Noteworthy Differences from other Languages¶

Noteworthy differences from MATLAB¶

Although MATLAB users may find Julia’s syntax familiar, 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:

• Arrays are indexed with square brackets, A[i,j].

• Arrays are assigned by reference. After A=B, assigning into B will modify A as well.

• Values are passed and assigned by reference. If a function modifies an array, the changes will be visible in the caller.

• Matlab combines allocation and assignment into single statements, e.g., a(4)=3.2 creates the array a=[0003.2] and a(5)=7 grows it. Julia separates allocation and assignment: if a is of length 4, a[5]=7 yields an error. Julia has a push! function which grows Vectors much more efficiently than Matlab’s a(end+1)=val.

• The imaginary unit sqrt(-1) is represented in julia with im.

• Literal numbers without a decimal point (such as 42) create integers instead of floating point numbers. Arbitrarily large integer literals are supported. But this means that some operations such as 2^-1 will throw a domain error as the result is not an integer (see the FAQ entry on domain errors for details).

• Multiple values are returned and assigned with parentheses, e.g.

  return(a,b) and (a,b)=f(x). The equivalent of nargout, which is often used in Matlab to do optional work based on the number of returned values does not exist in Julia. Instead, users can use optional and keyword arguments to achieve similar capabilities.

• Julia has 1-dimensional arrays. Column vectors are of size N, not Nx1. For example, rand(N) makes a 1-dimensional array.

• Concatenating scalars and arrays with the syntax [x,y,z] concatenates in the first dimension (“vertically”). For the second dimension (“horizontally”), use spaces as in [xyz]. To construct block matrices (concatenating in the first two dimensions), the syntax [ab;cd] is used to avoid confusion.

• Colons a:b and a:b:c construct Range objects. To construct a full vector, use linspace, or “concatenate” the range by enclosing it in brackets, [a:b].

• Functions return values using the return keyword, instead of by listing their names in the function definition (see The return Keyword for details).

• A file may contain any number of functions, and all definitions will be externally visible when the file is loaded.

• Reductions such as sum, prod, and max are performed over every element of an array when called with a single argument as in sum(A).

• Functions such as sort that operate column-wise by default (sort(A) is equivalent to sort(A,1)) do not have special behavior for 1xN arrays; the argument is returned unmodified since it still performs sort(A,1). To sort a 1xN matrix like a vector, use sort(A,2).

• If A is a 2-dimensional array fft(A) computes a 2D FFT. In particular, it is not equivalent to fft(A,1), which computes a 1D FFT acting column-wise.

• Parentheses must be used to call a function with zero arguments, as in tic() and toc().

• Do not use semicolons to end statements. The results of statements are not automatically printed (except at the interactive prompt), and lines of code do not need to end with semicolons. The function println can be used to print a value followed by a newline.

• If A and B are arrays, A==B doesn’t return an array of booleans. Use A.==B instead. Likewise for the other boolean operators, <, >, !=, etc.

• The operators &, |, and $ perform the bitwise operations and, or, and xor, respectively, and have precedence similar to Python’s bitwise operators (not like C). They can operate on scalars or elementwise across arrays and can be used to combine logical arrays, but note the difference in order of operations—parentheses may be required (e.g., to select elements of A equal to 1 or 2 use (A.==1)|(A.==2)).

• The elements of a collection can be passed as arguments to a function using ..., as in xs=[1,2];f(xs...).

• Julia’s svd returns singular values as a vector instead of as a full diagonal matrix.

• In Julia, ... is not used to continue lines of code. Instead, incomplete expressions automatically continue onto the next line.

• The variable ans is set to the value of the last expression issued in an interactive session, but not set when Julia code is run in other ways.

• The closest analog to Julia’s types are Matlab’s classes. Matlab’s structs behave somewhere between Julia’s types and Dicts; in particular, if you need to be able to add fields to a struct on-the-fly, use a Dict rather than a type.

Noteworthy differences from R¶

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 same 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 foriin[1,2,3] instead of for(iinc(1,2,3)) and ifi==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 iftrue.

• 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 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 discourages populating the main namespace with functions. Most statistical functionality for Julia is found in packages like the DataFrames and Distributions packages:

  • Distributions functions are found in the Distributions package.
  • The DataFrames package provides data frames.
  • Generalized linear models are provided by the GLM package.

• 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].

• max, min are the equivalent of pmax and pmin in R, but both arguments need to have the same dimensions. While maximum, minimum replace max and min in R, there are important differences.

• The functions sum, prod, maximum, minimum are different from their counterparts in R. They all accept one or two arguments. The first argument is an iterable collection such as an array. If there is a second argument, then this argument indicates the dimensions, over which the operation is carried out. For instance, let A=[[12],[34]] in Julia and B=rbind(c(1,2),c(3,4)) be the same matrix in R. Then sum(A) gives the same result as sum(B), but sum(A,1) is a row vector containing the sum over each column and sum(A,2) is a column vector containing the sum over each row. This contrasts to the behavior of R, where sum(B,1)=11 and sum(B,2)=12. If the second argument is a vector, then it specifies all the dimensions over which the sum is performed, e.g., sum(A,[1,2])=10. It should be noted that there is no error checking regarding the second argument.

• 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.

Noteworthy differences from Python¶

• Indexing of arrays, strings, etc. in Julia is 1-based not 0-based.
• The last element of a list or array is indexed with end in Julia, not -1 as in Python.
• Comprehensions in Julia do not (yet) have the optional if clause found in Python.
• For, if, while, etc. blocks in Julia are terminated by end; indentation is not significant.
• Julia has no line continuation syntax: if, at the end of a line, the input so far is a complete expression, it is considered done; otherwise the input continues. One way to force an expression to continue is to wrap it in parentheses.
• Julia arrays are column-major (Fortran ordered) whereas numpy arrays are row-major (C-ordered) by default. To get optimal performance when looping over arrays, the order of the loops should be reversed in Julia relative to numpy (see relevant section of Performance Tips).
• Julia evaluates default values of function arguments every time the method is invoked (not once when the function is defined as in Python). This means that function f(x=rand())=x returns a new random number every time it is invoked without argument. On the other hand function g(x=[1,2])=push!(x,3) returns [1,2,3] every time it is called as g().