Plotting with PlotlyJS.jl

Imports



In [1]:

    
using PlotlyJS
using DataFrames, RDatasets









    






 
 Plotly javascript loaded.
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init_notebook(true)

 






    



WARNING: Method definition describe(AbstractArray) in module StatsBase at /Users/mrestrep/.julia/v0.5/StatsBase/src/scalarstats.jl:573 overwritten in module DataFrames at /Users/mrestrep/.julia/v0.5/DataFrames/src/abstractdataframe/abstractdataframe.jl:407.

Basics

Simple line scatter

First define your traces with x, y as keyword inputs
Pass the trace to the plot() function



In [27]:

    
function linescatter()
    trace1 = scatter(;x=1:4, y=[10, 15, 13, 18])
    plot(trace1)
end
linescatter()









    



WARNING: Method definition linescatter() in module Main at In[8]:2 overwritten at In[27]:2.






    Out[27]:

Customize and add multiple traces to one plot

Customize markers vs lines: Trace attributes are part of the trace definition
Multiple traces in one plot: Define multiple traces and pass them as an array to plot()
JavaScript Docs



In [9]:

    
function multiple_scatter_traces()
    trace1 = scatter(;x=1:4, y=[10, 15, 13, 17], mode="markers", name="marker only")
    trace2 = scatter(;x=1:4, y=[16, 5, 11, 9], mode="lines", name="line")
    trace3 = scatter(;x=1:4, y=[12, 9, 15, 12], mode="lines+markers", name="line+marker")
    trace4 = scatter(;x=1:4, y=[5, 10, 8, 12], mode="lines", line_dash="dash", name="dash")
    plot([trace1, trace2, trace3, trace4])
end
multiple_scatter_traces()









    Out[9]:

Data labels and Figure Layout

Add the array of labels as an attribute to each trace
Layout modifies the "Figure Pane Attributes"



In [10]:

    
function data_labels()
    trace1 = scatter(;x=1:5, y=[1, 6, 3, 6, 1],
                      mode="markers+text", name="Team A",
                      textposition="top center",
                      text=["A-1", "A-2", "A-3", "A-4", "A-5"],
                      marker_size=12, textfont_family="Raleway, sans-serif")

    trace2 = scatter(;x=1:5+0.5, y=[4, 1, 7, 1, 4],
                      mode="markers+text", name= "Team B",
                      textposition="bottom center",
                      text= ["B-a", "B-b", "B-c", "B-d", "B-e"],
                      marker_size=12, textfont_family="Times New Roman")

    data = [trace1, trace2]

    layout = Layout(;title="Data Labels on the Plot", xaxis_range=[0.75, 5.25],
                     yaxis_range=[0, 8], legend_y=0.5, legend_yref="paper",
                     legend=attr(family="Arial, sans-serif", size=20,
                                 color="grey"))
    plot(data, layout)
end
data_labels()









    Out[10]:

Area



In [18]:

    
function area1()
    trace1 = scatter(;x=1:4, y=[0, 2, 3, 5], fill="tozeroy")
    trace2 = scatter(;x=1:4, y=[3, 5, 1, 7], fill="tonexty")
    plot([trace1, trace2])
end
area1()









    



WARNING: Method definition area1() in module Main at In[17]:2 overwritten at In[18]:2.






    Out[18]:

Other visual interpretations of Matrix Data and Subplots

Easy - just cancatenate plots, but...
3d subplots are buggy



In [19]:

    
function matrix_subplots()
    z = rand(10,10)
    trace0 = scatter(; y=z)

    trace1 = heatmap(; z=z, showscale=false)
    trace2 = contour(; z=z)
    trace3 = surface(; z=z)
    p = [plot(trace1) plot(trace2)]
end

matrix_subplots()









    Out[19]:

Advanced Layouts

Much more verbose
Fine-control of position of each element (powerful but time consuming)

Challenge: Finish the layout



In [20]:

    
function advanced_layouts()
    trace1 = scatter(;y=rand(10), mode="markers")
    trace2 = bar(;y=rand(10), xaxis="x2", yaxis="y2")
    trace3 = scatter(;y=rand(10), xaxis="x3", yaxis="y3")

    data = [trace1, trace2, trace3]
    xdomains = [[0,0.3], [0.33, 0.53] , [0.56, 0.78], [0.8, 1] ]
    ydomains = [[0,1],   [0, 0.27] , [0.33, 0.63], [0.66, 1] ]
    layout = Layout(; xaxis_domain=xdomains[1], 
                    xaxis2_domain=xdomains[2], yaxis2_domain=ydomains[2],yaxis2_anchor="x2", xaxis2_anchor="y2",
                    xaxis3_domain=xdomains[2], yaxis3_domain=ydomains[3],yaxis3_anchor="x3", xaxis3_anchor="y3"
                    )
    plot(data, layout)
end
p1 = advanced_layouts()









    Out[20]:

Stats Plots



In [21]:

    
function grouped_bar_example()
    trace1 = bar(;x=["giraffes", "orangutans", "monkeys"],
                  y=[20, 14, 23],
                  name="SF Zoo")
    trace2 = bar(;x=["giraffes", "orangutans", "monkeys"],
                  y=[12, 18, 29],
                  name="LA Zoo")
    data = [trace1, trace2]
    layout = Layout(;barmode="group")
    plot(data, layout)
end

grouped_bar_example()









    Out[21]:



In [22]:

    
function stacked_bar_example()
    trace1 = bar(;x=["giraffes", "orangutans", "monkeys"],
                  y=[20, 14, 23],
                  name="SF Zoo")
    trace2 = bar(x=["giraffes", "orangutans", "monkeys"],
                 y=[12, 18, 29],
                 name="LA Zoo")
    data = [trace1, trace2]
    layout = Layout(;barmode="stack")
    plot(data, layout)
end
stacked_bar_example()









    Out[22]:



In [23]:

    
function two_hists()
    x0 = randn(500)
    x1 = x0+1

    trace1 = histogram(x=x0, opacity=0.75)
    trace2 = histogram(x=x1, opacity=0.75)
    data = [trace1, trace2]
    layout = Layout(barmode="overlay")
    plot(data, layout)
end

two_hists()









    Out[23]:

Box (Whisker) Plots

A box plot is a convenient way of graphically depicting numerical data through their quartiles. The first quartile (Q1) is the middle number between the smallest number and the median of the data set. The second quartile (Q2) is the median of the data. The third quartile (Q3) is the middle value between the median and the highest value of the data set. Box plots may also have lines extending vertically from the boxes (whiskers) indicating variability outside the upper and lower quartiles. Outliers may be plotted as individual points.



In [24]:

    
function box_plot()
    x0 = ["day 1", "day 1", "day 1", "day 1", "day 1", "day 1",
          "day 2", "day 2", "day 2", "day 2", "day 2", "day 2"]
    trace1 = box(;y=[0.2, 0.2, 0.6, 1.0, 0.5, 0.4, 0.2, 0.7, 0.9, 0.1, 0.5, 0.3],
                  x=x0,
                  name="kale",
                  marker_color="#3D9970")
    trace2 = box(;y=[0.6, 0.7, 0.3, 0.6, 0.0, 0.5, 0.7, 0.9, 0.5, 0.8, 0.7, 0.2],
                  x=x0,
                  name="radishes",
                  marker_color="#FF4136")
    trace3 = box(;y=[0.1, 0.3, 0.1, 0.9, 0.6, 0.6, 0.9, 1.0, 0.3, 0.6, 0.8, 0.5],
                  x=x0,
                  name="carrots",
                  marker_color="#FF851B")
    data = [trace1, trace2, trace3]
    layout = Layout(;yaxis=attr(title="normalized moisture", zeroline=false),
                    boxmode="group")
    plot(data, layout)
end
box_plot()









    Out[24]:

Using DataFrames

Some documentation lives here and in the convinience methods section

Challenge: Is it possible to customize the markers of each grooup?



In [26]:

    
function data_frame_scatter()
    iris = dataset("datasets", "iris");
#     display(head(iris))
    my_trace = scatter(iris, x=:SepalLength, y=:SepalWidth, mode="markers", group=:Species)
    plot(my_trace)
    p = Plot(iris, x=:SepalLength, y=:SepalWidth, mode="markers", marker_size=8, group=:Species)
    _p = JupyterPlot(p) #In Atom use _p = ElectronPlot(p)
    display(_p)
end

data_frame_scatter()









    














    



WARNING: Method definition data_frame_scatter() in module Main at In[25]:2 overwritten at In[26]:2.