I have a matrix like this:
m1 <- matrix(c(1,2,3,4,5,6,7,8,9), nrow = 3, byrow = TRUE)
and I would like to have every column repeated "m" times, but transposing into files and concat the results horizontally. I mean, suppose "m" is 3, I would like to have something like this:
matrix(c(1,4,7,2,5,8,3,6,9,1,4,7,2,5,8,3,6,9,1,4,7,2,5,8,3,6,9),
nrow = 3, byrow = TRUE)
Is there any vectorized way to do this?
I have tried using rep to replicate the columns and then transposing, but I end with many rows
We can use rep
matrix(rep(m1, each=nrow(m1)), nrow=3)
Or
`dim<-`(rep(m1, each=nrow(m1)), dim(m1)*c(1,3))
Or
t(replicate(nrow(m1), c(m1)))
data
m1 <- matrix(c(1,2,3,4,5,6,7,8,9), nrow = 3, byrow = TRUE)
Related
I am totally new to R and I am struggling to write a code to find the numerical derivatives of vector fields. I have two matrices U and V, e.g.,
U <- matrix(runif(9), nrow = 3, ncol = 3, byrow = T)
V <- matrix(runif(9), nrow = 3, ncol = 3, byrow = T)
These matrices (not actual values obviously) represents the components of a 2D wind vector field. I would like to code the numerical derivatives of the 2 vector components du/dy and dv/dx. I have no idea how to do this in R. Please help. Sorry in advance if this question has been answered already.
What you are looking for is the diff() function. You can apply it efficiently over a dimension of a matrix using an apply
U <- matrix(runif(9), nrow = 3, ncol = 3, byrow = T) #Your wind component
apply(U,2,diff) #change the '1' by '2' to apply diff over the other dim
Hope this helped.
to put it simply, I have a list of DFMs created by quanteda package(LD1). each DFM has different texts of different lengths.
now, I want to calculate and compare lexical diversity for each text within DFMs and among DFMs.
lex.div <-lapply(LD1, function(x) {textstat_lexdiv(x,measure = "all")})
this leaves me with a list of S3 type data, and within each of which, there are different attributes that are lexical diversity measures.
lex.div[[1]]$TTR
[1] 0.2940000 0.2285000 0.2110000 0.1912500 0.1802000 0.1671667 0.1531429 0.1483750 0.1392222
[10] 0.1269000
lex.div[[2]]$TTR
[1] 0.3840000 0.2895000 0.2273333 0.2047500 0.1922000 0.1808333 0.1677143 0.1616250 0.1530000
[10] 0.1439000 0.1352727 0.1279167 0.1197692 0.1125000 0.1069333
here comes the problem. I need all the TTR values in one matrix. i want lex.div[[1]]$TTR to be the first row of the matrix, lex.div[[2]]$TTR to be the second, and so on. note that the length of lex.div[[1]]$TTR ≠ lex.div[[2]]$TTR.
here is what I've done so far:
m1 <-matrix(lex.div[[1]]$TTR, nrow = 1, ncol = length(lex.div[[1]]$TTR))
m.sup <- if(ncol(m1) < 30) {mat.to.add = matrix(NA, nrow = nrow(m1), ncol = 30 - ncol(m1))}
m1 <-cbind(m1, m.sup)
m2 <-matrix(lex.div[[2]]$TTR, nrow = 1, ncol = length(lex.div[[2]]$TTR))
m.sup <- if(ncol(m2) < 30) {mat.to.add = matrix(NA, nrow = nrow(m2), ncol = 30 - ncol(m2))}
m2 <-cbind(m2, m.sup)
m3 <-matrix(lex.div[[3]]$TTR, nrow = 1, ncol = length(lex.div[[3]]$TTR))
m.sup <- if(ncol(m3) < 30) {mat.to.add = matrix(NA, nrow = nrow(m3), ncol = 30 - ncol(m3))}
m3 <-cbind(m3, m.sup)
...
m.total <-rbind (m1,m2,m3...)
but I cannot do it this way. can you help me write a for loop or sth to get it done easier and quicker?
You can try the code below
TTRs <- lapply(lex.div, `[[`, "TTR")
m <- t(sapply(TTRs, `length<-`, max(lengths(TTRs))))
I would like to create a 3D array based on a couple of 2D matrices in R, but I have no idea. Let's say we have the following three matrices:
matrix1 <- matrix(1:1, nrow = 5, ncol = 5)
matrix2 <- matrix(2:2, nrow = 5, ncol = 5)
matrix3 <- matrix(3:3, nrow = 5, ncol = 5)
I would like to know how to create one [1:3, 1:5, 1:5] array, as a combination of the three matrices. Thank you!
We can concatenate the matrixes together into a vector, use array to construct a 3D array with specified dim
ar1 <- array(c(matrix1, matrix2, matrix3), c(5, 5, 3))
suppose i have a list of data frames, just like this:
M1 <- data.frame(matrix(1:4, nrow = 2, ncol = 2))
M2 <- data.frame(matrix(1:9, nrow = 3, ncol = 3))
M3 <- data.frame(matrix(1:4, nrow = 2, ncol = 2))
mlist <- list(M1, M2, M3)
and now i want to select X1 columns from all of dataframes, I tried :
M.X1 <- mlist$X1
but failed with NULL:
> mlist$X1
NULL
I don't want to use for to extract each data frames' X1, is there some better way to do this ? And what if extract columns X3 ? (which means some columns may not exists in other row)
Normally you can use lapply as below:
lapply(mlist, function(x) x$X2)
The 2nd parameter you define a function right inside to pass to each member of mlist.
I'm looking for an elegant, R-like way to capture rows in a dataframe that don't have their indices listed in a vector:
table.combos <- matrix(data = 1:12, nrow = 10, ncol = 6, byrow=T)
table.combos
not.these<-c(2,4,5,9)
x<-table.combos[c(not.these),]
#y<- everything not in x
Just use the same index vector as in:
y <- table.combos[-not.these,]
which tells chose all the rows from table.combos but those contained in not.these vector.