Building Blocks
Traces
A Plot instance will have a vector of traces. These should each be a subtype of AbstractTrace.
PlotlyJS.jl defines one such subtype:
mutable struct GenericTrace{T <: AbstractDict{Symbol,Any}} <: AbstractTrace
fields::T
endThe fields is an AbstractDict object that maps trace attributes to their values.
We create this wrapper around a Dict to provide some convnient syntax as described below.
Let’s consider an example. Suppose we would like to build the following JSON object:
{
"type": "scatter",
"x": [1, 2, 3, 4, 5],
"y": [1, 6, 3, 6, 1],
"mode": "markers+text",
"name": "Team A",
"text": ["A-1", "A-2", "A-3", "A-4", "A-5"],
"textposition": "top center",
"textfont": {
"family": "Raleway, sans-serif"
},
"marker": { "size": 12 }
}One way to do this in Julia is:
fields = Dict{Symbol,Any}(:type => "scatter",
:x => [1, 2, 3, 4, 5],
:y => [1, 6, 3, 6, 1],
:mode => "markers+text",
:name => "Team A",
:text => ["A-1", "A-2", "A-3", "A-4", "A-5"],
:textposition => "top center",
:textfont => Dict(:family => "Raleway, sans-serif"),
:marker => Dict(:size => 12))
GenericTrace("scatter", fields)scatter with fields marker, mode, name, text, textfont, textposition, type, x, and y
A more convenient syntax is:
t1 = scatter(;x=[1, 2, 3, 4, 5],
y=[1, 6, 3, 6, 1],
mode="markers+text",
name="Team A",
text=["A-1", "A-2", "A-3", "A-4", "A-5"],
textposition="top center",
textfont_family="Raleway, sans-serif",
marker_size=12)scatter with fields marker, mode, name, text, textfont, textposition, type, x, and y
Notice a few things:
-
The trace
typebecame the function name. There is a similar method for all
plotly.js traces types.
-
All other trace attributes were set using keyword arguments. This allows us
to avoid typing out the symbol prefix (:) and the arrows (=>) that were necessary when constructing the Dict
-
We can set nested attributes using underscores. Notice that the JSON
"marker": { "size": 12 } was written marker_size=12.
We can verify that this is indeed equivalent JSON by printing the JSON: (note the order of the attributes is different, but the content is identical):
print(JSON.json(t1, 2)){
"textfont": {
"family": "Raleway, sans-serif"
},
"mode": "markers+text",
"marker": {
"size": 12
},
"textposition": "top center",
"y": [
1,
6,
3,
6,
1
],
"type": "scatter",
"name": "Team A",
"text": [
"A-1",
"A-2",
"A-3",
"A-4",
"A-5"
],
"x": [
1,
2,
3,
4,
5
]
}
Accessing attributes
If we then wanted to extract a particular attribute, we can do so using getindex(t1, :attrname), or the syntactic sugar t1[:attrname]. Note that both symbols and strings can be used in a call to getindex:
julia> t1["marker"]
Dict{Any, Any} with 1 entry:
:size => 12
julia> t1[:marker]
Dict{Any, Any} with 1 entry:
:size => 12To access a nested property use parent.child
julia> t1["textfont.family"]
"Raleway, sans-serif"Setting additional attributes
We can also set additional attributes. Suppose we wanted to set marker.color to be red. We can do this with a call to setindex!(t1, "red", :marker_color), or equivalently t1["marker_color"] = "red":
julia> t1["marker_color"] = "red"
"red"
julia> println(JSON.json(t1, 2))
{
"textfont": {
"family": "Raleway, sans-serif"
},
"mode": "markers+text",
"marker": {
"color": "red",
"size": 12
},
"textposition": "top center",
"y": [
1,
6,
3,
6,
1
],
"type": "scatter",
"name": "Team A",
"text": [
"A-1",
"A-2",
"A-3",
"A-4",
"A-5"
],
"x": [
1,
2,
3,
4,
5
]
}Notice how the color attribute was correctly added within the existing marker attribute (alongside size), instead of replacing the marker attribute.
You can also use this syntax to add completely new nested attributes:
julia> t1["line_width"] = 5
5
julia> println(JSON.json(t1, 2))
{
"textfont": {
"family": "Raleway, sans-serif"
},
"mode": "markers+text",
"marker": {
"color": "red",
"size": 12
},
"line": {
"width": 5
},
"textposition": "top center",
"y": [
1,
6,
3,
6,
1
],
"type": "scatter",
"name": "Team A",
"text": [
"A-1",
"A-2",
"A-3",
"A-4",
"A-5"
],
"x": [
1,
2,
3,
4,
5
]
}Layouts
The Layout type is defined as
mutable struct Layout{T <: AbstractDict{Symbol,Any}} <: AbstractLayout
fields::T
subplots::_Maybe{Subplots}
endYou can construct a layout using the same convenient keyword argument syntax that we used for traces:
julia> l = Layout(;title="Penguins",
xaxis_range=[0, 42.0], xaxis_title="fish",
yaxis_title="Weight",
xaxis_showgrid=true, yaxis_showgrid=true,
legend_y=1.15, legend_x=0.7)
layout with fields legend, margin, template, title, xaxis, and yaxis
julia> println(JSON.json(l, 2))
{
"xaxis": {
"showgrid": true,
"range": [
0.0,
42.0
],
"title": {
"text": "fish"
}
},
"template": {
"data": {
"scatterpolargl": [
{
"type": "scatterpolargl",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"carpet": [
{
"baxis": {
"gridcolor": "white",
"endlinecolor": "#2a3f5f",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f",
"linecolor": "white"
},
"type": "carpet",
"aaxis": {
"gridcolor": "white",
"endlinecolor": "#2a3f5f",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f",
"linecolor": "white"
}
}
],
"scatterpolar": [
{
"type": "scatterpolar",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"parcoords": [
{
"line": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
},
"type": "parcoords"
}
],
"scatter": [
{
"type": "scatter",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"histogram2dcontour": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "histogram2dcontour",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"contour": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "contour",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"scattercarpet": [
{
"type": "scattercarpet",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"mesh3d": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "mesh3d"
}
],
"surface": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "surface",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"scattermapbox": [
{
"type": "scattermapbox",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"scattergeo": [
{
"type": "scattergeo",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"histogram": [
{
"type": "histogram",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"pie": [
{
"type": "pie",
"automargin": true
}
],
"choropleth": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "choropleth"
}
],
"heatmapgl": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "heatmapgl",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"bar": [
{
"type": "bar",
"error_y": {
"color": "#2a3f5f"
},
"error_x": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
}
}
],
"heatmap": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "heatmap",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"contourcarpet": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "contourcarpet"
}
],
"table": [
{
"type": "table",
"header": {
"line": {
"color": "white"
},
"fill": {
"color": "#C8D4E3"
}
},
"cells": {
"line": {
"color": "white"
},
"fill": {
"color": "#EBF0F8"
}
}
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
},
"type": "scatter3d",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"scattergl": [
{
"type": "scattergl",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"histogram2d": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "histogram2d",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"scatterternary": [
{
"type": "scatterternary",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"barpolar": [
{
"type": "barpolar",
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
}
}
]
},
"layout": {
"xaxis": {
"gridcolor": "white",
"zerolinewidth": 2,
"title": {
"standoff": 15
},
"ticks": "",
"zerolinecolor": "white",
"automargin": true,
"linecolor": "white"
},
"hovermode": "closest",
"paper_bgcolor": "white",
"geo": {
"showlakes": true,
"showland": true,
"landcolor": "#E5ECF6",
"bgcolor": "white",
"subunitcolor": "white",
"lakecolor": "white"
},
"colorscale": {
"sequential": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
],
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequentialminus": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
},
"yaxis": {
"gridcolor": "white",
"zerolinewidth": 2,
"title": {
"standoff": 15
},
"ticks": "",
"zerolinecolor": "white",
"automargin": true,
"linecolor": "white"
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"font": {
"color": "#2a3f5f"
},
"annotationdefaults": {
"arrowhead": 0,
"arrowwidth": 1,
"arrowcolor": "#2a3f5f"
},
"plot_bgcolor": "#E5ECF6",
"title": {
"x": 0.05
},
"coloraxis": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
},
"hoverlabel": {
"align": "left"
},
"mapbox": {
"style": "light"
},
"polar": {
"angularaxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
}
},
"autotypenumbers": "strict",
"ternary": {
"aaxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
},
"baxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
}
},
"scene": {
"xaxis": {
"gridcolor": "white",
"gridwidth": 2,
"backgroundcolor": "#E5ECF6",
"ticks": "",
"showbackground": true,
"zerolinecolor": "white",
"linecolor": "white"
},
"zaxis": {
"gridcolor": "white",
"gridwidth": 2,
"backgroundcolor": "#E5ECF6",
"ticks": "",
"showbackground": true,
"zerolinecolor": "white",
"linecolor": "white"
},
"yaxis": {
"gridcolor": "white",
"gridwidth": 2,
"backgroundcolor": "#E5ECF6",
"ticks": "",
"showbackground": true,
"zerolinecolor": "white",
"linecolor": "white"
}
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
]
}
},
"legend": {
"y": 1.15,
"x": 0.7
},
"margin": {
"l": 50,
"b": 50,
"r": 50,
"t": 60
},
"title": "Penguins",
"yaxis": {
"showgrid": true,
"title": {
"text": "Weight"
}
}
}attr
There is a special function named attr that allows you to apply the same keyword magic we saw in the trace and layout functions, but to nested attributes. Let’s revisit the previous example, but use attr to build up our xaxis and legend:
julia> l2 = Layout(;title="Penguins",
xaxis=attr(range=[0, 42.0], title="fish", showgrid=true),
yaxis_title="Weight", yaxis_showgrid=true,
legend=attr(x=0.7, y=1.15))
layout with fields legend, margin, template, title, xaxis, and yaxis
julia> println(JSON.json(l2, 2))
{
"xaxis": {
"showgrid": true,
"range": [
0.0,
42.0
],
"title": "fish"
},
"template": {
"data": {
"scatterpolargl": [
{
"type": "scatterpolargl",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"carpet": [
{
"baxis": {
"gridcolor": "white",
"endlinecolor": "#2a3f5f",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f",
"linecolor": "white"
},
"type": "carpet",
"aaxis": {
"gridcolor": "white",
"endlinecolor": "#2a3f5f",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f",
"linecolor": "white"
}
}
],
"scatterpolar": [
{
"type": "scatterpolar",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"parcoords": [
{
"line": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
},
"type": "parcoords"
}
],
"scatter": [
{
"type": "scatter",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"histogram2dcontour": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "histogram2dcontour",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"contour": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "contour",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"scattercarpet": [
{
"type": "scattercarpet",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"mesh3d": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "mesh3d"
}
],
"surface": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "surface",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"scattermapbox": [
{
"type": "scattermapbox",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"scattergeo": [
{
"type": "scattergeo",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"histogram": [
{
"type": "histogram",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"pie": [
{
"type": "pie",
"automargin": true
}
],
"choropleth": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "choropleth"
}
],
"heatmapgl": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "heatmapgl",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"bar": [
{
"type": "bar",
"error_y": {
"color": "#2a3f5f"
},
"error_x": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
}
}
],
"heatmap": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "heatmap",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"contourcarpet": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "contourcarpet"
}
],
"table": [
{
"type": "table",
"header": {
"line": {
"color": "white"
},
"fill": {
"color": "#C8D4E3"
}
},
"cells": {
"line": {
"color": "white"
},
"fill": {
"color": "#EBF0F8"
}
}
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
},
"type": "scatter3d",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"scattergl": [
{
"type": "scattergl",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"histogram2d": [
{
"colorbar": {
"ticks": "",
"outlinewidth": 0
},
"type": "histogram2d",
"colorscale": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
}
],
"scatterternary": [
{
"type": "scatterternary",
"marker": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
}
}
],
"barpolar": [
{
"type": "barpolar",
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
}
}
}
]
},
"layout": {
"xaxis": {
"gridcolor": "white",
"zerolinewidth": 2,
"title": {
"standoff": 15
},
"ticks": "",
"zerolinecolor": "white",
"automargin": true,
"linecolor": "white"
},
"hovermode": "closest",
"paper_bgcolor": "white",
"geo": {
"showlakes": true,
"showland": true,
"landcolor": "#E5ECF6",
"bgcolor": "white",
"subunitcolor": "white",
"lakecolor": "white"
},
"colorscale": {
"sequential": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
],
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequentialminus": [
[
0.0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1.0,
"#f0f921"
]
]
},
"yaxis": {
"gridcolor": "white",
"zerolinewidth": 2,
"title": {
"standoff": 15
},
"ticks": "",
"zerolinecolor": "white",
"automargin": true,
"linecolor": "white"
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"font": {
"color": "#2a3f5f"
},
"annotationdefaults": {
"arrowhead": 0,
"arrowwidth": 1,
"arrowcolor": "#2a3f5f"
},
"plot_bgcolor": "#E5ECF6",
"title": {
"x": 0.05
},
"coloraxis": {
"colorbar": {
"ticks": "",
"outlinewidth": 0
}
},
"hoverlabel": {
"align": "left"
},
"mapbox": {
"style": "light"
},
"polar": {
"angularaxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
}
},
"autotypenumbers": "strict",
"ternary": {
"aaxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
},
"baxis": {
"gridcolor": "white",
"ticks": "",
"linecolor": "white"
}
},
"scene": {
"xaxis": {
"gridcolor": "white",
"gridwidth": 2,
"backgroundcolor": "#E5ECF6",
"ticks": "",
"showbackground": true,
"zerolinecolor": "white",
"linecolor": "white"
},
"zaxis": {
"gridcolor": "white",
"gridwidth": 2,
"backgroundcolor": "#E5ECF6",
"ticks": "",
"showbackground": true,
"zerolinecolor": "white",
"linecolor": "white"
},
"yaxis": {
"gridcolor": "white",
"gridwidth": 2,
"backgroundcolor": "#E5ECF6",
"ticks": "",
"showbackground": true,
"zerolinecolor": "white",
"linecolor": "white"
}
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
]
}
},
"legend": {
"y": 1.15,
"x": 0.7
},
"margin": {
"l": 50,
"b": 50,
"r": 50,
"t": 60
},
"title": "Penguins",
"yaxis": {
"showgrid": true,
"title": {
"text": "Weight"
}
}
}Notice we got the exact same output as before, but we didn’t have to resort to building the Dict by hand or prefixing multiple arguments with xaxis_ or legend_.
Using DataFrames
|
New in version 0.6.0 |
You can also construct traces using the columns of any subtype of AbstractDataFrame (e.g. the DataFrame type from DataFrames.jl).
To demonstrate this functionality let’s load the famous iris data set:
julia> using DataFrames, RDatasets
julia> iris = dataset("datasets", "iris");
julia> first(iris, 10)
10×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64 │ Float64 │ Float64 │ Float64 │ Cat… │
├─────┼─────────────┼────────────┼─────────────┼────────────┼─────────┤
│ 1 │ 5.1 │ 3.5 │ 1.4 │ 0.2 │ setosa │
│ 2 │ 4.9 │ 3.0 │ 1.4 │ 0.2 │ setosa │
│ 3 │ 4.7 │ 3.2 │ 1.3 │ 0.2 │ setosa │
│ 4 │ 4.6 │ 3.1 │ 1.5 │ 0.2 │ setosa │
│ 5 │ 5.0 │ 3.6 │ 1.4 │ 0.2 │ setosa │
│ 6 │ 5.4 │ 3.9 │ 1.7 │ 0.4 │ setosa │
│ 7 │ 4.6 │ 3.4 │ 1.4 │ 0.3 │ setosa │
│ 8 │ 5.0 │ 3.4 │ 1.5 │ 0.2 │ setosa │
│ 9 │ 4.4 │ 2.9 │ 1.4 │ 0.2 │ setosa │
│ 10 │ 4.9 │ 3.1 │ 1.5 │ 0.1 │ setosa │Suppose that we wanted to construct a scatter trace with the SepalLength column as the x variable and the SepalWidth columns as the y variable. We do this by calling
julia> my_trace = scatter(iris, x=:SepalLength, y=:SepalWidth, marker_color=:red)
scatter with fields marker, type, x, and y
julia> [my_trace[:x][1:5] my_trace[:y][1:5]]
5×2 Array{Float64,2}:
5.1 3.5
4.9 3.0
4.7 3.2
4.6 3.1
5.0 3.6
julia> my_trace[:marker_color]
:redHow does this work? The basic rule is that if the value of any keyword argument is a Julia Symbol (i.e. created with :something), then the function creating the trace checks if that symbol is one of the column names in the DataFrame. If so, it extracts the column from the DataFrame and sets that as the value for the keyword argument. Otherwise it passes the symbol directly through.
In the above example, when we constructed my_trace the value of the keyword argument x was set to the Symbol :SepalLength. This did match a column name from iris so that column was extracted and replaced :SepalLength as the value for the x argument. The same holds for y and SepalWidth.
However, when setting marker_color=:red we found that :red is not one of the column names, so the value for the marker_color keyword argument remained :red.
The DataFrame interface becomes more useful when constructing whole plots. See the convenience methods section of the documentation for more information.
|
New in version 0.9.0 |
As of version 0.9.0, you can construct groups of traces using the DataFrame api. This is best understood by example, so let’s see it in action:
julia> iris = dataset("datasets", "iris");
julia> unique(iris[:Species])
3-element Array{String,1}:
"setosa"
"versicolor"
"virginica"
julia> traces = scatter(
iris, group=:Species, x=:SepalLength, y=:SepalWidth, mode="markers", marker_size=8
)
3-element Array{GenericTrace,1}:
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:x => [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9 … 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0],:mode => "markers",:y => [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1 … 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3],:type => "scatter",:name => CategoricalValue{String,UInt8} "setosa",:marker => Dict{Any,Any}(:size => 8)))
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:x => [7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2 … 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7],:mode => "markers",:y => [3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7 … 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8],:type => "scatter",:name => CategoricalValue{String,UInt8} "versicolor",:marker => Dict{Any,Any}(:size => 8)))
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:x => [6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2 … 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9],:mode => "markers",:y => [3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6 … 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0],:type => "scatter",:name => CategoricalValue{String,UInt8} "virginica",:marker => Dict{Any,Any}(:size => 8)))
julia> [t[:name] for t in traces]
3-element CategoricalArray{String,1,UInt8}:
"setosa"
"versicolor"
"virginica"Notice how there are three Species in the iris DataFrame, and when passing group=:Species to scatter we obtained three traces.
We can pass a Vector{Symbol} as group, to split the data on the value in more than one column:
julia> tips = dataset("reshape2", "tips");
julia> unique(tips[:Sex])
2-element Array{String,1}:
"Female"
"Male"
julia> unique(tips[:Day])
4-element Array{String,1}:
"Sun"
"Sat"
"Thur"
"Fri"
julia> traces = violin(tips, group=[:Sex, :Day], x=:TotalBill, orientation="h")
8-element Array{GenericTrace,1}:
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:type => "violin",:name => "(Female, Fri)",:orientation => "h",:x => [5.75, 16.32, 22.75, 11.35, 15.38, 13.42, 15.98, 16.27, 10.09]))
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:type => "violin",:name => "(Female, Sat)",:orientation => "h",:x => [20.29, 15.77, 19.65, 15.06, 20.69, 16.93, 26.41, 16.45, 3.07, 17.07 … 10.59, 10.63, 12.76, 13.27, 28.17, 12.9, 30.14, 22.12, 35.83, 27.18]))
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:type => "violin",:name => "(Female, Sun)",:orientation => "h",:x => [16.99, 24.59, 35.26, 14.83, 10.33, 16.97, 10.29, 34.81, 25.71, 17.31, 29.85, 25.0, 13.39, 16.21, 17.51, 9.6, 20.9, 18.15]))
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:type => "violin",:name => "(Female, Thur)",:orientation => "h",:x => [10.07, 34.83, 10.65, 12.43, 24.08, 13.42, 12.48, 29.8, 14.52, 11.38 … 18.64, 11.87, 19.81, 43.11, 13.0, 12.74, 13.0, 16.4, 16.47, 18.78]))
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:type => "violin",:name => "(Male, Fri)",:orientation => "h",:x => [28.97, 22.49, 40.17, 27.28, 12.03, 21.01, 12.46, 12.16, 8.58, 13.42]))
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:type => "violin",:name => "(Male, Sat)",:orientation => "h",:x => [20.65, 17.92, 39.42, 19.82, 17.81, 13.37, 12.69, 21.7, 9.55, 18.35 … 15.69, 11.61, 10.77, 15.53, 10.07, 12.6, 32.83, 29.03, 22.67, 17.82]))
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:type => "violin",:name => "(Male, Sun)",:orientation => "h",:x => [10.34, 21.01, 23.68, 25.29, 8.77, 26.88, 15.04, 14.78, 10.27, 15.42 … 34.63, 34.65, 23.33, 45.35, 23.17, 40.55, 20.69, 30.46, 23.1, 15.69]))
GenericTrace{Dict{Symbol,Any}}(Dict{Symbol,Any}(:type => "violin",:name => "(Male, Thur)",:orientation => "h",:x => [27.2, 22.76, 17.29, 19.44, 16.66, 32.68, 15.98, 13.03, 18.28, 24.71 … 9.78, 7.51, 28.44, 15.48, 16.58, 7.56, 10.34, 13.51, 18.71, 20.53]))
julia> [t[:name] for t in traces]
8-element Array{String,1}:
"(Female, Fri)"
"(Female, Sat)"
"(Female, Sun)"
"(Female, Thur)"
"(Male, Fri)"
"(Male, Sat)"
"(Male, Sun)"
"(Male, Thur)"Also new in version 0.9.0, when using the DataFrame API you are allowed to pass a function as the value for a keyword argument. When the each trace is constructed, the value will be replaced by calling the function on whatever DataFrame is being used. When used in conjunction with the group argument, this allows you to compute group specific trace attributes on the fly.
See the docstring for GenericTrace and the violin_side_by_side example on the Violin example page more details.
Facets
|
New in PlotlyBase version 0.6.5 (PlotlyJS version 0.16.4) |
When plotting a DataFrame (let’s call it df), the keyword arguments facet_row and facet_col allow you to create a matrix of subplots. The rows of this matrix correspond unique(df[:facet_row]), where :facet_row is a placeholder for the actual value passed as the facet_row argument. Similarly, the columns of the matrix of subplots come from unique(df[:facet_col]).
Each subplot will have the same structure, as defined by the keyword arguments passed to plot, but will only show data for a single value of facet_row and facet_col at a time.
Below is an example of how this works
julia> using PlotlyJS, CSV, DataFrames
julia> df = dataset(DataFrame, "tips")
244×7 DataFrame
Row │ total_bill tip sex smoker day time size
│ Float64 Float64 String7 String3 String7 String7 Int64
─────┼────────────────────────────────────────────────────────────────
1 │ 16.99 1.01 Female No Sun Dinner 2
2 │ 10.34 1.66 Male No Sun Dinner 3
3 │ 21.01 3.5 Male No Sun Dinner 3
4 │ 23.68 3.31 Male No Sun Dinner 2
5 │ 24.59 3.61 Female No Sun Dinner 4
6 │ 25.29 4.71 Male No Sun Dinner 4
7 │ 8.77 2.0 Male No Sun Dinner 2
8 │ 26.88 3.12 Male No Sun Dinner 4
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
238 │ 32.83 1.17 Male Yes Sat Dinner 2
239 │ 35.83 4.67 Female No Sat Dinner 3
240 │ 29.03 5.92 Male No Sat Dinner 3
241 │ 27.18 2.0 Female Yes Sat Dinner 2
242 │ 22.67 2.0 Male Yes Sat Dinner 2
243 │ 17.82 1.75 Male No Sat Dinner 2
244 │ 18.78 3.0 Female No Thur Dinner 2
229 rows omitted
julia> plot(
df, x=:total_bill, y=:tip, xbingroyp="x", ybingroup="y", kind="histogram2d",
facet_row=:sex, facet_col=:smoker
)
data: [
"histogram2d with fields type, x, xaxis, xbingroyp, y, yaxis, and ybingroup",
"histogram2d with fields type, x, xaxis, xbingroyp, y, yaxis, and ybingroup",
"histogram2d with fields type, x, xaxis, xbingroyp, y, yaxis, and ybingroup",
"histogram2d with fields type, x, xaxis, xbingroyp, y, yaxis, and ybingroup"
]
layout: "layout with fields annotations, legend, margin, template, xaxis, xaxis2, xaxis3, xaxis4, yaxis, yaxis2, yaxis3, and yaxis4"