel_ranges[[1]]$x.range)
I'd like to plot the frequency of a variable color coded for 2 factor levels for example blue bars should be the hist of level A and green the hist of level B both n the same graph? Is this possible with the hist command? The help of hist does not allow for a factor. Is there another way around?
I managed to do this by barplots manually but i want to ask if there is a more automatic method
Many thanks
EC
PS. I dont need density plots
Just in case the others haven't answered this is a way that satisfies. I had to deal with stacking histograms recently, and here's what I did:
data_sub <- subset(data, data$V1 == "Yes") #only samples that have V1 as "yes" in my dataset #are added to the subset
hist(data$HL)
hist(data_sub$HL, col="red", add=T)
Hopefully, this is what you meant?
It's rather unclear what you have as a data layout. A histogram requires that you have a variable that is ordinal or continuous so that breaks can be created. If you also have a separate grouping factor you can plot histograms conditional on that factor. A nice worked example of such a grouping and overlaying a density curve is offered in the second example on the help page for the histogram function in the lattice package.
A nice resource for learning relative merits of lattice and ggplot2 plotting is the Learning R blog. This is from the first of a multipart series on side-by=side comparison of the two plotting systems:
library(lattice)
library(ggplot2)
data(Chem97, package = "mlmRev")
#The lattice method:
pl <- histogram(~gcsescore | factor(score), data = Chem97)
print(pl)
# The ggplot method:
pg <- ggplot(Chem97, aes(gcsescore)) + geom_histogram(binwidth = 0.5) +
facet_wrap(~score)
print(pg)
I don't think you can do that easily with a bar histogram, as you would have to "interlace" the bars from both factor levels... It would need some kind of "discretization" of the now continuous x axis (i.e. it would have to be split in "categories" and in each category you would have 2 bars, for each factor level...
But it is quite easy and without problems if you are just fine with plotting the density line function:
y <- rnorm(1000, 0, 1)
x <- rnorm(1000, 0.5, 2)
dx <- density(x)
dy <- density(y)
plot(dx, xlim = range(dx$x, dy$x), ylim = range(dx$y, dy$y),
type = "l", col = "red")
lines(dy, col = "blue")
It's very possible.
I didn't have data to work with but here's an example of a histogram with different colored bars. From here you'd need to use my code and figure out how to make it work for factors instead of tails.
BASIC SETUP
histogram <- hist(scale(vector)), breaks= , plot=FALSE)
plot(histogram, col=ifelse(abs(histogram$breaks) < #of SD, Color 1, Color 2))
#EXAMPLE
x<-rnorm(1000)
histogram <- hist(scale(x), breaks=20 , plot=FALSE)
plot(histogram, col=ifelse(abs(histogram$breaks) < 2, "red", "green"))
I agree with the others that a density plot is more useful than merging colored bars of a histogram, particularly if the group's values are intermixed. This would be very difficult and wouldn't really tell you much. You've got some great suggestions from others on density plots, here's my 2 cents for density plots that I sometimes use:
y <- rnorm(1000, 0, 1)
x <- rnorm(1000, 0.5, 2)
DF <- data.frame("Group"=c(rep(c("y","x"), each=1000)), "Value"=c(y,x))
library(sm)
with(DF, sm.density.compare(Value, Group, xlab="Grouping"))
title(main="Comparative Density Graph")
legend(-9, .4, levels(DF$Group), fill=c("red", "darkgreen"))