Getting started with Julia programming language
Table of contents
Download
Download Julia from julialang.org and Julia IDEs from

Juno from junolab.org

IJulia notebook from github.com/JuliaLang/IJulia.jl
 To use IJulia you will need the Ipython Notebook which now is called Jupyter
Juno is a good IDE for writing and evaluating julia code quickly. IJulia notebook is good for writing tutorials and reports with julia code results embeded in the document.
Once you’ve installed everything I recommend opening up the Juno IDE and going through the tutorial.
Quick start
I execute all Julia code below in IJulia. I suggest you create a folder on your desktop and make it your working directory where we will be able to write files. First, a couple of basic commands. To evaluate code in Juno you just need to press CtrlD
(its in the Juno tutrial):
VERSION # print julia version number
pwd() # print working directory
homedir() # print the default home directory
# set working directory to DirectoryPath "C:/Users/TimDz/Desktop/julialang"
cd("C:/Users/TimDz/Desktop/julialang")
3+5 # => 8
5*7 # => 35
3^17 # => 129140163
3^(1+3im) # im stands for imaginary number => 2.964383781426573  0.46089998526262876im
log(7) # natural log of 7 => 1.9459101490553132
Interesting that julia has imaginary number built in. Now, variables and functions:
a = cos(pi) + im*sin(pi) # assigning to a variable
1.0 + 1.2246467991473532e16im
b = e^(im*pi)
1.0 + 1.2246467991473532e16im
a == b # boolean expression. It is an euler identity.
true
Lets see how to define functions. Here is a chapter on functions in julia docs for more info.
plus2(x) = x + 2 # a compact way
function plustwo(x) # traditional function definition
return x+2
end
plustwo (generic function with 1 method)
plus2(11)
13
plustwo(11)
13
Here is a julia cheatsheet with above and additional information in a concise form. Next, lets write a function that will generate some data which we will write to a csv file, plot, and save the plot.
Data frames, plotting, and file Input/Output
So I decided to write a function $f(x)$ that performs the process from the Collatz conjecture. Basically, if x is even divide by $2$, if x is odd multiply by three and add $1$. Repeat the process until you reach one. The Collatz conjecture proposes that regardless of what number you start with you will always reach one. Here it is in explicit form
The function collatz(x)
will count the number of iterations it took for the starting number to reach $1$.
function collatz(x)
# Given a number x
#  divide by 2 if x is even
#  multiply by 3 and add 1 if x is odd
# until x reaches 1
count = 0
while x != 1
if x % 2 == 0
x = x/2
count += 1
else
x = 3*x + 1
count += 1
end
end
return count
end
collatz(2)
1
collatz(3)
7
Data frames
Now, lets create a data frame with the number of steps needed to reach 1 for each number from 1 to 1000. We will use the DataFrames
package because the base julia library does not have data frames.
# Pkg.add("DataFrames")
using DataFrames
# Before populating the dataframe with collatz data lets see how to create a dataframe
df = DataFrame(Col1 = 1:10, Col2 = ["a","b","c","d","e","f","a","b","c","d"])
# Lets use collatz data
df = DataFrame(Number = 1:1000, NumofSteps = map(collatz,1:1000))
head(df)
Number  NumofSteps  

1  1  0 
2  2  1 
3  3  7 
4  4  2 
5  5  5 
6  6  8 
map()
applies collatz()
function to every number in the 1:1000
array which is an array of numbers [1,2,3,...,1000]
. In this instance map()
returns an array of numbers that went went through collatz()
function.
# To get descriptive statistics
describe(df)
Number
Min 1.0
1st Qu. 250.75
Median 500.5
Mean 500.5
3rd Qu. 750.25
Max 1000.0
NAs 0
NA% 0.0%
NumofSteps
Min 0.0
1st Qu. 26.0
Median 43.0
Mean 59.542
3rd Qu. 99.0
Max 178.0
NAs 0
NA% 0.0%
Before we save it lets categorize the points based on whether the original number is even or odd.
# create new evenodd column
df = hcat(df, map(x > if x % 2 == 0 "even" else "odd" end, 1:1000))
rename!(df, :x1, :evenodd) #rename it to evenodd
head(df)
Number  NumofSteps  evenodd  

1  1  0  odd 
2  2  1  even 
3  3  7  odd 
4  4  2  even 
5  5  5  odd 
6  6  8  even 
hcat(df, column(s))
horizontally concatenates data frames. I use the map()
function with an anonymous function x > if x % 2 == 0 "even" else "odd" end
which checks for divisibility by two to create a column with “even” and “odd” as entries. Finally, I rename the new column “evenodd”. Lets save it:
# To save the data frame in the working directory (make sure to set the wd as described in
# the beginning of the tutorial)
writetable("collatz.csv", df)
Plotting data
To plot the data we will use the Gadfly
package. Cairo
is needed to be able to save plots as PDFs and PNG. First, I will do a simple plot.
# Pkg.add("Gadfly")
# Pkg.add("Cairo")
# Pkg.add("Compose")
# Pkg.update()
using Gadfly, Cairo
plot(df,x="Number", y="NumofSteps", Geom.point)
Looks pretty. You should be able to zoom in/out. Lets color the points based on whether the original number is even or odd. I will assign the plot to a variable and save it.
# assign plot to variable
a = plot(df,x="Number", y="NumofSteps", color = "evenodd", Geom.point)
It looks like odd numbers overlay the even numbers points. Let’s plot even and odd numbers side by side.
a = plot(df[df[:evenodd] .== "odd",:],
x="Number", y="NumofSteps",
Geom.point,
Guide.xlabel("Odd"))
b = plot(df[df[:evenodd] .== "even",:],
x="Number", y="NumofSteps",
Geom.point,
Guide.xlabel("Even"),
Theme(default_color=colorant"#d4ca59"))
vstack(a,b)
Even numbers up to 1000 require fewer steps to reach one through the Collatz procedure than odd numbers. We can save this plot.
# Save the plot in the working directory
draw(PNG("collatzplot.png", 8inch, 10inch), vstack(a,b))
Conclusion
Julia is a comfortable language to work with and many say it is the future of scientific computing. It may very well be true. One of the main reasons is Julia’s JIT compiler which makes Julia almost as fast and sometimes faster than C. At this point, I find Julia not as good as R simply because R is more mature and has a bigger commmunity. R aslo has better documentation and more questions on Stackoverflow. There are $109419$ questions with an R tag in contrast to $1251$ questions with julialang tag as of 10/12/2015 and $1631$ as of 3/7/2016.
Julia is up and coming and given enough time it could create competition for R. Unlikely that Julia is going to be a competitor in the industry against Python, SAS and R, but in academia it is a different story.