diff --git a/docs/hanamicloth_book.datasets.html b/docs/hanamicloth_book.datasets.html index 4f41818..c0340ba 100644 --- a/docs/hanamicloth_book.datasets.html +++ b/docs/hanamicloth_book.datasets.html @@ -157,6 +157,12 @@ 3  Walkthrough + +
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    + + 4  Plotlycloth Walkthrough (experimental 🛠) +
    • diff --git a/docs/hanamicloth_book.plotlycloth_walkthrough.html b/docs/hanamicloth_book.plotlycloth_walkthrough.html new file mode 100644 index 0000000..447b654 --- /dev/null +++ b/docs/hanamicloth_book.plotlycloth_walkthrough.html @@ -0,0 +1,1474 @@ + + + + + + + + + +Hanamicloth – 4  Plotlycloth Walkthrough (experimental 🛠) + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + +
    + + + +
    + +
    +
    +

    4  Plotlycloth Walkthrough (experimental 🛠)

    +
    + + + +
    + + + + +
    + + + +
    + + + + + + + + +

    Plotlycloth is a Clojure API for creating Plotly.js plots through layered pipelines. It is part of the Hanamicloth library.

    +

    Here, we provide a walkthrough of the API.

    +

    🛠 This part of Hanamicloth 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 Setup

    +

    For this tutorial, we require:

    + +
    +
    (ns hanamicloth-book.plotlycloth-walkthrough
+  (:require [scicloj.hanamicloth.v1.plotlycloth :as ploclo]
+            [tablecloth.api :as tc]
+            [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]
+            [hanamicloth-book.datasets :as datasets]))
    +
    +
    +
    +

    4.2 Basic usage

    +

    Plotlycloth plots are created by passing datasets to a pipeline of layer functions.

    +

    Additional parameters to the functions are passed as maps. By convention, the 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})
+    (ploclo/layer-point
+     {:=x :sepal-width
+      :=y :sepal-length
+      :=color :species
+      :=mark-size 20
+      :=mark-opacity 0.6}))
    +
    +
    +
    +
    +

    4.3 Templates and parameters

    +

    (💡 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})
+      (ploclo/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
+ {:=x0 :com.rpl.specter.impl/NONE,
+  :=y-type
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=x0-after-stat :=x0,
+  :=layers
+  [{:y :=y-after-stat,
+    :trace-base {:mode :=mode, :type :=type, :opacity :=mark-opacity},
+    :color-type :=color-type,
+    :group :=group,
+    :color :=color,
+    :mark :=mark,
+    :x-title :=x-title,
+    :name :=name,
+    :y1 :=y1-after-stat,
+    :size :=size,
+    :size-type :=size-type,
+    :aerial.hanami.templates/defaults
+    {:=x0 :com.rpl.specter.impl/NONE,
+     :=y-type
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=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[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=height :com.rpl.specter.impl/NONE,
+     :=name :com.rpl.specter.impl/NONE,
+     :=mark-opacity 0.6,
+     :=inferred-group
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=mode
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=y-title :com.rpl.specter.impl/NONE,
+     :=size :com.rpl.specter.impl/NONE,
+     :=group :=inferred-group,
+     :=y0 :com.rpl.specter.impl/NONE,
+     :=mark-size 20,
+     :=size-type
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=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)",
+     :=type
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=x-type-after-stat
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=traces
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=x-type
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=histogram-nbins 10,
+     :=stat :com.rpl.specter.impl/NONE,
+     :=width :com.rpl.specter.impl/NONE,
+     :=color-type
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=xaxis-gridcolor "rgb(255,255,255)",
+     :=mark :point,
+     :=dataset-after-stat
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=x-title :com.rpl.specter.impl/NONE,
+     :=layout
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+     :=y :sepal-length,
+     :=x1-after-stat :=x1,
+     :=dataset #<WrappedValue@28277720: 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(229,229,229)",
+     :=y0-after-stat :=y0,
+     :=y-after-stat :=y,
+     :=predictors [:=x],
+     :=marker-size-key
+     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},
+    :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}],
+  :=x1 :com.rpl.specter.impl/NONE,
+  :=title :com.rpl.specter.impl/NONE,
+  :=y1 :com.rpl.specter.impl/NONE,
+  :=y-type-after-stat
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=height :com.rpl.specter.impl/NONE,
+  :=name :com.rpl.specter.impl/NONE,
+  :=mark-opacity :com.rpl.specter.impl/NONE,
+  :=inferred-group
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=mode
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=y-title :com.rpl.specter.impl/NONE,
+  :=size :com.rpl.specter.impl/NONE,
+  :=group :=inferred-group,
+  :=y0 :com.rpl.specter.impl/NONE,
+  :=mark-size :com.rpl.specter.impl/NONE,
+  :=size-type
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=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)",
+  :=type
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=x-type-after-stat
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=traces
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=x-type
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=histogram-nbins 10,
+  :=stat :com.rpl.specter.impl/NONE,
+  :=width :com.rpl.specter.impl/NONE,
+  :=color-type
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=xaxis-gridcolor "rgb(255,255,255)",
+  :=mark :point,
+  :=dataset-after-stat
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=x-title :com.rpl.specter.impl/NONE,
+  :=layout
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],
+  :=y :y,
+  :=x1-after-stat :=x1,
+  :=dataset #<WrappedValue@28277720: 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(229,229,229)",
+  :=y0-after-stat :=y0,
+  :=y-after-stat :=y,
+  :=predictors [:=x],
+  :=marker-size-key
+  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},
+ :kindly/f #'scicloj.hanamicloth.v1.plotlycloth/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}
    +
    +
    +
    (:kindly/f example1)
    +
    +
    +
    #'scicloj.hanamicloth.v1.plotlycloth/plotly-xform
    +
    +
    +
    +

    4.4 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
+    ploclo/plot
+    kind/pprint)
    +
    +
    +
    {:data
+ [{:mode :markers,
+   :type :scatter,
+   :opacity 0.6,
+   :name "setosa",
+   :x [3.4 3.4 2.3],
+   :y [5.0 4.6 4.5],
+   :marker {:color "#1B9E77", :size 20}}
+  {:mode :markers,
+   :type :scatter,
+   :opacity 0.6,
+   :name "versicolor",
+   :x [2.9 3.0 2.7 2.0 2.5],
+   :y [5.7 6.1 5.6 5.0 6.3],
+   :marker {:color "#D95F02", :size 20}}
+  {:mode :markers,
+   :type :scatter,
+   :opacity 0.6,
+   :name "virginica",
+   :x [2.8 3.3],
+   :y [6.2 6.7],
+   :marker {:color "#7570B3", :size 20}}],
+ :layout
+ {:width nil,
+  :height nil,
+  :plot_bgcolor "rgb(229,229,229)",
+  :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
+    ploclo/plot
+    meta)
    +
    +
    +
    #:kindly{:kind :kind/plotly}
    +
    +

    This can be useful if you wish to process the actual Plotly.js spec rather than use Plotlycloth’s API. Let us change the background colour, for example:

    +
    +
    (-> example1
+    ploclo/plot
+    (assoc-in [:layout :plot_bgcolor] "#eeeedd"))
    +
    +
    +
    +
    +

    4.5 Field type inference

    +

    Plotlycloth 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
+    (ploclo/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
+    (ploclo/layer-point
+     {:=x :mpg
+      :=y :disp
+      :=color :cyl
+      :=color-type :nominal
+      :=mark-size 20}))
    +
    +
    +
    +
    +

    4.6 More examples

    +
    +

    4.6.1 Boxplot

    +
    +
    (-> datasets/mtcars
+    (ploclo/layer-boxplot
+     {:=x :cyl
+      :=y :disp}))
    +
    +
    +
    +
    +

    4.6.2 Segment plot

    +
    +
    (-> datasets/iris
+    (ploclo/layer-segment
+     {:=x0 :sepal-width
+      :=y0 :sepal-length
+      :=x1 :petal-width
+      :=y1 :petal-length
+      :=mark-opacity 0.4
+      :=mark-size 3
+      :=color :species}))
    +
    +
    +
    +
    +
    +

    4.7 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
+    (ploclo/base {:=title "IJKLMNOP"})
+    (ploclo/layer-point {:=x :ABCD
+                         :=y :EFGH
+                         :=color :IJKL
+                         :=size :MNOP
+                         :=name "QRST1"})
+    (ploclo/layer-line
+     {:=title "IJKL MNOP"
+      :=x :ABCD
+      :=y :ABCD
+      :=name "QRST2"
+      :=mark-color "magenta"
+      :=mark-size 20
+      :=mark-opacity 0.2})
+    ploclo/plot)
    +
    +
    +
    +
    +

    4.8 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")))
+    (ploclo/layer-line
+     {:=x :date
+      :=y :value
+      :=mark-color "purple"}))
    +
    +
    +
    +
    +

    4.9 Multiple layers

    +

    We can draw more than one layer:

    +
    +
    (-> datasets/economics-long
+    (tc/select-rows #(-> % :variable (= "unemploy")))
+    (ploclo/layer-point {:=x :date
+                         :=y :value
+                         :=mark-color "green"
+                         :=mark-size 20
+                         :=mark-opacity 0.5})
+    (ploclo/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")))
+    (ploclo/base {:=x :date
+                  :=y :value})
+    (ploclo/layer-point {:=mark-color "green"
+                         :=mark-size 20
+                         :=mark-opacity 0.5})
+    (ploclo/layer-line {:=mark-color "purple"}))
    +
    +
    +
    +
    +

    4.10 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")))
+    (ploclo/base {:=x :date
+                  :=y :value})
+    (ploclo/layer-line {:=mark-color "purple"})
+    (ploclo/update-data tc/random 5)
+    (ploclo/layer-point {:=mark-color "green"
+                         :=mark-size 15
+                         :=mark-opacity 0.5}))
    +
    +
    +
    +
    +

    4.11 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 ggplot’s 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
+    (ploclo/base {:=title "dummy"
+                  :=mark-color "green"
+                  :=x :sepal-width
+                  :=y :sepal-length})
+    ploclo/layer-point
+    (ploclo/layer-smooth {:=mark-color "orange"})
+    ploclo/plot)
    +
    +
    +

    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
+    (ploclo/base {:=x :sepal-width
+                  :=y :sepal-length})
+    ploclo/layer-point
+    (ploclo/layer-smooth {:=predictors [:petal-width
+                                        :petal-length]
+                          :=mark-opacity 0.5})
+    ploclo/plot)
    +
    +
    +
    +
    +

    4.12 Grouping

    +

    The regression computed by haclo/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
+    (ploclo/base {:=title "dummy"
+                  :=color :species
+                  :=x :sepal-width
+                  :=y :sepal-length})
+    ploclo/layer-point
+    ploclo/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
+    (ploclo/base {:=title "dummy"
+                  :=color :species
+                  :=group []
+                  :=x :sepal-width
+                  :=y :sepal-length})
+    ploclo/layer-point
+    ploclo/layer-smooth)
    +
    +
    +
    +
    +

    4.13 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
    22971967-07-01unemploy2944.00.02044683Past
    22981967-08-01unemploy2945.00.02052578Past
    22991967-09-01unemploy2958.00.02155206Past
    ………………
    2015-07-02Future
    2015-07-03Future
    2015-07-04Future
    2015-07-05Future
    +
    +

    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
    22971967-07-01unemploy2944.00.02044683Past1967723611
    22981967-08-01unemploy2945.00.02052578Past1967823612
    22991967-09-01unemploy2958.00.02155206Past1967923613
    ………………………
    2022-12-01Future20221224276
    2023-01-01Future2023124277
    2023-02-01Future2023224278
    2023-03-01Future2023324279
    +
    +

    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))))
+    (ploclo/base {:=x :date
+                  :=y :value})
+    (ploclo/layer-smooth {:=color :relative-time
+                          :=mark-size 20
+                          :=group []
+                          :=predictors [:yearmonth]})
+    ;; Keep only the past for the following layer:
+    (ploclo/update-data (fn [dataset]
+                          (-> dataset
+                              (tc/select-rows (fn [row]
+                                                (-> row :relative-time (= "Past")))))))
+    (ploclo/layer-line {:=mark-color "purple"
+                        :=mark-size 3}))
    +
    +
    +
    +
    +

    4.14 Histograms

    +

    Histograms can also be represented as layers with statistical processing:

    +
    +
    (-> datasets/iris
+    (ploclo/layer-histogram {:=x :sepal-width}))
    +
    +
    +
    +
    (-> datasets/iris
+    (ploclo/layer-histogram {:=x :sepal-width
+                             :=histogram-nbins 30}))
    +
    +
    +
    +
    source: notebooks/hanamicloth_book/plotlycloth_walkthrough.clj
    + + +
    + +
    + + +
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t.preventDefault();this._searchTimeout(t)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(t){clearTimeout(this.searching),this.close(t),this._change(t)}}),this._initSource(),this.menu=V(" 
    (ns hanamicloth-book.walkthrough
@@ -252,9 +259,7 @@ 
                [scicloj.kindly.v4.kind :as kind]
             [clojure.string :as str]
             [scicloj.kindly.v4.api :as kindly]
-            [hanamicloth-book.datasets :as datasets]
-            [aerial.hanami.common :as hc])
-  (:import java.time.LocalDate))
    + [hanamicloth-book.datasets :as datasets]))
    @@ -612,136 +617,135 @@

    < :kindly/f #'scicloj.hanamicloth.v1.api/vega-lite-xform, :aerial.hanami.templates/defaults {:=x2 :com.rpl.specter.impl/NONE, - :=base-dataset - #<WrappedValue@36ca353f: ggplot2/economics_long [574 5]: - -| :rownames | :date | :variable | :value | :value01 | -|----------:|------------|-----------|-------:|-----------:| -| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 | -| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 | -| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 | -| 2300 | 1967-10-01 | unemploy | 3143.0 | 0.03615694 | -| 2301 | 1967-11-01 | unemploy | 3066.0 | 0.03007816 | -| 2302 | 1967-12-01 | unemploy | 3018.0 | 0.02628878 | -| 2303 | 1968-01-01 | unemploy | 2878.0 | 0.01523644 | -| 2304 | 1968-02-01 | unemploy | 3001.0 | 0.02494671 | -| 2305 | 1968-03-01 | unemploy | 2877.0 | 0.01515750 | -| 2306 | 1968-04-01 | unemploy | 2709.0 | 0.00189469 | -| ... | ... | ... | ... | ... | -| 2860 | 2014-06-01 | unemploy | 9460.0 | 0.53485435 | -| 2861 | 2014-07-01 | unemploy | 9608.0 | 0.54653825 | -| 2862 | 2014-08-01 | unemploy | 9599.0 | 0.54582774 | -| 2863 | 2014-09-01 | unemploy | 9262.0 | 0.51922318 | -| 2864 | 2014-10-01 | unemploy | 8990.0 | 0.49775006 | -| 2865 | 2014-11-01 | unemploy | 9090.0 | 0.50564459 | -| 2866 | 2014-12-01 | unemploy | 8717.0 | 0.47619799 | -| 2867 | 2015-01-01 | unemploy | 8903.0 | 0.49088182 | -| 2868 | 2015-02-01 | unemploy | 8610.0 | 0.46775085 | -| 2869 | 2015-03-01 | unemploy | 8504.0 | 0.45938265 | -| 2870 | 2015-04-01 | unemploy | 8526.0 | 0.46111944 | ->, - :=layer [], - :=y-type - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=x2-encoding - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=opacity :com.rpl.specter.impl/NONE, - :=y2-after-stat :=y2, - :=x2-after-stat :=x2, - :=usermeta {:embedOptions {:renderer :=renderer}}, - :=title :com.rpl.specter.impl/NONE, - :=y-type-after-stat - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=height 300, - :=renderer :svg, - :=mark-opacity :com.rpl.specter.impl/NONE, - :=y-title :com.rpl.specter.impl/NONE, - :=size :com.rpl.specter.impl/NONE, - :=group - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=mark-size :com.rpl.specter.impl/NONE, - :=layer? :com.rpl.specter.impl/NONE, - :=size-type - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=layer-dataset - #<WrappedValue@36ca353f: ggplot2/economics_long [574 5]: - -| :rownames | :date | :variable | :value | :value01 | -|----------:|------------|-----------|-------:|-----------:| -| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 | -| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 | -| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 | -| 2300 | 1967-10-01 | unemploy | 3143.0 | 0.03615694 | -| 2301 | 1967-11-01 | unemploy | 3066.0 | 0.03007816 | -| 2302 | 1967-12-01 | unemploy | 3018.0 | 0.02628878 | -| 2303 | 1968-01-01 | unemploy | 2878.0 | 0.01523644 | -| 2304 | 1968-02-01 | unemploy | 3001.0 | 0.02494671 | -| 2305 | 1968-03-01 | unemploy | 2877.0 | 0.01515750 | -| 2306 | 1968-04-01 | unemploy | 2709.0 | 0.00189469 | -| ... | ... | ... | ... | ... | -| 2860 | 2014-06-01 | unemploy | 9460.0 | 0.53485435 | -| 2861 | 2014-07-01 | unemploy | 9608.0 | 0.54653825 | -| 2862 | 2014-08-01 | unemploy | 9599.0 | 0.54582774 | -| 2863 | 2014-09-01 | unemploy | 9262.0 | 0.51922318 | -| 2864 | 2014-10-01 | unemploy | 8990.0 | 0.49775006 | -| 2865 | 2014-11-01 | unemploy | 9090.0 | 0.50564459 | -| 2866 | 2014-12-01 | unemploy | 8717.0 | 0.47619799 | -| 2867 | 2015-01-01 | unemploy | 8903.0 | 0.49088182 | -| 2868 | 2015-02-01 | unemploy | 8610.0 | 0.46775085 | -| 2869 | 2015-03-01 | unemploy | 8504.0 | 0.45938265 | -| 2870 | 2015-04-01 | unemploy | 8526.0 | 0.46111944 | ->, - :=color :com.rpl.specter.impl/NONE, - :=mark-color "purple", - :=x :date, - :=y2 :com.rpl.specter.impl/NONE, - :=x-after-stat :=x, - :DFMT {:type "csv"}, - :=encoding - {:color {:field :=color, :type :=color-type}, - :size {:field :=size, :type :=size-type}, - :x - {:field :=x-after-stat, - :type :=x-type-after-stat, - :title :=x-title, - :bin :=x-bin}, - :y - {:field :=y-after-stat, - :type :=y-type-after-stat, - :title :=y-title, - :bin :=y-bin}, - :x2 :=x2-encoding, - :y2 :=y2-encoding}, - :=x-type-after-stat - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=x-bin :com.rpl.specter.impl/NONE, - :=x-type - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=histogram-nbins 10, - :=stat :com.rpl.specter.impl/NONE, - :=width 400, - :=color-type - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=mark "circle", - :=dataset-after-stat - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=y-bin :com.rpl.specter.impl/NONE, - :=x-title :com.rpl.specter.impl/NONE, - :=csv-data - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :=y :value, - :=data {:values :=csv-data, :format {:type "csv"}}, - :=dataset - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465], - :VALDATA :=csv-data, - :=mark-tooltip true, - :=background "floralwhite", - :=y-after-stat :=y, - :=predictors [:=x], - :=y2-encoding - #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465]}, - :height :=height, - :data :=data} + :=base-dataset #<WrappedValue@37e5aed: ggplot2/economics_long [574 5]: + +| :rownames | :date | :variable | :value | :value01 | +|----------:|------------|-----------|-------:|-----------:| +| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 | +| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 | +| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 | +| 2300 | 1967-10-01 | unemploy | 3143.0 | 0.03615694 | +| 2301 | 1967-11-01 | unemploy | 3066.0 | 0.03007816 | +| 2302 | 1967-12-01 | unemploy | 3018.0 | 0.02628878 | +| 2303 | 1968-01-01 | unemploy | 2878.0 | 0.01523644 | +| 2304 | 1968-02-01 | unemploy | 3001.0 | 0.02494671 | +| 2305 | 1968-03-01 | unemploy | 2877.0 | 0.01515750 | +| 2306 | 1968-04-01 | unemploy | 2709.0 | 0.00189469 | +| ... | ... | ... | ... | ... | +| 2860 | 2014-06-01 | unemploy | 9460.0 | 0.53485435 | +| 2861 | 2014-07-01 | unemploy | 9608.0 | 0.54653825 | +| 2862 | 2014-08-01 | unemploy | 9599.0 | 0.54582774 | +| 2863 | 2014-09-01 | unemploy | 9262.0 | 0.51922318 | +| 2864 | 2014-10-01 | unemploy | 8990.0 | 0.49775006 | +| 2865 | 2014-11-01 | unemploy | 9090.0 | 0.50564459 | +| 2866 | 2014-12-01 | unemploy | 8717.0 | 0.47619799 | +| 2867 | 2015-01-01 | unemploy | 8903.0 | 0.49088182 | +| 2868 | 2015-02-01 | unemploy | 8610.0 | 0.46775085 | +| 2869 | 2015-03-01 | unemploy | 8504.0 | 0.45938265 | +| 2870 | 2015-04-01 | unemploy | 8526.0 | 0.46111944 | +>, + :=layer [], + :=y-type + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=x2-encoding + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=opacity :com.rpl.specter.impl/NONE, + :=y2-after-stat :=y2, + :=x2-after-stat :=x2, + :=usermeta {:embedOptions {:renderer :=renderer}}, + :=title :com.rpl.specter.impl/NONE, + :=y-type-after-stat + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=height 300, + :=renderer :svg, + :=mark-opacity :com.rpl.specter.impl/NONE, + :=y-title :com.rpl.specter.impl/NONE, + :=size :com.rpl.specter.impl/NONE, + :=group + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=mark-size :com.rpl.specter.impl/NONE, + :=layer? :com.rpl.specter.impl/NONE, + :=size-type + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=layer-dataset + #<WrappedValue@37e5aed: ggplot2/economics_long [574 5]: + +| :rownames | :date | :variable | :value | :value01 | +|----------:|------------|-----------|-------:|-----------:| +| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 | +| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 | +| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 | +| 2300 | 1967-10-01 | unemploy | 3143.0 | 0.03615694 | +| 2301 | 1967-11-01 | unemploy | 3066.0 | 0.03007816 | +| 2302 | 1967-12-01 | unemploy | 3018.0 | 0.02628878 | +| 2303 | 1968-01-01 | unemploy | 2878.0 | 0.01523644 | +| 2304 | 1968-02-01 | unemploy | 3001.0 | 0.02494671 | +| 2305 | 1968-03-01 | unemploy | 2877.0 | 0.01515750 | +| 2306 | 1968-04-01 | unemploy | 2709.0 | 0.00189469 | +| ... | ... | ... | ... | ... | +| 2860 | 2014-06-01 | unemploy | 9460.0 | 0.53485435 | +| 2861 | 2014-07-01 | unemploy | 9608.0 | 0.54653825 | +| 2862 | 2014-08-01 | unemploy | 9599.0 | 0.54582774 | +| 2863 | 2014-09-01 | unemploy | 9262.0 | 0.51922318 | +| 2864 | 2014-10-01 | unemploy | 8990.0 | 0.49775006 | +| 2865 | 2014-11-01 | unemploy | 9090.0 | 0.50564459 | +| 2866 | 2014-12-01 | unemploy | 8717.0 | 0.47619799 | +| 2867 | 2015-01-01 | unemploy | 8903.0 | 0.49088182 | +| 2868 | 2015-02-01 | unemploy | 8610.0 | 0.46775085 | +| 2869 | 2015-03-01 | unemploy | 8504.0 | 0.45938265 | +| 2870 | 2015-04-01 | unemploy | 8526.0 | 0.46111944 | +>, + :=color :com.rpl.specter.impl/NONE, + :=mark-color "purple", + :=x :date, + :=y2 :com.rpl.specter.impl/NONE, + :=x-after-stat :=x, + :DFMT {:type "csv"}, + :=encoding + {:color {:field :=color, :type :=color-type}, + :size {:field :=size, :type :=size-type}, + :x + {:field :=x-after-stat, + :type :=x-type-after-stat, + :title :=x-title, + :bin :=x-bin}, + :y + {:field :=y-after-stat, + :type :=y-type-after-stat, + :title :=y-title, + :bin :=y-bin}, + :x2 :=x2-encoding, + :y2 :=y2-encoding}, + :=x-type-after-stat + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=x-bin :com.rpl.specter.impl/NONE, + :=x-type + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=histogram-nbins 10, + :=stat :com.rpl.specter.impl/NONE, + :=width 400, + :=color-type + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=mark "circle", + :=dataset-after-stat + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=y-bin :com.rpl.specter.impl/NONE, + :=x-title :com.rpl.specter.impl/NONE, + :=csv-data + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :=y :value, + :=data {:values :=csv-data, :format {:type "csv"}}, + :=dataset + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211], + :VALDATA :=csv-data, + :=mark-tooltip true, + :=background "floralwhite", + :=y-after-stat :=y, + :=predictors [:=x], + :=y2-encoding + #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]}, + :height :=height, + :data :=data}

    The structure returned by haclo/base is a Hanami template (with local defaults). When it is displayed, it goes through the Hanami transform to recieve the Vega-Lite spec.

    When we use base, we can keep processing the template in a pipeline of transformations. We will use it soon with layers.

    @@ -803,7 +807,7 @@

    (haclo/layer-point {:=mark-color "green" :=mark-size 200})) -
    +

    You see, we have lots of data for the lines, but only five random points.

    @@ -843,7 +847,7 @@

    :y {:field :value, :type :quantitative}}, :data {:values - "rownames,date,variable,value,value01\n2782,2007-12-01,unemploy,7645.0,0.3915686429304492\n2717,2002-07-01,unemploy,8390.0,0.45038288466092996\n2594,1992-04-01,unemploy,9415.0,0.531301807847162\n2789,2008-07-01,unemploy,8937.0,0.49356595879055815\n2798,2009-04-01,unemploy,13853.0,0.8816610089208179\n", + "rownames,date,variable,value,value01\n2851,2013-09-01,unemploy,11270.0,0.6777453224915134\n2833,2012-03-01,unemploy,12713.0,0.7916633772795453\n2636,1995-10-01,unemploy,7328.0,0.3665429857109023\n2419,1977-09-01,unemploy,6751.0,0.32099155285387226\n2574,1990-08-01,unemploy,7188.0,0.35549064498302674\n", :format {:type "csv"}}}], :height 300, :data @@ -864,7 +868,7 @@

    haclo/plot (assoc :background "lightgrey")) -
    +

    3.11 Smoothing

    @@ -1650,6 +1654,9 @@

    + + 4  Plotlycloth Walkthrough (experimental 🛠) + diff --git a/docs/index.html b/docs/index.html index 1ce850d..c6a1b06 100644 --- a/docs/index.html +++ b/docs/index.html @@ -156,6 +156,12 @@ 3  Walkthrough + +
  • +
    + + 4  Plotlycloth Walkthrough (experimental 🛠) +
    • @@ -225,8 +231,12 @@

    Hanamicloth

    (haclo/layer-point {:=mark-color "green" :=mark-size 200})) -
    -

    See more examples in the 👣 Walkthrough.

    +
    +

    See more examples:

    +

    1 Preface

    Hanamicloth is a composition of Hanami data visualization templates and Tablecloth datasets.

    @@ -234,16 +244,14 @@

    1 Preface

    The current draft was written by Daniel Slutsky, mentored by jsa-aerial (Hanami author) and Kira McLean.

    Source: (GiuHub repo)

    Artifact: (Clojars coordinates)

    -

    Status: The existing API will soon move to beta stage, but still lacks some important features.

    +

    Status: The api namespace will soon move to beta stage, but still lacks some important features. The plotlycloth namespace is still experimental.

    An early version of this library was demonstrated in Kira Mclean’s April 2024 talk at London Clojurians:

    1.1 Near term plan

      -
    • Add minor improvements to the current Vega-Lite-based API.
      • -
    • Add a Plotly.js-based API.
      • -
    • Stabilize both as Beta stage.
      • +
    • Stabilize both the Vega-Lite-based API and the Plotly.js-based API as Beta stage.
    • Keep developing main ly the Plotly.js-based API (as it will be more flexible to extend).
    @@ -276,6 +284,7 @@

  • Some datasets

  • Walkthrough

    • +

  • Plotlycloth Walkthrough (experimental 🛠)

    • diff --git a/docs/notebooks/chapters.edn b/docs/notebooks/chapters.edn index a84c0a5..e5918b1 100644 --- a/docs/notebooks/chapters.edn +++ b/docs/notebooks/chapters.edn @@ -1 +1 @@ -["datasets" "walkthrough"] +["datasets" "walkthrough" "plotlycloth_walkthrough"] diff --git a/docs/search.json b/docs/search.json index 5c16b7a..572bbff 100644 --- a/docs/search.json +++ b/docs/search.json @@ -4,7 +4,7 @@ "href": "index.html", "title": "Hanamicloth", "section": "", - "text": "1 Preface\nHanamicloth is a composition of Hanami data visualization templates and Tablecloth datasets.\nIt adds a simplified set of Hanami templates and defaults alongside those of Hanami, as well as a set of template-processing functions inspired by ggplot2’s layered grammar of graphics.\nThe current draft was written by Daniel Slutsky, mentored by jsa-aerial (Hanami author) and Kira McLean.\nSource:\nArtifact:\nStatus: The existing API will soon move to beta stage, but still lacks some important features.\nAn early version of this library was demonstrated in Kira Mclean’s April 2024 talk at London Clojurians:", + "text": "1 Preface\nHanamicloth is a composition of Hanami data visualization templates and Tablecloth datasets.\nIt adds a simplified set of Hanami templates and defaults alongside those of Hanami, as well as a set of template-processing functions inspired by ggplot2’s layered grammar of graphics.\nThe current draft was written by Daniel Slutsky, mentored by jsa-aerial (Hanami author) and Kira McLean.\nSource:\nArtifact:\nStatus: The api namespace will soon move to beta stage, but still lacks some important features. The plotlycloth namespace is still experimental.\nAn early version of this library was demonstrated in Kira Mclean’s April 2024 talk at London Clojurians:", "crumbs": [ "1  Preface" ] @@ -14,7 +14,7 @@ "href": "index.html#near-term-plan", "title": "Hanamicloth", "section": "1.1 Near term plan", - "text": "1.1 Near term plan\n\nAdd minor improvements to the current Vega-Lite-based API.\nAdd a Plotly.js-based API.\nStabilize both as Beta stage.\nKeep developing main ly the Plotly.js-based API (as it will be more flexible to extend).", + "text": "1.1 Near term plan\n\nStabilize both the Vega-Lite-based API and the Plotly.js-based API as Beta stage.\nKeep developing main ly the Plotly.js-based API (as it will be more flexible to extend).", "crumbs": [ "1  Preface" ] @@ -44,7 +44,7 @@ "href": "index.html#chapters-in-this-book", "title": "Hanamicloth", "section": "1.4 Chapters in this book:", - "text": "1.4 Chapters in this book:\n\nSome datasets\nWalkthrough\n\n\nsource: notebooks/index.clj", + "text": "1.4 Chapters in this book:\n\nSome datasets\nWalkthrough\nPlotlycloth Walkthrough (experimental 🛠)\n\n\nsource: notebooks/index.clj", "crumbs": [ "1  Preface" ] @@ -104,7 +104,7 @@ "href": "hanamicloth_book.walkthrough.html#setup", "title": "3  Walkthrough", "section": "", - "text": "Hanamicloth’s main API namepace\nHanami\nTablecloth\nthe datetime namespace of dtype-next,\nthe print namespace of tech.ml.dataset for customized dataset printing,\nKindly (which allows us to specify how values should be visualized).\nthe datasets defined in the Datasets chapter.\n\n\n(ns hanamicloth-book.walkthrough\n (:require [scicloj.hanamicloth.v1.api :as haclo]\n [aerial.hanami.templates :as ht]\n [tablecloth.api :as tc]\n [tech.v3.datatype.datetime :as datetime]\n [tech.v3.dataset.print :as print]\n [scicloj.kindly.v4.kind :as kind]\n [clojure.string :as str]\n [scicloj.kindly.v4.api :as kindly]\n [hanamicloth-book.datasets :as datasets]\n [aerial.hanami.common :as hc])\n (:import java.time.LocalDate))", + "text": "Hanamicloth’s main API namepace\nHanami\nTablecloth\nthe datetime namespace of dtype-next\nthe print namespace of tech.ml.dataset for customized dataset printing\nKindly (to specify how certaiun values should be visualized)\nthe datasets defined in the Datasets chapter\n\n\n(ns hanamicloth-book.walkthrough\n (:require [scicloj.hanamicloth.v1.api :as haclo]\n [aerial.hanami.templates :as ht]\n [tablecloth.api :as tc]\n [tech.v3.datatype.datetime :as datetime]\n [tech.v3.dataset.print :as print]\n [scicloj.kindly.v4.kind :as kind]\n [clojure.string :as str]\n [scicloj.kindly.v4.api :as kindly]\n [hanamicloth-book.datasets :as datasets]))", "crumbs": [ "3  Walkthrough" ] @@ -164,7 +164,7 @@ "href": "hanamicloth_book.walkthrough.html#delayed-transformation", "title": "3  Walkthrough", "section": "3.7 Delayed transformation", - "text": "3.7 Delayed transformation\nInstead of the haclo/plot function, it is possible to used haclo/base:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/base haclo/line-chart\n {:=x :date\n :=y :value\n :=mark-color \"purple\"}))\n\n\nThe result is displayed the same way, but the internal representation delays the Hanami transformation of templates.\nLet us compare the two:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/plot haclo/line-chart\n {:=x :date\n :=y :value\n :=mark-color \"purple\"})\n kind/pprint)\n\n\n{:encoding\n {:x {:field :date, :type :temporal},\n :y {:field :value, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :mark {:type \"line\", :color \"purple\", :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n \"rownames,date,variable,value,value01\\n2297,1967-07-01,unemploy,2944.0,0.020446830346569828\\n2298,1967-08-01,unemploy,2945.0,0.020525775637483226\\n2299,1967-09-01,unemploy,2958.0,0.021552064419357385\\n2300,1967-10-01,unemploy,3143.0,0.036156943238335834\\n2301,1967-11-01,unemploy,3066.0,0.030078155838004262\\n2302,1967-12-01,unemploy,3018.0,0.026288781874161206\\n2303,1968-01-01,unemploy,2878.0,0.015236441146285624\\n2304,1968-02-01,unemploy,3001.0,0.024946711928633458\\n2305,1968-03-01,unemploy,2877.0,0.015157495855372228\\n2306,1968-04-01,unemploy,2709.0,0.0018946869819215285\\n2307,1968-05-01,unemploy,2740.0,0.004341991000236836\\n2308,1968-06-01,unemploy,2938.0,0.019973158601089443\\n2309,1968-07-01,unemploy,2883.0,0.01563116760085261\\n2310,1968-08-01,unemploy,2768.0,0.006552459145811952\\n2311,1968-09-01,unemploy,2686.0,7.894529091339702E-5\\n2312,1968-10-01,unemploy,2689.0,3.157811636535881E-4\\n2313,1968-11-01,unemploy,2715.0,0.0023683587274019105\\n2314,1968-12-01,unemploy,2685.0,0.0\\n2315,1969-01-01,unemploy,2718.0,0.0026051946001421017\\n2316,1969-02-01,unemploy,2692.0,5.526170363937791E-4\\n2317,1969-03-01,unemploy,2712.0,0.0021315228546617193\\n2318,1969-04-01,unemploy,2758.0,0.005763006236677982\\n2319,1969-05-01,unemploy,2713.0,0.0022104681455751164\\n2320,1969-06-01,unemploy,2816.0,0.010341833109655009\\n2321,1969-07-01,unemploy,2868.0,0.014446988237151653\\n2322,1969-08-01,unemploy,2856.0,0.01349964474619089\\n2323,1969-09-01,unemploy,3040.0,0.02802557827425594\\n2324,1969-10-01,unemploy,3049.0,0.028736085892476514\\n2325,1969-11-01,unemploy,2856.0,0.01349964474619089\\n2326,1969-12-01,unemploy,2884.0,0.015710112891766007\\n2327,1970-01-01,unemploy,3201.0,0.04073577011131286\\n2328,1970-02-01,unemploy,3453.0,0.06062998342148891\\n2329,1970-03-01,unemploy,3635.0,0.07499802636772716\\n2330,1970-04-01,unemploy,3797.0,0.08778716349569748\\n2331,1970-05-01,unemploy,3919.0,0.09741848898713192\\n2332,1970-06-01,unemploy,4071.0,0.10941817320596826\\n2333,1970-07-01,unemploy,4175.0,0.11762848346096155\\n2334,1970-08-01,unemploy,4256.0,0.1240230520249467\\n2335,1970-09-01,unemploy,4456.0,0.13981211020762613\\n2336,1970-10-01,unemploy,4591.0,0.1504697244809347\\n2337,1970-11-01,unemploy,4898.0,0.1747059287913476\\n2338,1970-12-01,unemploy,5076.0,0.18875819057393226\\n2339,1971-01-01,unemploy,4986.0,0.18165311439172654\\n2340,1971-02-01,unemploy,4903.0,0.1751006552459146\\n2341,1971-03-01,unemploy,4987.0,0.18173205968263992\\n2342,1971-04-01,unemploy,4959.0,0.17952159153706482\\n2343,1971-05-01,unemploy,4996.0,0.1824425673008605\\n2344,1971-06-01,unemploy,4949.0,0.17873213862793083\\n2345,1971-07-01,unemploy,5035.0,0.185521433646483\\n2346,1971-08-01,unemploy,5134.0,0.1933370174469093\\n2347,1971-09-01,unemploy,5042.0,0.18607405068287677\\n2348,1971-10-01,unemploy,4954.0,0.17912686508249784\\n2349,1971-11-01,unemploy,5161.0,0.19546854030157101\\n2350,1971-12-01,unemploy,5154.0,0.19491592326517723\\n2351,1972-01-01,unemploy,5019.0,0.18425830899186862\\n2352,1972-02-01,unemploy,4928.0,0.1770742875187495\\n2353,1972-03-01,unemploy,5038.0,0.18575826951922317\\n2354,1972-04-01,unemploy,4959.0,0.17952159153706482\\n2355,1972-05-01,unemploy,4922.0,0.17660061577326913\\n2356,1972-06-01,unemploy,4923.0,0.17667956106418253\\n2357,1972-07-01,unemploy,4913.0,0.17589010815504855\\n2358,1972-08-01,unemploy,4939.0,0.17794268571879687\\n2359,1972-09-01,unemploy,4849.0,0.17083760953659113\\n2360,1972-10-01,unemploy,4875.0,0.17289018710033946\\n2361,1972-11-01,unemploy,4602.0,0.15133812268098207\\n2362,1972-12-01,unemploy,4543.0,0.14668035051709166\\n2363,1973-01-01,unemploy,4326.0,0.1295492223888845\\n2364,1973-02-01,unemploy,4452.0,0.13949632904397252\\n2365,1973-03-01,unemploy,4394.0,0.1349175021709955\\n2366,1973-04-01,unemploy,4459.0,0.1400489460803663\\n2367,1973-05-01,unemploy,4329.0,0.1297860582616247\\n2368,1973-06-01,unemploy,4363.0,0.13247019815268019\\n2369,1973-07-01,unemploy,4305.0,0.12789137127970315\\n2370,1973-08-01,unemploy,4305.0,0.12789137127970315\\n2371,1973-09-01,unemploy,4350.0,0.13144390937080602\\n2372,1973-10-01,unemploy,4144.0,0.11518117944264625\\n2373,1973-11-01,unemploy,4396.0,0.1350753927528223\\n2374,1973-12-01,unemploy,4489.0,0.1424173048077682\\n2375,1974-01-01,unemploy,4644.0,0.15465382489934476\\n2376,1974-02-01,unemploy,4731.0,0.1615220652088103\\n2377,1974-03-01,unemploy,4634.0,0.15386437199021077\\n2378,1974-04-01,unemploy,4618.0,0.15260124733559643\\n2379,1974-05-01,unemploy,4705.0,0.15946948764506197\\n2380,1974-06-01,unemploy,4927.0,0.1769953422278361\\n2381,1974-07-01,unemploy,5063.0,0.1877319017920581\\n2382,1974-08-01,unemploy,5022.0,0.18449514486460883\\n2383,1974-09-01,unemploy,5437.0,0.21725744059366858\\n2384,1974-10-01,unemploy,5523.0,0.22404673561222074\\n2385,1974-11-01,unemploy,6140.0,0.27275598010578667\\n2386,1974-12-01,unemploy,6636.0,0.3119128443988316\\n2387,1975-01-01,unemploy,7501.0,0.38020052103892005\\n2388,1975-02-01,unemploy,7520.0,0.3817004815662745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0367095602748\\n2857,2014-03-01,unemploy,10380.0,0.60748401357859\\n2858,2014-04-01,unemploy,9702.0,0.5539591063393069\\n2859,2014-05-01,unemploy,9859.0,0.5663535170127102\\n2860,2014-06-01,unemploy,9460.0,0.5348543459382648\\n2861,2014-07-01,unemploy,9608.0,0.5465382489934475\\n2862,2014-08-01,unemploy,9599.0,0.5458277413752269\\n2863,2014-09-01,unemploy,9262.0,0.5192231783374122\\n2864,2014-10-01,unemploy,8990.0,0.4977500592089682\\n2865,2014-11-01,unemploy,9090.0,0.5056445883003079\\n2866,2014-12-01,unemploy,8717.0,0.4761979947896108\\n2867,2015-01-01,unemploy,8903.0,0.49088181889950266\\n2868,2015-02-01,unemploy,8610.0,0.4677508486618773\\n2869,2015-03-01,unemploy,8504.0,0.4593826478250572\\n2870,2015-04-01,unemploy,8526.0,0.46111944422515194\\n\",\n :format {:type \"csv\"}}}\n\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/base haclo/line-chart\n {:=x :date\n :=y :value\n :=mark-color \"purple\"})\n kind/pprint)\n\n\n{:encoding :=encoding,\n :usermeta :=usermeta,\n :mark\n {:type \"line\",\n :color :=mark-color,\n :size :=mark-size,\n :opacity :=mark-opacity,\n :tooltip :=mark-tooltip},\n :width :=width,\n :background :=background,\n :title :=title,\n :layer :=layer,\n :kindly/f #'scicloj.hanamicloth.v1.api/vega-lite-xform,\n :aerial.hanami.templates/defaults\n {:=x2 :com.rpl.specter.impl/NONE,\n :=base-dataset\n #<WrappedValue@36ca353f: ggplot2/economics_long [574 5]:\n\n| :rownames | :date | :variable | :value | :value01 |\n|----------:|------------|-----------|-------:|-----------:|\n| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 |\n| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 |\n| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 |\n| 2300 | 1967-10-01 | unemploy | 3143.0 | 0.03615694 |\n| 2301 | 1967-11-01 | unemploy | 3066.0 | 0.03007816 |\n| 2302 | 1967-12-01 | unemploy | 3018.0 | 0.02628878 |\n| 2303 | 1968-01-01 | unemploy | 2878.0 | 0.01523644 |\n| 2304 | 1968-02-01 | unemploy | 3001.0 | 0.02494671 |\n| 2305 | 1968-03-01 | unemploy | 2877.0 | 0.01515750 |\n| 2306 | 1968-04-01 | unemploy | 2709.0 | 0.00189469 |\n| ... | ... | ... | ... | ... |\n| 2860 | 2014-06-01 | unemploy | 9460.0 | 0.53485435 |\n| 2861 | 2014-07-01 | unemploy | 9608.0 | 0.54653825 |\n| 2862 | 2014-08-01 | unemploy | 9599.0 | 0.54582774 |\n| 2863 | 2014-09-01 | unemploy | 9262.0 | 0.51922318 |\n| 2864 | 2014-10-01 | unemploy | 8990.0 | 0.49775006 |\n| 2865 | 2014-11-01 | unemploy | 9090.0 | 0.50564459 |\n| 2866 | 2014-12-01 | unemploy | 8717.0 | 0.47619799 |\n| 2867 | 2015-01-01 | unemploy | 8903.0 | 0.49088182 |\n| 2868 | 2015-02-01 | unemploy | 8610.0 | 0.46775085 |\n| 2869 | 2015-03-01 | unemploy | 8504.0 | 0.45938265 |\n| 2870 | 2015-04-01 | unemploy | 8526.0 | 0.46111944 |\n>,\n :=layer [],\n :=y-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=x2-encoding\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=opacity :com.rpl.specter.impl/NONE,\n :=y2-after-stat :=y2,\n :=x2-after-stat :=x2,\n :=usermeta {:embedOptions {:renderer :=renderer}},\n :=title :com.rpl.specter.impl/NONE,\n :=y-type-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=height 300,\n :=renderer :svg,\n :=mark-opacity :com.rpl.specter.impl/NONE,\n :=y-title :com.rpl.specter.impl/NONE,\n :=size :com.rpl.specter.impl/NONE,\n :=group\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=mark-size :com.rpl.specter.impl/NONE,\n :=layer? :com.rpl.specter.impl/NONE,\n :=size-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=layer-dataset\n #<WrappedValue@36ca353f: ggplot2/economics_long [574 5]:\n\n| :rownames | :date | :variable | :value | :value01 |\n|----------:|------------|-----------|-------:|-----------:|\n| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 |\n| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 |\n| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 |\n| 2300 | 1967-10-01 | unemploy | 3143.0 | 0.03615694 |\n| 2301 | 1967-11-01 | unemploy | 3066.0 | 0.03007816 |\n| 2302 | 1967-12-01 | unemploy | 3018.0 | 0.02628878 |\n| 2303 | 1968-01-01 | unemploy | 2878.0 | 0.01523644 |\n| 2304 | 1968-02-01 | unemploy | 3001.0 | 0.02494671 |\n| 2305 | 1968-03-01 | unemploy | 2877.0 | 0.01515750 |\n| 2306 | 1968-04-01 | unemploy | 2709.0 | 0.00189469 |\n| ... | ... | ... | ... | ... |\n| 2860 | 2014-06-01 | unemploy | 9460.0 | 0.53485435 |\n| 2861 | 2014-07-01 | unemploy | 9608.0 | 0.54653825 |\n| 2862 | 2014-08-01 | unemploy | 9599.0 | 0.54582774 |\n| 2863 | 2014-09-01 | unemploy | 9262.0 | 0.51922318 |\n| 2864 | 2014-10-01 | unemploy | 8990.0 | 0.49775006 |\n| 2865 | 2014-11-01 | unemploy | 9090.0 | 0.50564459 |\n| 2866 | 2014-12-01 | unemploy | 8717.0 | 0.47619799 |\n| 2867 | 2015-01-01 | unemploy | 8903.0 | 0.49088182 |\n| 2868 | 2015-02-01 | unemploy | 8610.0 | 0.46775085 |\n| 2869 | 2015-03-01 | unemploy | 8504.0 | 0.45938265 |\n| 2870 | 2015-04-01 | unemploy | 8526.0 | 0.46111944 |\n>,\n :=color :com.rpl.specter.impl/NONE,\n :=mark-color \"purple\",\n :=x :date,\n :=y2 :com.rpl.specter.impl/NONE,\n :=x-after-stat :=x,\n :DFMT {:type \"csv\"},\n :=encoding\n {:color {:field :=color, :type :=color-type},\n :size {:field :=size, :type :=size-type},\n :x\n {:field :=x-after-stat,\n :type :=x-type-after-stat,\n :title :=x-title,\n :bin :=x-bin},\n :y\n {:field :=y-after-stat,\n :type :=y-type-after-stat,\n :title :=y-title,\n :bin :=y-bin},\n :x2 :=x2-encoding,\n :y2 :=y2-encoding},\n :=x-type-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=x-bin :com.rpl.specter.impl/NONE,\n :=x-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=histogram-nbins 10,\n :=stat :com.rpl.specter.impl/NONE,\n :=width 400,\n :=color-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=mark \"circle\",\n :=dataset-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=y-bin :com.rpl.specter.impl/NONE,\n :=x-title :com.rpl.specter.impl/NONE,\n :=csv-data\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :=y :value,\n :=data {:values :=csv-data, :format {:type \"csv\"}},\n :=dataset\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465],\n :VALDATA :=csv-data,\n :=mark-tooltip true,\n :=background \"floralwhite\",\n :=y-after-stat :=y,\n :=predictors [:=x],\n :=y2-encoding\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--47465]},\n :height :=height,\n :data :=data}\n\nThe structure returned by haclo/base is a Hanami template (with local defaults). When it is displayed, it goes through the Hanami transform to recieve the Vega-Lite spec.\nWhen we use base, we can keep processing the template in a pipeline of transformations. We will use it soon with layers.", + "text": "3.7 Delayed transformation\nInstead of the haclo/plot function, it is possible to used haclo/base:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/base haclo/line-chart\n {:=x :date\n :=y :value\n :=mark-color \"purple\"}))\n\n\nThe result is displayed the same way, but the internal representation delays the Hanami transformation of templates.\nLet us compare the two:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/plot haclo/line-chart\n {:=x :date\n :=y :value\n :=mark-color \"purple\"})\n kind/pprint)\n\n\n{:encoding\n {:x {:field :date, :type :temporal},\n :y {:field :value, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :mark {:type \"line\", :color \"purple\", :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n 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0367095602748\\n2857,2014-03-01,unemploy,10380.0,0.60748401357859\\n2858,2014-04-01,unemploy,9702.0,0.5539591063393069\\n2859,2014-05-01,unemploy,9859.0,0.5663535170127102\\n2860,2014-06-01,unemploy,9460.0,0.5348543459382648\\n2861,2014-07-01,unemploy,9608.0,0.5465382489934475\\n2862,2014-08-01,unemploy,9599.0,0.5458277413752269\\n2863,2014-09-01,unemploy,9262.0,0.5192231783374122\\n2864,2014-10-01,unemploy,8990.0,0.4977500592089682\\n2865,2014-11-01,unemploy,9090.0,0.5056445883003079\\n2866,2014-12-01,unemploy,8717.0,0.4761979947896108\\n2867,2015-01-01,unemploy,8903.0,0.49088181889950266\\n2868,2015-02-01,unemploy,8610.0,0.4677508486618773\\n2869,2015-03-01,unemploy,8504.0,0.4593826478250572\\n2870,2015-04-01,unemploy,8526.0,0.46111944422515194\\n\",\n :format {:type \"csv\"}}}\n\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/base haclo/line-chart\n {:=x :date\n :=y :value\n :=mark-color \"purple\"})\n kind/pprint)\n\n\n{:encoding :=encoding,\n :usermeta :=usermeta,\n :mark\n {:type \"line\",\n :color :=mark-color,\n :size :=mark-size,\n :opacity :=mark-opacity,\n :tooltip :=mark-tooltip},\n :width :=width,\n :background :=background,\n :title :=title,\n :layer :=layer,\n :kindly/f #'scicloj.hanamicloth.v1.api/vega-lite-xform,\n :aerial.hanami.templates/defaults\n {:=x2 :com.rpl.specter.impl/NONE,\n :=base-dataset #<WrappedValue@37e5aed: ggplot2/economics_long [574 5]:\n\n| :rownames | :date | :variable | :value | :value01 |\n|----------:|------------|-----------|-------:|-----------:|\n| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 |\n| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 |\n| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 |\n| 2300 | 1967-10-01 | unemploy | 3143.0 | 0.03615694 |\n| 2301 | 1967-11-01 | unemploy | 3066.0 | 0.03007816 |\n| 2302 | 1967-12-01 | unemploy | 3018.0 | 0.02628878 |\n| 2303 | 1968-01-01 | unemploy | 2878.0 | 0.01523644 |\n| 2304 | 1968-02-01 | unemploy | 3001.0 | 0.02494671 |\n| 2305 | 1968-03-01 | unemploy | 2877.0 | 0.01515750 |\n| 2306 | 1968-04-01 | unemploy | 2709.0 | 0.00189469 |\n| ... | ... | ... | ... | ... |\n| 2860 | 2014-06-01 | unemploy | 9460.0 | 0.53485435 |\n| 2861 | 2014-07-01 | unemploy | 9608.0 | 0.54653825 |\n| 2862 | 2014-08-01 | unemploy | 9599.0 | 0.54582774 |\n| 2863 | 2014-09-01 | unemploy | 9262.0 | 0.51922318 |\n| 2864 | 2014-10-01 | unemploy | 8990.0 | 0.49775006 |\n| 2865 | 2014-11-01 | unemploy | 9090.0 | 0.50564459 |\n| 2866 | 2014-12-01 | unemploy | 8717.0 | 0.47619799 |\n| 2867 | 2015-01-01 | unemploy | 8903.0 | 0.49088182 |\n| 2868 | 2015-02-01 | unemploy | 8610.0 | 0.46775085 |\n| 2869 | 2015-03-01 | unemploy | 8504.0 | 0.45938265 |\n| 2870 | 2015-04-01 | unemploy | 8526.0 | 0.46111944 |\n>,\n :=layer [],\n :=y-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x2-encoding\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=opacity :com.rpl.specter.impl/NONE,\n :=y2-after-stat :=y2,\n :=x2-after-stat :=x2,\n :=usermeta {:embedOptions {:renderer :=renderer}},\n :=title :com.rpl.specter.impl/NONE,\n :=y-type-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=height 300,\n :=renderer :svg,\n :=mark-opacity :com.rpl.specter.impl/NONE,\n :=y-title :com.rpl.specter.impl/NONE,\n :=size :com.rpl.specter.impl/NONE,\n :=group\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=mark-size :com.rpl.specter.impl/NONE,\n :=layer? :com.rpl.specter.impl/NONE,\n :=size-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=layer-dataset\n #<WrappedValue@37e5aed: ggplot2/economics_long [574 5]:\n\n| :rownames | :date | :variable | :value | :value01 |\n|----------:|------------|-----------|-------:|-----------:|\n| 2297 | 1967-07-01 | unemploy | 2944.0 | 0.02044683 |\n| 2298 | 1967-08-01 | unemploy | 2945.0 | 0.02052578 |\n| 2299 | 1967-09-01 | unemploy | 2958.0 | 0.02155206 |\n| 2300 | 1967-10-01 | unemploy | 3143.0 | 0.03615694 |\n| 2301 | 1967-11-01 | unemploy | 3066.0 | 0.03007816 |\n| 2302 | 1967-12-01 | unemploy | 3018.0 | 0.02628878 |\n| 2303 | 1968-01-01 | unemploy | 2878.0 | 0.01523644 |\n| 2304 | 1968-02-01 | unemploy | 3001.0 | 0.02494671 |\n| 2305 | 1968-03-01 | unemploy | 2877.0 | 0.01515750 |\n| 2306 | 1968-04-01 | unemploy | 2709.0 | 0.00189469 |\n| ... | ... | ... | ... | ... |\n| 2860 | 2014-06-01 | unemploy | 9460.0 | 0.53485435 |\n| 2861 | 2014-07-01 | unemploy | 9608.0 | 0.54653825 |\n| 2862 | 2014-08-01 | unemploy | 9599.0 | 0.54582774 |\n| 2863 | 2014-09-01 | unemploy | 9262.0 | 0.51922318 |\n| 2864 | 2014-10-01 | unemploy | 8990.0 | 0.49775006 |\n| 2865 | 2014-11-01 | unemploy | 9090.0 | 0.50564459 |\n| 2866 | 2014-12-01 | unemploy | 8717.0 | 0.47619799 |\n| 2867 | 2015-01-01 | unemploy | 8903.0 | 0.49088182 |\n| 2868 | 2015-02-01 | unemploy | 8610.0 | 0.46775085 |\n| 2869 | 2015-03-01 | unemploy | 8504.0 | 0.45938265 |\n| 2870 | 2015-04-01 | unemploy | 8526.0 | 0.46111944 |\n>,\n :=color :com.rpl.specter.impl/NONE,\n :=mark-color \"purple\",\n :=x :date,\n :=y2 :com.rpl.specter.impl/NONE,\n :=x-after-stat :=x,\n :DFMT {:type \"csv\"},\n :=encoding\n {:color {:field :=color, :type :=color-type},\n :size {:field :=size, :type :=size-type},\n :x\n {:field :=x-after-stat,\n :type :=x-type-after-stat,\n :title :=x-title,\n :bin :=x-bin},\n :y\n {:field :=y-after-stat,\n :type :=y-type-after-stat,\n :title :=y-title,\n :bin :=y-bin},\n :x2 :=x2-encoding,\n :y2 :=y2-encoding},\n :=x-type-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x-bin :com.rpl.specter.impl/NONE,\n :=x-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=histogram-nbins 10,\n :=stat :com.rpl.specter.impl/NONE,\n :=width 400,\n :=color-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=mark \"circle\",\n :=dataset-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=y-bin :com.rpl.specter.impl/NONE,\n :=x-title :com.rpl.specter.impl/NONE,\n :=csv-data\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=y :value,\n :=data {:values :=csv-data, :format {:type \"csv\"}},\n :=dataset\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :VALDATA :=csv-data,\n :=mark-tooltip true,\n :=background \"floralwhite\",\n :=y-after-stat :=y,\n :=predictors [:=x],\n :=y2-encoding\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},\n :height :=height,\n :data :=data}\n\nThe structure returned by haclo/base is a Hanami template (with local defaults). When it is displayed, it goes through the Hanami transform to recieve the Vega-Lite spec.\nWhen we use base, we can keep processing the template in a pipeline of transformations. We will use it soon with layers.", "crumbs": [ "3  Walkthrough" ] @@ -194,7 +194,7 @@ "href": "hanamicloth_book.walkthrough.html#processing-raw-vega-lite", "title": "3  Walkthrough", "section": "3.10 Processing raw vega-lite", - "text": "3.10 Processing raw vega-lite\nDuring a pipeline, we may call haclo/plot to apply the Hanami transform and realize the Vega-Lite spec.\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/base {:=x :date\n :=y :value})\n (haclo/layer-line {:=mark-color \"purple\"})\n (haclo/update-data tc/random 5)\n (haclo/layer-point {:=mark-color \"green\"\n :=mark-size 200})\n haclo/plot\n kind/pprint)\n\n\n{:encoding\n {:x {:field :date, :type :temporal},\n :y {:field :value, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :width 400,\n :background \"floralwhite\",\n :layer\n [{:mark {:type \"line\", :color \"purple\", :tooltip true},\n :encoding\n {:x {:field :date, :type :temporal},\n :y {:field :value, :type :quantitative}},\n :data\n {:values\n 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:format {:type \"csv\"}}}\n\nWhile this in itself does not affect the display of the plot, it allows us to keep editing it as a Vega-Lite spec. For example, let us change the backgound colour this way:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/base {:=x :date\n :=y :value})\n (haclo/layer-line {:=mark-color \"purple\"})\n (haclo/update-data tc/random 5)\n (haclo/layer-point {:=mark-color \"green\"\n :=mark-size 200})\n haclo/plot\n (assoc :background \"lightgrey\"))", + "text": "3.10 Processing raw vega-lite\nDuring a pipeline, we may call haclo/plot to apply the Hanami transform and realize the Vega-Lite spec.\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/base {:=x :date\n :=y :value})\n (haclo/layer-line {:=mark-color \"purple\"})\n (haclo/update-data tc/random 5)\n (haclo/layer-point {:=mark-color \"green\"\n :=mark-size 200})\n haclo/plot\n kind/pprint)\n\n\n{:encoding\n {:x {:field :date, :type :temporal},\n :y {:field :value, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :width 400,\n :background \"floralwhite\",\n :layer\n [{:mark {:type \"line\", :color \"purple\", :tooltip true},\n :encoding\n {:x {:field :date, :type :temporal},\n :y {:field :value, :type :quantitative}},\n :data\n {:values\n \"rownames,date,variable,value,value01\\n2297,1967-07-01,unemploy,2944.0,0.020446830346569828\\n2298,1967-08-01,unemploy,2945.0,0.020525775637483226\\n2299,1967-09-01,unemploy,2958.0,0.021552064419357385\\n2300,1967-10-01,unemploy,3143.0,0.036156943238335834\\n2301,1967-11-01,unemploy,3066.0,0.030078155838004262\\n2302,1967-12-01,unemploy,3018.0,0.026288781874161206\\n2303,1968-01-01,unemploy,2878.0,0.015236441146285624\\n2304,1968-02-01,unemploy,3001.0,0.024946711928633458\\n2305,1968-03-01,unemploy,2877.0,0.015157495855372228\\n2306,1968-04-01,unemploy,2709.0,0.0018946869819215285\\n2307,1968-05-01,unemploy,2740.0,0.004341991000236836\\n2308,1968-06-01,unemploy,2938.0,0.019973158601089443\\n2309,1968-07-01,unemploy,2883.0,0.01563116760085261\\n2310,1968-08-01,unemploy,2768.0,0.006552459145811952\\n2311,1968-09-01,unemploy,2686.0,7.894529091339702E-5\\n2312,1968-10-01,unemploy,2689.0,3.157811636535881E-4\\n2313,1968-11-01,unemploy,2715.0,0.0023683587274019105\\n2314,1968-12-01,unemploy,2685.0,0.0\\n2315,1969-01-01,unemploy,2718.0,0.0026051946001421017\\n2316,1969-02-01,unemploy,2692.0,5.526170363937791E-4\\n2317,1969-03-01,unemploy,2712.0,0.0021315228546617193\\n2318,1969-04-01,unemploy,2758.0,0.005763006236677982\\n2319,1969-05-01,unemploy,2713.0,0.0022104681455751164\\n2320,1969-06-01,unemploy,2816.0,0.010341833109655009\\n2321,1969-07-01,unemploy,2868.0,0.014446988237151653\\n2322,1969-08-01,unemploy,2856.0,0.01349964474619089\\n2323,1969-09-01,unemploy,3040.0,0.02802557827425594\\n2324,1969-10-01,unemploy,3049.0,0.028736085892476514\\n2325,1969-11-01,unemploy,2856.0,0.01349964474619089\\n2326,1969-12-01,unemploy,2884.0,0.015710112891766007\\n2327,1970-01-01,unemploy,3201.0,0.04073577011131286\\n2328,1970-02-01,unemploy,3453.0,0.06062998342148891\\n2329,1970-03-01,unemploy,3635.0,0.07499802636772716\\n2330,1970-04-01,unemploy,3797.0,0.08778716349569748\\n2331,1970-05-01,unemploy,3919.0,0.09741848898713192\\n2332,1970-06-01,unemploy,4071.0,0.10941817320596826\\n2333,1970-07-01,unemploy,4175.0,0.11762848346096155\\n2334,1970-08-01,unemploy,4256.0,0.1240230520249467\\n2335,1970-09-01,unemploy,4456.0,0.13981211020762613\\n2336,1970-10-01,unemploy,4591.0,0.1504697244809347\\n2337,1970-11-01,unemploy,4898.0,0.1747059287913476\\n2338,1970-12-01,unemploy,5076.0,0.18875819057393226\\n2339,1971-01-01,unemploy,4986.0,0.18165311439172654\\n2340,1971-02-01,unemploy,4903.0,0.1751006552459146\\n2341,1971-03-01,unemploy,4987.0,0.18173205968263992\\n2342,1971-04-01,unemploy,4959.0,0.17952159153706482\\n2343,1971-05-01,unemploy,4996.0,0.1824425673008605\\n2344,1971-06-01,unemploy,4949.0,0.17873213862793083\\n2345,1971-07-01,unemploy,5035.0,0.185521433646483\\n2346,1971-08-01,unemploy,5134.0,0.1933370174469093\\n2347,1971-09-01,unemploy,5042.0,0.18607405068287677\\n2348,1971-10-01,unemploy,4954.0,0.17912686508249784\\n2349,1971-11-01,unemploy,5161.0,0.19546854030157101\\n2350,1971-12-01,unemploy,5154.0,0.19491592326517723\\n2351,1972-01-01,unemploy,5019.0,0.18425830899186862\\n2352,1972-02-01,unemploy,4928.0,0.1770742875187495\\n2353,1972-03-01,unemploy,5038.0,0.18575826951922317\\n2354,1972-04-01,unemploy,4959.0,0.17952159153706482\\n2355,1972-05-01,unemploy,4922.0,0.17660061577326913\\n2356,1972-06-01,unemploy,4923.0,0.17667956106418253\\n2357,1972-07-01,unemploy,4913.0,0.17589010815504855\\n2358,1972-08-01,unemploy,4939.0,0.17794268571879687\\n2359,1972-09-01,unemploy,4849.0,0.17083760953659113\\n2360,1972-10-01,unemploy,4875.0,0.17289018710033946\\n2361,1972-11-01,unemploy,4602.0,0.15133812268098207\\n2362,1972-12-01,unemploy,4543.0,0.14668035051709166\\n2363,1973-01-01,unemploy,4326.0,0.1295492223888845\\n2364,1973-02-01,unemploy,4452.0,0.13949632904397252\\n2365,1973-03-01,unemploy,4394.0,0.1349175021709955\\n2366,1973-04-01,unemploy,4459.0,0.1400489460803663\\n2367,1973-05-01,unemploy,4329.0,0.1297860582616247\\n2368,1973-06-01,unemploy,4363.0,0.13247019815268019\\n2369,1973-07-01,unemploy,4305.0,0.12789137127970315\\n2370,1973-08-01,unemploy,4305.0,0.12789137127970315\\n2371,1973-09-01,unemploy,4350.0,0.13144390937080602\\n2372,1973-10-01,unemploy,4144.0,0.11518117944264625\\n2373,1973-11-01,unemploy,4396.0,0.1350753927528223\\n2374,1973-12-01,unemploy,4489.0,0.1424173048077682\\n2375,1974-01-01,unemploy,4644.0,0.15465382489934476\\n2376,1974-02-01,unemploy,4731.0,0.1615220652088103\\n2377,1974-03-01,unemploy,4634.0,0.15386437199021077\\n2378,1974-04-01,unemploy,4618.0,0.15260124733559643\\n2379,1974-05-01,unemploy,4705.0,0.15946948764506197\\n2380,1974-06-01,unemploy,4927.0,0.1769953422278361\\n2381,1974-07-01,unemploy,5063.0,0.1877319017920581\\n2382,1974-08-01,unemploy,5022.0,0.18449514486460883\\n2383,1974-09-01,unemploy,5437.0,0.21725744059366858\\n2384,1974-10-01,unemploy,5523.0,0.22404673561222074\\n2385,1974-11-01,unemploy,6140.0,0.27275598010578667\\n2386,1974-12-01,unemploy,6636.0,0.3119128443988316\\n2387,1975-01-01,unemploy,7501.0,0.38020052103892005\\n2388,1975-02-01,unemploy,7520.0,0.3817004815662745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:format {:type \"csv\"}}}\n\nWhile this in itself does not affect the display of the plot, it allows us to keep editing it as a Vega-Lite spec. For example, let us change the backgound colour this way:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (haclo/base {:=x :date\n :=y :value})\n (haclo/layer-line {:=mark-color \"purple\"})\n (haclo/update-data tc/random 5)\n (haclo/layer-point {:=mark-color \"green\"\n :=mark-size 200})\n haclo/plot\n (assoc :background \"lightgrey\"))", "crumbs": [ "3  Walkthrough" ] @@ -248,5 +248,155 @@ "crumbs": [ "3  Walkthrough" ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html", + "href": "hanamicloth_book.plotlycloth_walkthrough.html", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "", + "text": "4.1 Setup\nFor this tutorial, we require:", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#setup", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#setup", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "", + "text": "The plotlycloth API namepace\nTablecloth for dataset processing\nthe datetime namespace of dtype-next\nthe print namespace of tech.ml.dataset for customized dataset printing\nKindly (to specify how certaiun values should be visualized)\nthe datasets defined in the Datasets chapter\n\n\n(ns hanamicloth-book.plotlycloth-walkthrough\n (:require [scicloj.hanamicloth.v1.plotlycloth :as ploclo]\n [tablecloth.api :as tc]\n [tech.v3.datatype.datetime :as datetime]\n [tech.v3.dataset.print :as print]\n [scicloj.kindly.v4.kind :as kind]\n [clojure.string :as str]\n [scicloj.kindly.v4.api :as kindly]\n [hanamicloth-book.datasets :as datasets]))", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#basic-usage", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#basic-usage", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.2 Basic usage", + "text": "4.2 Basic usage\nPlotlycloth plots are created by passing datasets to a pipeline of layer functions.\nAdditional parameters to the functions are passed as maps. By convention, the map keys begin with = (e.g., :=color).\nFor example, let us plot a scatterplot (a layer of points) of 10 random items from the Iris dataset.\n\n(-> datasets/iris\n (tc/random 10 {:seed 1})\n (ploclo/layer-point\n {:=x :sepal-width\n :=y :sepal-length\n :=color :species\n :=mark-size 20\n :=mark-opacity 0.6}))", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#templates-and-parameters", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#templates-and-parameters", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.3 Templates and parameters", + "text": "4.3 Templates and parameters\n(💡 You do neet need to understand these details for basic usage.)\nTechnically, 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.\nThe layer functions return a Hanami template. Let us print the resulting structure of the previous plot.\n\n(def example1\n (-> datasets/iris\n (tc/random 10 {:seed 1})\n (ploclo/layer-point\n {:=x :sepal-width\n :=y :sepal-length\n :=color :species\n :=mark-size 20\n :=mark-opacity 0.6})))\n\n\n(kind/pprint example1)\n\n\n{:data :=traces,\n :layout :=layout,\n :aerial.hanami.templates/defaults\n {:=x0 :com.rpl.specter.impl/NONE,\n :=y-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x0-after-stat :=x0,\n :=layers\n [{:y :=y-after-stat,\n :trace-base {:mode :=mode, :type :=type, :opacity :=mark-opacity},\n :color-type :=color-type,\n :group :=group,\n :color :=color,\n :mark :=mark,\n :x-title :=x-title,\n :name :=name,\n :y1 :=y1-after-stat,\n :size :=size,\n :size-type :=size-type,\n :aerial.hanami.templates/defaults\n {:=x0 :com.rpl.specter.impl/NONE,\n :=y-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x0-after-stat :=x0,\n :=layers [],\n :=x1 :com.rpl.specter.impl/NONE,\n :=title :com.rpl.specter.impl/NONE,\n :=y1 :com.rpl.specter.impl/NONE,\n :=y-type-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=height :com.rpl.specter.impl/NONE,\n :=name :com.rpl.specter.impl/NONE,\n :=mark-opacity 0.6,\n :=inferred-group\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=mode\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=y-title :com.rpl.specter.impl/NONE,\n :=size :com.rpl.specter.impl/NONE,\n :=group :=inferred-group,\n :=y0 :com.rpl.specter.impl/NONE,\n :=mark-size 20,\n :=size-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=color :species,\n :=mark-color :com.rpl.specter.impl/NONE,\n :=y1-after-stat :=y1,\n :=x :sepal-width,\n :=x-after-stat :=x,\n :=yaxis-gridcolor \"rgb(255,255,255)\",\n :=type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x-type-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=traces\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=histogram-nbins 10,\n :=stat :com.rpl.specter.impl/NONE,\n :=width :com.rpl.specter.impl/NONE,\n :=color-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=xaxis-gridcolor \"rgb(255,255,255)\",\n :=mark :point,\n :=dataset-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x-title :com.rpl.specter.impl/NONE,\n :=layout\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=y :sepal-length,\n :=x1-after-stat :=x1,\n :=dataset #<WrappedValue@28277720: datasets/iris [10 6]:\n\n| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width | :species |\n|----------:|--------------:|-------------:|--------------:|-------------:|------------|\n| 27 | 5.0 | 3.4 | 1.6 | 0.4 | setosa |\n| 97 | 5.7 | 2.9 | 4.2 | 1.3 | versicolor |\n| 127 | 6.2 | 2.8 | 4.8 | 1.8 | virginica |\n| 92 | 6.1 | 3.0 | 4.6 | 1.4 | versicolor |\n| 7 | 4.6 | 3.4 | 1.4 | 0.3 | setosa |\n| 95 | 5.6 | 2.7 | 4.2 | 1.3 | versicolor |\n| 125 | 6.7 | 3.3 | 5.7 | 2.1 | virginica |\n| 61 | 5.0 | 2.0 | 3.5 | 1.0 | versicolor |\n| 73 | 6.3 | 2.5 | 4.9 | 1.5 | versicolor |\n| 42 | 4.5 | 2.3 | 1.3 | 0.3 | setosa |\n>,\n :=background \"rgb(229,229,229)\",\n :=y0-after-stat :=y0,\n :=y-after-stat :=y,\n :=predictors [:=x],\n :=marker-size-key\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},\n :y0 :=y0-after-stat,\n :inferred-group :=inferred-group,\n :marker-override\n {:color :=mark-color, :=marker-size-key :=mark-size},\n :x :=x-after-stat,\n :x1 :=x1-after-stat,\n :x0 :=x0-after-stat,\n :y-title :=y-title,\n :dataset :=dataset-after-stat}],\n :=x1 :com.rpl.specter.impl/NONE,\n :=title :com.rpl.specter.impl/NONE,\n :=y1 :com.rpl.specter.impl/NONE,\n :=y-type-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=height :com.rpl.specter.impl/NONE,\n :=name :com.rpl.specter.impl/NONE,\n :=mark-opacity :com.rpl.specter.impl/NONE,\n :=inferred-group\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=mode\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=y-title :com.rpl.specter.impl/NONE,\n :=size :com.rpl.specter.impl/NONE,\n :=group :=inferred-group,\n :=y0 :com.rpl.specter.impl/NONE,\n :=mark-size :com.rpl.specter.impl/NONE,\n :=size-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=color :com.rpl.specter.impl/NONE,\n :=mark-color :com.rpl.specter.impl/NONE,\n :=y1-after-stat :=y1,\n :=x :x,\n :=x-after-stat :=x,\n :=yaxis-gridcolor \"rgb(255,255,255)\",\n :=type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x-type-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=traces\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=histogram-nbins 10,\n :=stat :com.rpl.specter.impl/NONE,\n :=width :com.rpl.specter.impl/NONE,\n :=color-type\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=xaxis-gridcolor \"rgb(255,255,255)\",\n :=mark :point,\n :=dataset-after-stat\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=x-title :com.rpl.specter.impl/NONE,\n :=layout\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n :=y :y,\n :=x1-after-stat :=x1,\n :=dataset #<WrappedValue@28277720: datasets/iris [10 6]:\n\n| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width | :species |\n|----------:|--------------:|-------------:|--------------:|-------------:|------------|\n| 27 | 5.0 | 3.4 | 1.6 | 0.4 | setosa |\n| 97 | 5.7 | 2.9 | 4.2 | 1.3 | versicolor |\n| 127 | 6.2 | 2.8 | 4.8 | 1.8 | virginica |\n| 92 | 6.1 | 3.0 | 4.6 | 1.4 | versicolor |\n| 7 | 4.6 | 3.4 | 1.4 | 0.3 | setosa |\n| 95 | 5.6 | 2.7 | 4.2 | 1.3 | versicolor |\n| 125 | 6.7 | 3.3 | 5.7 | 2.1 | virginica |\n| 61 | 5.0 | 2.0 | 3.5 | 1.0 | versicolor |\n| 73 | 6.3 | 2.5 | 4.9 | 1.5 | versicolor |\n| 42 | 4.5 | 2.3 | 1.3 | 0.3 | setosa |\n>,\n :=background \"rgb(229,229,229)\",\n :=y0-after-stat :=y0,\n :=y-after-stat :=y,\n :=predictors [:=x],\n :=marker-size-key\n #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},\n :kindly/f #'scicloj.hanamicloth.v1.plotlycloth/plotly-xform}\n\nThis 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.\n\n(meta example1)\n\n\n#:kindly{:kind :kind/fn}\n\n\n(:kindly/f example1)\n\n\n#'scicloj.hanamicloth.v1.plotlycloth/plotly-xform", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#realizing-the-plot", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#realizing-the-plot", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.4 Realizing the plot", + "text": "4.4 Realizing the plot\nIf 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:\n\n(-> example1\n ploclo/plot\n kind/pprint)\n\n\n{:data\n [{:mode :markers,\n :type :scatter,\n :opacity 0.6,\n :name \"setosa\",\n :x [3.4 3.4 2.3],\n :y [5.0 4.6 4.5],\n :marker {:color \"#1B9E77\", :size 20}}\n {:mode :markers,\n :type :scatter,\n :opacity 0.6,\n :name \"versicolor\",\n :x [2.9 3.0 2.7 2.0 2.5],\n :y [5.7 6.1 5.6 5.0 6.3],\n :marker {:color \"#D95F02\", :size 20}}\n {:mode :markers,\n :type :scatter,\n :opacity 0.6,\n :name \"virginica\",\n :x [2.8 3.3],\n :y [6.2 6.7],\n :marker {:color \"#7570B3\", :size 20}}],\n :layout\n {:width nil,\n :height nil,\n :plot_bgcolor \"rgb(229,229,229)\",\n :xaxis {:gridcolor \"rgb(255,255,255)\", :title :sepal-width},\n :yaxis {:gridcolor \"rgb(255,255,255)\", :title :sepal-length},\n :title nil}}\n\nIt is annotated as kind/plotly, so that visual tools know how to render it.\n\n(-> example1\n ploclo/plot\n meta)\n\n\n#:kindly{:kind :kind/plotly}\n\nThis can be useful if you wish to process the actual Plotly.js spec rather than use Plotlycloth’s API. Let us change the background colour, for example:\n\n(-> example1\n ploclo/plot\n (assoc-in [:layout :plot_bgcolor] \"#eeeedd\"))", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#field-type-inference", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#field-type-inference", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.5 Field type inference", + "text": "4.5 Field type inference\nPlotlycloth infers the type of relevant fields from the data.\nThe example above was colored as it were since :species column was nominal, so it was assigned distinct colours.\nIn the following example, the coloring is by a quantitative column, so a color gradient is used:\n\n(-> datasets/mtcars\n (ploclo/layer-point\n {:=x :mpg\n :=y :disp\n :=color :cyl\n :=mark-size 20}))\n\n\nWe can override the inferred types and thus affect the generated plot:\n\n(-> datasets/mtcars\n (ploclo/layer-point\n {:=x :mpg\n :=y :disp\n :=color :cyl\n :=color-type :nominal\n :=mark-size 20}))", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#more-examples", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#more-examples", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.6 More examples", + "text": "4.6 More examples\n\n4.6.1 Boxplot\n\n(-> datasets/mtcars\n (ploclo/layer-boxplot\n {:=x :cyl\n :=y :disp}))\n\n\n\n\n4.6.2 Segment plot\n\n(-> datasets/iris\n (ploclo/layer-segment\n {:=x0 :sepal-width\n :=y0 :sepal-length\n :=x1 :petal-width\n :=y1 :petal-length\n :=mark-opacity 0.4\n :=mark-size 3\n :=color :species}))", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#varying-color-and-size", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#varying-color-and-size", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.7 Varying color and size", + "text": "4.7 Varying color and size\n\n(-> {:ABCD (range 1 11)\n :EFGH [5 2.5 5 7.5 5 2.5 7.5 4.5 5.5 5]\n :IJKL [:A :A :A :A :A :B :B :B :B :B]\n :MNOP [:C :D :C :D :C :D :C :D :C :D]}\n tc/dataset\n (ploclo/base {:=title \"IJKLMNOP\"})\n (ploclo/layer-point {:=x :ABCD\n :=y :EFGH\n :=color :IJKL\n :=size :MNOP\n :=name \"QRST1\"})\n (ploclo/layer-line\n {:=title \"IJKL MNOP\"\n :=x :ABCD\n :=y :ABCD\n :=name \"QRST2\"\n :=mark-color \"magenta\"\n :=mark-size 20\n :=mark-opacity 0.2})\n ploclo/plot)", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#time-series", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#time-series", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.8 Time series", + "text": "4.8 Time series\nDate and time fields are handle appropriately. Let us, for example, draw the time series of unemployment counts.\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (ploclo/layer-line\n {:=x :date\n :=y :value\n :=mark-color \"purple\"}))", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#multiple-layers", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#multiple-layers", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.9 Multiple layers", + "text": "4.9 Multiple layers\nWe can draw more than one layer:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (ploclo/layer-point {:=x :date\n :=y :value\n :=mark-color \"green\"\n :=mark-size 20\n :=mark-opacity 0.5})\n (ploclo/layer-line {:=x :date\n :=y :value\n :=mark-color \"purple\"}))\n\n\nWe can also use the base function for the common parameters across layers:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (ploclo/base {:=x :date\n :=y :value})\n (ploclo/layer-point {:=mark-color \"green\"\n :=mark-size 20\n :=mark-opacity 0.5})\n (ploclo/layer-line {:=mark-color \"purple\"}))", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#updating-data", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#updating-data", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.10 Updating data", + "text": "4.10 Updating data\nWe can use the update-data function to vary the dataset along a plotting pipeline, affecting the layers that follow.\nThis functionality is inspired by ggbuilder and metamorph.\nHere, for example, we draw a line, then sample 5 data rows, and draw them as points:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (ploclo/base {:=x :date\n :=y :value})\n (ploclo/layer-line {:=mark-color \"purple\"})\n (ploclo/update-data tc/random 5)\n (ploclo/layer-point {:=mark-color \"green\"\n :=mark-size 15\n :=mark-opacity 0.5}))", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#smoothing", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#smoothing", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.11 Smoothing", + "text": "4.11 Smoothing\nlayer-smooth is a layer that applies some statistical processing to the dataset to model it as a smooth shape. It is inspired by ggplot’s geom_smooth.\nAt the moment, it can only be used to model :=y by linear regression. Soon we will add more ways of modelling the data.\n\n(-> datasets/iris\n (ploclo/base {:=title \"dummy\"\n :=mark-color \"green\"\n :=x :sepal-width\n :=y :sepal-length})\n ploclo/layer-point\n (ploclo/layer-smooth {:=mark-color \"orange\"})\n ploclo/plot)\n\n\nBy 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.\n\n(-> datasets/iris\n (ploclo/base {:=x :sepal-width\n :=y :sepal-length})\n ploclo/layer-point\n (ploclo/layer-smooth {:=predictors [:petal-width\n :petal-length]\n :=mark-opacity 0.5})\n ploclo/plot)", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#grouping", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#grouping", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.12 Grouping", + "text": "4.12 Grouping\nThe regression computed by haclo/layer-smooth is affected by the inferred grouping of the data.\nFor example, here we recieve three regression lines, each for every species.\n\n(-> datasets/iris\n (ploclo/base {:=title \"dummy\"\n :=color :species\n :=x :sepal-width\n :=y :sepal-length})\n ploclo/layer-point\n ploclo/layer-smooth)\n\n\nThis happened because the :color field was :species, which is of :nominal type.\nBut we may override this using the :group key. For example, let us avoid grouping:\n\n(-> datasets/iris\n (ploclo/base {:=title \"dummy\"\n :=color :species\n :=group []\n :=x :sepal-width\n :=y :sepal-length})\n ploclo/layer-point\n ploclo/layer-smooth)", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#example-out-of-sample-predictions", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#example-out-of-sample-predictions", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.13 Example: out-of-sample predictions", + "text": "4.13 Example: out-of-sample predictions\nHere is a slighly more elaborate example inpired by the London Clojurians talk mentioned in the preface.\nAssume 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):\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (tc/add-column :relative-time \"Past\")\n (tc/concat (tc/dataset {:date (-> datasets/economics-long\n :date\n last\n (datetime/plus-temporal-amount (range 96) :days))\n :relative-time \"Future\"}))\n (print/print-range 6))\n\n\nggplot2/economics_long [670 6]:\n\n\n\n\n\n\n\n\n\n\n\n:rownames\n:date\n:variable\n:value\n:value01\n:relative-time\n\n\n\n\n2297\n1967-07-01\nunemploy\n2944.0\n0.02044683\nPast\n\n\n2298\n1967-08-01\nunemploy\n2945.0\n0.02052578\nPast\n\n\n2299\n1967-09-01\nunemploy\n2958.0\n0.02155206\nPast\n\n\n…\n…\n…\n…\n…\n…\n\n\n\n2015-07-02\n\n\n\nFuture\n\n\n\n2015-07-03\n\n\n\nFuture\n\n\n\n2015-07-04\n\n\n\nFuture\n\n\n\n2015-07-05\n\n\n\nFuture\n\n\n\n\nLet us represent our dates as numbers, so that we can use them in linear regression:\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (tc/add-column :relative-time \"Past\")\n (tc/concat (tc/dataset {:date (-> datasets/economics-long\n :date\n last\n (datetime/plus-temporal-amount (range 96) :months))\n :relative-time \"Future\"}))\n (tc/add-column :year #(datetime/long-temporal-field :years (:date %)))\n (tc/add-column :month #(datetime/long-temporal-field :months (:date %)))\n (tc/map-columns :yearmonth [:year :month] (fn [y m] (+ m (* 12 y))))\n (print/print-range 6))\n\n\nggplot2/economics_long [670 9]:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n:rownames\n:date\n:variable\n:value\n:value01\n:relative-time\n:year\n:month\n:yearmonth\n\n\n\n\n2297\n1967-07-01\nunemploy\n2944.0\n0.02044683\nPast\n1967\n7\n23611\n\n\n2298\n1967-08-01\nunemploy\n2945.0\n0.02052578\nPast\n1967\n8\n23612\n\n\n2299\n1967-09-01\nunemploy\n2958.0\n0.02155206\nPast\n1967\n9\n23613\n\n\n…\n…\n…\n…\n…\n…\n…\n…\n…\n\n\n\n2022-12-01\n\n\n\nFuture\n2022\n12\n24276\n\n\n\n2023-01-01\n\n\n\nFuture\n2023\n1\n24277\n\n\n\n2023-02-01\n\n\n\nFuture\n2023\n2\n24278\n\n\n\n2023-03-01\n\n\n\nFuture\n2023\n3\n24279\n\n\n\n\nLet 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.\n\n(-> datasets/economics-long\n (tc/select-rows #(-> % :variable (= \"unemploy\")))\n (tc/add-column :relative-time \"Past\")\n (tc/concat (tc/dataset {:date (-> datasets/economics-long\n :date\n last\n (datetime/plus-temporal-amount (range 96) :months))\n :relative-time \"Future\"}))\n (tc/add-column :year #(datetime/long-temporal-field :years (:date %)))\n (tc/add-column :month #(datetime/long-temporal-field :months (:date %)))\n (tc/map-columns :yearmonth [:year :month] (fn [y m] (+ m (* 12 y))))\n (ploclo/base {:=x :date\n :=y :value})\n (ploclo/layer-smooth {:=color :relative-time\n :=mark-size 20\n :=group []\n :=predictors [:yearmonth]})\n ;; Keep only the past for the following layer:\n (ploclo/update-data (fn [dataset]\n (-> dataset\n (tc/select-rows (fn [row]\n (-> row :relative-time (= \"Past\")))))))\n (ploclo/layer-line {:=mark-color \"purple\"\n :=mark-size 3}))", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] + }, + { + "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#histograms", + "href": "hanamicloth_book.plotlycloth_walkthrough.html#histograms", + "title": "4  Plotlycloth Walkthrough (experimental 🛠)", + "section": "4.14 Histograms", + "text": "4.14 Histograms\nHistograms can also be represented as layers with statistical processing:\n\n(-> datasets/iris\n (ploclo/layer-histogram {:=x :sepal-width}))\n\n\n\n(-> datasets/iris\n (ploclo/layer-histogram {:=x :sepal-width\n :=histogram-nbins 30}))\n\n\n\nsource: notebooks/hanamicloth_book/plotlycloth_walkthrough.clj", + "crumbs": [ + "4  Plotlycloth Walkthrough (experimental 🛠)" + ] } ] \ No newline at end of file