# Julia 101

## Lesson 09: Data Visualization in Julia

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Data visualization is a crucial part of data analysis and interpretation. In Julia, the Plots library stands as a versatile tool for creating visualizations. In this article, we explore data visualization in Julia using the Plots library.

**Installing Plots**

We begin by installing the Plots library. Let’s open our Julia REPL and execute the following command

`using Pkg`

Pkg.add("Plots")

Once Plots is installed, we can start creating visualizations by importing the Plots module

`using Plots`

**Our First Plot**

Let’s begin with a simple line plot. Imagine we want to visualize cosine function

`x = 0:0.01:4*pi`

y = cos.(x)

plot(x,y)

xlabel!("x")

ylabel!("y")

title!("cos(x)")

**Customizing Our Plot**

Plots offers various customization options for enhanced plots. We can customize line colors, line style, line width and legend.

`x = 0:0.01:4*pi`

y = cos.(x)

plot(x,y,label="cos(x)",linestyle=:dash,linecolor="red",linewidth=5,grid=true)

xlabel!("x")

ylabel!("y")

title!("cos(x)")

Similarly, in scatter plot we can customize marker colors, marker style and marker width

`x = 0:0.1:4*pi`

y = cos.(x)

plot(x,y,label="cos(x)",marker=:diamond,markercolor="red",markerwidth=5,grid=false)

xlabel!("x")

ylabel!("y")

title!("cos(x)")

We can turn off the grid by setting `grid=false`

**Multiple Plots**

We can plot multiple lines in the same figure

`x = 0:0.01:4*pi`

plot(x,[cos.(x) sin.(x)],label=["cos(x)" "sin(x)"],linewidth=3)

xlabel!("x")

ylabel!("y")

or we can use `layout`

to divide them into subplots.

`x = 0:0.01:4*pi`

plot(x,[cos.(x) sin.(x)],label=["cos(x)" "sin(x)"],layout(2,1),linewidth=3)

xlabel!("x")

ylabel!("y")

Using the `layout`

, we created two subplots in a single figure, specifying two rows and one column.

## Saving Plot

After creating our plot, we can save it as an image file using the `savefig()`

command

`savefig("plot.png")`

This line saves the current plot as “plot.png” in the current working directory.

For more advanced features, it’s highly recommended to refer to the official documentation of Plots library.