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Plots.jl.jl
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Plots.jl.jl
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### A Pluto.jl notebook ###
# v0.19.9
#> [frontmatter]
#> author_url = "https://github.com/JuliaPluto"
#> image = "https://user-images.githubusercontent.com/6933510/174067386-c0a1296f-dba8-4dbf-b936-1c7f81933f94.png"
#> tags = ["basic", "classic", "plotting"]
#> author_name = "Pluto.jl"
#> description = "An introduction to Plots.jl"
#> license = "Unlicense"
using Markdown
using InteractiveUtils
# βββ‘ 5e611449-c239-4547-9d13-5db33a081e58
using Plots
# βββ‘ 7b93882c-9ad8-11ea-0288-0941e163f9d5
md"""
# Plotting in Pluto
Pluto is an excellent environment to visualise your data. This notebook shows a few examples of using plots in Pluto using the `Plots` package.
I wrote this notebook so you can understand it even if you have never used `Plots`. However, it is not intended as a complete tutorial for the package. If you want to start making your own plots, I recommend looking at the package [documentation](https://docs.juliaplots.org/latest/) for a full tutorial as well.
Let's start by importing the `Plots` package. _(Pluto's package manager automatically takes care of installing and tracking dependencies!)_
"""
# βββ‘ e5abaf40-f105-11ea-1494-2da858d7db5b
md"We need to choose a _backend_ for Plots.jl. We choose `plotly` because it works with no additional dependencies. You can [read more about backends](http://docs.juliaplots.org/latest/backends/#backends) in Plots.jl - it's one of its coolest features!"
# βββ‘ 9414a092-f105-11ea-10cd-23f84e47d876
# β ββ‘ show_logs = false
plotly()
# βββ‘ 5ae65950-9ad9-11ea-2e14-35119d369acd
md"""
## The basics
Now we can get started. We can begin by making some X and Y values. I typed out some Y values so you can alter them and see the effect.
"""
# βββ‘ aaa805d4-9ad8-11ea-21c2-3b20580fea0e
years = 2001:2010
# βββ‘ c02bc44a-9ad8-11ea-117d-3997e1f3cab0
apples = [15, 25, 80, 75, 50, 30, 35, 15, 25, 35]
# βββ‘ a405ae4c-9ad9-11ea-0008-f763e098846d
md"""
Great, let's plot them! The basic syntax of `Plots` is very simple.
"""
# βββ‘ 12a8c222-9ad9-11ea-2544-355bd080367f
plot(years, apples)
# βββ‘ 19660da0-9ada-11ea-1942-a9d4ddd7753b
md"""
You can change the properties of the plot by giving different arguments to the function call.
"""
# βββ‘ 5410faba-9ada-11ea-2fc5-35f0bdd40d96
plot(years, apples, legend=false, title="Number of apples per year")
# βββ‘ 90c47cae-9ada-11ea-10da-91f4dcf0f2d6
md"""
Alternatively, you can use `plot!()`. This function takes the same arguments, but alters our existing plot.
"""
# βββ‘ b2727d2e-9ada-11ea-23d3-bddab9ec8de1
begin
plot(years, apples)
plot!(legend=false)
plot!(title="Number of apples per year")
end
# βββ‘ eb218bea-9adc-11ea-2f3a-298c41caf8a1
md"""
## More options
We can easily plot multiple series.
"""
# βββ‘ bd30bdf4-9add-11ea-26d4-75ac3f0b8610
oranges = rand(1:100, 10)
# βββ‘ dc50d480-9add-11ea-2821-018a98fab262
begin
plot(years, apples, label="apples")
plot!(years, oranges, label="oranges")
end
# βββ‘ c9a64e98-9b67-11ea-1d6a-3b1f7548cb8d
md"""
Different plot types have their own functions.
"""
# βββ‘ 13428812-9ade-11ea-3865-1d293993203d
scatter(apples, oranges, legend=false)
# βββ‘ d76fd0f6-9b67-11ea-0a73-e9ddb60ca291
md"""
You can also plot a function instead of an output array!
"""
# βββ‘ 69fa7744-9b67-11ea-26df-3b4446dfb3ea
begin
function myfunction(x)
return sin(x / 2)
end
plot(years, sin, label="sine")
plot!(years, myfunction, label="my function")
end
# βββ‘ af31a428-9adf-11ea-280a-45e2b891c4f9
md"""
## Naming plots
Plots are also just a type of variable, so you can give them a name.
"""
# βββ‘ 5ed7c444-9adf-11ea-2925-8573f018e820
apples_plot = plot(years, apples,
legend=false, xlabel="year", ylabel="apples")
# βββ‘ 932b6b3c-a64d-11ea-2aba-2bf577ce3cf8
apples_vs_oranges = scatter(apples, oranges,
legend=false, xlabel="apples", ylabel="oranges")
# βββ‘ 030c1d10-9b69-11ea-11b7-ff04c60892b9
md"""
This way, you can show them again
"""
# βββ‘ d7bdab72-9b65-11ea-18c0-5353a214749d
apples_plot
# βββ‘ 22992cec-9adb-11ea-078e-c7c21489c053
md"""
## Combining subplots
We can combine multiple plots in a single image using the `layout` argument. One option is to provide a matrix of values and call a single `plot`.
"""
# βββ‘ 5d723220-9ade-11ea-37db-c110dda60a15
pears = rand(1:100, 10)
# βββ‘ 632cb26c-9ade-11ea-1bc7-39b8207190e2
bananas = rand(1:100, 10)
# βββ‘ a01bf430-9ade-11ea-2ae4-23287ba11406
fruits = hcat(apples, oranges, pears, bananas)
# βββ‘ 69e045b0-9ade-11ea-0c55-05fa0da5893c
plot(years, fruits, layout=(4, 1), legend=false)
# βββ‘ 054e0c4e-9adf-11ea-3deb-4daf2b6a2548
md"""
This works when we have a nice output matrix. For more complicated options, named plots are usually a good solution. When we have created a few plots, we can put them together in a single plot.
"""
# βββ‘ 91b4eedc-9adf-11ea-37aa-250ceec26640
plot(apples_plot, apples_vs_oranges, layout=(1, 2))
# βββ‘ a7abf64e-9adb-11ea-2ae4-47886125fe60
md"""
## The Pluto way of plotting
For the most part, plotting in Pluto is not different from anywhere else. However, there are a few things to keep in mind.
The `plot!()` function alters an existing plot. In reactive programming, you are not supposed to alter the value of a variable you defined in a different cell. I strongly recommend that you only use `plot!()` to alter plots you initialised in the same cell.
For the sake of demonstration, here is what happens if you use `plot!()` in its own cell. Let's start by making a new plot.
"""
# βββ‘ 2617a7b6-9ae2-11ea-1461-d914c7cfdb09
pears_plot = plot(years, pears, label="pears")
# βββ‘ 46388876-9ae2-11ea-2c5f-073698e45d44
md"""
The cell below adds a new series to the plot. It shows the altered plot, while our original plot still looks intact.
Maybe that's not so bad. It seems like we could use this to build a plot one step at a time. But try changing the plot below so that it plots oranges instead of bananas.
"""
# βββ‘ 2598a29a-9ae2-11ea-02cc-21f3fa5a9472
plot!(pears_plot, years, bananas, label="bananas")
# βββ‘ a9cdba00-9ae2-11ea-3bf9-41d07e7a785f
md"""
Did you get three series? It's because the `plot` still has the `bananas` data added to it. So the bananas are still plotted, even though you now don't have any code doing that. This is not great for coding: it means that if you close your notebook, your plots might not look the same when you open it again.
Try running the cell a few times. You keep adding series! Also not great when working on a plot.
"""
# βββ‘ f22b6fda-9ae3-11ea-3ff2-ebcf1374f595
md"""
So what if you do want to add things to a plot one at a time, instead of using a single cell? My recommendation is to define your plot in a function.
"""
# βββ‘ 8819edf0-9ae4-11ea-15f4-f17e9e9db8ea
function plot_pears()
myplot = plot(years, pears, label="pears")
return myplot
end
# βββ‘ 9e4d1462-9ae4-11ea-3c0f-774d68a671c3
plot_pears()
# βββ‘ a7906786-9ae4-11ea-0175-4f860c05a8a2
begin
plot_pears()
plot!(years, bananas, label="bananas")
end
# βββ‘ d3cbc548-9ae4-11ea-261f-7fb956839e53
md"""
Try changing the function for the base plot, or the addition in the bottom cell. No weird effects!
"""
# βββ‘ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
[compat]
Plots = "~1.31.4"
"""
# βββ‘ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
[[Adapt]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "af92965fb30777147966f58acb05da51c5616b5f"
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version = "3.3.3"
[[ArgTools]]
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[[Artifacts]]
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
[[Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
[[Bzip2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2"
uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
version = "1.0.8+0"
[[Cairo_jll]]
deps = ["Artifacts", "Bzip2_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
git-tree-sha1 = "4b859a208b2397a7a623a03449e4636bdb17bcf2"
uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a"
version = "1.16.1+1"
[[ChainRulesCore]]
deps = ["Compat", "LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "80ca332f6dcb2508adba68f22f551adb2d00a624"
uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
version = "1.15.3"
[[ChangesOfVariables]]
deps = ["ChainRulesCore", "LinearAlgebra", "Test"]
git-tree-sha1 = "38f7a08f19d8810338d4f5085211c7dfa5d5bdd8"
uuid = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0"
version = "0.1.4"
[[CodecZlib]]
deps = ["TranscodingStreams", "Zlib_jll"]
git-tree-sha1 = "ded953804d019afa9a3f98981d99b33e3db7b6da"
uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
version = "0.7.0"
[[ColorSchemes]]
deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "Random"]
git-tree-sha1 = "1fd869cc3875b57347f7027521f561cf46d1fcd8"
uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
version = "3.19.0"
[[ColorTypes]]
deps = ["FixedPointNumbers", "Random"]
git-tree-sha1 = "eb7f0f8307f71fac7c606984ea5fb2817275d6e4"
uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
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[[ColorVectorSpace]]
deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "SpecialFunctions", "Statistics", "TensorCore"]
git-tree-sha1 = "d08c20eef1f2cbc6e60fd3612ac4340b89fea322"
uuid = "c3611d14-8923-5661-9e6a-0046d554d3a4"
version = "0.9.9"
[[Colors]]
deps = ["ColorTypes", "FixedPointNumbers", "Reexport"]
git-tree-sha1 = "417b0ed7b8b838aa6ca0a87aadf1bb9eb111ce40"
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[[Compat]]
deps = ["Dates", "LinearAlgebra", "UUIDs"]
git-tree-sha1 = "924cdca592bc16f14d2f7006754a621735280b74"
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[[CompilerSupportLibraries_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
[[Contour]]
git-tree-sha1 = "d05d9e7b7aedff4e5b51a029dced05cfb6125781"
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[[Downloads]]
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[[EarCut_jll]]
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