I want to the find the closest match down to their each character's position if possible.
I'm not sure should I dive into writing a loop to check each character? Or is there any more efficient or elegant alternatives?
Sample Data:
look up in lookUp_List for the one that is closest to the item and in this sample, ideally it should be bmw6 or the next, not so ideal could be bmws as well.
Best if could have both methods then I can apply to my data and check which is better.
Thank you so much in advanced!!
item = "bmwx650isport(a)"
lookUp_List = c("bmw5", "bmw6", "bmws")
Trial:
amatch(item, lookUp_List, maxDist = 30)
# [1] 1
stringdist(item, lookUp_List)
# [1] 12 12 12
Related
I have a boolean vector in which I want to count the number of occurrences of some patterns.
For example, for the pattern "(1,1)" and the vector "(1,1,1,0,1,1,1)", the answer should be 4.
The only built-in function I found to help is grepRaw, which finds the occurrences of a particular string in a longer string. However, it seems to fail when the sub-strings matching the pattern overlap:
length(grepRaw("11","1110111",all=TRUE))
# [1] 2
Do you have any ideas to obtain the right answer in this case?
Edit 1
I'm afraid that Rich's answer works for the particular example I posted, but fails in a more general setting:
> sum(duplicated(rbind(c(FALSE,FALSE),embed(c(TRUE,TRUE,TRUE,FALSE,TRUE,TRUE,TRUE),2))))
[1] 3
In this other example, the expected answer would be 0.
Using the function rollapply you can apply a moving window of width = 2 summing the values. Then you can sum the records where the result is equal to 2 i.e. sum(c(1,1))
library(zoo)
z <- c(1,1,1,0,1,1,1)
sum(rollapply(z, 2, sum) == 2)
Background:
On this combinatorics question, the issue is how to determine the sample space: the ways 8 different soccer teams can be paired up for the next round of competition. Two different answers have been advanced for that part of the problem: 28 (see comments OP) and 105 (see edit within OP and answer).
I'd like to do this manually to try to hone down on the mistake in whichever answer is incorrect.
What I have tried:
teams = 1:8
names(teams) = c("RM", "BCN", "SEV", "JUV", "ROM", "MC", "LIV", "BYN")
split(sample(teams), rep(1:(length(teams)/2), each=2))
Unfortunately, the output is a list, and I wanted a vector to be able to run something like:
unique(...,MARGIN=2)
Is there a way of doing this in an elegant manner?
After a now erased answer (thank you), I would go with
a <- replicate(1e5, unlist(split(sample(teams), rep(1:(length(teams)/2), each=2))))
to simulate 100,000 random samples, and later run
unique(a, MARGIN = 2).
But how can I account for the fact that the order of the 4 pairings of opponents doesn't matter, and that LIV-BYN and BYN-LIV, for example, is the same pairing (field advantage notwithstanding)?
> u = ncol(unique(replicate(1e6, unlist(split(sample(teams), rep(1:(length(teams)/2), each=2)))), MARGIN = 2))
> u / (factorial(4) * 2^4)
[1] 105
The idea of unlist is from #Song Zhengyi, and if his answer is un-deleted, I'll accept it. The complete answer is in the lines above.
u needs to be divided by 4! because
BCN-RM, BYN-SEV, JUV-ROM, LIV-MC
is exactly the same as
LIV-MC, BCN-RM, BYN-SEV, JUV-ROM
or
BCN-RM, LIV-MC, BYN-SEV, JUV-ROM
etc.
The term 2^4 is to avoid over-counting since for every possible unique draw, each one of the pairings can be flipped without loss (discarding field advantage): BCN-RM is the same as RM-BCN, and there are 4 pairs in each draw.
If field advantage is a consideration (real life)...
> u/factorial(4)
[1] 1680
we end up with 1,680 possible draws.
I am trying to create some lines on a graph based on a third coordinate (x,y, temp). I would like to get a vector of indexes so I can split them into x and y vectors for each duplicate temperature. To make this more clear, I will include my actual data set:
DataFrame
I am trying to make multiple lines that have the same temp value. For example, I would like to have the following coordinates on the same line [0,14] [0,22] [0,26] [0,28]. They all have the temp value of 5.8. Once I find the duplicates, I will record the indexes in a vector which will allow me to retrieve the x and y coordinates. One other aspect is that I will not always know how many entries are going to be in the data.frame.
My question is how can I find the duplicates and store their indices in a vector? Once I have the indices for the duplicate temps, I can be sure to grab their x y coordinates and use that to create lines.
If you can answer my question or have any advice on how I can do this better, all help is appreciated
Consider the following:
df <- data.frame(temp = sample.int(n=3, size=5, replace=T))
df
temp
1 3
2 3
3 1
4 3
5 1
duplicated(df$temp)
[1] FALSE TRUE FALSE TRUE TRUE
which(duplicated(df$temp))
[1] 2 4 5
You've stated in the comments that you're looking to make an isopleth graph. The procedure you have described will not generate anything resembling an isopleth graph. Since it looks like your data is arranged in a regular grid, you should do something like the solutions presented in this question and answer, which use functions specifically designed for extracting contours from a grid of values. Another option is the contourLines function in the gDevices package. If you want higher-resolution, less jagged contours, you might look into using either the interp.surface or Krig functions from the fields package to interpolate your data to the resolution you require.
Relatively simple problem, but I'm thinking around in circles, and not reaching a solution. Essentially, I have x=sample(1:30,20) and I need to find the coordinates of each individual number. If I use grep, then I get where each number that has a 1 (including 10, 19, 21 etc.) is. I have a feeling there's an obscenely simple solution to this, but I can't think of it for some reason.
for example: if x=c(2,3,1,10,12),
then
f(1,x)
[1] 3
and
f(3,x)
[1] 2
Note: I tried using fixed = T, but that did not help.
you want which.
x <- c(2,3,1,10,12)
which(x==10)
[1] 4
I'm working on a dataset that consists of ~10^6 values which clustered into a variable number of bins. In the course of my analysis, I am trying to randomize my clustering, but keeping bin size constant. As a toy example (in pseudocode), this would look something like this:
data <- list(c(1,5,6,3), c(2,4,7,8), c(9), c(10,11,15), c(12,13,14));
sizes <- lapply(data, length);
for (rand in 1:no.of.randomizations) {
rand.data <- partition.sample(seq(1,15), partitions=sizes, replace=F)
}
So, I am looking for a function like "partition.sample" that will take a vector (like seq(1,15)) and randomly sample from it, returning a list with the data partitioned into the right bin sizes given already by "sizes".
I've been trying to write one such function myself, since the task seems to be not so hard. However, the partitioning of a vector into given bin sizes looks like it would be a lot faster and more efficient if done "under the hood", meaning probably not in native R. So I wonder whether I have just missed the name of the appropriate function, or whether someone could please point me to a smart solution that is around :-)
Your help & time are very much appreciated! :-)
Best,
Lymond
UPDATE:
By "no.of.randomizations" I mean the actual number of times I run through the whole "randomization loop". This will, later on, obviously include more steps than just the actual sampling.
Moreover, I would in addition be interested in a trick to do the above feat for sampling without replacement.
Thanks in advance, your help is very much appreciated!
Revised: This should be fairly efficient. It's complexity should be primarily in the permutation step:
# A single step:
x <- sample( unlist(data))
list( one=x[1:4], two=x[5:8], three=x[9], four=x[10:12], five=x[13:16])
As mentioned above the "no.of.randomizations" may have been the number of repeated applications of this proces, in which case you may want to wrap replicate around that:
replic <- replicate(n=4, { x <- sample(unlist(data))
list( x[1:4], x[5:8], x[9], x[10:12], x[13:15]) } )
After some more thinking and googling, I have come up with a feasible solution. However, I am still not convinced that this is the fastest and most efficient way to go.
In principle, I can generate one long vector of a uniqe permutation of "data" and then split it into a list of vectors of lengths "sizes" by going via a factor argument supplied to split. For this, I need an additional ID scheme for my different groups of "data", which I happen to have in my case.
It becomes clearer when viewed as code:
data <- list(c(1,5,6,3), c(2,4,7,8), c(9), c(10,11,15), c(12,13,14));
sizes <- lapply(data, length);
So far, everything as above
names <- c("set1", "set2", "set3", "set4", "set5");
In my case, I am lucky enough to have "names" already provided from the data. Otherwise, I would have to obtain them as (e.g.)
names <- seq(1, length(data));
This "names" vector can then be expanded by "sizes" using rep:
cut.by <- rep(names, times = sizes);
[1] 1 1 1 1 2 2 2 2 3 4 4 4 5
[14] 5 5
This new vector "cut.by" can then by provided as argument to split()
rand.data <- split(sample(1:15, 15), cut.by)
$`1`
[1] 8 9 14 4
$`2`
[1] 10 2 15 13
$`3`
[1] 12
$`4`
[1] 11 3 5
$`5`
[1] 7 6 1
This does the job I was looking for alright. It samples from the background "1:15" and splits the result into vectors of lengths "sizes" through the vector "cut.by".
However, I am still not happy to have to go via an additional (possibly) long vector to indicate the split positions, such as "cut.by" in the code above. This definitely works, but for very long data vectors, it could become quite slow, I guess.
Thank you anyway for the answers and pointers provided! Your help is very much appreciated :-)