I often determine that when plotting in R not all relevant tick-marks are drawn. Relevant here means that there is data present.
See this example
> set.seed(NULL)
> d <- data.frame(a=sample(1:10, replace=TRUE), b=sample(11:30))
> plot(d)
The resulting plot where you can see values on the X-axis at 3, 5, 7 and 9. But the tick-marks for them are missing.
The focus of my question is to understand why R acts like that. What is the algorithm and logic behind it?
btw: I know how to solve it. I can draw the X-axis myself. But that is not part of the question.
You could find a brief description of the algorithm for plotting the tick marks using?axis.
plot() is a generic function to plot a wide sort of data. In your example, you are using discrete data. For continuous data, it does not make much sense to have a single tick mark for every single value, which would make unreadable the axes.
However, you can easily adjust the ticks in your plot using axis()
Related
I have performed PCA Analysis using the prcomp function apart of the FactoMineR package on quite a substantial dataset of 3000 x 500.
I have tried plotting the main Principal Components that cover up to 100% of cumulative variance proportion with a fviz_eig plot. However, this is a very large plot due to the large dimensions of the dataset. Is there any way in R to split a plot into multiple plots using a for loop or any other way?
Here is a visual of my plot that only cover 80% variance due to the fact it being large. Could I split this plot into 2 plots?
Large Dataset Visualisation
I have tried splitting the plot up using a for loop...
for(i in data[1:20]) {
fviz_eig(data, addlabels = TRUE, ylim = c(0, 30))
}
But this doesn't work.
Edited Reproducible example:
This is only a small reproducible example using an already available dataset in R but I used a similar method for my large dataset. It will show you how the plot actually works.
# Already existing data in R.
install.packages("boot")
library(boot)
data(frets)
frets
dataset_pca <- prcomp(frets)
dataset_pca$x
fviz_eig(dataset_pca, addlabels = TRUE, ylim = c(0, 100))
However, my large dataset has a lot more PCs that this one (possibly 100 or more to cover up to 100% of cumulative variance proportion) and therefore this is why I would like a way to split the single plot into multiple plots for better visualisation.
Update:
I have performed what was said by #G5W below...
data <- prcomp(data, scale = TRUE, center = TRUE)
POEV = data$sdev^2 / sum(data$sdev^2)
barplot(POEV, ylim=c(0,0.22))
lines(0.7+(0:10)*1.2, POEV, type="b", pch=20)
text(0.7+(0:10)*1.2, POEV, labels = round(100*POEV, 1), pos=3)
barplot(POEV[1:40], ylim=c(0,0.22), main="PCs 1 - 40")
text(0.7+(0:6)*1.2, POEV[1:40], labels = round(100*POEV[1:40], 1),
pos=3)
and I have now got a graph as follows...
Graph
But I am finding it difficult getting the labels to appear above each bar. Can someone help or suggest something for this please?
I am not 100% sure what you want as your result,
but I am 100% sure that you need to take more control over
what is being plotted, i.e. do more of it yourself.
So let me show an example of doing that. The frets data
that you used has only 4 dimensions so it is hard to illustrate
what to do with more dimensions, so I will instead use the
nuclear data - also available in the boot package. I am going
to start by reproducing the type of graph that you displayed
and then altering it.
library(boot)
data(nuclear)
N_PCA = prcomp(nuclear)
plot(N_PCA)
The basic plot of a prcomp object is similar to the fviz_eig
plot that you displayed but has three main differences. First,
it is showing the actual variances - not the percent of variance
explained. Second, it does not contain the line that connects
the tops of the bars. Third, it does not have the text labels
that tell the heights of the boxes.
Percent of Variance Explained. The return from prcomp contains
the raw information. str(N_PCA) shows that it has the standard
deviations, not the variances - and we want the proportion of total
variation. So we just create that and plot it.
POEV = N_PCA$sdev^2 / sum(N_PCA$sdev^2)
barplot(POEV, ylim=c(0,0.8))
This addresses the first difference from the fviz_eig plot.
Regarding the line, you can easily add that if you feel you need it,
but I recommend against it. What does that line tell you that you
can't already see from the barplot? If you are concerned about too
much clutter obscuring the information, get rid of the line. But
just in case, you really want it, you can add the line with
lines(0.7+(0:10)*1.2, POEV, type="b", pch=20)
However, I will leave it out as I just view it as clutter.
Finally, you can add the text with
text(0.7+(0:10)*1.2, POEV, labels = round(100*POEV, 1), pos=3)
This is also somewhat redundant, but particularly if you change
scales (as I am about to do), it could be helpful for making comparisons.
OK, now that we have the substance of your original graph, it is easy
to separate it into several parts. For my data, the first two bars are
big so the rest are hard to see. In fact, PC's 5-11 show up as zero.
Let's separate out the first 4 and then the rest.
barplot(POEV[1:4], ylim=c(0,0.8), main="PC 1-4")
text(0.7+(0:3)*1.2, POEV[1:4], labels = round(100*POEV[1:4], 1),
pos=3)
barplot(POEV[5:11], ylim=c(0,0.0001), main="PC 5-11")
text(0.7+(0:6)*1.2, POEV[5:11], labels = round(100*POEV[5:11], 4),
pos=3, cex=0.8)
Now we can see that even though PC 5 is much smaller that any of 1-4,
it is a good bit bigger than 6-11.
I don't know what you want to show with your data, but if you
can find an appropriate way to group your components, you can
zoom in on whichever PCs you want.
I'm working on trying to create a key for a heatmap, but as far as I know, I cannot use the existing tools for adding a legend since I've generated the colors myself (I manually turn a scaled variable into rgb values for a short rainbow ( [255,0,0] to [0,0,255] ).
Basically, all I want to do is use the rightmost 10th of the screen to create a rectangle with these 10 colors: "#0000FF", "#0072FF", "#00E3FF", "#00FFAA", "#00FF38", "#39FF00", "#AAFF00", "#FFE200", "#FF7100", "#FF0000"
with three numerical labels - at 0, max/2, and max
In essence, I want to manually produce an object that looks like a rudimentary heatmap color key.
As far as I know, split.screen can only split the screen in half, which isn't what I'm looking for. I want the graphic I already know how to produce to take up the leftmost 90% of the screen, and I want this colored rectangle to take up the other 10%.
Thanks.
EDIT: I greatly appreciate the advice about the best way to the the plot - that said, I still would like to know the best way to do the task originally asked - creating the legend by hand; I already am able to produce the exact heatmap graphic that I'm looking for - the false coloring wasn't the only problem with ggplot that I was having - it was just the final factor convincing me to switch. I need a non ggplot solution.
EDIT #2: This is close to the solution I am looking for, except this only goes up to 10 instead of accepting a maximum value as a parameter (I will be running this code on multiple data-sets, all with different maximum values - I want the legend to reflect this). Additionally, if I change the size of the graph, the key falls apart into disconnected squares.
Take a look at the layouts function (link). I think you want something like this:
layout(matrix(c(1,2), 1, 2, byrow = TRUE), widths=c(9,1))
## plot heatmap
## plot legend
I would also recommend the ggplot2 package and the geom_tile function which will take care of all of this for you.
Assuming your data is in a data frame with the x and y coordinates and heatmap value (e.g. gdat <- data.frame(x_coord=c(1,2,...), y_coord=c(1,1,...), val=c(6,2,...))) Then you should be able to produce your desired heat map plot with the following ggplot command:
ggplot(gdat) + geom_tile(aes(x=x_coord, y=y_coord, fill=val)) +
scale_fill_gradient(low="#0000FF", high="#FF0000")
To get your data into the following format you may want to look into the very useful reshape2 package.
Given a script no ggplot restriction on this answer here is how one could produce the plot with just base R.
colors <- c("#0000FF", "#0072FF", "#00E3FF", "#00FFAA", "#00FF38",
"#39FF00", "#AAFF00", "#FFE200", "#FF7100", "#FF0000")
layout(matrix(c(1,2), 1, 2, byrow = TRUE), widths=c(9,1))
plot(rnorm(20), rnorm(20), col=sample(colors, 20, replace=TRUE))
par(mar=c(0,0,0,0))
plot(x=rep(1,10), y=1:10, col=colors, pch=15, cex=7.1)
You may have to adjust the cex for your device.
I have two problems that I am having trouble to solve for. Firstly when I do a multiple column matrix plot using lattice xyplot, I find that all the points are connected. How can I get separate disconnected lines?
x<-cbind(rnorm(10),rnorm(10))
xyplot(x~1:nrow(x),type="l")
Secondly, I am having trouble figuring out how to make one line thicker than the other. For example, given that I want column 1, then column 1's line will be thicker than that of column 2.
The lattice plotting paradigm,like that of ggplot2 that followed it, expects data to be in long format in dataframes:
dfrm <- data.frame( y=c(rnorm(10),rnorm(10)),
x=1:10,
grp=rep(c("a","b"),each=10))
xyplot(y~x, group=grp, type="l", data=dfrm, col=c("red","blue"))
This might not be the most elegant solution but it gets the job done:
x<-cbind(rnorm(10),rnorm(10))
plot1<-xyplot(x[,1]~1:nrow(x),type="l",col="red",lwd=3)
plot2<-xyplot(x[,2]~1:nrow(x),type="l")
library(latticeExtra)
plot1+plot2
I assumed that you wanted V1 and V2 plotted against the number of observations.
Otherwise you indeed only have one line.
You can adjust the axis and labels according to taste.
I have two related problems.
Problem 1: I'm currently using the code below to generate a histogram overlayed with a density plot:
hist(x,prob=T,col="gray")
axis(side=1, at=seq(0,100, 20), labels=seq(0,100,20))
lines(density(x))
I've pasted the data (i.e. x above) here.
I have two issues with the code as it stands:
the last tick and label (100) of the x-axis does not appear on the histogram/plot. How can I put these on?
I'd like the y-axis to be of count or frequency rather than density, but I'd like to retain the density plot as an overlay on the histogram. How can I do this?
Problem 2: using a similar solution to problem 1, I now want to overlay three density plots (not histograms), again with frequency on the y-axis instead of density. The three data sets are at:
http://pastebin.com/z5X7yTLS
http://pastebin.com/Qg8mHg6D
http://pastebin.com/aqfC42fL
Here's your first 2 questions:
myhist <- hist(x,prob=FALSE,col="gray",xlim=c(0,100))
dens <- density(x)
axis(side=1, at=seq(0,100, 20), labels=seq(0,100,20))
lines(dens$x,dens$y*(1/sum(myhist$density))*length(x))
The histogram has a bin width of 5, which is also equal to 1/sum(myhist$density), whereas the density(x)$x are in small jumps, around .2 in your case (512 even steps). sum(density(x)$y) is some strange number definitely not 1, but that is because it goes in small steps, when divided by the x interval it is approximately 1: sum(density(x)$y)/(1/diff(density(x)$x)[1]) . You don't need to do this later because it's already matched up with its own odd x values. Scale 1) for the bin width of hist() and 2) for the frequency of x length(x), as DWin says. The last axis tick became visible after setting the xlim argument.
To do your problem 2, set up a plot with the correct dimensions (xlim and ylim), with type = "n", then draw 3 lines for the densities, scaled using something similar to the density line above. Think however about whether you want those semi continuous lines to reflect the heights of imaginary bars with bin width 5... You see how that might make the density lines exaggerate the counts at any particular point?
Although this is an aged thread, if anyone catches this. I would only think it is a 'good idea' to forego translating the y density to count scales based on what the user is attempting to do.
There are perfectly good reasons for using frequency as the y value. One idea in particular that comes to mind is that using counts for the y scale value can give an analyst a good idea about where to begin the 'data hunt' for stratifying heterogenous data, if a mixed distribution model cannot soundly or intuitively be applied.
In practice, overlaying a density estimate over the observed histogram can be very useful in data quality checks. For example, in the above, if I were looking at the above graphic as a single source of data with the assumption that it describes "1 thing" and I wish to model this as "1 thing", I have an issue. That is, I have heterogeneous data which may require some level of stratification. The density overlay then becomes a simple visual tool for detecting heterogeneity (apart from using log transformations to smooth between-interval variation), and a direction (locations of the mixed distributions) for stratifying the data.
I am a newbie to R and I am trying to do some clustering on a data table where rows represent individual objects and columns represent the features that have been measured for these objects. I've worked through some clustering tutorials and I do get some output, however, the heatmap that I get after clustering does not correspond at all to the heatmap produced from the same data table with another programme. While the heatmap of that programme does indicate clear differences in marker expression between the objects, my heatmap doesn't show much differences and I cannot recognize any clustering (i.e., colour) pattern on the heatmap, it just seems to be a randomly jumbled set of colours that are close to each other (no big contrast). Here is an example of the code I am using, maybe someone has an idea on what I might be doing wrong.
mydata <- read.table("mydata.csv")
datamat <- as.matrix(mydata)
datalog <- log(datamat)
I am using log values for the clustering because I know that the other programme does so, too
library(gplots)
hr <- hclust(as.dist(1-cor(t(datalog), method="pearson")), method="complete")
mycl <- cutree(hr, k=7)
mycol <- sample(rainbow(256)); mycol <- mycol[as.vector(mycl)]
heatmap(datamat, Rowv=as.dendrogram(hr), Colv=NA,
col=colorpanel(40, "black","yellow","green"),
scale="column", RowSideColors=mycol)
Again, I plot the original colours but use the log-clusters because I know that this is what the other programme does.
I tried to play around with the methods, but I don't get anything that would at least somehow look like a clustered heatmap. When I take out the scaling, the heatmap becomes extremely dark (and I am actually quite sure that I have somehow to scale or normalize the data by column). I also tried to cluster with k-means, but again, this didn't help. My idea was that the colour scale might not be used completely because of two outliers, but although removing them slightly increased the range of colours plotted on the heatmap, this still did not reveal proper clusters.
Is there anything else I could play around with?
And is it possible to change the colour scale with heatmap so that outliers are found in the last bin that has a range of "everything greater than a particular value"? I tried to do this with heatmap.2 (argument "breaks"), but I didn't quite succeed and also I didn't manage to put the row side colours that I use with the heatmap function.
If you are okay with using heatmap.2 from the gplots package that will allow you to add breaks to assign colors to ranges represented in your heatmap.
For example if you had 3 colors blue, white, and red with the values going from low to high you could do something like this:
my.breaks <- c(seq(-5, -.6, length.out=6),seq(-.5999999, .1, length.out=4),seq(.100009,5, length.out=7))
result <- heatmap.2(mtscaled, Rowv=T, scale='none', dendrogram="row", symm = T, col=bluered(16), breaks=my.breaks)
In this case you have 3 sets of values that correspond to the 3 colors, the values will differ of course depending on what values you have with your data.
One thing you are doing in your program is to call hclust on your data then to call heatmap on it, however if you look in the heatmap manual page it states:
Defaults to hclust.
So I don't think you need to do that. You might want to take a look at some similar questions that I had asked that might help to point you in the right direction:
Heatmap Question 1
Heatmap Question 2
If you post an image of the heatmap you get and an image of the heatmap that the other program is making it will be easier for us to help you out more.