Assume I want to plot the following dataframe:
df <- data.frame(expand.grid(1:10,1:10),rep(10,100))
colnames(df) <- c("x","y","z")
with the lattice wireframe() function:
wireframe(z~x*y,df,colorkey=TRUE,drape=TRUE)
How do I get it to plot the given coordinates? I would assume it has something to do with having to scale/adjust the z-axis as the automatic scaling within wireframe is probably confused by all z-coordinates being equal.
This is from the help page scales section: "The most common use for this argument is to set arrows=FALSE, which causes tick marks and labels to be used instead of arrows being drawn (the default)." So just add that as a list value to 'scales':
wireframe(z~x*y,df,colorkey=TRUE,drape=TRUE,
scales=list(arrows=FALSE), zlim=c(0,10.1))
The failure of wireframe to display anything when the plotted plane is at one of the extremes seems to be at least "unexpected behavior" if not a bug. I suspect you would not see this in real data. Your use of drape doesn't make much sense since the entire data-plane plane gets displayed at the white midpoint. (Again this is probably not a problem if you have something other than this pathological example.)
Just add a zlim argument.
wireframe(z~x*y,df,colorkey=TRUE,drape=TRUE, zlim=c(0,20))
Related
I am attempting to project data onto a plot in R and see the correlation between the points. I have added a line to let the reader see the connection between these points. I am however stumped when it comes to inputting arrows to show the direction of the line. Rddproj was just an arbitrary name given to the data. Three sets of x and y coordinates are plotted x=c(-0.7159425, -0.8129311, -0.7392371); y=0.7743088, 0.7732762, 0.7490996) Here is the example below.
x<-rddproj[1:3,1]; y<-rddproj[1:3,2]
plot(x,y)
My concern is that the second group of coordinates is the greatest negative point on the x-axis. In drawing a line with arrows, the arrow will most likely point towards this point, when it should be forming a V with that point in the middle. Is it possible to plot an arrow to reflect the placement of points in a group and not just the most positive point to the most negative point or vice versa?
The arrows function ( a modified segments function) is used for this purpose (to the extent that I understand the question) in base R:
# fixed your assignment code.
plot(NA, xlim=range(x), ylim=range(y) )
arrows(head(x,-1),head(y,-1),tail(x,-1), tail(y,-1), angle=30)
An alternative reading of your question would have the glaringly obvious solution : plot(x,y) which I hope is not what you were asking since that should have been satisfactory.
I am using forest.rma in the R package metafor. I want to use the xlim argument to specify the width of the plot. But this gets overridden when one of the x-values is outside that range. Here are two examples:
library(metafor)
par(mfrow=c(2,1))
rmaObject <- rma.uni(yi=c(1.3, -16), sei=c(0.8, 0.7))
forest(rmaObject, atransf=exp, xlim=c(-5,12), alim=c(-5,12))$xlim
forest(rmaObject, atransf=exp, xlim=c(-5,12))$xlim
In both cases I think the function should follow my instruction to use xlim=c(-5,12) and draw just a left arrowhead for the second bar, to show that the whole of the bar lies outside the left of the plot. But as the console output shows the first one sets xlim to c(-16,12) and the second one sets it to c(-20,12).
In other words forest forces xlim to encompass both bars completely, but I don't think it should do that if I specify xlim, and especially not if I specify alim as well. Is there an easy way for me to fix this?
The numbers in this example are real, though I have omitted most of my studies (i.e. values of yi and sei), which are mostly in the area of 0 or 1.
You can install the development version of metafor as described here:
http://www.metafor-project.org/doku.php/installation#development_version
It deals with this better. It still forces xlim to encompass the yi values, but no longer for alim. The summary polygon at the bottom still looks like crap though (I haven't built in any code to cut off the polygon shape at the alim values -- not as trivial as it may seem), but this may not be an issue once you add the rest of the studies.
I'm trying to plot the cluster obtained from fuzzy c-means clustering.
The plot should look like this.
code for the plot
plot(data$Longitude, data$Latitude, main="Fuzzy C-Means",col=data$Revised, pch=16, cex=.6,
xlab="Longitude",ylab="Latitude")
library(maps)
map("state", add=T)
However, when I tried to use clusplot the plot is displaying in opposite direction(both top and bottom and left and right) as below.
I wanna know if there's a way to reverse the plot to show in the order as the above picture.
Also, for the very dense area, it's hard to find the ellipse label. I wanna know if there's a way to show the label inside the ellipse instead of outside.
code for 2nd pic
library(cluster)
clusplot(cbind(Geocode$Longitude, Geocode$Latitude), cluster, color=TRUE,shade=TRUE,
labels=4, lines=0,col.p=cluster,
xlab="Longitude",ylab="Latitude",cex=1)
clusplot is a function that performs a lot of magic for you. In particular it projects the data set - which happens in a way you don't like, unfortunately. (Also note the scales - it centered and scaled the data, too)
clusplot.default: Creates a bivariate plot visualizing a partition (clustering) of the data. All observation are represented by points in the plot, using principal components or multidimensional scaling.
As far as I can tell, clusplot doesn't have map support, but you will want such a map I guess...
While maybe you can use the s.x.2d parameter to specify the exact projection (and this way disable automatic scaling), it probably is still difficult to add the map. Maybe look at the source of clusplot instead, and take only the parts you want?
I have a plot, where the lines are only in the negative range and when I plot it, the axis is automatically changed, i.e. negative larger values are going up and not down, in a normal plot. Currently I have the following plot:
But I want to have the y axis the other way round, so that negative larger values are going down and not up, I hope it is understandable what I mean.
How can I achieve this in R?
My code with my specific data is just using the normal plot() function.
As Ben Bolker said, the following has to be said:
I set the ylim range wrong, I set it like
ylim=c(-0.05,-1)
but
ylim=c(-1,-0.05)
should do what I want!
I am in my way of finishing the graphs for a paper and decided (after a discussion on stats.stackoverflow), in order to transmit as much information as possible, to create the following graph that present both in the foreground the means and in the background the raw data:
However, one problem remains and that is overplotting. For example, the marked point looks like it reflects one data point, but in fact 5 data points exists with the same value at that place.
Therefore, I would like to know if there is a way to deal with overplotting in base graph using points as the function.
It would be ideal if e.g., the respective points get darker, or thicker or,...
Manually doing it is not an option (too many graphs and points like this). Furthermore, ggplot2 is also not what I want to learn to deal with this single problem (one reason is that I tend to like dual-axes what is not supprted in ggplot2).
Update: I wrote a function which automatically creates the above graphs and avoids overplotting by adding vertical or horizontal jitter (or both): check it out!
This function is now available as raw.means.plot and raw.means.plot2 in the plotrix package (on CRAN).
Standard approach is to add some noise to the data before plotting. R has a function jitter() which does exactly that. You could use it to add the necessary noise to the coordinates in your plot. eg:
X <- rep(1:10,10)
Z <- as.factor(sample(letters[1:10],100,replace=T))
plot(jitter(as.numeric(Z),factor=0.2),X,xaxt="n")
axis(1,at=1:10,labels=levels(Z))
Besides jittering, another good approach is alpha blending which you can obtain (on the graphics devices supporing it) as the fourth color parameter. I provided an example for 'overplotting' of two histograms in this SO question.
One additional idea for the general problem of showing the number of points is using a rug plot (rug function), this places small tick marks along the margin that can show how many points contribute (still use jittering or alpha blending for ties). This allows the actual points to show their true rather than jittered values, but the rug can then indicate which parts of the plot have more values.
For the example plot direct jittering or alpha blending is probably best, but in some other cases the rug plot can be useful.
You may also use sunflowerplot, while it would be hard to implement it here. I would use alpha-blending, as Dirk suggested.