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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:
Is it possible to overlay multiple layers of ScatterD3 plots on top of one another? I haven't been able to find this anywhere in either the vignettes or searching StackExchange/Google.
I'm curious, since folks have been able to make PCA Vector Loading plots using ScatterD3. If one could overlay this on top of another plot with the points (akin to what's possible with ggplot2 or ggvis layers), you could have a gorgeous and interactive PCA plot. Additionally, you might be able to outline points (since point stroke currently isn't an option).
Does anyone have any insight or workarounds?
It is possible, but more difficult. I would recommend using plotly package. You'll be able to use the View tab in the RStudio and more easily examine your 3D scatter by rotating. The color scheme is also easier to add This post attempts to tackle a similar, though not identical. A good (free) tutorial for plotly can be found here through DataCamp.
Question answered here thanks to the author of ScatterD3. To generate a full PCA plot, you need to redefine the dataframe being plotted like so:
library(FactoMineR)
library (ScatterD3)
out<-PCA(iris[,1:4],scale.unit = TRUE, graph=FALSE)
cc1<-data.frame(out$ind$coord)
cc2<-data.frame(out$var$coord)
points <- data.frame(x = cc1$Dim.1,
y = cc1$Dim.2,
color = iris$Species,
lab = row.names(iris),
type = rep("point", 150))
arrows <- data.frame(x = cc2$Dim.1,
y = cc2$Dim.2,
color = "Blue",
lab = row.names(cc2),
type = rep("arrow", 4))
data1 <- rbind(points, arrows)
scatterD3(data1, x = x, y = y,
lab = lab, type_var = data1$type, col_var = color)
I am using the latticeExtra library "ecdfplot" to plot my error. I want to add gridlines.
The following does not seem to work:
ecdfplot(err)
grid(ny=10)
It gives the following (gridless) result:
I really would love to give a "graphical summary" where the quantiles are indicated by lines, and their intersections with the data are shown on the x-axis.
Can you tell me how to add gridlines?
How about adding vertical lines at a particular x-location?
Try the argument axis = axis.grid
require(latticeExtra)
data(singer, package = "lattice")
ecdfplot(~height, data = singer, add=TRUE, axis = axis.grid, par.settings = theEconomist.theme())
I'm creating a histogram in R which displays the frequency of several events in a vector. Each event is represented by an integer in the range [1, 9]. I'm displaying the label for each count vertically below the chart. Here's the code:
hist(vector, axes = FALSE, breaks = chartBreaks)
axis(1, at = tickMarks, labels = eventTypes, las = 2, tick = FALSE)
Unfortunately, the labels are too long, so they are cut off by the bottom of the window. How can I make them visible? Am I even using the right chart?
Look at help(par), in particular fields mar (for the margin) and oma (for outer margin).
It may be as simple as
par(mar=c(5,3,1,1)) # extra large bottom margin
hist(vector, axes = FALSE, breaks = chartBreaks)
axis(1, at = tickMarks, labels = eventTypes, las = 2, tick = FALSE)
This doesn't sound like a job for a histogram - the event is not a continuous variable. A barplot or dotplot may be more suitable.
Some dummy data
set.seed(123)
vec <- sample(1:9, 100, replace = TRUE)
vec <- factor(vec, labels = paste("My long event name", 1:9))
A barplot is produced via the barplot() function - we provide it the counts of each event using the table() function for convenience. Here we need to rotate labels using las = 2 and create some extra space of the labels in the margin
## lots of extra space in the margin for side 1
op <- par(mar = c(10,4,4,2) + 0.1)
barplot(table(vec), las = 2)
par(op) ## reset
A dotplot is produced via function dotchart() and has the added convenience of sorting out the plot margins for us
dotchart(table(vec))
The dotplot has the advantage over the barplot of using much less ink to display the same information and focuses on the differences in counts across groups rather than the magnitudes of the counts.
Note how I've set the data up as a factor. This allows us to store the event labels as the labels for the factor - thus automating the labelling of the axes in the plots. It also is a natural way of storing data like I understand you to have.
Perhaps adding \n into your labels so they will wrap onto 2 lines? It's not optimal, but it may work.
You might want to look at this post from Cross Validated
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: