I have a vector called "combined" with 1's and 0's
combined
1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I sampled twice from this vector, each with a sample size of 3 and put it into a contingency table of counts as follows.
2 1
1 2
I want to reiterate this sampling 1000 times such that I end with 1000 contingency tables each with counts of 1s and 0s from the sampling.
This is what I tried:
sample1 = as.vector(replicate(10000, sample(combined, 3)))
sample2 = as.vector(replicate(10000, sample(combined, 3)))
con_table = table(sample1,sample2)
but I ended up only getting 1 table instead of 10000. Hoping to get some help.
8109 7573
7306 7012
You need to wrap the entire expression, sample and table inside replicate. Add a conversion to a factor to ensure you always get a 2x2 table. E.g. a simple version with 2 replications:
combined <- rep(0:1,each=10)
combined <- as.factor(combined)
replicate(2, table(sample(combined,3), sample(combined,3)), simplify=FALSE)
#[[1]]
#
# 0 1
# 0 0 1
# 1 1 1
#
#[[2]]
#
# 0 1
# 0 1 1
# 1 0 1
Related
From a given dataframe:
# Create dataframe with 4 variables and 10 obs
set.seed(1)
df<-data.frame(replicate(4,sample(0:1,10,rep=TRUE)))
I would like to compute a substract operation between in all columns combinations by pairs, but only keeping one substact, i.e column A- column B but not column B-column A and so on.
What I got is very manual, and this tend to be not so easy when there are lots of variables.
# Result
df_result <- as.data.frame(list(df$X1-df$X2,
df$X1-df$X3,
df$X1-df$X4,
df$X2-df$X3,
df$X2-df$X4,
df$X3-df$X4))
Also the colname of the feature name should describe the operation i.e.(x1_x2) being x1-x2.
You can use combn:
COMBI = combn(colnames(df),2)
res = data.frame(apply(COMBI,2,function(i)df[,i[1]]-df[,i[2]]))
colnames(res) = apply(COMBI,2,paste0,collapse="minus")
head(res)
X1minusX2 X1minusX3 X1minusX4 X2minusX3 X2minusX4 X3minusX4
1 0 0 -1 0 -1 -1
2 1 1 0 0 -1 -1
3 0 0 0 0 0 0
4 0 0 -1 0 -1 -1
5 1 1 1 0 0 0
6 -1 0 0 1 1 0
As I am new to R, this question may seem to you piece of a cake.
I have a data in txt format. The first column has Cluster Number and the second column has names of different organisms.
For example:
0 org4|gene759
1 org1|gene992
2 org1|gene1101
3 org4|gene757
4 org1|gene1702
5 org1|gene989
6 org1|gene990
7 org1|gene1699
9 org1|gene1102
10 org4|gene2439
10 org1|gene1374
I need to re-arrange/reshape the data in following format.
Cluster No. Org 1 Org 2 org3 org4
0 0 0 1
1 0 0 0
I could not figure out how to do it in R.
Thanks
We could use table
out <- cbind(ClusterNo = seq_len(nrow(df1)), as.data.frame.matrix(table(seq_len(nrow(df1)),
factor(sub("\\|.*", "", df1[[2]]), levels = paste0("org", 1:4)))))
head(out, 2)
# ClusterNo org1 org2 org3 org4
#1 1 0 0 0 1
#2 2 1 0 0 0
It is also possible that we need to use the first column to get the frequency
out1 <- as.data.frame.matrix(table(df1[[1]],
factor(sub("\\|.*", "", df1[[2]]), levels = paste0("org", 1:4))))
Reading the table into R can be done with
input <- read.table('filename.txt')
Then we can extract the relevant number from the org4|gene759 string using a regular expression, and set this to a third column of our input:
input[, 3] <- gsub('^org(.+)\\|.*', '\\1', input[, 2])
Our input data now looks like this:
> input
V1 V2 V3
1 0 org4|gene759 4
2 1 org1|gene992 1
3 2 org1|gene1101 1
4 3 org4|gene757 4
5 4 org1|gene1702 1
6 5 org1|gene989 1
7 6 org1|gene990 1
8 7 org1|gene1699 1
9 9 org1|gene1102 1
10 10 org4|gene2439 4
11 10 org1|gene1374 1
Then we need to list the possible values of org:
possibleOrgs <- seq_len(max(input[, 3])) # = c(1, 2, 3, 4)
Now for the tricky part. The following function takes each unique cluster number in turn (I notice that 10 appears twice in your example data), takes all the rows relating to that cluster, and looks at the org value for those rows.
result <- vapply(unique(input[, 1]), function (x)
possibleOrgs %in% input[input[, 1] == x, 3], logical(4)))
We can then format this result as we like, perhaps using t to transform its orientation, * 1 to convert from TRUEs and FALSEs to 1s and 0s, and colnames to title its columns:
result <- t(result) * 1
colnames (result) <- paste0('org', possibleOrgs)
rownames(result) <- unique(input[, 1])
I hope that this is what you were looking for -- it wasn't quite clear from your question!
Output:
> result
org1 org2 org3 org4
0 0 0 0 1
1 1 0 0 0
2 1 0 0 0
3 0 0 0 1
4 1 0 0 0
5 1 0 0 0
6 1 0 0 0
7 1 0 0 0
9 1 0 0 0
10 1 0 0 1
Suppose I have matrix D which consists of death counts per year by specific ages.
I want to fill this matrix with appropriate death counts that is stored in
vector Age, but the following code gives me wrong answer. How should I write the code without making a loop?
# Year and age grid for tables
Years=c(2007:2017)
Ages=c(60:70)
#Data.frame of deaths
D=data.frame(matrix(ncol=length(Years),nrow=length(Ages))); D[is.na(D)]=0
colnames(D)=Years
rownames(D)=Ages
Age=c(60,61,62,65,65,65,68,69,60)
year=2010
D[as.character(Age),as.character(year)]<-
D[as.character(Age),as.character(year)]+1
D[,'2010'] # 1 1 1 0 0 1 0 0 1 1 0
# Should be 2 1 1 0 0 3 0 0 1 1 0
You need to use table
AgeTable = table(Age)
D[names(AgeTable), as.character(year)] = AgeTable
D[,'2010']
[1] 2 1 1 0 0 3 0 0 1 1 0
I was wondering if there's a fast way to get an incidence matrix for this such a problem. I've got two data frames with three columns (the join keys)
df1 <- data.frame(K1=c(1,1,0,1,3,2,2),K2=c(1,2,1,0,2,0,1),K3=c(0,0,3,2,1,3,0))
df2 <- data.frame(K1=c(1,2,0,3),K2=c(0,1,2,0),K3=c(2,0,3,1))
and I need to obtain the corresponding incidence matrix
# IM:
# 1 2 3 4
# 1 1 1 0 0
# 2 1 0 1 0
# 3 0 1 1 0
# 4 1 0 0 0
# 5 0 0 1 1
# 6 0 1 1 0
# 7 0 1 0 0
where it's set 1 if there's a match between the corresponding KEY (column value) of rows of the two data frames.
I would do by using multiple loops
for (j in seq_len(nrow(df2)))
for (k in seq_len(ncol(df2))) {
if (df2[j,k])
m[which(df1[,k] == df2[j,k]),j] <- 1
}
but it's a C approach and maybe there's something faster in R. Do you have any other ideas? Besides, when the data.frame are quite big (around 50k and 20k rows), I cannot allocate the matrix as it seems too big.
I am definitely not an R coder but am trying to stumble my way through this code. I have a dataframe that looks like this--with 200 rows (just 8 shown here).
Ind.ID V1 V2 V3 V4 V5 V6 V7 Captures
1 1 0 0 1 1 0 0 0 2
2 2 0 0 1 0 0 0 1 2
3 3 1 1 0 1 1 0 1 5
4 4 0 0 1 1 0 0 0 2
5 5 1 0 0 0 0 1 0 2
6 6 0 1 1 0 0 0 0 2
7 7 0 0 1 1 1 0 0 3
8 8 1 0 0 0 1 0 0 2
I am trying to sample from the Captures column (which is the sum of the row) and output the Ind.ID value. If there is a 0 in the Captures column, I want it to subtract 1 from i (i=i-1) and resample--to ensure that I get the correct number of samples. I also want to then subtract 1 from the sampled column (i.e., decrease the Captures value by 1 if it was sampled), and then resample. I am trying to get 400 samples (I think the current code will get me only 200, but I can't figure out how to get 400).
i want my output to be
23
45
197
64
.....
Here's my code:
sess1<-(numeric(200)) #create a place for output
for(i in 1:length(dep.pop$Captures)){
if(dep.pop[i,'Captures']!=0){ #if the value of Captures is not 0, sample and
sample(dep.pop$Captures, size=1, replace=TRUE) #want to resample the row if Captures >1
#code here to decrease the value of the sampled Captures column by 1. create new vector for resampling?
}
else {
if(dep.pop[i,'Captures']==0){ #if the value of Captures = 0
i<-i-1 #decrease the value of i by 1 to ensure 200 samples
sample(dep.pop$Captures, size=1, replace=TRUE) #and resample
}
#sess1<- #store the value from a different column (ID column) that represents the sampled row
}}
Thanks!
Assuming sum(dep.pop$Captures) is at least 400 then the following code may meet your needs to sample up to the number of captures for each individual id:
sample(rep(dep.pop$Ind.ID, times=dep.pop$Captures), size=400)
If you wish to sample with replacement (so you do not need to worry about the total number of captures) but still want to use the number of captures per individual id as sampling weights, then perhaps
sample(dep.pop$Ind.ID, size=400, replace=TRUE, prob=dep.pop$Captures)