I would like to merge two matrices with different length on their incommon row.names with a function:
My first matrix (T) looks similar to this:
1 2 3 4
1 -4 3 2 2
1 2 1 1 5
2 3 -2 4 6
2 -2 1 -1 -9
Now I want to join this function into my new matrix (M), however in this matrix there should be only the colsum of the matching rows which are >=0 plus 1:
1 2 3 4
1 2 3 3 3
2 2 2 2 2
I tried following formula, which I found here in the forum, however it does not work:
merge.default(as.data.frame(M), as.data.frame(T), by = "row.names", function(x){colSums(T[,]>0)+1})
Do you have an idea, where my mistake is?
Thank you very much
EDIT: my desired output would be my Matrix T, which is at the moment empty:
T now:
1 2 3 4
1
2
T after merge which is now filled with the function:
colsums(T[,] >=0)+1
1 2 3 4
1 2 3 3 3
2 2 2 2 2
T[1,1]= 2 as there is 1 value in Matrix M which is >=0 and then I add 1 to it
T[2,1]= 3 : two values >=0 and plus 1
Related
I have two vectors, which are basically starting and ending Row indices.
I want to group them using this vectors.
Example
a<-c(1,4,7,12)
b<-c(3,6,11,15)
my output vector should be
d <- c(1,1,1,2,2,2,3,3,3,3,3,4,4,4,4)
You can use rep to repeat value b-a times.
rep(seq_along(a), (b - a) + 1)
#[1] 1 1 1 2 2 2 3 3 3 3 3 4 4 4 4
Will this work:
> rep(1:length(a), c(b[1],diff(b)))
[1] 1 1 1 2 2 2 3 3 3 3 3 4 4 4 4
>
We can use
rep(seq_len(length(a)), (b - a) + 1)
#[1] 1 1 1 2 2 2 3 3 3 3 3 4 4 4 4
I'm working on a data frame that requires to calculate Fleiss's Kappa for inter-rater agreements. I'm using the 'irr' package for that.
Besides that, I need to count, for each observation, how many of raters are in agreement.
My data looks like these:
a b c
1 1 1 1
2 1 2 2
3 2 3 2
4 3 3 1
5 4 2 1
I'm expecting something like this, , where count stands for number of raters on agreement
a b c count
1 1 1 1 3
2 1 2 2 2
3 2 3 2 2
4 3 3 1 2
5 4 2 1 0
Thanks a lot.
Alternative solution if your data is in a data frame called abc:
as.numeric(apply(abc,1,function(x) {
ux<-unique(x);
tab <- tabulate(match(x, ux));
mode <- ux[tab == max(tab)];
ifelse(length(mode)==1,length(which(x==mode)),NA_character_);
} ))
When you run it gives:
[1] 3 2 2 2 NA
Suppose I have a vector from 1 to 5,
a<-c(1:5)
What I need to do is to repeat the vector by losing one element continuously. That is, the final outcome should be like
1 2 3 4 5 1 2 3 4 1 2 3 1 2 1
We can reverse the vector and apply sequence
sequence(rev(a))
#[1] 1 2 3 4 5 1 2 3 4 1 2 3 1 2 1
Or another option is toeplitz
m1 <- toeplitz(a)
m1[lower.tri(m1, diag=TRUE)]
#[1] 1 2 3 4 5 1 2 3 4 1 2 3 1 2 1
I have the following dataset called asteroids
3 4 3 3 1 4 1 3 2 3
1 1 4 2 3 3 2 6 1 1
3 3 2 2 2 2 1 3 2 1
6 1 3 2 2 1 2 2 4 2
I need to find out what proportion of this dataset is 1.
If you have a specific value in mind you can just do an equality comparison and then use mean on the resulting logical vector.
> asteroids <- scan(what=numeric())
1: 3 4 3 3 1 4 1 3 2 3 1 1 4 2 3 3 2 6 1 1 3 3 2 2 2 2 1 3 2 1 6 1 3 2 2 1 2 2 4 2
41:
Read 40 items
> mean(asteroids == 1)
[1] 0.25
This works since the equality comparison will give TRUE and FALSE and when T/F are coerced numerically they become 1s and 0s so mean ends up giving us the proportion of TRUEs.
I assumed asteroids was a vector. You don't specify in your question but if it's a different type of structure you'll probably need to coerce it into a vector in some way or another.
Assuming that 'asteroids' is a data.frame, unlist it, get the table and find the proportion with prop.table.
prop.table(table(unlist(asteroids)==1))
# FALSE TRUE
# 0.75 0.25
Or as #Richard Scriven mentioned, we can convert the data.frame to a logical matrix, and use table directly on it as 'matrix' is a vector with dim attributes.
prop.table(table(asteroids == 1))
Say I have a list in R like so,
[1] 3 5 4 7
And I want to generate all "drawings" from this list, from 1 up to the value of each number. For example,
1 1 1 1
1 1 1 2
1 1 1 3
...
2 3 3 1
2 3 3 2
2 3 3 3
...
3 5 4 7
I know I have used rep() in the past to do something very similar, which works for lists of 2 or 3 numbers (i.e. something like 1 4 5), but I'm not sure how to generalize this here.
Thoughts?
As suggested in comments, use Map function to apply seq to elements of your vector, then use expand.grid to generate data.frame with Cartesian product of result's elements:
head(expand.grid(Map(seq,c(3,5,4,7))))
Var1 Var2 Var3 Var4
1 1 1 1 1
2 2 1 1 1
3 3 1 1 1
4 1 2 1 1
5 2 2 1 1
6 3 2 1 1