Suppose I have a matrix such like:
x <- matrix(rnorm(1000000), nrow = 500)
How to track or show the progress bar for a single function:
dist(x)
I tried a package pbapply::pblapply(x, dist) but it seems it calculated the dist for each value rather than for the matrix.
I found it can be realized by:
pbapply::pblapply(list(x), dist)[[1]]
But it might be not the best way.
Related
I need to create something like a spider chart in R without using any libraries. That’s my code for now. It creates a figure with points number equal to the length of vector ‘a’. However, I’d like each point to be at the distance from the coordinates center equal to a respective number in a vector, for example one point at a distance 1, another at 2, so on. Is it possible to do so?
a <- 1:6
angle <- seq(0, 2*pi, (2*pi)/length(a))
x <- cos(angle)
y <- sin(angle)
plot(x, y,
type = "l")
See ?stars:
a <- 1:6
stars(matrix(a, nrow=1), scale=FALSE)
For future reference, using R's built-in help search would have found this with ??spider
Sorry this might be basic but I am a newbie. I will be making lots of curves so some advice will be useful for me.
I have a function which I want to plot:
f <- function(x) sum(4*sin(x*seq(1,21,2))/(pi*seq(1,21,2)))
using
curve(f, -pi, pi, n=100)
Unfortunately ,this does not work for me. Please advise.
Thanks
You function isn't vectorized. At the moment it will only take a single scalar input and output a single return value. curve expects that it should be able to feed in a vector of the x values it wants to plot for and should receive a vector of response values. The easiest solution is to just use Vectorize to automatically convert your function into one that can take vector inputs.
f2 <- Vectorize(f)
curve(f2, -pi, pi, n = 100)
However, you might just want to write a vectorized version of the function directly.
I'm trying to arcsine squareroot data lying on [-1,1]. Using transf.arcsine from the metafor package produces NaNs when trying to squareroot the negative datapoints. Conceptually, I want to use arcsin(sgn(x)√|x|) i.e. square the absolute value, apply its previous sign, then arcsine transform it. The trouble is I have no idea how to begin doing this in R. Help would be appreciated.
x <- seq(-1, 1, length = 20)
asin(sign(x) * sqrt(abs(x)))
or as a function
trans.arcsine <- function(x){
asin(sign(x) * sqrt(abs(x)))
}
trans.arcsine(x)
Help in R is just help() or help.search(). So, let's try the obvious,
> help(arcsin)
No documentation for ‘arcsin’ in specified packages and libraries:
OK, that's not good. But it must be able to trig... let's try something even simpler.
help(sin)
There's all the trig functions. And I note, there's a link to Math on the page. Clicking that seems to provide all of the functions you need. It turns out that I could have just typed..
help(Math)
also,
help.search('trigonometry')
I had a similar prob. I wanted to arcsine transform most of the dataset "logmeantd.ascvr" & approached it in this manner:
First make are data range has been transformed b/t -1 and 1 (in this case they were expressed as percentages):
logmeantd.ascvr[1:12] <- logmeantd.ascvr[1:12] * 0.01
Next apply the square root function, sqrt():
logmeantd.ascvr[1:12] <- sqrt(logmeantd.ascvr[1:12])
lastly apply the arc sine function, asin():
logmeantd.ascvr[1:12] <- asin(logmeantd.ascvr[1:12])
*note in this instance I had excluded the MEAN variable of my dataset because I wanted to apply a log function to it, log():
logmeantd.ascvr$MEAN <- log(logmeantd.ascvr$MEAN)
The following loop takes too lonng to run (2mins/iteration)
The tumor_signals is size 950000x422
The normal_signals is size 950000x772
Any ideas for how to speed it up?
for(i in 1:ncol(tumor_signals)){
x <- as.vector(tumor_signals[,i])
print("Assigned x")
y <- t((t(normal_signals) - x)^2)
print("assigned y")
y <- t(sqrt(colSums(y)))
print("done")
#all_distance <- cbind(all_distance,matrix(distance))
print(i)
}
There's a bug in your code -- you don't need to take the transpose of normal_signals. As I understand it, you are trying to compute, for all i = 1,2,...422, and j=1,2,...,772, the Euclidean distance between tumor_signals[,i] and normal_signals[,j]. You would probably want the results in a 422 x 772 matrix. There's a function rdist() in the package fields that will do this for you:
require(fields)
result <- rdist(t(tumor_signals), t(normal_signals))
Incidentally, a Google search for [R Euclidean distance] would have easily found this package.
Learning R language - I know how to do a moving average but I need to do more - but I am not a statistician - unfortunately all the docs seem to be written for statisticians.
I do this in excel a lot, it's really handy for analysis of operational activities.
Here are the fields on each row to make bollinger bands:
Value could be # of calls, complaint ratio, anything
TimeStamp | Value | Moving Average | Moving STDEVP | Lower Control | Upper Control
Briefly, the moving avg and the stdevP point to the prior 8 or so values in the series. Lower control at a given point in time is = moving average - 2*moving stdevP and upper control = moving average + 2*moving stdevP
This can easily be done in excel for a single file, but if I can find a way to make R work R will be better for my needs. Hopefully faster and more reliable when automated, too.
links or tips would be appreciated.
You could use the function rollapply() from the zoo package, providing you work with a zoo series :
TimeSeries <- cumsum(rnorm(1000))
ZooSeries <- as.zoo(TimeSeries)
BollLines <- rollapply(ZooSeries,9,function(x){
M <- mean(x)
SD <- sd(x)
c(M,M+SD*2,M-SD*2)
})
Now you have to remember that rollapply uses a centered frame, meaning that it takes the values to the left and the right of the current day. This is also more convenient and more true to the definition of the Bollinger Band than your suggestion of taking x prior values.
If you don't want to convert to zoo, you can use the vectors as well and write your own function. I added an S3 based plotting function that allows you to easily plot the calculations as well. With these functions, you could do something like :
TimeSeries <- cumsum(rnorm(1000))
X <- BollingerBands(TimeSeries,80)
plot(X,TimeSeries,type="l",main="An Example")
to get :
The function codes :
BollingerBands <- function(x,width){
Start <- width +1
Stop <- length(x)
Trail <- rep(NA,ceiling(width/2))
Tail <- rep(NA,floor(width/2))
Lines <- sapply(Start:Stop,function(i){
M <- mean(x[(i-width):i])
SD <- sd(x[(i-width):i])
c(M,M+2*SD,M-2*SD)
})
Lines <- apply(Lines,1,function(i)c(Trail,i,Tail))
Out <- data.frame(Lines)
names(Out) <- c("Mean","Upper","Lower")
class(Out) <- c("BollingerBands",class(Out))
Out
}
plot.BollingerBands <- function(x,data,lcol=c("red","blue","blue"),...){
plot(data,...)
for(i in 1:3){
lines(x[,i],col=lcol[i])
}
}
There is an illustration in the R Graph Gallery (65) giving code both for calculating the bands and for plotting share prices.
The 2005 code still seems to work six years later and will give IBM's current share price and going back several months
The most obvious bug is the width of the bandwidth and volume lower charts which have been narrowed; there may be another over the number of days covered.