# Learning Julia

## 2021-04-10

Julia is a programming language I’ve been wanting to learn for a while. I’ve been encountering larger and larger datasets during my PhD, both with terrestrial LiDAR data and huge species-by-site diversity matrices. Running everything in R is becoming restrictive when I want to crunch lots of data within a reasonable amount of time.

Julia promises that it looks like Python, feels like Lisp, and runs like C, supposedly straddling that trade-off between speed, expressiveness, and generalisability.

I watched this Youtube tutorial to get me started on the basic syntax, which is quite pleasant to read, and seems familiar to me having a background in R with some Python and shell-scripting. I also found that the official Julia documentation is pretty useful for understanding some of the finer points in more detail.

As my first practice I aimed to calculate the Shannon and Simpson diversity indices for a huge species by site matrix.

I created the matrix in R and wrote it to a .csv file:

mat <- matrix(sample(0:1000, 10^7, replace = TRUE), nrow = 100000)

# Write matrix for use in Julia
write.csv(mat, "mat.csv", row.names = FALSE)

Then in R I used the {vegan} package to calculate the Shannon and Simpson diversity indices, and the {microbenchmark} package to time how long it took:

library(vegan)

div <- function(x) {
shannon <- diversity(x, "shannon")
simpson <- diversity(x, "simpson")
return(list(shannon, simpson))
}

microbenchmark(
div(mat),
times = 100)

The mean time to complete was 2167 milliseconds.

In Julia the overhead is a bit larger, just because I’m writing my own functions for the diversity indices:

# Packages
using CSV
using BenchmarkTools
using Tables

# Import matrix from .csv
mat = CSV.File("mat.csv") |> Tables.matrix

# Define Shannon function
function shannon(x)
xno = [i for i=x if i != 0]
N = sum(xno)
p = xno / N
return -sum(p .* log.(p))
end

# Define Simpson function
function simpson(x)
N = sum(x)
p = x / N
return sum(p.^2)
end

# Iterate over columns
function testfunc()
shanout = []
simpout = []
for col in eachcol(mat)
push!(shanout, shannon(col))
push!(simpout, simpson(col))
end
end

# Benchmark
@benchmark testfunc()

The median time to complete was only 521 ms.

The main issue I’m having at the moment which is tripping me up a lot is the way Julia handles reassigning variables. While in R I could do something like this:

x <- c(1,2,3)
y <- x
y[1] <- 10
y != x

and have the last line evaluate as true, in Julia unless I use copy() when reassigning the variable, y will continue to equal x:

x = [1,2,3]
y = x
y[1] = 10
y != x

y = copy(x)
y[1] = 100
y != x