According to the documentation, the explanation for the jitter function is "Add a small amount of noise to a numeric vector."
What does this mean?
Is a random number associated with each number in the vector and added to it?
Jittering indeed means just adding random noise to a vector of numeric values, by default this is done in jitter-function by drawing samples from the uniform distribution. The range of values in the jittering is chosen according to the data, if amount-parameter is not provided.
I think term 'jittering' covers other distributions than uniform, and it is typically used to better visualize overlapping values, such as integer covariates. This helps grasp where the density of observations is high. It is good practice to mention in the figure legend if some of the values have been jittered, even if it is obvious. Here is an example visualization with the jitter-function as well as a normal distribution jittering where I arbitrarily threw in value sd=0.1:
n <- 500
set.seed(1)
dat <- data.frame(integer = rep(1:3, each=n), continuous = c(rnorm(n, mean=1), rnorm(n, mean=2), rnorm(n, mean=3))^2)
par(mfrow=c(3,1))
plot(dat, main="No jitter for x-axis", xlab="Integer", ylab="Continuous")
plot(jitter(dat[,1]), dat[,2], main="Jittered x-axis (uniform distr.)", xlab="Integer", ylab="Continuous")
plot(dat[,1]+rnorm(3*n, sd=0.1), dat[,2], main="Jittered x-axis (normal distr.)", xlab="Integer", ylab="Continuous")
A really good explanation of the Jitter effect and why it is necessary can be found in the Swirl course on Regression Models in R.
It takes the Sir Francis Galton's data on the relationship between heights of parents and their children and plots it out on the graph without jitter and then with jitter.
This is the one without jitter (plot(child ~ parent, galton)):
This is the one with jitter (please ignore the regression lines) (plot(jitter(child,4) ~ parent,galton)):
The course says that if you do not have jitter, many people will have the same height, so points falls on top of each other which is why some of the circles in the first plot look darker than others. However, by using R's function "jitter" on the children's heights, we can spread out the data to simulate the measurement errors and make high frequency heights more visible.
Related
I want to change the x axis range of my histogram.
How can I remove all hours for which there is no data from the x axis?
Help would be appreciated.
One way you can artificially squeeze the values together is to change the bin size. Here is a randomly generated extreme bimodal distribution like yours. In each bar is 500 counts of data:
hist(rbinom(n=1000,
size=1,
prob = .50))
If we simply reduce the number of bins that the values go into, it reduces the gap in between the values and labeling below where they lie:
hist(rbinom(n=1000,
size=1,
prob = .50),
breaks = 3)
The problem lies in that data won't be represented accurately depending on what you're doing, so I would suggest explaining why you made that decision if you share this elsewhere.
A less extreme example below with various counts but still gaps:
hist(rbinom(n=1000,
size=10,
prob = .20))
And here if we shift the bins again, they will distribute in the plot with less gaps, making it more right skewed but more distribution within the bars:
hist(rbinom(n=1000,
size=10,
prob = .20),
breaks = 6)
As far as I'm aware there isn't really a way to just remove the values in between the plot. For one, its a distribution plot, so whatever values you remove a priori will just redistribute when you generate the plot. Second, if there is in fact a way, and I wouldn't doubt there is, your x axis would look strange, as you would have a bunch of missing data unaccounted for, looking like this:
Therefore, changing the bins, showing where the data is actually allocated, is probably the best choice.
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.
Sorry for the newbie R question...
I have a data.frame that contains measurements of a single variable. These measurements will be distributed differently depending on whether the thing being measured is of type A or type B; that is, you can imagine that my column names are: measurement, type label (A or B). I want to plot the histograms of the measurements for A and B separately, and put the two histograms in the same plot, with each histogram normalised to unit area (this is because I expect the proportions of A and B to differ significantly). By unit area, I mean that A and B each have unit area, not that A+B have unit area. Basically, I want something like geom_density, but I don't want a smoothed distributions for each; I want the histogram bars. Not interleaved, but plotted one on top of the other. Not stacked, although it would be interesting to know how to do this also. (The purpose of this plot is to explore differences in the shapes of the distributions that would indicate that there are quantitative differences between A and B that could be used to distinguish between them.) That's all. Two or more histograms -- not smoothed density plots -- in the same plot with each normalised to unit area. Thanks!
Something like this?
# generate example
set.seed(1)
df <- data.frame(Type=c(rep("A",1000),rep("B",4000)),
Value=c(rnorm(1000,mean=25,sd=10),rchisq(4000,15)))
# you start here...
library(ggplot2)
ggplot(df, aes(x=Value))+
geom_histogram(aes(y=..density..,fill=Type),color="grey80")+
facet_grid(Type~.)
Note that there are 4 times as many samples of type B.
You can also set the y-axis scales to float using: scales="free_y" in the call to facet_grid(...).
I am trying to plot a set of data in R
x <- c(1,4,5,3,2,25)
my Y scale is fixed at 20 so that the last datapoint would effectively not be visible on the plot if i execute the following code
plot(x, ylim=c(0,20), type='l')
i wanted to show the range of the outlying datapoint by showing a smaller box above the plot, with an independent Y scale, representing only this last datapoint.
is there any package or way to approach this problem?
You may try axis.break (plotrix package) http://rss.acs.unt.edu/Rdoc/library/plotrix/html/axis.break.html, with which you can define the axis to break, the style, size and color of the break marker.
The potential disadvantage of this approach is that the trend perception might be fooled. Good luck!
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.