# Plotting

Plotting in julia can be obtained using a specific plotting package (e.g. [Gadfly](https://github.com/dcjones/Gadfly.jl), [Winston](https://github.com/nolta/Winston.jl)) or, as I prefer, use the [Plots](https://github.com/JuliaPlots/Plots.jl) package that provide a unified API to several supported backends

Backends are chosen running `chosenbackend()` (that is, the name of the corresponding backend package, but written all in lower case) before calling the `plot` function. You need to install at least one backend before being able to use the `Plots` package. My preferred one is [PlotlyJS](https://github.com/sglyon/PlotlyJS.jl) (a julia interface to the [plotly.js](https://plot.ly) visualization library. ), but you may be interested also in [PyPlot](https://github.com/JuliaPy/PyPlot.jl) (that use the excellent python [matplotlib](http://matplotlib.org/api/pyplot_api.html) **VERSION 2**).

For example:

```
Pkg.add("Plots")
Pkg.add("PyPlot.jl") # or  Pkg.add("PlotlyJS")
using Plots
pyplot()             # or plotlyjs()
plot(sin, -2pi, pi, label="sine function")
```

![sine\_plot](https://1436292136-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LK5kRxHC0DakZquGiEE%2F-LLikz62LJ6cTeDyEtiY%2F-LLikze8Q6k4EE3g1BPK%2Fsine_plot.png?generation=1536235405596808\&alt=media)

Attention not to mix using different plotting packages (e.g.`Plots` and one of its backends). I had troubles with that. If you have already imported a plot package and you want to use an other package, always restart the julia kernel (this is not necessary, and it is one of the advantage, when switching between different bakends of the `Plots` package).

You can find some useful documentation on `Plots` backends:

* [Which backend to choose ?](http://docs.juliaplots.org/latest/backends/)
* [Charts and attributes supported by the various backends](http://docs.juliaplots.org/latest/supported/)

### Plotting multiple groups of series from a DataFrame

The following example uses [StatsPlots](https://github.com/JuliaPlots/StatsPlots.jl) in order to work directly on DataFrames (rather than on arrays). Passing the dataFrame as first argument of the `@df` macro, you can access its columns by name and split the overall series using a third column.

```julia
using DataFrames, Plots, StatsPlots
df = DataFrame(
  fruit       = ["orange","orange","orange","orange","apple","apple","apple","apple"],
  year        = [2010,2011,2012,2013,2010,2011,2012,2013],
  production  = [120,150,170,160,100,130,165,158],
  consumption = [70,90,100,95,   80,95,110,120]
)
pyplot()
mycolours = [:green :orange] # note that the serie is piled up alphabetically
@df df plot(:year, :production, group=:fruit, linestyle = :solid, linewidth=3, label = reshape(("Production of " .* sort(unique(:fruit))),(1,:)), color=mycolours)
@df df plot!(:year, :consumption, group=:fruit, linestyle = :dot, linewidth=3, label = reshape(("Consumption of " .* sort(unique(:fruit))),(1,:)), color=mycolours)
```

![fruits\_plot](https://1436292136-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LK5kRxHC0DakZquGiEE%2F-LLikz62LJ6cTeDyEtiY%2F-LLikzeBI4f6tAjOyGs5%2Ffruits_plot.png?generation=1536235405602451\&alt=media)

The first call to `plot()` create a new plot. Calling `plot!()` modify instead the plot that is passed as first argument (if none, the latest plot is modified)

## Printing area charts

Use the `fill(fillrange,fillalpha,fillcolor)` attribute, e.g. `fill = (0, 0.5, :blue)`.

## Saving

To save the figure just call one of the following:

```
savefig("fruits_plot.svg")
savefig("fruits_plot.pdf")
savefig("fruits_plot.png")
```

*While an updated, expanded and revised version of this chapter is available in "Chapter 9 - Working with Data" of* [*Antonello Lobianco (2019), "Julia Quick Syntax Reference", Apress*](https://julia-book.com)*, this tutorial remains in active development.*