I'm reading a sparse table from a file which looks like:
1 0 7 0 0 1 0 0 0 5 0 0 0 0 2 0 0 0 0 1 0 0 0 1
1 0 0 1 0 0 0 3 0 0 0 0 1 0 0 0 1
0 0 0 1 0 0 0 2 0 0 0 0 1 0 0 0 1 0 1 0 0 1
1 0 0 1 0 3 0 0 0 0 1 0 0 0 1
0 0 0 1 0 0 0 2 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 2 1 0 1 0 1
Note row lengths are different.
Each row represents a single simulation. The value in the i-th column in each row says how many times value i-1 was observed in this simulation. For example, in the first simulation (first row), we got a single result with value '0' (first column), 7 results with value '2' (third column) etc.
I wish to create an average cumulative distribution function (CDF) for all the simulation results, so I could later use it to calculate an empirical p-value for true results.
To do this I can first sum up each column, but I need to take zeros for the undef columns.
How do I read such a table with different row lengths? How do I sum up columns replacing 'undef' values with 0'? And finally, how do I create the CDF? (I can do this manually but I guess there is some package which can do that).
This will read the data in:
dat <- textConnection("1 0 7 0 0 1 0 0 0 5 0 0 0 0 2 0 0 0 0 1 0 0 0 1
1 0 0 1 0 0 0 3 0 0 0 0 1 0 0 0 1
0 0 0 1 0 0 0 2 0 0 0 0 1 0 0 0 1 0 1 0 0 1
1 0 0 1 0 3 0 0 0 0 1 0 0 0 1
0 0 0 1 0 0 0 2 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 2 1 0 1 0 1")
df <- data.frame(scan(dat, fill = TRUE, what = as.list(rep(1, 29))))
names(df) <- paste("Val", 1:29)
close(dat)
Resulting in:
> head(df)
Val 1 Val 2 Val 3 Val 4 Val 5 Val 6 Val 7 Val 8 Val 9 Val 10 Val 11 Val 12
1 1 0 7 0 0 1 0 0 0 5 0 0
2 1 0 0 1 0 0 0 3 0 0 0 0
3 0 0 0 1 0 0 0 2 0 0 0 0
4 1 0 0 1 0 3 0 0 0 0 1 0
5 0 0 0 1 0 0 0 2 0 0 0 0
....
If the data are in a file, provide the file name instead of dat. This code presumes that there are a maximum of 29 columns, as per the data you supplied. Alter the 29 to suit the real data.
We get the column sums using
df.csum <- colSums(df, na.rm = TRUE)
the ecdf() function generates the ECDF you wanted,
df.ecdf <- ecdf(df.csum)
and we can plot it using the plot() method:
plot(df.ecdf, verticals = TRUE)
You can use the ecdf() (in base R) or Ecdf() (from the Hmisc package) functions.
Related
I am trying to create a sequence consisting of 1 and 0 using Rstudio.
My desired output is a sequence that first has five 1 then six 0, followed by four 1 then six 0. Then this should all be repeat until the end of a given vector.
The result should be like this:
1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 .....
Hope someone has a good solution, and sorry if I have some grammar mistakes
Best,
HB
rep(c(rep(1,5),rep(0,6),rep(1,4),rep(0,6)),n)
repeating your pattern n times.
You could use Map.
unlist(Map(function(x, ...) c(rep(x, ...), rep(0, 6)), 1, times=length(v):1))
# [1] 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0
Instead of length(v):1 you may also use rev(seq(v)) but it's slower.
Data
v <- c("Vector", "of", "specific", "length", "five")
I'm currently stuck on a part of my code that feels intuitive but I can't figure a way to do it. I have a very big data frame (nrows = 34036, ncol = 43) in which I want to create a continuous sequence of the variables where the value of the row is 1 (without having multiple columns with 1). It consists of only zeros and ones similar to the following:
A B C D
1 0 0 0
0 0 0 1
0 0 0 1
0 0 0 0
0 0 0 0
1 0 1 0
1 0 1 0
0 1 0 0
0 1 0 0
1 0 0 1
I was able to remove the zeroes using:
#find the sum of each row
placeholderData <- transform(placeholderData, sum=rowSums(placeholderData))
placeholderData <- placeholderData[!(placeholderData$sum <= 0),]
And the data frame now looks like:
A B C D sum
1 0 0 0 1
0 0 0 1 1
0 0 0 1 1
1 0 1 0 2
1 0 1 0 2
0 1 0 0 1
0 1 0 0 1
1 0 0 1 2
My main problem comes when there are two or more 1's in a row. To try to solve this, I used the following code to identify the columns that have a sum of 2 or more:
placeholderData$Matches <- lapply(apply(placeholderData == 1, 1, which), names)
Which added the following column to the data frame:
A B C D sum Matches
1 0 0 0 1 A
0 0 0 1 1 D
0 0 0 1 1 D
1 0 1 0 2 c("A","C")
1 0 1 0 2 c("A","C")
0 1 0 0 1 B
0 1 0 0 1 B
1 0 0 1 2 c("A", "D")
I added the Matches column as an approach to solve the problem, but I'm not sure how would I do it without using a lot of logical operators (I don't know what columns have matches or not). What I would like to do is to aggregate the rows that have more than (or equal to) two 1's into a new column, to be able to have a data frame like this:
A B C D AC AD sum Matches
1 0 0 0 0 0 1 A
0 0 0 1 0 0 1 D
0 0 0 1 0 0 1 D
0 0 0 0 1 0 1 c("A","C")
0 0 0 0 1 0 1 c("A","C")
0 1 0 0 0 0 1 B
0 1 0 0 0 0 1 B
0 0 0 0 0 1 1 c("A", "D")
Then, I would be able to use my code as normal (It works just fine when there are no repeated values in rows). I tried searching to find similar questions, but I'm not sure if I was even asking the right question. I was wondering if anyone could provide some help or some ideas that I could try.
Thank you very much!
This seems a lot like making dummy variables, so I would use the model.matrix function commonly used for dummy variables (one-hot encoding):
m = read.table(header = T, text = "A B C D
1 0 0 0
0 0 0 1
0 0 0 1
0 0 0 0
0 0 0 0
1 0 1 0
1 0 1 0
0 1 0 0
0 1 0 0
1 0 0 1")
m = m[rowSums(m) > 0, ]
d = factor(sapply(apply(m == 1, 1, which), function(x) paste(names(m)[x], collapse = "")))
result = data.frame(model.matrix(~ d + 0))
names(result) = levels(d)
# A AC AD B D
# 1 1 0 0 0 0
# 2 0 0 0 0 1
# 3 0 0 0 0 1
# 4 0 1 0 0 0
# 5 0 1 0 0 0
# 6 0 0 0 1 0
# 7 0 0 0 1 0
# 8 0 0 1 0 0
I'm having an odd problem while trying to set up a design matrix to do downstream pairwise differential expression analysis on RNAseq data.
For the design matrix, I have both the donor information and each condition:
group<-factor(y$samples$group) #44 samples, 6 different conditions
sample<-factor(y$samples$samples) #44 samples, 11 different donors.
design<- model.matrix(~0+sample+group)
head(design)
Donor11.CD8 Donor12.CD8 Donor14.CD8 Donor15.CD8 Donor16.CD8
1 1 0 0 0 0
2 1 0 0 0 0
3 1 0 0 0 0
4 1 0 0 0 0
5 1 0 0 0 0
6 1 0 0 0 0
Donor17.CD8 Donor18.CD8 Donor19.CD8 Donor20.CD8 Donor3.CD8
1 0 0 0 0 0
2 0 0 0 0 0
3 0 0 0 0 0
4 0 0 0 0 0
5 0 0 0 0 0
6 0 0 0 0 0
Donor4.CD8 Treatment2 Treatment3 Treatment4 Treatment5
1 0 0 0 0 0
2 0 0 0 0 1
3 0 0 0 1 0
4 0 0 0 0 0
5 0 0 1 0 0
6 0 1 0 0 0
Treatment6
1 1
2 0
3 0
4 0
5 0
6 0
>
The issue is that I seem to be losing a condition (treatment 1) when I form the design matrix, and I'm not sure why.
Many thanks, in advance, for your help!
That's not a problem. Treatment 1 is indicated by all 0 for the columns in the design matrix. Look at row 4 - zero for Treatments 2 through 6. That means it is Treatment 1. This is called a "treatment contrast" because the coefficients in the model contrast the named treatment against the "base" level, in this case the base level is Treatment1.
I have a matrix that is for example like this:
rownames V1
a 1
c 3
b 2
d 4
y 2
q 4
i 1
j 1
r 3
I want to make a Symmetric binary matrix that it's dimnames of that is the same as rownames of above matrix. I want to fill these matrix by 1 & 0 in such a way that 1 indicated placing variables that has the same number in front of it and 0 for the opposite situation.This matrix would be like
dimnames
a c b d y q i j r
a 1 0 0 0 0 0 1 1 0
c 0 1 0 0 0 0 0 0 1
b 0 0 1 0 1 0 0 0 0
d 0 0 0 1 0 1 0 0 0
y 0 0 1 0 1 0 0 0 0
q 0 0 0 1 0 1 0 0 0
i 1 0 0 0 0 0 1 1 0
j 1 0 0 0 0 0 1 1 0
r 0 1 0 0 0 0 0 0 1
Anybody know how can I do that?
Use dist:
DF <- read.table(text = "rownames V1
a 1
c 3
b 2
d 4
y 2
q 4
i 1
j 1
r 3", header = TRUE)
res <- as.matrix(dist(DF$V1)) == 0L
#alternatively:
#res <- !as.matrix(dist(DF$V1))
#diag(res) <- 0L #for the first version of the question, i.e. a zero diagonal
res <- +(res) #for the second version, i.e. to coerce to an integer matrix
dimnames(res) <- list(DF$rownames, DF$rownames)
# 1 2 3 4 5 6 7 8 9
#1 1 0 0 0 0 0 1 1 0
#2 0 1 0 0 0 0 0 0 1
#3 0 0 1 0 1 0 0 0 0
#4 0 0 0 1 0 1 0 0 0
#5 0 0 1 0 1 0 0 0 0
#6 0 0 0 1 0 1 0 0 0
#7 1 0 0 0 0 0 1 1 0
#8 1 0 0 0 0 0 1 1 0
#9 0 1 0 0 0 0 0 0 1
You can do this using table and crossprod.
tcrossprod(table(DF))
# rownames
# rownames a b c d i j q r y
# a 1 0 0 0 1 1 0 0 0
# b 0 1 0 0 0 0 0 0 1
# c 0 0 1 0 0 0 0 1 0
# d 0 0 0 1 0 0 1 0 0
# i 1 0 0 0 1 1 0 0 0
# j 1 0 0 0 1 1 0 0 0
# q 0 0 0 1 0 0 1 0 0
# r 0 0 1 0 0 0 0 1 0
# y 0 1 0 0 0 0 0 0 1
If you want the row and column order as they are found in the data, rather than alphanumerically, you can subset
tcrossprod(table(DF))[DF$rownames, DF$rownames]
or use factor
tcrossprod(table(factor(DF$rownames, levels=unique(DF$rownames)), DF$V1))
If your data is large or sparse, you can use the sparse matrix algebra in xtabs, with similar ways to change the order of the resulting table as before.
Matrix::tcrossprod(xtabs(data=DF, ~ rownames + V1, sparse=TRUE))
I am working on the adjacency matrix to find the results of the egonet package function. But when I run the command index.egonet, it gives me an error.
My adjacency matrix "p2":
p2
1 2 3 4 5 7 8 9 6
1 0 1 1 1 1 0 0 0 0
2 1 0 0 0 1 1 1 1 0
3 1 0 0 0 0 1 0 1 1
4 1 0 0 0 0 0 0 0 0
5 1 1 0 0 0 0 0 0 0
7 0 1 1 0 0 0 0 0 0
8 0 1 0 0 0 0 0 0 0
9 0 1 1 0 0 0 0 0 0
6 0 0 1 0 0 0 0 0 0
I apply this command on the adjacency for the desired results but it gives me an error
index.egonet(p2)
Error in dati[ego.name, y] : subscript out of bounds
So any alternative or solution to current code error will highly be appreciated.
The ego name must be "EGO" in capital letters, as far as I could understand from working with that function.
colnames(p2) <- rownames(p2) <- c("EGO", 2:ncol(p2))
index.egonet(p2)
this should work...