> mm <- matrix(rnorm(1000000), 1000, 1000)
> system.time(eigen(mm))
user system elapsed
5.26 0.00 5.25
IPy [1] >>> xx = np.random.rand(1000000).reshape(1000, 1000)
IPy [2] >>> %timeit(np.linalg.eig(xx))
1 loops, best of 3: 1.28 s per loop
But where R really stinks is memory access:
> system.time(for(x in 1:1000) for(y in 1:1000) mm[x, y] <- 1)
user system elapsed
1.09 0.00 1.11
IPy [7] >>> def do():
...: for x in range(1000):
...: for y in range(1000):
...: xx[x, y] = 1
...:
IPy [10] >>> %timeit do()
10 loops, best of 3: 134 ms per loop
Growing lists in R is even worse with all the append nonsense. Exponential time slower.
