Data visualization in Julia

For the following Julia program, we use a package called Plots. The command used to install the package is Pkg.add("Plots"). Here, we run Julia programs via a Jupyter notebook. The Julia program is presented in the following screenshot:

After clicking Kernel on the menu bar, and then Restart and Run All, we get the following:

Again, the srand(123) command guarantees that any user who applies the same random seed will get the same set of random numbers. Because of this, he/she would get the same graph shown previously. The next example is a scatter plot using a Julia package called PyPlot:

using PyPlot 
n=50 
srand(333) 
x = 100*rand(n) 
y = 100*rand(n) 
areas = 800*rand(n) 
fig = figure("pyplot_scatterplot",figsize=(10,10)) 
ax = axes() 
scatter(x,y,s=areas,alpha=0.5) 
title("using PyPlot: Scatter Plot") 
xlabel("X") 
ylabel("Y") 
grid("on") 

The related graph is shown here:

The next Julia program is borrowed from Sargent and Stachurski (2017):

using QuantEcon: meshgrid...