I've search a lot in this forum. However, I didn't found a similar problem as the one I'm facing.
My question is:
I have two vectors
x <- c(1,1,2,2,3,3,3,4,4,4,6,7,8) and z <- c(1,1,2,4,5,5,5)
I need to count the number of times x or z appears in each other including if they are repeated or not.
The answer for this problem should be 4 because :
There are two number 1, one number 2, and one number 4 in each vector.
Functions like match() don't help since they will return the answer of repeated for non repeated numbers. Using unique() will also alter the final answer from 4 to 3
What I came up with was a loop that every time it found one number in the other, it would remove from the list so it won't be counted again.
The loop works fine for this size of this example; however, searching for larger vectors numerous times makes my loop inefficient and too slow for my purposes.
system.time({
for(n in 1:1000){
x <- c(1,1,2,2,3,3,3,4,4,4,6,7,8)
z <- c(1,1,2,4,5,5,5)
score <- 0
for(s in spectrum){
if(s %in% sequence){
sequence <- sequence[-which(sequence==s)[1]]
score <- score + 1
}
}
}
})
Can someone suggest a better method?
I've tried using lapply, for short vectors it is faster, but it became slower for longer ones..
Use R's vectorization to your advantage here. There's no looping necessary.
You could use a table to look at the frequencies,
table(z[z %in% x])
#
# 1 2 4
# 2 1 1
And then take the sum of the table for the total
sum(table(z[z %in% x]))
# [1] 4
Related
I am trying to create a vector from another vector where I multiply the numbers in the vector one more each time.
For example if I had (1,2,3) the new vector would be (1, 1 x 2, 1 x 2 x 3)=(1,2,6)
I tried to create a loop for this as seen below. It seems to work for whole numbers but not decimals. I am not sure why.
x <- c(0.99,0.98,0.97,0.96,0.95)
for(i in 1:5){x[i]=prod(x[1:i])}
The result given is 0.9900000 0.9702000 0.9316831 0.8590845 0.7303385
which is incorrect as prod(x) = 0.8582777. Which is not the same as the last element of the vector.
Does anyone know why this is the case? Or have a suggestion for improvement in my code to get the correct answer.
test<-c(1,2,3)
cumprod(test)
[1] 1 2 6
As #akrun suggests, one can achieve the same with:
Reduce("*", test, accumulate = TRUE)
I am trying to select relevant rows from a large time-series data set. The tricky bit is, that the needed rows are before and after certain values in a column.
# example data
x <- rnorm(100)
y <- rep(0,100)
y[c(13,44,80)] <- 1
y[c(20,34,92)] <- 2
df <- data.frame(x,y)
In this case the critical values are 1 and 2 in the df$y column. If, e.g., I want to select 2 rows before and 4 after df$y==1 I can do:
ones<-which(df$y==1)
selection <- NULL
for (i in ones) {
jj <- (i-2):(i+4)
selection <- c(selection,jj)
}
df$selection <- 0
df$selection[selection] <- 1
This, arguably, scales poorly for more values. For df$y==2 I would have to repeat with:
twos<-which(df$y==2)
selection <- NULL
for (i in twos) {
jj <- (i-2):(i+4)
selection <- c(selection,jj)
}
df$selection[selection] <- 2
Ideal scenario would be a function doing something similar to this imaginary function selector(data=df$y, values=c(1,2), before=2, after=5, afterafter = FALSE, beforebefore=FALSE), where values is fed with the critical values, before with the amount of rows to select before and correspondingly after.
Whereas, afterafter would allow for the possibility to go from certain rows until certain rows after the value, e.g. after=5,afterafter=10 (same but going into the other direction with afterafter).
Any tips and suggestions are very welcome!
Thanks!
This is easy enough with rep and its each argument.
df$y[rep(which(df$y == 2), each=7L) + -2:4] <- 2
Here, rep repeats the row indices that your criterion 7 times each (two before, the value, and four after, the L indicates that the argument should be an integer). Add values -2 through 4 to get these indices. Now, replace.
Note that for some comparisons, == will not be adequate due to numerical precision. See the SO post why are these numbers not equal for a detailed discussion of this topic. In these cases, you could use something like
which(abs(df$y - 2) < 0.001)
or whatever precision measure will work for your problem.
I want a list of all possible sets of five (or n) numbers between 1 and 63 (or more generalizably 1 and k)
If computing time wasn't an issue, I could do something like
#Get all combenations of numbers between 1 and 63
indexCombinations <- expand.grid(1:63, 1:63, 1:63, 1:63, 1:63)
#Throw out the rows that have more than one of the same number in them
allDifferent <- apply(indexCombinations, 1, function(x){
length(x) == length(unique(x))
} # function
) # apply
indexCombinationsValid <- indexCombinations[allDifferent,]
# And then just take the unique values
indexCombinationsValidUnique <- unique(indexCombinationsValid)
The finding of unique values, I am concerned, is going to be prohibitively slow. Furthermore, I end up having to make a bunch of rows in the first place I never use. I was wondering if anyone has a more elegant and efficient way of getting a data frame or matrix of unique combinations of each of five numbers (or n numbers) between one and some some range of values.
Credit to #SymbolixAU for a very elegant solution, which I re-post here as an answer:
n <- 1:63; x <- combn(n, m = 5)
I am trying to create a function that takes the sum of the first n odd integers, i.e the summation from i=1 to n of (2i-1).
If n = 1 it should output 1
If n = 2 it should output 4
I'm having problems using a for loop which only outputs the nth term
n <-2
for (i in 1:n)
{
y<-((2*i)-1)
}
y
In R programming we try avoiding for loops
cumsum ( seq(1,2*n, by=2) )
Or just use 'sum' if you don't want the series of partial sums.
There's actually no need to use a loop or to construct the sequence of the first n odd numbers here -- this is an arithmetic series so we know the sum of the first n elements in closed form:
sum.first.n.odd <- function(n) n^2
sum.first.n.odd(1)
[1] 1
sum.first.n.odd(2)
[1] 4
sum.first.n.odd(100)
[1] 10000
This should be a good deal more efficient than any solution based on for or sum because it never computes the elements of the sequence.
[[Just seeing the title -- the OP apparently knows the analytic result and wanted something else...]]
Try this:
sum=0
n=2
for(i in seq(1,2*n,2)){
sum=sum+i
}
But, of course, R is rather slow when working with loops. That's why one should avoid them.
I have a numeric vector of length 5,000,000
>head(coordvec)
[1] 47286545 47286546 47286547 47286548 47286549 472865
and a 3 x 1,400,000 numeric matrix
>head(subscores)
V1 V2 V3
1 47286730 47286725 0.830
2 47286740 47286791 0.065
3 47286750 47286806 -0.165
4 47288371 47288427 0.760
5 47288841 47288890 0.285
6 47288896 47288945 0.225
What I am trying to accomplish is that for each number in coordvec, find the average of V3 for rows in subscores in which V1 and V2 encompass the number in coordvec. To do that, I am taking the following approach:
results<-numeric(length(coordvec))
for(i in 1:length(coordvec)){
select_rows <- subscores[, 1] < coordvec[i] & subscores[, 2] > coordvec[i]
scores_subset <- subscores[select_rows, 3]
results[m]<-mean(scores_subset)
}
This is very slow, and would take a few days to finish. Is there a faster way?
Thanks,
Dan
I think there are two challenging parts to this question. The first is finding the overlaps. I'd use the IRanges package from Bioconductor (?findInterval in the base package might also be useful)
library(IRanges)
creating width 1 ranges representing the coordinate vector, and set of ranges representing the scores; I sort the coordinate vectors for convenience, assuming that duplicate coordinates can be treated the same
coord <- sort(sample(.Machine$integer.max, 5000000))
starts <- sample(.Machine$integer.max, 1200000)
scores <- runif(length(starts))
q <- IRanges(coord, width=1)
s <- IRanges(starts, starts + 100L)
Here we find which query overlaps which subject
system.time({
olaps <- findOverlaps(q, s)
})
This takes about 7s on my laptop. There are different types of overlaps (see ?findOverlaps) so maybe this step requires a bit of refinement.
The result is a pair of vectors indexing the query and overlapping subject.
> olaps
Hits of length 281909
queryLength: 5000000
subjectLength: 1200000
queryHits subjectHits
<integer> <integer>
1 19 685913
2 35 929424
3 46 1130191
4 52 37417
I think this is the end of the first complicated part, finding the 281909 overlaps. (I don't think the data.table answer offered elsewhere addresses this, though I could be mistaken...)
The next challenging part is calculating a large number of means. The built-in way would be something like
olaps0 <- head(olaps, 10000)
system.time({
res0 <- tapply(scores[subjectHits(olaps0)], queryHits(olaps0), mean)
})
which takes about 3.25s on my computer and appears to scale linearly, so maybe 90s for the 280k overlaps. But I think we can accomplish this tabulation efficiently with data.table. The original coordinates are start(v)[queryHits(olaps)], so as
require(data.table)
dt <- data.table(coord=start(q)[queryHits(olaps)],
score=scores[subjectHits(olaps)])
res1 <- dt[,mean(score), by=coord]$V1
which takes about 2.5s for all 280k overlaps.
Some more speed can be had by recognizing that the query hits are ordered. We want to calculate a mean for each run of query hits. We start by creating a variable to indicate the ends of each query hit run
idx <- c(queryHits(olaps)[-1] != queryHits(olaps)[-length(olaps)], TRUE)
and then calculate the cumulative scores at the ends of each run, the length of each run, and the difference between the cumulative score at the end and at the start of the run
scoreHits <- cumsum(scores[subjectHits(olaps)])[idx]
n <- diff(c(0L, seq_along(idx)[idx]))
xt <- diff(c(0L, scoreHits))
And finally, the mean is
res2 <- xt / n
This takes about 0.6s for all the data, and is identical to (though more cryptic than?) the data.table result
> identical(res1, res2)
[1] TRUE
The original coordinates corresponding to the means are
start(q)[ queryHits(olaps)[idx] ]
Something like this might be faster :
require(data.table)
subscores <- as.data.table(subscores)
subscores[, cond := V1 < coordvec & V2 > coordvec]
subscores[list(cond)[[1]], mean(V3)]
list(cond)[[1]] because: "When i is a single variable name, it is not considered an expression of column names and is instead evaluated in calling scope." source: ?data.table
Since your answer isn't easily reproducible and even if it were, none of your subscores meet your boolean condition, I'm not sure if this does exactly what you're looking for but you can use one of the apply family and a function.
myfun <- function(x) {
y <- subscores[, 1] < x & subscores[, 2] > x
mean(subscores[y, 3])
}
sapply(coordvec, myfun)
You can also take a look at mclapply. If you have enough memory this will probably speed things up significantly. However, you could also look at the foreach package with similar results. You've got your for loop "correct" by assigning into results rather than growing it, but really, you're doing a lot of comparisons. It will be hard to speed this up much.