Julia installation is straightforward, whether using precompiled binaries or compiling from source. Download and install Julia by following the instructions at http://julialang.org/downloads/.
The easiest way to learn and experiment with Julia is by starting an interactive session (also known as a read-eval-print loop or “repl”):
$ julia _ _ _ _(_)_ | (_) | (_) (_) | A fresh approach to technical computing. _ _ _| |_ __ _ | | | | | | | |/ _` | | Version 0 (pre-release) | | |_| | | | (_| | | Commit 61847c5aa7 (2011-08-20 06:11:31)* _/ |\__'_|_|_|\__'_| | |__/ | julia> 1 + 2 3 julia> ans 3
To exit the interactive session, type ^D — the control key together with the d key or type quit(). When run in interactive mode, julia displays a banner and prompts the user for input. Once the user has entered a complete expression, such as 1 + 2, and hits enter, the interactive session evaluates the expression and shows its value. If an expression is entered into an interactive session with a trailing semicolon, its value is not shown. The variable ans is bound to the value of the last evaluated expression whether it is shown or not.
To evaluate expressions written in a source file file.jl, write include("file.jl").
To run code in a file non-interactively, you can give it as the first argument to the julia command:
$ julia script.jl arg1 arg2...
As the example implies, the following command-line arguments to julia are taken as command-line arguments to the program script.jl, passed in the global constant ARGS. ARGS is also set when script code is given using the -e option on the command line (see the julia help output below). For example, to just print the arguments given to a script, you could do this:
$ julia -e 'for x in ARGS; println(x); end' foo bar foo bar
Or you could put that code into a script and run it:
$ echo 'for x in ARGS; println(x); end' > script.jl $ julia script.jl foo bar foo bar
If you have code that you want executed whenever julia is run, you can put it in ~\.juliarc.jl:
$ echo 'println("Greetings! 你好! 안녕하세요?")' > ~/.juliarc.jl $ julia Greetings! 你好! 안녕하세요? ...
There are various ways to run Julia code and provide options, similar to those available for the perl and ruby programs:
julia [options] [program] [args...] -v --version Display version information -q --quiet Quiet startup without banner -H --home=<dir> Load files relative to <dir> -T --tab=<size> Set REPL tab width to <size> -e --eval=<expr> Evaluate <expr> -E --print=<expr> Evaluate and show <expr> -P --post-boot=<expr> Evaluate <expr> right after boot -L --load=file Load <file> right after boot on all processors -J --sysimage=file Start up with the given system image file -p n Run n local processes --machinefile file Run processes on hosts listed in file --no-history Don't load or save history -f --no-startup Don't load ~/.juliarc.jl -F Load ~/.juliarc.jl, then handle remaining inputs -h --help Print this message
A few walkthrough-style tutorials are available online:
MATLAB users may find Julia’s syntax familiar. However, Julia is in no way a MATLAB clone: there are major syntactic and functional differences. The following are some noteworthy differences that may trip up Julia users accustomed to MATLAB:
One of Julia’s goals is to provide an effective language for data analysis and statistical programming. For users coming to Julia from R, these are some noteworthy differences:
Julia uses = for assignment. Julia does not provide any operator like <- or <<-.
Julia constructs vectors using brackets. Julia’s [1, 2, 3] is the equivalent of R’s c(1, 2, 3).
Julia’s matrix operations are more like traditional mathematical notation than R’s. If A and B are matrices, then A * B defines a matrix multiplication in Julia equivalent to R’s A %*% B. In R, this some notation would perform an elementwise Hadamard product. To get the elementwise multiplication operation, you need to write A .* B in Julia.
Julia performs matrix transposition using the ' operator. Julia’s A' is therefore equivalent to R’s t(A).
Julia does not require parentheses when writing if statements or for loops: use for i in [1, 2, 3] instead of for (i in c(1, 2, 3)) and if i == 1 instead of if (i == 1).
Julia does not treat the numbers 0 and 1 as Booleans. You cannot write if (1) in Julia, because if statements accept only booleans. Instead, you can write if true.
Julia does not provide nrow and ncol. Instead, use size(M, 1) for nrow(M) and size(M, 2) for ncol(M).
Julia’s SVD is not thinned by default, unlike R. To get results like R’s, you will often want to call svd(X, true) on a matrix X.
Julia is very careful to distinguish scalars, vectors and matrices. In R, 1 and c(1) are the same. In Julia, they can not be used interchangeably. One potentially confusing result of this is that x' * y for vectors x and y is a 1-element vector, not a scalar. To get a scalar, use dot(x, y).
Julia’s diag() and diagm() are not like R’s.
Julia cannot assign to the results of function calls on the left-hand of an assignment operation: you cannot write diag(M) = ones(n).
Julia provides tuples and real hash tables, but not R’s lists. When returning multiple items, you should typically use a tuple: instead of list(a = 1, b = 2), use (1, 2).
Julia encourages all users to write their own types. Julia’s types are much easier to use than S3 or S4 objects in R. Julia’s multiple dispatch system means that table(x::TypeA) and table(x::TypeB) act like R’s table.TypeA(x) and table.TypeB(x).
In Julia, values are passed and assigned by reference. If a function modifies an array, the changes will be visible in the caller. This is very different from R and allows new functions to operate on large data structures much more efficiently.
Concatenation of vectors and matrices is done using hcat and vcat, not c, rbind and cbind.
A Julia range object like a:b is not shorthand for a vector like in R, but is a specialized type of object that is used for iteration without high memory overhead. To convert a range into a vector, you need to wrap the range with brackets [a:b].
Julia has several functions that can mutate their arguments. For example, it has sort(v) and sort!(v).
colMeans() and rowMeans(), size(m, 1) and size(m, 2)
In R, performance requires vectorization. In Julia, almost the opposite is true: the best performing code is often achieved by using devectorized loops.
Unlike R, there is no delayed evaluation in Julia. For most users, this means that there are very few unquoted expressions or column names.
Julia does not support the NULL type.
There is no equivalent of R’s assign or get in Julia.