Julia

Crash course in Julia

Author

Matthew DeHaven

Published

April 2, 2025

Julia Overview

Similar to R and Python in syntax
Very fast
Builtin package and environment manager
Unicode support
Broadcasting

Julia Background

Extremely new language.

. . .

Development began in 2009. Version 1.0 was launched in 2018.

. . .

Developed by a group at MIT: “Why We Created Julia”

. . .

Designed to be:

As readable as R and Python
As fast as C

(C is very fast, but hard to read and write)

Julia Microbenchmarks

Julia Installs

Installing Julia

Julia can be installed with…

julialang.org

. . .

(Macos) Homebrew: brew install julia

Running Julia in VS Code

You can run a whole file of Julia code by clicking the play arrow.

. . .

Or you can run a single line or selection…

hitting Shift+Enter

. . .

Both of these will execute the Julia code in a terminal, called a REPL—read-eval-print-loop.

Hello World Example

x = "Hello Class!"
print(x)

Hello Class!

a = 0.1
b = 0.1
c = 0.1

print(a + b + c == 0.3)

false

Julia Environments and Packages

Pkg.jl

Julia has a built in package and environment manager: Pkg.jl

. . .

It has its own REPL, which can be launched from Julia by typing a right square bracket: ]

. . .

But all of its commands can also be run from withing Julia.

ex: Pkg.add("DataFrames")

. . .

I will show commands should be run in the Pkg REPL with: pkg>

Creating an Environment

In a Julia REPL, activate the Pkg REPL with: ]

. . .

Then you can create and activate an environment:

pkg>

activate myenv

This will create an environment in a new folder, “myenv”.

. . .

If you want to use your current folder, run

pkg>

activate .

Adding a Package

In Julia, you can install packages both system wide

i.e. in the default installation environment (called something like “v1.10”)

or in an environment you have created.

. . .

To add the DataFrames.jl package:

pkg>

add DataFrames

. . .

This is equivalent to running the following in Julia:

import Pkg
Pkg.add("DataFrames")

Pkg TOMLs

Once you have a package added to your environment, you will see two files:

. . .

“Project.toml”

Lists the packages you have installed for this environment.

. . .

“Manifest.toml”

Lists the

Julia version
Installed package versions
Every package dependency version

. . .

With these two files, Julia can always recreate your environment.

Connect Environment to VS Code Workspace

In order to have your VS Code workspace automatically use the correct Julia environment, you have to connect it.

. . .

Open the Command Palette (Shift+Cmd+P), then search “Julia: Change Current Environment”

. . .

Click on “Julia env:” at the bottom left of the VS Code window

. . .

After selecting your environment, VS Code will add a “.vscode/settings.json” file to the folder. This file contains a path to the environment.

Using Packages

Once you have a package installed, you can:

Load the entire namespace:

using DataFrames
DataFrame(a = 1:2, b = 3:4)

. . .

Import the package and use the . to reference functions

import DataFrames
DataFrames.DataFrame(a = 1:2, b = 3:4)

. . .

Import the package and rename it

import DataFrames as DF
DF.DataFrame(a = 1:2, b = 3:4)

Julia Basics

Variable Assignment

Julia uses a single = for assignment

x = 42

. . .

Just like R, functions can be assigned to new variables:

a = sqrt
a(10)

3.1622776601683795

Basic Math

Math is not that different:

3 + 3

3 - 3

3 * 3

3 / 3

1.0

3 ^ 3 # Power

3 % 3  # remainder

Logic

Logic operators are written with double && or ||

1 > 2

false

1 < 2

true

1 > 2 && 1 < 2

false

1 > 2 || 1 < 2

true

!false

true

Julia Data Types

Booleans
Numbers: Integer, Floating
Strings
Collections:
- Arrays, Tuples

Making Arrays

A basic array is constructed as comma-separated elements in square brackets

my_array = [1, 5, 2, 8]

4-element Vector{Int64}:
 1
 5
 2
 8

. . .

Single dimension arrays are referred to as Vectors.

Array Types

Arrays will default to the most general type at construction.

int_array = [1, 5, 2, 8]

4-element Vector{Int64}:
 1
 5
 2
 8

If even one of the elements is a float at construction:

float_array = [1, 5.2, 2, 8]

4-element Vector{Float64}:
 1.0
 5.2
 2.0
 8.0

Array Types are Immutable

We cannot add a float to an already made Int Array:

int_array[2] = 9.1

LoadError: InexactError: Int64(9.1)
InexactError: Int64(9.1)

Stacktrace:
 [1] Int64
   @ ./float.jl:994 [inlined]
 [2] convert
   @ ./number.jl:7 [inlined]
 [3] setindex!(A::Vector{Int64}, x::Float64, i::Int64)
   @ Base ./array.jl:987
 [4] top-level scope
   @ In[20]:1

Arrays of Any Type

But at construction, arrays can be very flexible on the types included:

a = [1, 3.14, "hi", false]

4-element Vector{Any}:
     1
     3.14
      "hi"
 false

Julia will default to a general type “Any” to allow an integer, float, string, and boolean to exist in the same array.

Enforcing an Array Type

You can be explicit about the type for an array by listing it before the square brackets.

int_array = Int[1, 2, 3]

3-element Vector{Int64}:
 1
 2
 3

. . .

If we wanted this to be a Float array:

fl_array = Float64[1, 2, 3]

3-element Vector{Float64}:
 1.0
 2.0
 3.0

Empty Arrays

This is most useful when creating an empty array—which defaults to type Any.

empty_array = []

Any[]

Empty array with a type:

string_array = String[]

String[]

Array Dimensions

You can make…

arrays of multiple dimensions (2d: matrices, Nd).

m = [[1, 2, 3] [4, 5, 6]]

3×2 Matrix{Int64}:
 1  4
 2  5
 3  6

. . .

Arrays of arrays:

aa = [[1, 2, 3], [4, 5, 6]]

2-element Vector{Vector{Int64}}:
 [1, 2, 3]
 [4, 5, 6]

Tuples

Tuples are constructed as comma-separated elements in parentheses.

my_tuple = (1, "hi", 2.2, false)

(1, "hi", 2.2, false)

. . .

Tuples are immutable; arrays are mutable.

i.e. you cannot change values of tuples once created, or add additional elements

my_tuple[1] = 4

LoadError: MethodError: no method matching setindex!(::Tuple{Int64, String, Float64, Bool}, ::Int64, ::Int64)
The function `setindex!` exists, but no method is defined for this combination of argument types.
MethodError: no method matching setindex!(::Tuple{Int64, String, Float64, Bool}, ::Int64, ::Int64)
The function `setindex!` exists, but no method is defined for this combination of argument types.

Stacktrace:
 [1] top-level scope
   @ In[29]:1

Julia Indexing

Julia starts indexing from 1.

x = ["a", "b", "c"]
x[1]

"a"

For Loops

Julia is not space sensitive.

A for loop instead ends with an end keyword

for i in 1:10
  print(i)
end

12345678910

. . .

Leaving out the space is fine (though ugly).

for i in 1:10
print(i)
end

12345678910

Julia Functions

Julia functions are defined with the keyword function and end:

function hi()
  print("Hello!")
end

hi()

Hello!

. . .

You can define arguments and return values.

function my_function(a, b)
  c = a ^ 2 + b ^ 2
  return c
end

my_function(3, 4)

Short Function Declaration

For one line functions, you can define them with:

f(x, y) = x + y * x

f(2, 3)

Fun Feature: Unicode Support

Julia has built in unicode support.

Unicode is a text encoding standard that supports characters from all major writing systems and emojis!

. . .

print("Hi! 😃")

Hi! 😃

. . .

But what’s really fun, is that you can use emojis as variables too…

😃 = 4
💛 = 2


f(😃, 💛)

Practical Unicode

What this is actually incredibly convenient for is writing greek letters for equations:

β = 0.99
γ = 1.2

U(C) = β * (1 / γ) * C ^ γ

U(10)

13.075368837804186

. . .

It is a lot tidier than writing out “beta” and “gamma”,

Writing out Unicode

In VS Code, to get one of these unicode symbols, you type:

\:smiley: for 😃

. . .

Once you start typing \:smi… you can use tab to autocomplete.

. . .

Similarily,

\beta for β

. . .

For a full list, see the Unicode Julia documentation.

Vectorization in Julia

Julia can vectorize (broadcast) any function, which is incredibly useful.

. . .

By default, Julia functions aren’t vectorized:

abs([-7, 2, -5])

MethodError: no method matching abs(::Vector{Int64})
The function `abs` exists, but no method is defined for this combination of argument types.

Closest candidates are:
  abs(::Bool)
   @ Base bool.jl:153
  abs(::Missing)
   @ Base missing.jl:101
  abs(::Pkg.Resolve.VersionWeight)
   @ Pkg /opt/homebrew/Cellar/julia/1.11.3/share/julia/stdlib/v1.11/Pkg/src/Resolve/versionweights.jl:32
  ...


Stacktrace:
 [1] top-level scope
   @ In[39]:1

Vectorization in Julia

Julia can vectorize (broadcast) any function, which is incredibly useful.

By default, Julia functions aren’t vectorized:

abs([-7, 2, -5])

To apply the abs function to each element, add the . operator:

abs.([-7, 2, -5])

3-element Vector{Int64}:
 7
 2
 5

Broadcasting Your Own Functions

Broadcasting with the . operator works for any functions:

f(x) = x ^ 2 + 7 - x

f.(1:6)

6-element Vector{Int64}:
  7
  9
 13
 19
 27
 37

Broadcasting with Math Operators

By default, Julia won’t multiply two vectors element by element:

vec1 = [1, 4, 3]
vec2 = [1, 2, 7]

vec1 * vec2

MethodError: no method matching *(::Vector{Int64}, ::Vector{Int64})
The function `*` exists, but no method is defined for this combination of argument types.

Closest candidates are:
  *(::Any, ::Any, ::Any, ::Any...)
   @ Base operators.jl:596
  *(::LinearAlgebra.Adjoint{<:Any, <:SparseArrays.CHOLMOD.Sparse}, ::VecOrMat)
   @ SparseArrays /opt/homebrew/Cellar/julia/1.11.3/share/julia/stdlib/v1.11/SparseArrays/src/solvers/cholmod.jl:1419
  *(::LinearAlgebra.Adjoint{<:Number, <:AbstractVector}, ::AbstractVector{<:Number})
   @ LinearAlgebra /opt/homebrew/Cellar/julia/1.11.3/share/julia/stdlib/v1.11/LinearAlgebra/src/adjtrans.jl:478
  ...


Stacktrace:
 [1] top-level scope
   @ In[42]:4

Broadcasting with Math Operators

By default, Julia won’t multiply two vectors element by element:

vec1 = [1, 4, 3]
vec2 = [1, 2, 7]

vec1 * vec2

Instead, use broadcasting to get this behavior

vec1 .* vec2

3-element Vector{Int64}:
  1
  8
 21

DataFrames

Working with Data

DataFrames is a package that implements dataframes in Julia.

First, you need to install DataFrames, then import it.

pkg>

add DataFrames

And then in your Julia code:

using DataFrames

DataFrame Example

Constructing a dataframe by hand:

df = DataFrame(
  a = [1, 5, 6, 8],
  b = ["Y", "Y", "N", "N"],
  c = [true, false, false, true]
)

4×3 DataFrame

Row	a	b	c
	Int64	String	Bool
1	1	Y	true
2	5	Y	false
3	6	N	false
4	8	N	true

The “Bang!” Convention

In Julia it is convention that functions that modify their inputs always end with a bang: !

. . .

For example, if we want to add a row to the DataFrame:

push!(df, [2, "Y", false])

5×3 DataFrame

Row	a	b	c
	Int64	String	Bool
1	1	Y	true
2	5	Y	false
3	6	N	false
4	8	N	true
5	2	Y	false

Data Science with DataFrames

All of the data science operations we saw with dplyr and data.table are possible with Julia and DataFrames.jl

group by
summarize
adding new columns
etc.

. . .

For reading and writing CSVs, use the CSV.jl package.

Plotting in Julia

Lots of possible plotting packages in Julia.

Plots.jl the most used
Gadfly.jl is a grammar of graphics package
UnicodePLots.jl makes plots in the terminal
Makie.jl is a high performance plotting package (can use GPUs for big plots)
AlgebraOfGraphics.jl is a grammar of graphics package that wraps Makie.jl
PlotlyJS.jl is a wrapper for Plotly

Plot Example with `PLots.jl`

using Plots
x = 1:10
y = rand(10)
plot(x, y, title="My Plot")

Just-in-time Compilation

Programming languages have to be compiled to machine code before they can be run.

Julia does this “just-in-time” (JIT)—i.e. right before the code is run.

This means that Julia can be very fast, but it also means that the first time you run a function, it will be slow.

. . .

The speed advantage comes from compiling code for only the specific types of the inputs.

. . .

An example of the disadvantage is the “time to first plot” problem.

the first plot you run in a Julia session takes a while, the second is fast

Summary

Lecture Summary

Similar to R and Python in syntax
Very fast
Builtin package and environment manager
Unicode support
Broadcasting
JIT compilation

Live Coding Example

Launch Julia REPL
Create a new environment ] activate .
Install Gadfly and RDatasets

using Gadfly, RDatasets

iris = dataset("datasets", "iris")
plot(iris, x = :SepalLength, y = :SepalWidth, Geom.point)

mtcars = dataset("datasets", "mtcars")
plot(mtcars, x = :MPG, y = :HP, Geom.point)

Julia Overview

Julia Background

Julia Microbenchmarks

Julia Installs

Installing Julia

Running Julia in VS Code

Hello World Example

Julia Environments and Packages

Pkg.jl

Creating an Environment

Adding a Package

Pkg TOMLs

Connect Environment to VS Code Workspace

Using Packages

Julia Basics

Variable Assignment

Basic Math

Logic

Julia Data Types

Making Arrays

Array Types

Array Types are Immutable

Arrays of Any Type

Enforcing an Array Type

Empty Arrays

Array Dimensions

Tuples

Julia Indexing

For Loops

Julia Functions

Short Function Declaration

Fun Feature: Unicode Support

Practical Unicode

Writing out Unicode

Vectorization in Julia

Vectorization in Julia

Broadcasting Your Own Functions

Broadcasting with Math Operators

Broadcasting with Math Operators

DataFrames

Working with Data

DataFrame Example

The “Bang!” Convention

Data Science with DataFrames

Plotting in Julia

Plot Example with PLots.jl

Just-in-time Compilation

Summary

Lecture Summary

Live Coding Example

Plot Example with `PLots.jl`