Although MATLAB users may find Julia's syntax familiar, Julia is not 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:
A[i,j].A = B, changing elements of B will modify A as well.a(4) = 3.2 can create the array a = [0 0 0 3.2] and a(5) = 7 can grow it into a = [0 0 0 3.2 7], the corresponding Julia statement a[5] = 7 throws an error if the length of a is less than 5 or if this statement is the first use of the identifier a. Julia has push! and append!, which grow Vectors much more efficiently than MATLAB's a(end+1) = val.sqrt(-1) is represented in Julia as im, not i or j as in MATLAB.42) create integers instead of floating point numbers. As a result, some operations can throw a domain error if they expect a float; for example, julia> a = -1; 2^a throws a domain error, as the result is not an integer (see the FAQ entry on domain errors for details).(a, b) = (1, 2) or a, b = 1, 2. MATLAB's 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.N, not Nx1. For example, rand(N) makes a 1-dimensional array.[x,y,z] will always construct a 3-element array containing x, y and z.vcat(x,y,z) or separate with semicolons ([x; y; z]).hcat(x,y,z) or separate with spaces ([x y z]).hvcat or combine spaces and semicolons ([a b; c d]).a:b and a:b:c construct AbstractRange objects. To construct a full vector like in MATLAB, use collect(a:b). Generally, there is no need to call collect though. An AbstractRange object will act like a normal array in most cases but is more efficient because it lazily computes its values. This pattern of creating specialized objects instead of full arrays is used frequently, and is also seen in functions such as range, or with iterators such as enumerate, and zip. The special objects can mostly be used as if they were normal arrays.return keyword instead of listing the names of variables to return in the function definition (see The return Keyword for details).sum, prod, and max are performed over every element of an array when called with a single argument, as in sum(A), even if A has more than one dimension.rand().println or @printf can be used to print specific output.A and B are arrays, logical comparison operations like A == B do not return an array of booleans. Instead, use A .== B, and similarly for the other boolean operators like <, >.&, |, and ⊻ (xor) perform the bitwise operations equivalent to and, or, and xor respectively in MATLAB, and have precedence similar to Python's bitwise operators (unlike C). They can operate on scalars or element-wise 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))...., as in xs=[1,2]; f(xs...).svd returns singular values as a vector instead of as a dense diagonal matrix.... is not used to continue lines of code. Instead, incomplete expressions automatically continue onto the next line.ans is set to the value of the last expression issued in an interactive session. In Julia, unlike MATLAB, ans is not set when Julia code is run in non-interactive mode.structs do not support dynamically adding fields at runtime, unlike MATLAB's classes. Instead, use a Dict.x(x>3) or in the statement x(x>3) = [] to modify x in-place. In contrast, Julia provides the higher order functions filter and filter!, allowing users to write filter(z->z>3, x) and filter!(z->z>3, x) as alternatives to the corresponding transliterations x[x.>3] and x = x[x.>3]. Using filter! reduces the use of temporary arrays.vertcat(A{:}) in MATLAB, is written using the splat operator in Julia, e.g. as vcat(A...).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's single quotes enclose characters, not strings.
Julia can create substrings by indexing into strings. In R, strings must be converted into character vectors before creating substrings.
In Julia, like Python but unlike R, strings can be created with triple quotes """ ... """. This syntax is convenient for constructing strings that contain line breaks.
In Julia, varargs are specified using the splat operator ..., which always follows the name of a specific variable, unlike R, for which ... can occur in isolation.
In Julia, modulus is mod(a, b), not a %% b. % in Julia is the remainder operator.
In Julia, not all data structures support logical indexing. Furthermore, logical indexing in Julia is supported only with vectors of length equal to the object being indexed. For example:
c(1, 2, 3, 4)[c(TRUE, FALSE)] is equivalent to c(1, 3).c(1, 2, 3, 4)[c(TRUE, FALSE, TRUE, FALSE)] is equivalent to c(1, 3).[1, 2, 3, 4][[true, false]] throws a BoundsError.[1, 2, 3, 4][[true, false, true, false]] produces [1, 3].Like many languages, Julia does not always allow operations on vectors of different lengths, unlike R where the vectors only need to share a common index range. For example, c(1, 2, 3, 4) + c(1, 2) is valid R but the equivalent [1, 2, 3, 4] + [1, 2] will throw an error in Julia.
Julia allows an optional trailing comma when that comma does not change the meaning of code. This can cause confusion among R users when indexing into arrays. For example, x[1,] in R would return the first row of a matrix; in Julia, however, the comma is ignored, so x[1,] == x[1], and will return the first element. To extract a row, be sure to use :, as in x[1,:].
Julia's map takes the function first, then its arguments, unlike lapply(<structure>, function, ...) in R. Similarly Julia's equivalent of apply(X, MARGIN, FUN, ...) in R is mapslices where the function is the first argument.
Multivariate apply in R, e.g. mapply(choose, 11:13, 1:3), can be written as broadcast(binomial, 11:13, 1:3) in Julia. Equivalently Julia offers a shorter dot syntax for vectorizing functions binomial.(11:13, 1:3).
Julia uses end to denote the end of conditional blocks, like if, loop blocks, like while/ for, and functions. In lieu of the one-line if ( cond ) statement, Julia allows statements of the form if cond; statement; end, cond && statement and !cond || statement. Assignment statements in the latter two syntaxes must be explicitly wrapped in parentheses, e.g. cond && (x = value).
In Julia, <-, <<- and -> are not assignment operators.
Julia's -> creates an anonymous function.
Julia constructs vectors using brackets. Julia's [1, 2, 3] is the equivalent of R's c(1, 2, 3).
Julia's * operator can perform matrix multiplication, unlike in R. If A and B are matrices, then A * B denotes a matrix multiplication in Julia, equivalent to R's A %*% B. In R, this same notation would perform an element-wise (Hadamard) product. To get the element-wise multiplication operation, you need to write A .* B in Julia.
Julia performs matrix transposition using the transpose function and conjugated transposition using the ' operator or the adjoint function. Julia's transpose(A) is therefore equivalent to R's t(A). Additionally a non-recursive transpose in Julia is provided by the permutedims function.
Julia does not require parentheses when writing if statements or for/while 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, if Bool(1), or if 1==1.
Julia does not provide nrow and ncol. Instead, use size(M, 1) for nrow(M) and size(M, 2) for ncol(M).
Julia is careful to distinguish scalars, vectors and matrices. In R, 1 and c(1) are the same. In Julia, they cannot be used interchangeably.
Julia cannot assign to the results of function calls on the left hand side of an assignment operation: you cannot write diag(M) = fill(1, n).
Julia discourages populating the main namespace with functions. Most statistical functionality for Julia is found in packages under the JuliaStats organization. For example:
Julia provides tuples and real hash tables, but not R-style lists. When returning multiple items, you should typically use a tuple or a named tuple: instead of list(a = 1, b = 2), use (1, 2) or (a=1, b=2).
Julia encourages users to write their own types, which are 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 not copied when assigned or passed to a function. 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.
In Julia, vectors and matrices are concatenated using hcat, vcat and hvcat, not c, rbind and cbind like in R.
In Julia, a range like a:b is not shorthand for a vector like in R, but is a specialized AbstractRange object that is used for iteration without high memory overhead. To convert a range into a vector, use collect(a:b).
Julia's max and min are the equivalent of pmax and pmin respectively in R, but both arguments need to have the same dimensions. While maximum and minimum replace max and min in R, there are important differences.
Julia's sum, prod, maximum, and minimum are different from their counterparts in R. They all accept an optional keyword argument dims, which indicates the dimensions, over which the operation is carried out. For instance, let A = [1 2; 3 4] 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, dims=1) is a row vector containing the sum over each column and sum(A, dims=2) is a column vector containing the sum over each row. This contrasts to the behavior of R, where separate colSums(B) and rowSums(B) functions provide these functionalities. If the dims keyword argument is a vector, then it specifies all the dimensions over which the sum is performed, while retaining the dimensions of the summed array, e.g. sum(A, dims=(1,2)) == hcat(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 both sort and sort!.
In R, performance requires vectorization. In Julia, almost the opposite is true: the best performing code is often achieved by using devectorized loops.
Julia is eagerly evaluated and does not support R-style lazy evaluation. For most users, this means that there are very few unquoted expressions or column names.
Julia does not support the NULL type. The closest equivalent is nothing, but it behaves like a scalar value rather than like a list. Use x === nothing instead of is.null(x).
In Julia, missing values are represented by the missing object rather than by NA. Use ismissing(x) (or ismissing.(x) for element-wise operation on vectors) instead of is.na(x). The skipmissing function is generally used instead of na.rm=TRUE (though in some particular cases functions take a skipmissing argument).
Julia lacks the equivalent of R's assign or get.
In Julia, return does not require parentheses.
In R, an idiomatic way to remove unwanted values is to use logical indexing, like in the expression x[x>3] or in the statement x = x[x>3] to modify x in-place. In contrast, Julia provides the higher order functions filter and filter!, allowing users to write filter(z->z>3, x) and filter!(z->z>3, x) as alternatives to the corresponding transliterations x[x.>3] and x = x[x.>3]. Using filter! reduces the use of temporary arrays.
end to end a block. Unlike Python, Julia has no pass keyword.a[2:3] in Julia is a[1:3] in Python.end in Julia, not -1 as in Python.for, if, while, etc. blocks are terminated by the end keyword. Indentation level is not significant as it is in Python.+=, -=, ...) are not in-place whereas NumPy's are. This means A = [1, 1]; B = A; B += [3, 3] doesn't change values in A, it rather rebinds the name B to the result of the right-hand side B = B + 3, which is a new array. For in-place operation, use B .+= 3 (see also dot operators), explicit loops, or InplaceOps.jl.f(x=rand()) = x returns a new random number every time it is invoked without argument. On the other hand, the function g(x=[1,2]) = push!(x,3) returns [1,2,3] every time it is called as g().% is the remainder operator, whereas in Python it is the modulus.Int type corresponds to the machine integer type (Int32 or Int64). This means it will overflow, such that 2^64 == 0. If you need larger values use another appropriate type, such as Int128, BigInt or a floating point type like Float64.A[i,j]. This syntax is not just syntactic sugar for a reference to a pointer or address as in C/C++. See the Julia documentation for the syntax for array construction (it has changed between versions).A = B, changing elements of B will modify A as well. Updating operators like += do not operate in-place, they are equivalent to A = A + B which rebinds the left-hand side to the result of the right-hand side expression.42) create signed integers, of type Int, but literals too large to fit in the machine word size will automatically be promoted to a larger size type, such as Int64 (if Int is Int32), Int128, or the arbitrarily large BigInt type. There are no numeric literal suffixes, such as L, LL, U, UL, ULL to indicate unsigned and/or signed vs. unsigned. Decimal literals are always signed, and hexadecimal literals (which start with 0x like C/C++), are unsigned. Hexadecimal literals also, unlike C/C++/Java and unlike decimal literals in Julia, have a type based on the length of the literal, including leading 0s. For example, 0x0 and 0x00 have type UInt8, 0x000 and 0x0000 have type UInt16, then literals with 5 to 8 hex digits have type UInt32, 9 to 16 hex digits type UInt64 and 17 to 32 hex digits type UInt128. This needs to be taken into account when defining hexadecimal masks, for example ~0xf == 0xf0 is very different from ~0x000f == 0xfff0. 64 bit Float64 and 32 bit Float32 bit literals are expressed as 1.0 and 1.0f0 respectively. Floating point literals are rounded (and not promoted to the BigFloat type) if they can not be exactly represented. Floating point literals are closer in behavior to C/C++. Octal (prefixed with 0o) and binary (prefixed with 0b) literals are also treated as unsigned." or """, """ delimited literals can contain " characters without quoting it like "\"". String literals can have values of other variables or expressions interpolated into them, indicated by $variablename or $(expression), which evaluates the variable name or the expression in the context of the function.// indicates a Rational number, and not a single-line comment (which is # in Julia)#= indicates the start of a multiline comment, and =# ends it.return keyword. Multiple values can be returned from functions and assigned as tuples, e.g. (a, b) = myfunction() or a, b = myfunction(), instead of having to pass pointers to values as one would have to do in C/C++ (i.e. a = myfunction(&b).println or @printf can be used to print specific output. In the REPL, ; can be used to suppress output. ; also has a different meaning within [ ], something to watch out for. ; can be used to separate expressions on a single line, but are not strictly necessary in many cases, and are more an aid to readability.⊻ (xor) performs the bitwise XOR operation, i.e. ^ in C/C++. Also, the bitwise operators do not have the same precedence as C/++, so parenthesis may be required.^ is exponentiation (pow), not bitwise XOR as in C/C++ (use ⊻, or xor, in Julia)>> and >>>. >>> performs an arithmetic shift, >> always performs a logical shift, unlike C/C++, where the meaning of >> depends on the type of the value being shifted.-> creates an anonymous function, it does not access a member via a pointer.if statements or for/while loops: use for i in [1, 2, 3] instead of for (int i=1; i <= 3; i++) and if i == 1 instead of if (i == 1).0 and 1 as Booleans. You cannot write if (1) in Julia, because if statements accept only booleans. Instead, you can write if true, if Bool(1), or if 1==1.end to denote the end of conditional blocks, like if, loop blocks, like while/ for, and functions. In lieu of the one-line if ( cond ) statement, Julia allows statements of the form if cond; statement; end, cond && statement and !cond || statement. Assignment statements in the latter two syntaxes must be explicitly wrapped in parentheses, e.g. cond && (x = value), because of the operator precedence.@ character, and have both a function-like syntax, @mymacro(arg1, arg2, arg3), and a statement-like syntax, @mymacro arg1 arg2 arg3. The forms are interchangeable; the function-like form is particularly useful if the macro appears within another expression, and is often clearest. The statement-like form is often used to annotate blocks, as in the distributed for construct: @distributed for i in 1:n; #= body =#; end. Where the end of the macro construct may be unclear, use the function-like form.@enum(name, value1, value2, ...) For example: @enum(Fruit, banana=1, apple, pear)
! at the end of the name, for example push!.this, using the most-specific-declaration rule).
© 2009–2019 Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and other contributors
Licensed under the MIT License.
https://docs.julialang.org/en/v1.2.0/manual/noteworthy-differences/