**Publishable Stuff**, and kindly contributed to R-bloggers)

The Matlab syntax for creating matrices is pretty and convenient. Here is a 2×3 matrix in Matlab syntax where `,`

marks a new column and `;`

marks a new row:

[1, 2, 3; 4, 5, 6]

Here is how to create the corresponding matrix in R:

matrix(c(1,4,2,5,3,6), 2, 3)

## [,1] [,2] [,3] ## [1,] 1 2 3 ## [2,] 4 5 6

Functional but not as pretty, plus the default is to specify the values column wise. A better solution is to use

`rbind`

:

rbind(c(1,2,3), c(4,5,6))

## [,1] [,2] [,3] ## [1,] 1 2 3 ## [2,] 4 5 6

Lately I’ve been reading up on the metaprogramming capabilities of R in Hadley Wickham’s great Advanced R programming (while it is freely available online, you can already pre-order the IRL version here). Using metaprogramming we can hack together a function that allow us to create matrices in a similar way as in Matlab. I’ll first show some examples of how the function works and after that I’ll show you the code. The function is called

`qm`

as in “quick matrix” where`,`

is used to separate columns and`|`

is used to separate rows:

qm(1,2,3| 4,5,6)

## [,1] [,2] [,3] ## [1,] 1 2 3 ## [2,] 4 5 6

qm(1:3 | rnorm(3) | 0, 0, 0)

## [,1] [,2] [,3] ## [1,] 1.000 2.0000 3.000 ## [2,] 2.421 0.9801 1.202 ## [3,] 0.000 0.0000 0.000

`qm(1|2|3|4)`

## [,1] ## [1,] 1 ## [2,] 2 ## [3,] 3 ## [4,] 4

# qm is useful when defining covariance matrices, for example: library(MASS) sigma = qm( 1 , 0.7| 0.7, 1 ) xy <- mvrnorm(100, c(0, 0), sigma) plot(xy, xlab = "x", ylab = "y")

Pretty cool, right? 🙂

Here is finally the full

`qm`

function. The trick is roughly to grab the arguments to`qm`

as a list, split every argument with a`|`

into two, and finally form the rows of the matrix by evaluating and concatenating the arguments between each`|`

. (`qm`

is not extensively tested so use it at your own risk and please tell me if you find any way to improve the code!)

qm <- function(...) { # Get the arguments as a list arg <- eval(substitute(alist(...))) # Initialize l as a list of vecors, each vector in l corresponds to one row # of the matrix. l <- list(c()) # rhl_l is a list where we will push the rhs of expressions like 1 | 2 | 3 , # which parses as (1 | 2) | 3 , while we deal with the left hand side (1 | # 2) rhl_l <- list() while (length(arg) > 0) { a <- arg[[1]] arg <- tail(arg, -1) if (a[[1]] == "|") { # Push the left hand side of the ... | ... expression back on the arguments # list and push the rhs onto rhl_l arg <- c(a[[2]], arg) rhl_l <- c(a[[3]], rhl_l) } else { # Just a normal element, that we'll evaluate and append to the last # vector/row in l. l[[length(l)]] <- c(l[[length(l)]], eval(a)) # If there are rhs elements left in rhs_l we'll append them as new # vectors/rows on l and then we empty rhs_l. for (i in seq_along(rhl_l)) { l[[length(l) + 1]] <- eval(rhl_l[[i]]) } rhl_l <- list() } } do.call(rbind, l) }

While I’m not sure that

`qm`

is really a super useful function, I still think it is a nice example of what you can hack together using the metaprogramming facilities of R.Toleave a commentfor the author, please follow the link and comment on their blog:Publishable Stuff.

R-bloggers.com offersdaily e-mail updatesabout R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...