4 Plotly walkthrough π£ - experimental π
Tableplot offers a Clojure API for creating Plotly.js plots through layered pipelines.
The API not uses Hanami templates but is completely separate from the classical Hanami templates and parameters.
Here, we provide a walkthrough of that API.
π This part of Tableplot is still in experimental stage. Some of the details will change soon. Feedback and comments will help.
Soon, we will provide more in-depth explanations in additional chapters.
4.1 Known issues
Plot legends are missing in some cases.
Breaking changes are expected.
4.2 Setup
For this tutorial, we require:
The Tablecloth plotly API namepace
Tablecloth for dataset processing
the datetime namespace of dtype-next
the print namespace of tech.ml.dataset for customized dataset printing
Kindly (to specify how certaiun values should be visualized)
the datasets defined in the Datasets chapter
(ns tableplot-book.plotly-walkthrough
(:require [scicloj.tableplot.v1.plotly :as plotly]
[tablecloth.api :as tc]
[tablecloth.column.api :as tcc]
[tech.v3.datatype.datetime :as datetime]
[tech.v3.dataset.print :as print]
[scicloj.kindly.v4.kind :as kind]
[clojure.string :as str]
[scicloj.kindly.v4.api :as kindly]
[tableplot-book.datasets :as datasets]
[aerial.hanami.templates :as ht]))4.3 Basic usage
Plotly plots are created by passing datasets to a pipeline of layer functions.
Additional parameters to the functions are passed as maps. Map keys begin with = (e.g., :=color).
For example, let us plot a scatterplot (a layer of points) of 10 random items from the Iris dataset.
(-> datasets/iris
(tc/random 10 {:seed 1})
(plotly/layer-point
{:=x :sepal-width
:=y :sepal-length
:=color :species
:=mark-size 20
:=mark-opacity 0.6}))4.4 Templates and parameters
(π‘ You do neet need to understand these details for basic usage.)
Technically, the parameter maps contain Hanami substitution keys, which means they are processed by a simple set of rules, but you do not need to understand what this means yet.
The layer functions return a Hanami template. Let us print the resulting structure of the previous plot.
(def example1
(-> datasets/iris
(tc/random 10 {:seed 1})
(plotly/layer-point
{:=x :sepal-width
:=y :sepal-length
:=color :species
:=mark-size 20
:=mark-opacity 0.6})))(kind/pprint example1){:data :=traces,
:layout :=layout,
:aerial.hanami.templates/defaults
{:=textfont :com.rpl.specter.impl/NONE,
:=x0 :com.rpl.specter.impl/NONE,
:=y-type #function[clojure.lang.AFunction/1],
:=coordinates :com.rpl.specter.impl/NONE,
:=x0-after-stat :=x0,
:=layers
[{:y :=y-after-stat,
:trace-base
{:mode :=mode,
:type :=type,
:opacity :=mark-opacity,
:textfont :=textfont},
:color-type :=color-type,
:r :=r,
:coordinates :=coordinates,
:group :=group,
:color :=color,
:mark :=mark,
:x-title :=x-title,
:name :=name,
:y1 :=y1-after-stat,
:theta :=theta,
:size :=size,
:size-type :=size-type,
:aerial.hanami.templates/defaults
{:=textfont :com.rpl.specter.impl/NONE,
:=x0 :com.rpl.specter.impl/NONE,
:=y-type #function[clojure.lang.AFunction/1],
:=coordinates :com.rpl.specter.impl/NONE,
:=x0-after-stat :=x0,
:=layers [],
:=x1 :com.rpl.specter.impl/NONE,
:=title :com.rpl.specter.impl/NONE,
:=y1 :com.rpl.specter.impl/NONE,
:=y-type-after-stat #function[clojure.lang.AFunction/1],
:=height 400,
:=name :com.rpl.specter.impl/NONE,
:=mark-opacity 0.6,
:=inferred-group #function[clojure.lang.AFunction/1],
:=mode #function[clojure.lang.AFunction/1],
:=y-title :com.rpl.specter.impl/NONE,
:=size :com.rpl.specter.impl/NONE,
:=model-options {:model-type :fastmath/ols},
:=group :=inferred-group,
:=y0 :com.rpl.specter.impl/NONE,
:=mark-size 20,
:=design-matrix #function[clojure.lang.AFunction/1],
:=size-type #function[clojure.lang.AFunction/1],
:=r :com.rpl.specter.impl/NONE,
:=color :species,
:=mark-color :com.rpl.specter.impl/NONE,
:=y1-after-stat :=y1,
:=x :sepal-width,
:=x-after-stat :=x,
:=yaxis-gridcolor "rgb(255,255,255)",
:=text :com.rpl.specter.impl/NONE,
:=type #function[clojure.lang.AFunction/1],
:=x-type-after-stat #function[clojure.lang.AFunction/1],
:=traces #function[clojure.lang.AFunction/1],
:=x-type #function[clojure.lang.AFunction/1],
:=histogram-nbins 10,
:=stat :com.rpl.specter.impl/NONE,
:=width 500,
:=color-type #function[clojure.lang.AFunction/1],
:=xaxis-gridcolor "rgb(255,255,255)",
:=mark :point,
:=dataset-after-stat #function[clojure.lang.AFunction/1],
:=x-title :com.rpl.specter.impl/NONE,
:=layout #function[clojure.lang.AFunction/1],
:=y :sepal-length,
:=x1-after-stat :=x1,
:=dataset #<WrappedValue@17098b05: datasets/iris [10 6]:
| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width | :species |
|----------:|--------------:|-------------:|--------------:|-------------:|------------|
| 27 | 5.0 | 3.4 | 1.6 | 0.4 | setosa |
| 97 | 5.7 | 2.9 | 4.2 | 1.3 | versicolor |
| 127 | 6.2 | 2.8 | 4.8 | 1.8 | virginica |
| 92 | 6.1 | 3.0 | 4.6 | 1.4 | versicolor |
| 7 | 4.6 | 3.4 | 1.4 | 0.3 | setosa |
| 95 | 5.6 | 2.7 | 4.2 | 1.3 | versicolor |
| 125 | 6.7 | 3.3 | 5.7 | 2.1 | virginica |
| 61 | 5.0 | 2.0 | 3.5 | 1.0 | versicolor |
| 73 | 6.3 | 2.5 | 4.9 | 1.5 | versicolor |
| 42 | 4.5 | 2.3 | 1.3 | 0.3 | setosa |
>,
:=background "rgb(235,235,235)",
:=theta :com.rpl.specter.impl/NONE,
:=y0-after-stat :=y0,
:=y-after-stat :=y,
:=predictors [:=x],
:=marker-size-key #function[clojure.lang.AFunction/1]},
:y0 :=y0-after-stat,
:inferred-group :=inferred-group,
:marker-override
{:color :=mark-color, :=marker-size-key :=mark-size},
:x :=x-after-stat,
:x1 :=x1-after-stat,
:x0 :=x0-after-stat,
:y-title :=y-title,
:dataset :=dataset-after-stat,
:text :=text}],
:=x1 :com.rpl.specter.impl/NONE,
:=title :com.rpl.specter.impl/NONE,
:=y1 :com.rpl.specter.impl/NONE,
:=y-type-after-stat #function[clojure.lang.AFunction/1],
:=height 400,
:=name :com.rpl.specter.impl/NONE,
:=mark-opacity :com.rpl.specter.impl/NONE,
:=inferred-group #function[clojure.lang.AFunction/1],
:=mode #function[clojure.lang.AFunction/1],
:=y-title :com.rpl.specter.impl/NONE,
:=size :com.rpl.specter.impl/NONE,
:=model-options {:model-type :fastmath/ols},
:=group :=inferred-group,
:=y0 :com.rpl.specter.impl/NONE,
:=mark-size :com.rpl.specter.impl/NONE,
:=design-matrix #function[clojure.lang.AFunction/1],
:=size-type #function[clojure.lang.AFunction/1],
:=r :com.rpl.specter.impl/NONE,
:=color :com.rpl.specter.impl/NONE,
:=mark-color :com.rpl.specter.impl/NONE,
:=y1-after-stat :=y1,
:=x :x,
:=x-after-stat :=x,
:=yaxis-gridcolor "rgb(255,255,255)",
:=text :com.rpl.specter.impl/NONE,
:=type #function[clojure.lang.AFunction/1],
:=x-type-after-stat #function[clojure.lang.AFunction/1],
:=traces #function[clojure.lang.AFunction/1],
:=x-type #function[clojure.lang.AFunction/1],
:=histogram-nbins 10,
:=stat :com.rpl.specter.impl/NONE,
:=width 500,
:=color-type #function[clojure.lang.AFunction/1],
:=xaxis-gridcolor "rgb(255,255,255)",
:=mark :point,
:=dataset-after-stat #function[clojure.lang.AFunction/1],
:=x-title :com.rpl.specter.impl/NONE,
:=layout #function[clojure.lang.AFunction/1],
:=y :y,
:=x1-after-stat :=x1,
:=dataset #<WrappedValue@17098b05: datasets/iris [10 6]:
| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width | :species |
|----------:|--------------:|-------------:|--------------:|-------------:|------------|
| 27 | 5.0 | 3.4 | 1.6 | 0.4 | setosa |
| 97 | 5.7 | 2.9 | 4.2 | 1.3 | versicolor |
| 127 | 6.2 | 2.8 | 4.8 | 1.8 | virginica |
| 92 | 6.1 | 3.0 | 4.6 | 1.4 | versicolor |
| 7 | 4.6 | 3.4 | 1.4 | 0.3 | setosa |
| 95 | 5.6 | 2.7 | 4.2 | 1.3 | versicolor |
| 125 | 6.7 | 3.3 | 5.7 | 2.1 | virginica |
| 61 | 5.0 | 2.0 | 3.5 | 1.0 | versicolor |
| 73 | 6.3 | 2.5 | 4.9 | 1.5 | versicolor |
| 42 | 4.5 | 2.3 | 1.3 | 0.3 | setosa |
>,
:=background "rgb(235,235,235)",
:=theta :com.rpl.specter.impl/NONE,
:=y0-after-stat :=y0,
:=y-after-stat :=y,
:=predictors [:=x],
:=marker-size-key #function[clojure.lang.AFunction/1]},
:kindly/f #'scicloj.tableplot.v1.plotly/plotly-xform}This template has all the necessary knowledge, including the substitution keys, to turn into a plot. This happens when your visual tool (e.g., Clay) displays the plot. The tool knows what to do thanks to the Kindly metadata and a special function attached to the plot.
(meta example1)#:kindly{:kind :kind/fn, :options nil}(:kindly/f example1)#'scicloj.tableplot.v1.plotly/plotly-xform4.5 Realizing the plot
If you wish to see the resulting plot specification before displaying it as a plot, you can use the plot function. In this case, it generates a Plotly.js plot:
(-> example1
plotly/plot
kind/pprint){:data
[{:y [5.0 4.6 4.5],
:r nil,
:name "setosa",
:marker {:color "#1B9E77", :size 20},
:mode :markers,
:type "scatter",
:theta nil,
:opacity 0.6,
:x [3.4 3.4 2.3],
:text nil}
{:y [5.7 6.1 5.6 5.0 6.3],
:r nil,
:name "versicolor",
:marker {:color "#D95F02", :size 20},
:mode :markers,
:type "scatter",
:theta nil,
:opacity 0.6,
:x [2.9 3.0 2.7 2.0 2.5],
:text nil}
{:y [6.2 6.7],
:r nil,
:name "virginica",
:marker {:color "#7570B3", :size 20},
:mode :markers,
:type "scatter",
:theta nil,
:opacity 0.6,
:x [2.8 3.3],
:text nil}],
:layout
{:width 500,
:height 400,
:plot_bgcolor "rgb(235,235,235)",
:xaxis {:gridcolor "rgb(255,255,255)", :title :sepal-width},
:yaxis {:gridcolor "rgb(255,255,255)", :title :sepal-length},
:title nil}}It is annotated as kind/plotly, so that visual tools know how to render it.
(-> example1
plotly/plot
meta)#:kindly{:kind :kind/plotly, :options nil}This can be useful if you wish to process the Actual Plotly.js spec rather than use the Tableplot Plotly API. Let us change the background colour, for example:
(-> example1
plotly/plot
(assoc-in [:layout :plot_bgcolor] "#eeeedd"))For another example, let us use a logarithmic scale for the y axis:
(-> example1
plotly/plot
(assoc-in [:layout :yaxis :type] "log"))4.6 Field type inference
Tableplot infers the type of relevant fields from the data.
The example above was colored as it were since :species column was nominal, so it was assigned distinct colours.
In the following example, the coloring is by a quantitative column, so a color gradient is used:
(-> datasets/mtcars
(plotly/layer-point
{:=x :mpg
:=y :disp
:=color :cyl
:=mark-size 20}))We can override the inferred types and thus affect the generated plot:
(-> datasets/mtcars
(plotly/layer-point
{:=x :mpg
:=y :disp
:=color :cyl
:=color-type :nominal
:=mark-size 20}))4.7 More examples
4.7.1 Boxplot
(-> datasets/mtcars
(plotly/layer-boxplot
{:=x :cyl
:=y :disp}))4.7.2 Bar chart
(-> datasets/mtcars
(tc/group-by [:cyl])
(tc/aggregate {:total-disp #(-> % :disp tcc/sum)})
(plotly/layer-bar
{:=x :cyl
:=y :total-disp}))4.7.3 Text
(-> datasets/mtcars
(plotly/layer-text
{:=x :mpg
:=y :disp
:=text :cyl
:=mark-size 20}))(-> datasets/mtcars
(plotly/layer-text
{:=x :mpg
:=y :disp
:=text :cyl
:=textfont {:family "Courier New, monospace"
:size 16
:color :purple}
:=mark-size 20}))4.7.4 Segment plot
(-> datasets/iris
(plotly/layer-segment
{:=x0 :sepal-width
:=y0 :sepal-length
:=x1 :petal-width
:=y1 :petal-length
:=mark-opacity 0.4
:=mark-size 3
:=color :species}))4.8 Varying color and size
(-> {:ABCD (range 1 11)
:EFGH [5 2.5 5 7.5 5 2.5 7.5 4.5 5.5 5]
:IJKL [:A :A :A :A :A :B :B :B :B :B]
:MNOP [:C :D :C :D :C :D :C :D :C :D]}
tc/dataset
(plotly/base {:=title "IJKLMNOP"})
(plotly/layer-point {:=x :ABCD
:=y :EFGH
:=color :IJKL
:=size :MNOP
:=name "QRST1"})
(plotly/layer-line
{:=title "IJKL MNOP"
:=x :ABCD
:=y :ABCD
:=name "QRST2"
:=mark-color "magenta"
:=mark-size 20
:=mark-opacity 0.2}))4.9 Time series
Date and time fields are handle appropriately. Let us, for example, draw the time series of unemployment counts.
(-> datasets/economics-long
(tc/select-rows #(-> % :variable (= "unemploy")))
(plotly/layer-line
{:=x :date
:=y :value
:=mark-color "purple"}))4.10 Multiple layers
We can draw more than one layer:
(-> datasets/economics-long
(tc/select-rows #(-> % :variable (= "unemploy")))
(plotly/layer-point {:=x :date
:=y :value
:=mark-color "green"
:=mark-size 20
:=mark-opacity 0.5})
(plotly/layer-line {:=x :date
:=y :value
:=mark-color "purple"}))We can also use the base function for the common parameters across layers:
(-> datasets/economics-long
(tc/select-rows #(-> % :variable (= "unemploy")))
(plotly/base {:=x :date
:=y :value})
(plotly/layer-point {:=mark-color "green"
:=mark-size 20
:=mark-opacity 0.5})
(plotly/layer-line {:=mark-color "purple"}))Layers can be named:
(-> datasets/economics-long
(tc/select-rows #(-> % :variable (= "unemploy")))
(plotly/base {:=x :date
:=y :value})
(plotly/layer-point {:=mark-color "green"
:=mark-size 20
:=mark-opacity 0.5
:=name "points"})
(plotly/layer-line {:=mark-color "purple"
:=name "line"}))4.11 Updating data
We can use the update-data function to vary the dataset along a plotting pipeline, affecting the layers that follow.
This functionality is inspired by ggbuilder and metamorph.
Here, for example, we draw a line, then sample 5 data rows, and draw them as points:
(-> datasets/economics-long
(tc/select-rows #(-> % :variable (= "unemploy")))
(plotly/base {:=x :date
:=y :value})
(plotly/layer-line {:=mark-color "purple"})
(plotly/update-data tc/random 5)
(plotly/layer-point {:=mark-color "green"
:=mark-size 15
:=mark-opacity 0.5}))4.12 Overriding specific layer data (experimental)
(-> (tc/dataset {:x (range 4)
:y [1 2 5 9]})
tc/dataset
(tc/sq :y :x)
(plotly/layer-point {:=mark-size 20})
(plotly/layer-line {:=dataset (plotly/dataset {:x [0 3]
:y [1 10]})
:=mark-size 5}))4.13 Smoothing
layer-smooth is a layer that applies some statistical processing to the dataset to model it as a smooth shape. It is inspired by ggplotβs geom_smooth.
At the moment, it can only be used to model :=y by linear regression. Soon we will add more ways of modelling the data.
(-> datasets/iris
(plotly/base {:=x :sepal-width
:=y :sepal-length})
(plotly/layer-point {:=mark-color "green"
:=name "Actual"})
(plotly/layer-smooth {:=mark-color "orange"
:=name "Predicted"}))By default, the regression is computed with only one predictor variable, which is :=x. But this can be overriden using the :predictors key. We may compute a regression with more than one predictor.
(-> datasets/iris
(plotly/base {:=x :sepal-width
:=y :sepal-length})
(plotly/layer-point {:=mark-color "green"
:=name "Actual"})
(plotly/layer-smooth {:=predictors [:petal-width
:petal-length]
:=mark-opacity 0.5
:=name "Predicted"}))We can also provide the design matrix.
(-> datasets/iris
(plotly/base {:=x :sepal-width
:=y :sepal-length})
(plotly/layer-point {:=mark-color "green"
:=name "Actual"})
(plotly/layer-smooth {:=design-matrix [[:sepal-width '(identity sepal-width)]
[:sepal-width-2 '(* sepal-width
sepal-width)]]
:=mark-opacity 0.5
:=name "Predicted"}))Inspired by Sami Kallinenβs Heart of Clojure talk:
(-> datasets/iris
(plotly/base {:=x :sepal-width
:=y :sepal-length})
(plotly/layer-point {:=mark-color "green"
:=name "Actual"})
(plotly/layer-smooth {:=design-matrix [[:sepal-width '(identity sepal-width)]
[:sepal-width-2 '(* sepal-width
sepal-width)]
[:sepal-width-3 '(* sepal-width
sepal-width
sepal-width)]]
:=mark-opacity 0.5
:=name "Predicted"}))We can also provide the regression model details as metamorph.ml options:
(require 'scicloj.ml.tribuo)(def regression-tree-options
{:model-type :scicloj.ml.tribuo/regression
:tribuo-components [{:name "cart"
:type "org.tribuo.regression.rtree.CARTRegressionTrainer"
:properties {:maxDepth "8"
:fractionFeaturesInSplit "1.0"
:seed "12345"
:impurity "mse"}}
{:name "mse"
:type "org.tribuo.regression.rtree.impurity.MeanSquaredError"}]
:tribuo-trainer-name "cart"})(-> datasets/iris
(plotly/base {:=x :sepal-width
:=y :sepal-length})
(plotly/layer-point {:=mark-color "green"
:=name "Actual"})
(plotly/layer-smooth {:=model-options regression-tree-options
:=mark-opacity 0.5
:=name "Predicted"}))An example inspired by Plotlyβs ML Regressoin in Python example.
(-> datasets/tips
(tc/split :holdout {:seed 1})
(plotly/base {:=x :total_bill
:=y :tip})
(plotly/layer-point {:=color :$split-name})
(plotly/update-data (fn [ds]
(-> ds
(tc/select-rows #(-> % :$split-name (= :train))))))
(plotly/layer-smooth {:=model-options regression-tree-options
:=name "prediction"
:=mark-color "purple"}))4.14 Grouping
The regression computed by layer-smooth is affected by the inferred grouping of the data.
For example, here we recieve three regression lines, each for every species.
(-> datasets/iris
(plotly/base {:=title "dummy"
:=color :species
:=x :sepal-width
:=y :sepal-length})
plotly/layer-point
plotly/layer-smooth)This happened because the :color field was :species, which is of :nominal type.
But we may override this using the :group key. For example, let us avoid grouping:
(-> datasets/iris
(plotly/base {:=title "dummy"
:=color :species
:=group []
:=x :sepal-width
:=y :sepal-length})
plotly/layer-point
plotly/layer-smooth)Alternatively, we may assign the :=color only to the points layer without affecting the smoothing layer.
(-> datasets/iris
(plotly/base {:=title "dummy"
:=x :sepal-width
:=y :sepal-length})
(plotly/layer-point {:=color :species})
(plotly/layer-smooth {:=name "Predicted"
:=mark-color "blue"}))4.15 Example: out-of-sample predictions
Here is a slighly more elaborate example inpired by the London Clojurians talk mentioned in the preface.
Assume we wish to predict the unemployment rate for 96 months. Let us add those months to our dataset, and mark them as Future (considering the original data as Past):
(-> datasets/economics-long
(tc/select-rows #(-> % :variable (= "unemploy")))
(tc/add-column :relative-time "Past")
(tc/concat (tc/dataset {:date (-> datasets/economics-long
:date
last
(datetime/plus-temporal-amount (range 96) :days))
:relative-time "Future"}))
(print/print-range 6))ggplot2/economics_long [670 6]:
| :rownames | :date | :variable | :value | :value01 | :relative-time |
|---|---|---|---|---|---|
| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 | Past |
| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 | Past |
| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 | Past |
| β¦ | β¦ | β¦ | β¦ | β¦ | β¦ |
| 2015-07-02 | Future | ||||
| 2015-07-03 | Future | ||||
| 2015-07-04 | Future | ||||
| 2015-07-05 | Future |
Let us represent our dates as numbers, so that we can use them in linear regression:
(-> datasets/economics-long
(tc/select-rows #(-> % :variable (= "unemploy")))
(tc/add-column :relative-time "Past")
(tc/concat (tc/dataset {:date (-> datasets/economics-long
:date
last
(datetime/plus-temporal-amount (range 96) :months))
:relative-time "Future"}))
(tc/add-column :year #(datetime/long-temporal-field :years (:date %)))
(tc/add-column :month #(datetime/long-temporal-field :months (:date %)))
(tc/map-columns :yearmonth [:year :month] (fn [y m] (+ m (* 12 y))))
(print/print-range 6))ggplot2/economics_long [670 9]:
| :rownames | :date | :variable | :value | :value01 | :relative-time | :year | :month | :yearmonth |
|---|---|---|---|---|---|---|---|---|
| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 | Past | 1967 | 7 | 23611 |
| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 | Past | 1967 | 8 | 23612 |
| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 | Past | 1967 | 9 | 23613 |
| β¦ | β¦ | β¦ | β¦ | β¦ | β¦ | β¦ | β¦ | β¦ |
| 2022-12-01 | Future | 2022 | 12 | 24276 | ||||
| 2023-01-01 | Future | 2023 | 1 | 24277 | ||||
| 2023-02-01 | Future | 2023 | 2 | 24278 | ||||
| 2023-03-01 | Future | 2023 | 3 | 24279 |
Let us use the same regression line for the Past and Future groups. To do this, we avoid grouping by assigning [] to :=group. The line is affected only by the past, since in the Future, :=y is missing. We use the numerical field :yearmonth as the regression predictor, but for plotting, we still use the :temporal field :date.
(-> datasets/economics-long
(tc/select-rows #(-> % :variable (= "unemploy")))
(tc/add-column :relative-time "Past")
(tc/concat (tc/dataset {:date (-> datasets/economics-long
:date
last
(datetime/plus-temporal-amount (range 96) :months))
:relative-time "Future"}))
(tc/add-column :year #(datetime/long-temporal-field :years (:date %)))
(tc/add-column :month #(datetime/long-temporal-field :months (:date %)))
(tc/map-columns :yearmonth [:year :month] (fn [y m] (+ m (* 12 y))))
(plotly/base {:=x :date
:=y :value})
(plotly/layer-smooth {:=color :relative-time
:=mark-size 15
:=group []
:=predictors [:yearmonth]})
;; Keep only the past for the following layer:
(plotly/update-data (fn [dataset]
(-> dataset
(tc/select-rows (fn [row]
(-> row :relative-time (= "Past")))))))
(plotly/layer-line {:=mark-color "purple"
:=mark-size 3
:=name "Actual"}))4.16 Histograms
Histograms can also be represented as layers with statistical processing:
(-> datasets/iris
(plotly/layer-histogram {:=x :sepal-width}))(-> datasets/iris
(plotly/layer-histogram {:=x :sepal-width
:=histogram-nbins 30}))4.17 Coordinates
(WIP)
4.17.1 geo
4.17.2 polar
Monthly rain amounts - polar bar-chart
(def rain-data
(tc/dataset
{:month [:Jan :Feb :Mar :Apr
:May :Jun :Jul :Aug
:Sep :Oct :Nov :Dec]
:rain (repeatedly #(rand-int 200))}))(-> rain-data
(plotly/layer-bar
{:=r :rain
:=theta :month
:=coordinates :polar
:=mark-size 20
:=mark-opacity 0.6}))Controlling the polar layout (by manipulating the raw Plotly.js spec):
(-> rain-data
(plotly/base
{})
(plotly/layer-bar
{:=r :rain
:=theta :month
:=coordinates :polar
:=mark-size 20
:=mark-opacity 0.6})
plotly/plot
(assoc-in [:layout :polar]
{:angularaxis {:tickfont {:size 16}
:rotation 90
:direction "counterclockwise"}
:sector [0 180]}))A polar random walk - polar line-chart
(let [n 50]
(-> {:r (->> (repeatedly n #(- (rand) 0.5))
(reductions +))
:theta (->> (repeatedly n #(* 10 (rand)))
(reductions +)
(map #(rem % 360)))
:color (range n)}
tc/dataset
(plotly/layer-point
{:=r :r
:=theta :theta
:=coordinates :polar
:=mark-size 10
:=mark-opacity 0.6})
(plotly/layer-line
{:=r :r
:=theta :theta
:=coordinates :polar
:=mark-size 3
:=mark-opacity 0.6})))4.18 Debugging (WIP)
4.18.1 Viewing the computational dag of substitution keys:
(def example-to-debug
(-> datasets/iris
(tc/random 10 {:seed 1})
(plotly/layer-point {:=x :sepal-width
:=y :sepal-length
:=color :species})))(-> example-to-debug
plotly/dag)4.18.2 Viewing intermediate values in the computational dag:
Layers (tableplotβs intermediate data representation)
(-> example-to-debug
(plotly/debug :=layers))[{:y :sepal-length,
:trace-base {:mode :markers, :type "scatter"},
:color-type :nominal,
:group (:species),
:color :species,
:mark :point,
:inferred-group (:species),
:x :sepal-width,
:dataset #<WrappedValue@4ddf50a4: datasets/iris [10 6]:
| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width | :species |
|----------:|--------------:|-------------:|--------------:|-------------:|------------|
| 27 | 5.0 | 3.4 | 1.6 | 0.4 | setosa |
| 97 | 5.7 | 2.9 | 4.2 | 1.3 | versicolor |
| 127 | 6.2 | 2.8 | 4.8 | 1.8 | virginica |
| 92 | 6.1 | 3.0 | 4.6 | 1.4 | versicolor |
| 7 | 4.6 | 3.4 | 1.4 | 0.3 | setosa |
| 95 | 5.6 | 2.7 | 4.2 | 1.3 | versicolor |
| 125 | 6.7 | 3.3 | 5.7 | 2.1 | virginica |
| 61 | 5.0 | 2.0 | 3.5 | 1.0 | versicolor |
| 73 | 6.3 | 2.5 | 4.9 | 1.5 | versicolor |
| 42 | 4.5 | 2.3 | 1.3 | 0.3 | setosa |
>}]Traces (part of the Plotly spec)
(-> example-to-debug
(plotly/debug :=traces))[{:y [5.0 4.6 4.5],
:r nil,
:name "setosa",
:marker {:color "#1B9E77"},
:mode :markers,
:type "scatter",
:theta nil,
:x [3.4 3.4 2.3],
:text nil}
{:y [5.7 6.1 5.6 5.0 6.3],
:r nil,
:name "versicolor",
:marker {:color "#D95F02"},
:mode :markers,
:type "scatter",
:theta nil,
:x [2.9 3.0 2.7 2.0 2.5],
:text nil}
{:y [6.2 6.7],
:r nil,
:name "virginica",
:marker {:color "#7570B3"},
:mode :markers,
:type "scatter",
:theta nil,
:x [2.8 3.3],
:text nil}]Both
(-> example-to-debug
(plotly/debug {:layers :=layers
:traces :=traces})){:layers
[{:y :sepal-length,
:trace-base {:mode :markers, :type "scatter"},
:color-type :nominal,
:group (:species),
:color :species,
:mark :point,
:inferred-group (:species),
:x :sepal-width,
:dataset #<WrappedValue@4ddf50a4: datasets/iris [10 6]:
| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width | :species |
|----------:|--------------:|-------------:|--------------:|-------------:|------------|
| 27 | 5.0 | 3.4 | 1.6 | 0.4 | setosa |
| 97 | 5.7 | 2.9 | 4.2 | 1.3 | versicolor |
| 127 | 6.2 | 2.8 | 4.8 | 1.8 | virginica |
| 92 | 6.1 | 3.0 | 4.6 | 1.4 | versicolor |
| 7 | 4.6 | 3.4 | 1.4 | 0.3 | setosa |
| 95 | 5.6 | 2.7 | 4.2 | 1.3 | versicolor |
| 125 | 6.7 | 3.3 | 5.7 | 2.1 | virginica |
| 61 | 5.0 | 2.0 | 3.5 | 1.0 | versicolor |
| 73 | 6.3 | 2.5 | 4.9 | 1.5 | versicolor |
| 42 | 4.5 | 2.3 | 1.3 | 0.3 | setosa |
>}],
:traces
[{:y [5.0 4.6 4.5],
:r nil,
:name "setosa",
:marker {:color "#1B9E77"},
:mode :markers,
:type "scatter",
:theta nil,
:x [3.4 3.4 2.3],
:text nil}
{:y [5.7 6.1 5.6 5.0 6.3],
:r nil,
:name "versicolor",
:marker {:color "#D95F02"},
:mode :markers,
:type "scatter",
:theta nil,
:x [2.9 3.0 2.7 2.0 2.5],
:text nil}
{:y [6.2 6.7],
:r nil,
:name "virginica",
:marker {:color "#7570B3"},
:mode :markers,
:type "scatter",
:theta nil,
:x [2.8 3.3],
:text nil}]}