I know I can use
head(sample(x),m)
to print a random selection of m rows from my dataset, but in this case each new draw is randomized. What if, instead of randomizing every draw, I wanted to randomize only the starting position for the first draw, while preserving the order of subsequent rows?
To illustrate, imagine we have a dataset of n rows and I wanted to print m of them in order, starting from a random position. The randomly drawn starting position is 5, so my desired function would print 5, 6, 7, ..., m < n.
This is more of a theoretical question, not a diagnostic one, so I don't believe a MWE example is needed...please let me know if you think it is and I will be happy to provide one.
We create a numeric index using the sample element and adding with the sequence of 'n' rows that should follow it. If the sampled index is say the last row, then we can create a condition to check for those cases
i1 <- sample(nrow(df1), 1)+ 0:3
df1[ i1[i1 <= nrow(df1)], ]
Related
I am having some trouble coming up with a solution that properly handles classifying a variable number of neighbors for any given observation in a data frame based on some condition. I would like to be able to add a simple, binary indicator variable to a data frame that will equal 1 if the condition is satisfied, and 0 if it is not.
Where I am getting stuck is I am unsure how to iteratively check the condition against neighboring observations only, in either direction (i.e., to check if out of 4 neighboring observations in a given column in my data frame, that at least 3 out of 4 of them contain the same value). I have tried first creating another indicator variable indicating if the condition is satisfied or not (1 or 0 = yes or no). Then, I tried setting up a series of ifelse() statements within a loop to try to assign the proper categorization of the observation where the initial condition is satisfied, +/- 2 observations in either direction. However, when I inspect the dataframe after running the loop, only the observation itself (not its neighbors) where the condition is satisfied is receiving the value, rather than all neighboring observations also receiving the value. Here is my code:
#sample data
sample_dat <- data.frame(initial_ind = c(0,1,0,1,0,0,1,1,0,1,0,0))
sample_dat$violate <- NULL
for(i in 1:nrow(dat_date_ord)){
sample_dat$violate[i] <- ifelse(sample_dat$initial_ind[i]==1 &
((sample_dat$initial_ind[i-2]==1 |
sample_dat$initial_ind[i-1]==1) &
(sample_dat$initial_ind[i+2]==1 |
sample_dat$initial_ind[i+1]==1)),
"trending",
"non-trending"
)
}
This loop correctly identifies one of the four points that needs to be labelled "trending", but it does not also assign "trending" to the correct neighbors. In other words, I expect the output to be "trending for observations 7-10, since 3/4 observations in that group of 4 all have a value of 1 in the initial indicator column. I feel like there might be an easier way to accomplish this - but what I need to ensure is that my code is robust enough to identify and assign observations to a group regardless of if I want 3/4 to indicate a group, 5/6, 2/5, etc.
Thank you for any and all advice.
You can use the rollapply function from the zoo package to apply a function to set intervals in your data. The question then becomes about creating a function that satisfies your needs. I'm not sure if I've understood correctly, but it seems you want a function that checks if the condition is true for at least 3/5 of the observation plus its four closest neighbors. In this case just adding the 1s up and checking if they're above 2 works.
library(zoo)
sample_dat <- data.frame(initial_ind = c(0,1,0,1,0,0,1,1,0,1,0,0))
trend_test = function(x){
ifelse(sum(x) > 2, "trending", "non-trending")
}
sample_dat$violate_new = rollapply(sample_dat$initial_ind, FUN = trend_test, width = 5, fill = NA)
Edit: If you want a function that checks if the observation and the next 3 observations have at least 3 1s, you can do something very similar, just by changing the align argument on rollapply:
trend_test_2 = function(x){
ifelse(sum(x) > 2, "trending", "non-trending")
}
sample_dat$violate_new = rollapply(sample_dat$initial_ind, FUN = trend_test_2, width = 4,
fill = NA, align = "left")
Summary: I have an array of 10 rows and 4 columns filled with numbers. I select one number from each row and want to avoid duplicates in the selection.
Elaborate:
I have a grid of 100*100 cells. In that grid are 10 cells that contain a "person". In an iterative process I want to make the persons "walk around" in the grid, but I do not want to occur that two persons are in the same cell at the same time.
I have a vector that describes the positions of the 10 persons. It contains the cell numbers with a person. These positions are counting across all rows and columns (i.e. ranges from 1:10000). For example: Position 234 would be in the 3rd row, 34th column).
Positions<-sample(1:10000,10) #Initial positions
What I did is to first make an array of the surrounding cells of each person (up, right, down, left) giving 4 positions for each person:
Surroundings<-array(c(Positions+100,Positions+1,Positions-100,Positions-1),dim=c(10,4))
I then take a random direction from each of the rows in Surroundings into vector PosNew. It is this last vector in which I want to avoid duplicates.
I could repeat the random selection process of PosNew until it has no duplicates, but this could take very long. There are probably more efficient ways to do this.
For simplicity sake, let's assume that persons do not walk off the grid and no other errors occur.
My script:
Positions<-sample(1:10000,10) #Initial positions
for(i in 1:50) {
Surroundings<-array(c(Positions+100,Positions+1,Positions-100,Positions-1),dim=c(10,4))
PosNew<-Surroundings[cbind(1:10,sample(1:4,10,replace=TRUE))]
Dups<-length(which(duplicated(PosNew)==TRUE))
Positions<-PosNew
}
I am looking for a way to check for duplicates in the selected new positions and make sure that Dups is never above zero. Any suggestions are welcome, including suggestions to make the code faster/more efficient.
Added: What could I do when at some point one or more of the persons really cannot move to an empty cell, because all 4 sides are occupied? I want that person to stay in its original cell. How to code that?
Thank you so much for your time!
As this is an iterative process, where every person's move depends on the locations of others, I don't think you can do much better then moving one person and sampling the position for the next from the difference of the sets of all directions and all occupied positions (note that this adds a bit of unfairness as the first person has the most freedom to move, so to speak).
So the code would be something like this:
Positions <- sample(1:10000, 10) #Initial positions
for (i in 1:50) {
Surroundings <-
array(c(Positions + 100, Positions + 1, Positions - 100, Positions - 1),
dim = c(10, 4))
# BEGIN NEW CODE
PosNew <- numeric(10)
for (i in 1:10) {
# PosNew[seq_len(i-1)] is the set of occupied positions
available <- setdiff(Surroundings[i, ], PosNew[seq_len(i-1)])
if (length(available) != 0)
PosNew[i] <- sample(available, 1)
else
PosNew[i] <- Positions[i] # stay where you are
}
# END NEW CODE
Dups <- sum(duplicated(PosNew)) # shorter version - sum logical values to get a count
Positions <- PosNew
}
Hope this helps!
I am dealing with some computational feature extracting problem from RNA data, and I found myself unable to deal with this question:
I have n sequences (say two for example) from which I obtained an iterated statistic i times (kind of doing a Monte Carlo iteration for analizing distribution of obtained statistics compared with original).
Example:
Say we iterate 10 times
n <- 10
I got a vector of 20 values with all the iterations, but this vector corresponds to two different sequences, so I must divide this vector in two equal parts (the iterations are ordered 1:10 - 1:10 for each sequence).
MFEit <- c(10, 12, 34, 32, 12 .....) ## vector of length 20
MFEit.split <- split(MFEit, ceiling(MFEit.along/n5))
This generates a list of two items each with 10 values, named $1 and $2
On the other hand I have a vector of two values which are the original statistics, each corresponding to each original sequence
MFE <- c(25, 15)
What I want to do is to know how many values of first item in the list MFEit.split, are equal or less than the first value of MFE, and, iteratively, how many values of second item in the list MFEit.split, are equal or less than the second value of MFE, and so on, provided that I would have more than two values or items.
I know how to do it one by one, say:
R <- length(subset(MFEit.split$`1`, MFEit.split$`1`<=MFE[1]))
R <- length(subset(MFEit.split$`2`, MFEit.split$`1`<=MFE[2]))
But... how to include this into a loop so that I can get iteratively each comparison, no matter how many MFE values or items in the list I have?
The desired output would be a vector called R, with n values corresponding to each comparison.
Any help?...
In this question Getting N random numbers that the sum is M, the object was to generate a set of random numbers that sums to a specific number N. After reading this question, I started playing around with the idea of generating sets of numbers that satisfy this condition
sum(A) == sum(B) && sum(B) == sum(A * B)
An example of this would be
A <- c(5, 5, -10, 6, 6, -12)
B <- c(5, -5, 0, 6, -6, 0)
In this case, the three sums equal zero. Obviously, those sets aren't random, but they satisfy the condition. Is there a way to generate 'random' sets of data that satisfy the above condition? (As opposed to using a little algorithm as in the above example.)
(Note: I tagged this as an R question, but the language really doesn't matter to me.)
You'd need to define the first vector in n-dimensional space, and the 2nd one will have N-2 degrees of freedom (i.e. random numbers) since the sum and one angle are already determined.
The 2nd vector would need to be transformed into N-dimensional space; There are infinitely many transforms that could work, so if you don't care about the probability distribution of the resulting vectors, just choose the one that's most intuitive to you.
There's a nice geometrical interpretation to the first constraint: it constrains the 2nd vector to a (hyper-)plane in N-dimensional space; the 2nd constraint doesn't have a simple geometric interpretation.
check out hyperspherical cooridnates.
You can generate one set completely randomly. And generate randomly all numbers in set B except for two numbers. Since you have two equations you should be able to solve for those two numbers.
I have a matrix, which includes 100 rows and 10 columns, here I want to compare the diversity between rows and sort them. And then, I want to select the 10 maximum dissimilarity rows from it, Which method can I use?
set.seed(123)
mat <- matrix(runif(100 * 10), nrow = 100, ncol = 10)
My initial method is to calculate the similarity (e.g. saying tanimoto coefficient or others: http://en.wikipedia.org/wiki/Jaccard_index ) between two rows, and dissimilairty = 1 - similarity, and then compare the dissimilarty values. At last I will sort all dissimilarity value, and select the 10 maximum dissimilarity values. But it seems that the result is a 100 * 100 matrix, maybe need efficient method to such calculation if there are a large number of rows. However, this is just my thought, maybe not right, so I need help.
[update]
After looking for some literatures. I find the one definition for the maximum dissimilarity method.
Maximum dissimilarity method: It begins by randomly choosing a data record as the first cluster center. The record maximally distant from the first point is selected as the next cluster center. The record maximally distant from both current points is selected after that . The process repeats itself until there is a sufficient number of cluster centers.
Here in my question, the sufficient number should be 10.
Thanks.
First of all, the Jacard Index is not right for you. From the wikipedia page
The Jaccard coefficient measures similarity between finite sample sets...
Your matrix has samples of floats, so you have a different problem (note that the Index in question is defined in terms of intersections; that should be a red flag right there :-).
So, you have to decide what you mean by dissimilarity. One natural interpretation would be to say row A is more dissimilar from the data set than row B if it has a greater Euclidean distance to the center of mass of the data set. You can think of the center of mass of the data set as the vector you get by taking the mean of each of the colums and putting them together (apply(mat, 2, mean)).
With this, you can take the distance of each row to that central vector, and then get an ordering on those distances. From that you can work back to the rows you desire from the original matrix.
All together:
center <- apply(mat, 2, mean)
# not quite the distances, actually, but their squares. That will work fine for us though, since the order
# will still be the same
dists <- apply(mat, 1, function(row) sum((row - center) ** 2))
# this gives us the row indices in order of least to greaest dissimiliarity
dist.order <- order(dists)
# Now we just grab the 10 most dissimilar of those
most.dissimilar.ids <- dist.order[91:100]
# and use them to get the corresponding rows of the matrix
most.dissimilar <- mat[most.dissimilar.ids,]
If I was actually writing this, I probably would have compressed the last three lines as most.dissimilar <- mat[order(dists)[91:100],], but hopefully having it broken up like this makes it a little easier to see what's going on.
Of course, if distance from the center of mass doesn't make sense as the best way of thinking of "dissimilarity" in your context, then you'll have to amend with something that does.