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I have data that is mostly centered in a small range (1-10) but there is a significant number of points (say, 10%) which are in (10-1000). I would like to plot a histogram for this data that will focus on (1-10) but will also show the (10-1000) data. Something like a log-scale for th histogram.
Yes, i know this means not all bins are of equal size
A simple hist(x) gives
while hist(x,breaks=c(0,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,3,4,5,7.5,10,15,20,50,100,200,500,1000,10000))) gives
none of which is what I want.
update
following the answers here I now produce something that is almost exactly what I want (I went with a continuous plot instead of bar-histogram):
breaks <- c(0,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,4,8)
ggplot(t,aes(x)) + geom_histogram(colour="darkblue", size=1, fill="blue") + scale_x_log10('true size/predicted size', breaks = breaks, labels = breaks)![alt text][3]
the only problem is that I'd like to match between the scale and the actual bars plotted. There two options for doing that : the one is simply use the actual margins of the plotted bars (how?) then get "ugly" x-axis labels like 1.1754,1.2985 etc. The other, which I prefer, is to control the actual bins margins used so they will match the breaks.
Log scale histograms are easier with ggplot than with base graphics. Try something like
library(ggplot2)
dfr <- data.frame(x = rlnorm(100, sdlog = 3))
ggplot(dfr, aes(x)) + geom_histogram() + scale_x_log10()
If you are desperate for base graphics, you need to plot a log-scale histogram without axes, then manually add the axes afterwards.
h <- hist(log10(dfr$x), axes = FALSE)
Axis(side = 2)
Axis(at = h$breaks, labels = 10^h$breaks, side = 1)
For completeness, the lattice solution would be
library(lattice)
histogram(~x, dfr, scales = list(x = list(log = TRUE)))
AN EXPLANATION OF WHY LOG VALUES ARE NEEDED IN THE BASE CASE:
If you plot the data with no log-transformation, then most of the data are clumped into bars at the left.
hist(dfr$x)
The hist function ignores the log argument (because it interferes with the calculation of breaks), so this doesn't work.
hist(dfr$x, log = "y")
Neither does this.
par(xlog = TRUE)
hist(dfr$x)
That means that we need to log transform the data before we draw the plot.
hist(log10(dfr$x))
Unfortunately, this messes up the axes, which brings us to workaround above.
Using ggplot2 seems like the most easy option. If you want more control over your axes and your breaks, you can do something like the following :
EDIT : new code provided
x <- c(rexp(1000,0.5)+0.5,rexp(100,0.5)*100)
breaks<- c(0,0.1,0.2,0.5,1,2,5,10,20,50,100,200,500,1000,10000)
major <- c(0.1,1,10,100,1000,10000)
H <- hist(log10(x),plot=F)
plot(H$mids,H$counts,type="n",
xaxt="n",
xlab="X",ylab="Counts",
main="Histogram of X",
bg="lightgrey"
)
abline(v=log10(breaks),col="lightgrey",lty=2)
abline(v=log10(major),col="lightgrey")
abline(h=pretty(H$counts),col="lightgrey")
plot(H,add=T,freq=T,col="blue")
#Position of ticks
at <- log10(breaks)
#Creation X axis
axis(1,at=at,labels=10^at)
This is as close as I can get to the ggplot2. Putting the background grey is not that straightforward, but doable if you define a rectangle with the size of your plot screen and put the background as grey.
Check all the functions I used, and also ?par. It will allow you to build your own graphs. Hope this helps.
A dynamic graph would also help in this plot. Use the manipulate package from Rstudio to do a dynamic ranged histogram:
library(manipulate)
data_dist <- table(data)
manipulate(barplot(data_dist[x:y]), x = slider(1,length(data_dist)), y = slider(10, length(data_dist)))
Then you will be able to use sliders to see the particular distribution in a dynamically selected range like this:
I have two data set, I need to plot them in same graph. Here is the two dataset.
The following is the code I used to plot the data. How to plot above data in same plot ? How to set the graph legend on the x-axis? I tried setting it but it didn't work I got some error.
m_bs = conpl$new(sample_data1$V1)
m_eq = conpl$new(sample_data2$V1)
est = estimate_xmin(m_bs, xmax=5e+5)
est_eq = estimate_xmin(m_eq, xmax=Inf)
m_bs$setXmin(est_bs)
m_eq$setXmin(est_eq)
plot(m_bs)
lines(m_bs)
d = plot(m_eq, draw =FALSE)
points(d$x, d$y, col=2)
lines(m_eq,col=2,lwd=2)
Kindly let me know thanks.
You code works find for me when I used simulated data. However, I think your problem is with your data. In particular, you need to set the xlim values in your plot command. Something like:
min_x = min(sample_data1$V1, sample_data1$V2)
max_x = max(sample_data1$V1, sample_data1$V2)
plot(m_bs, xlim=c(min_x, max_x))
Should do the trick. To add a legend, just use the legend function
legend("bottomleft", col=1:2, legend = c("BS", "EQ"), lty=1)
I have two multiple line graphs drawn in R using matplot().
The graphs are from the same time series data but showing two differing clusters. I want to draw the y-axis so that it is identical in each plot I draw regardless of the data (range, max, min etc) that is used.
Could anybody suggest a means to achieve this in R?
Thanks!
Setting the ylim parameter should achieve what you are trying to do
plot_max <- 200
plot_min <- 0
plot1 <- matplot(data_set1, ylim = c(plot_min, plot_max)
plot2 <- matplot(data_set2, ylim = c(plot_min, plot_max)
This should get you what you want.
I have created my bar plot in R and am moving on from using two-bar plots to three-bars.
I want to label the bars '1','2' and '3'along the x-axis
I understand how to use the axis function, and also that the 'at' function requires a numeric value or vector, it's just thatI don't know exactly what value it is that it requires!
Thanks very much in advance
Great news! You can use whatever values you like!
mat <- matrix(c(14,9,7))
barplot(mat[ ,1])
axis(1, at = c(.5,1.5,3.5), labels = 1:3, tick = FALSE, las = 2)
I have data that is mostly centered in a small range (1-10) but there is a significant number of points (say, 10%) which are in (10-1000). I would like to plot a histogram for this data that will focus on (1-10) but will also show the (10-1000) data. Something like a log-scale for th histogram.
Yes, i know this means not all bins are of equal size
A simple hist(x) gives
while hist(x,breaks=c(0,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,3,4,5,7.5,10,15,20,50,100,200,500,1000,10000))) gives
none of which is what I want.
update
following the answers here I now produce something that is almost exactly what I want (I went with a continuous plot instead of bar-histogram):
breaks <- c(0,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,4,8)
ggplot(t,aes(x)) + geom_histogram(colour="darkblue", size=1, fill="blue") + scale_x_log10('true size/predicted size', breaks = breaks, labels = breaks)![alt text][3]
the only problem is that I'd like to match between the scale and the actual bars plotted. There two options for doing that : the one is simply use the actual margins of the plotted bars (how?) then get "ugly" x-axis labels like 1.1754,1.2985 etc. The other, which I prefer, is to control the actual bins margins used so they will match the breaks.
Log scale histograms are easier with ggplot than with base graphics. Try something like
library(ggplot2)
dfr <- data.frame(x = rlnorm(100, sdlog = 3))
ggplot(dfr, aes(x)) + geom_histogram() + scale_x_log10()
If you are desperate for base graphics, you need to plot a log-scale histogram without axes, then manually add the axes afterwards.
h <- hist(log10(dfr$x), axes = FALSE)
Axis(side = 2)
Axis(at = h$breaks, labels = 10^h$breaks, side = 1)
For completeness, the lattice solution would be
library(lattice)
histogram(~x, dfr, scales = list(x = list(log = TRUE)))
AN EXPLANATION OF WHY LOG VALUES ARE NEEDED IN THE BASE CASE:
If you plot the data with no log-transformation, then most of the data are clumped into bars at the left.
hist(dfr$x)
The hist function ignores the log argument (because it interferes with the calculation of breaks), so this doesn't work.
hist(dfr$x, log = "y")
Neither does this.
par(xlog = TRUE)
hist(dfr$x)
That means that we need to log transform the data before we draw the plot.
hist(log10(dfr$x))
Unfortunately, this messes up the axes, which brings us to workaround above.
Using ggplot2 seems like the most easy option. If you want more control over your axes and your breaks, you can do something like the following :
EDIT : new code provided
x <- c(rexp(1000,0.5)+0.5,rexp(100,0.5)*100)
breaks<- c(0,0.1,0.2,0.5,1,2,5,10,20,50,100,200,500,1000,10000)
major <- c(0.1,1,10,100,1000,10000)
H <- hist(log10(x),plot=F)
plot(H$mids,H$counts,type="n",
xaxt="n",
xlab="X",ylab="Counts",
main="Histogram of X",
bg="lightgrey"
)
abline(v=log10(breaks),col="lightgrey",lty=2)
abline(v=log10(major),col="lightgrey")
abline(h=pretty(H$counts),col="lightgrey")
plot(H,add=T,freq=T,col="blue")
#Position of ticks
at <- log10(breaks)
#Creation X axis
axis(1,at=at,labels=10^at)
This is as close as I can get to the ggplot2. Putting the background grey is not that straightforward, but doable if you define a rectangle with the size of your plot screen and put the background as grey.
Check all the functions I used, and also ?par. It will allow you to build your own graphs. Hope this helps.
A dynamic graph would also help in this plot. Use the manipulate package from Rstudio to do a dynamic ranged histogram:
library(manipulate)
data_dist <- table(data)
manipulate(barplot(data_dist[x:y]), x = slider(1,length(data_dist)), y = slider(10, length(data_dist)))
Then you will be able to use sliders to see the particular distribution in a dynamically selected range like this: