Visual data summaries
Can we plot interesting charts for all columns of a dataset?
Keywords
datavis
When exploring a new dataset, we face an immediate challenge: How do we quickly understand the structure and distribution of all our columns?
This notebook explores a “show everything” approach:
- Visual summaries for each column (distributions, categories)
- Summary statistics paired with visualizations
- A scatterplot-matrix-like view showing all column combinations
The goal is to enable rapid visual discovery of patterns and relationships.
Starting with a Complete Dataset
Let’s load a well-known dataset and explore how to present its columns effectively:
(def penguins
(tc/drop-missing (rdatasets/palmerpenguins-penguins)))Option 1: Print the data
We could just print the first few rows, but that only shows a small sample:
penguinshttps://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv [333 9]:
| :rownames | :species | :island | :bill-length-mm | :bill-depth-mm | :flipper-length-mm | :body-mass-g | :sex | :year |
|---|---|---|---|---|---|---|---|---|
| 1 | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 |
| 2 | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 |
| 3 | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 |
| 5 | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 |
| 6 | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 |
| 7 | Adelie | Torgersen | 38.9 | 17.8 | 181 | 3625 | female | 2007 |
| 8 | Adelie | Torgersen | 39.2 | 19.6 | 195 | 4675 | male | 2007 |
| 13 | Adelie | Torgersen | 41.1 | 17.6 | 182 | 3200 | female | 2007 |
| 14 | Adelie | Torgersen | 38.6 | 21.2 | 191 | 3800 | male | 2007 |
| 15 | Adelie | Torgersen | 34.6 | 21.1 | 198 | 4400 | male | 2007 |
| … | … | … | … | … | … | … | … | … |
| 334 | Chinstrap | Dream | 49.3 | 19.9 | 203 | 4050 | male | 2009 |
| 335 | Chinstrap | Dream | 50.2 | 18.8 | 202 | 3800 | male | 2009 |
| 336 | Chinstrap | Dream | 45.6 | 19.4 | 194 | 3525 | female | 2009 |
| 337 | Chinstrap | Dream | 51.9 | 19.5 | 206 | 3950 | male | 2009 |
| 338 | Chinstrap | Dream | 46.8 | 16.5 | 189 | 3650 | female | 2009 |
| 339 | Chinstrap | Dream | 45.7 | 17.0 | 195 | 3650 | female | 2009 |
| 340 | Chinstrap | Dream | 55.8 | 19.8 | 207 | 4000 | male | 2009 |
| 341 | Chinstrap | Dream | 43.5 | 18.1 | 202 | 3400 | female | 2009 |
| 342 | Chinstrap | Dream | 49.6 | 18.2 | 193 | 3775 | male | 2009 |
| 343 | Chinstrap | Dream | 50.8 | 19.0 | 210 | 4100 | male | 2009 |
| 344 | Chinstrap | Dream | 50.2 | 18.7 | 198 | 3775 | female | 2009 |
Option 2: Summary statistics
We could compute statistics for a single column:
(fms/stats-map (:bill-length-mm penguins)){:MAD 4.700000000000003,
:UOF 76.40000000000003,
:Skewness 0.045340470420402026,
:Max 59.6,
:Variance 29.906333441875624,
:Size 333,
:LAV 32.1,
:UIF 62.52500000000002,
:Mode 41.1,
:Mean 43.99279279279281,
:Q1 39.4,
:Q3 48.650000000000006,
:Min 32.1,
:LIF 25.524999999999988,
:Range 27.5,
:Total 14649.600000000006,
:SD 5.468668342647561,
:IQR 9.250000000000007,
:Outliers (),
:UAV 59.6,
:LOF 11.649999999999977,
:SEM 0.29968117914670855,
:Kurtosis -0.8834182330572031,
:Median 44.5}But this requires mental effort to visualize what the numbers mean.
Option 3: Visual summaries
What if we automatically plot the distribution of every column? This lets us see patterns at a glance.
Visualization inference
(def plot-width 100)(def plot-height 100)Type Classification and Plot Selection
We use tablecloth’s type system to classify columns and make intelligent visualization choices based on their semantic role in the data:
- Quantitative: numerical data with meaningful magnitude and order
- Temporal: datetime/time data (often paired with quantitative for time series)
- Categorical: textual or logical data with discrete values
- Identity/Index: all values unique or nearly unique (IDs, timestamps, etc.)
(defn column-general-type
"Returns the general type category of a column: :quantitative, :temporal, :categorical, or :identity."
[col]
(cond
(tcc/typeof? col :numerical) :quantitative
(tcc/typeof? col :datetime) :temporal
(tcc/typeof? col :logical) :categorical
(tcc/typeof? col :textual) :categorical
:else :identity))(defn cardinality
"Count of unique non-missing values in a column."
[col]
(let [values (tcc/drop-missing col)]
(count (set values))))(defn is-identity-column?
"Returns true if column appears to be an identity/index (all or nearly all unique values)."
[col]
(let [values (tcc/drop-missing col)
n (count values)]
(>= (cardinality col) (* 0.95 n))))95%+ unique values
(defn is-numeric-type?
"Convenience function: returns true for quantitative columns."
[col]
(= :quantitative (column-general-type col)))(defn get-numeric-domain
"Get min and max of numeric column for scaling."
[col]
(let [values (remove nil? (tcc/drop-missing col))]
(when (seq values)
{:min (apply min values)
:max (apply max values)})))(defn scale-value
"Scale a value from domain to plot range."
[value domain plot-min plot-max]
(when (and value domain)
(let [{:keys [min max]} domain
range (- max min)]
(if (zero? range)
(/ (+ plot-min plot-max) 2)
(+ plot-min (* (/ (- value min) range) (- plot-max plot-min)))))))(defn plot-basic [g]
(let [{:keys [data mappings geometry]} (g 1)
{:keys [x y]} mappings
x-col (data x)
y-col (data y)
x-domain (when (tcc/typeof? x-col :numerical) (get-numeric-domain x-col))
y-domain (when (tcc/typeof? y-col :numerical) (get-numeric-domain y-col))]
(for [geom geometry]
(case geom
:bar (let [x-vals (remove nil? (data x))
categories (distinct x-vals)
counts (frequencies x-vals)
max-count (when (seq counts) (apply max (vals counts)))
bar-width (/ plot-width (count categories))]
(when max-count
(for [[i cat] (map-indexed vector categories)]
(let [count (get counts cat 0)
bar-height (* (/ count max-count) plot-height)]
[:rect {:x (* i bar-width)
:y (- plot-height bar-height)
:width bar-width
:height bar-height
:fill "lightblue"
:stroke "gray"
:stroke-width 0.5}]))))
:heatmap (let [x-vals (remove nil? (data x))
y-vals (remove nil? (data y))
x-cats (distinct x-vals)
y-cats (distinct y-vals)
x-count (count x-cats)
y-count (count y-cats)
;; Build contingency table
contingency (reduce (fn [acc i]
(assoc-in acc [(data y i) (data x i)]
(inc (get-in acc [(data y i) (data x i)] 0))))
{} (range (tc/row-count data)))
all-counts (for [y-cat y-cats x-cat x-cats] (get-in contingency [y-cat x-cat] 0))
max-count (when (seq all-counts) (apply max all-counts))
cell-width (/ plot-width x-count)
cell-height (/ plot-height y-count)]
(when (and max-count (> max-count 0))
(for [[y-idx y-cat] (map-indexed vector y-cats)
[x-idx x-cat] (map-indexed vector x-cats)]
(let [count (get-in contingency [y-cat x-cat] 0)
intensity (/ count max-count)
color (str "rgba(70, 130, 180, " intensity ")")]
[:rect {:x (* x-idx cell-width)
:y (* y-idx cell-height)
:width cell-width
:height cell-height
:fill color
:stroke "gray"
:stroke-width 0.5}]))))
:histogram (let [values (remove nil? (data x))
hist-result (when (seq values) (fms/histogram values))
bins (:bins-maps hist-result)]
(when (seq bins)
(let [max-count (apply max (map :count bins))
bin-width (/ plot-width (count bins))]
(for [[i bin] (map-indexed vector bins)]
(let [bar-height (* (/ (:count bin) max-count) plot-height)]
[:rect {:x (* i bin-width)
:y (- plot-height bar-height)
:width bin-width
:height bar-height
:fill "lightblue"
:stroke "gray"
:stroke-width 0.5}])))))
:point (let [rows (tc/rows data :as-maps)
xys (mapv (juxt x y) rows)]
(for [[x-val y-val] xys]
(when (and x-val y-val x-domain y-domain)
(let [cx (scale-value (double x-val) x-domain 5 (- plot-width 5))
cy (scale-value (double y-val) y-domain (- plot-height 5) 5)]
(when (and cx cy)
[:circle {:r 2, :cx cx, :cy cy, :fill "lightblue" :stroke "blue" :stroke-width 0.5}])))))
:line (let [rows (tc/rows data :as-maps)
xys (mapv (juxt #(get % x) #(get % y)) rows)
scaled-xys (for [[x-val y-val] xys]
(when (and x-val y-val x-domain y-domain)
[(scale-value (double x-val) x-domain 5 (- plot-width 5))
(scale-value (double y-val) y-domain (- plot-height 5) 5)]))
valid-xys (remove nil? scaled-xys)]
(when (seq valid-xys)
[:path {:d (str "M " (str/join ","
(first valid-xys))
" L " (str/join " "
(map #(str/join "," %)
(rest valid-xys))))
:stroke "lightblue"
:fill "none"
:stroke-width 0.5}]))
:identity (let [values (remove nil? (data x))
unique-vals (distinct values)
n (count unique-vals)
point-spacing (/ plot-width n)]
(for [[i val] (map-indexed vector unique-vals)]
[:circle {:r 1.5
:cx (* (+ i 0.5) point-spacing)
:cy (/ plot-height 2)
:fill "lightgray"}]))))))(defn plot-distribution [ds column geom]
^:kind/hiccup
[:svg {:width 100
:viewBox (str/join " " [0 0 plot-width plot-height])
:xmlns "http://www.w3.org/2000/svg"
:style {:border "solid 1px gray"}}
[:g {:stroke "gray", :fill "none"}
(plot-basic [:graphic {:data ds
:mappings {:x column}
:geometry geom}])]])(plot-distribution penguins :bill-length-mm [:histogram])Geometry Selection
Determines what type of chart works best for different data patterns
(defn select-geometry-single
"Select visualization geometry for a single column based on its type and cardinality."
[col]
(let [general-type (column-general-type col)
card (cardinality col)
n (count (tcc/drop-missing col))]
(cond
;; All/nearly all unique values → show domain, not distribution
(is-identity-column? col) :identity
;; Quantitative → histogram shows distribution
(= :quantitative general-type) :histogram
;; Temporal → histogram of counts also works
(= :temporal general-type) :histogram
;; Categorical → bar chart shows frequencies
(= :categorical general-type) :bar
:else :bar)))(defn select-geometry-pair
"Select visualization geometry for a pair of columns based on their types."
[col-a col-b]
(let [type-a (column-general-type col-a)
type-b (column-general-type col-b)]
(cond
;; Same column (diagonal) → sparse identity plot
(= col-a col-b) :identity
;; Quantitative × Quantitative → scatter plot reveals correlation
(and (= :quantitative type-a) (= :quantitative type-b)) :point
;; Temporal × Quantitative → line chart shows time series
(and (= :temporal type-a) (= :quantitative type-b)) :line
(and (= :quantitative type-a) (= :temporal type-b)) :line
;; Categorical × Categorical → heatmap shows contingency
(and (= :categorical type-a) (= :categorical type-b)) :heatmap
;; Categorical × Anything else → bar chart (show distribution by category)
(or (= :categorical type-a) (= :categorical type-b)) :bar
;; Fallback
:else :bar)))Pair test
(defn plot-pair
"Visualization for a pair of columns with automatic geometry selection."
[ds column-a column-b]
^:kind/hiccup
[:svg {:width 100
:viewBox (str/join " " [0 0 plot-width plot-height])
:xmlns "http://www.w3.org/2000/svg"
:style {:border "solid 1px gray"}}
[:g {:stroke "gray", :fill "none"}
(plot-basic [:graphic {:data ds
:mappings {:x column-a, :y column-b}
:geometry [(select-geometry-pair (ds column-a) (ds column-b))]}])]])(plot-pair penguins :bill-length-mm :bill-depth-mm)Single Column Summaries
The summarize function automatically selects the right visualization type:
- Quantitative columns → histogram (shows distribution shape)
- Temporal columns → histogram (shows frequency distribution)
- Categorical columns → bar chart (shows frequencies)
- Identity columns → sparse plot (shows all unique values)
(defn summarize
"Generate a single-column visualization with appropriate geometry."
[ds column]
(let [col (ds column)
geom (select-geometry-single col)]
(plot-distribution ds column [geom])))Companion function: provides numeric summaries alongside visualizations Shows count, mean, standard deviation, min/max for quantitative data Shows count and cardinality for categorical and identity data
(defn get-summary-stats
"Generate summary statistics appropriate to the column's type."
[ds column]
(let [col (ds column)
general-type (column-general-type col)]
(if (= :quantitative general-type)
(let [stats (tcc/descriptive-statistics col)]
(format "n: %d, μ: %.2f, σ: %.2f, min: %.2f, max: %.2f"
(:n-elems stats)
(:mean stats)
(:standard-deviation stats)
(:min stats)
(:max stats)))
(let [values (tcc/drop-missing col)
card (cardinality col)]
(str "n: " (count values) ", card: " card)))))Summary Table: All Columns at a Glance
Combines visualization + statistics for every column. This gives us a complete overview of the dataset’s structure.
(defn visual-summary [ds]
(kind/table
(doall (for [column-name (tc/column-names ds)]
[column-name (summarize ds column-name) (get-summary-stats ds column-name)]))))(defn visual-summary-grid
"Grid layout with columns as vertical strips, each showing name + viz + stats."
[ds]
^:kind/hiccup
[:div {:style {:display "grid"
:grid-template-columns "repeat(auto-fit, minmax(150px, 1fr))"
:gap "10px"
:padding "10px"}}
(doall (for [column-name (tc/column-names ds)]
[:div {:style {:border "1px solid #ddd"
:padding "10px"
:text-align "center"}}
[:h4 {:style {:margin "0 0 10px 0"
:font-size "14px"}} (name column-name)]
(summarize ds column-name)
[:div {:style {:margin-top "10px"
:font-size "12px"
:color "#666"}}
(get-summary-stats ds column-name)]]))])(defn visual-summary-cards
"Bootstrap card layout with each column as a card in a responsive grid."
[ds]
^:kind/hiccup
[:div {:class "container-fluid"}
[:div {:class "row"}
(doall (for [column-name (tc/column-names ds)]
[:div {:class "col-md-4 col-lg-3 mb-3"}
[:div {:class "card h-100"}
[:div {:class "card-header"}
[:h5 {:class "card-title mb-0"} (name column-name)]]
[:div {:class "card-body text-center d-flex flex-column justify-content-center"}
(summarize ds column-name)]
[:div {:class "card-footer mt-auto"}
[:small {:class "text-muted"}
(get-summary-stats ds column-name)]]]]))]])(defn visual-summary-rows
"Row-based layout with each column getting a full-width row."
[ds]
^:kind/hiccup
[:div {:style {:max-width "800px"
:margin "0 auto"}}
(doall (for [column-name (tc/column-names ds)]
[:div {:style {:border "1px solid #ddd"
:margin-bottom "20px"
:padding "15px"
:border-radius "5px"}}
[:div {:style {:display "flex"
:align-items "center"
:gap "20px"}}
[:div {:style {:flex "1 1 auto"}}
[:h4 {:style {:margin "0"}} (name column-name)]
[:div {:style {:margin-top "5px"
:font-size "12px"
:color "#666"}}
(get-summary-stats ds column-name)]]
[:div {:style {:flex "0 0 auto"
:text-align "right"}}
(summarize ds column-name)]]]))])(visual-summary penguins)| rownames | n: 333, μ: 174.32, σ: 98.39, min: 1.00, max: 344.00 | |
| species | n: 333, card: 3 | |
| island | n: 333, card: 3 | |
| bill-length-mm | n: 333, μ: 43.99, σ: 5.47, min: 32.10, max: 59.60 | |
| bill-depth-mm | n: 333, μ: 17.16, σ: 1.97, min: 13.10, max: 21.50 | |
| flipper-length-mm | n: 333, μ: 200.97, σ: 14.02, min: 172.00, max: 231.00 | |
| body-mass-g | n: 333, μ: 4207.06, σ: 805.22, min: 2700.00, max: 6300.00 | |
| sex | n: 333, card: 2 | |
| year | n: 333, μ: 2008.04, σ: 0.81, min: 2007.00, max: 2009.00 |
(visual-summary-grid penguins)rownames
n: 333, μ: 174.32, σ: 98.39, min: 1.00, max: 344.00
species
n: 333, card: 3
island
n: 333, card: 3
bill-length-mm
n: 333, μ: 43.99, σ: 5.47, min: 32.10, max: 59.60
bill-depth-mm
n: 333, μ: 17.16, σ: 1.97, min: 13.10, max: 21.50
flipper-length-mm
n: 333, μ: 200.97, σ: 14.02, min: 172.00, max: 231.00
body-mass-g
n: 333, μ: 4207.06, σ: 805.22, min: 2700.00, max: 6300.00
sex
n: 333, card: 2
year
n: 333, μ: 2008.04, σ: 0.81, min: 2007.00, max: 2009.00
(visual-summary-cards penguins)rownames
species
island
bill-length-mm
bill-depth-mm
flipper-length-mm
body-mass-g
sex
year
(visual-summary-rows penguins)rownames
n: 333, μ: 174.32, σ: 98.39, min: 1.00, max: 344.00
species
n: 333, card: 3
island
n: 333, card: 3
bill-length-mm
n: 333, μ: 43.99, σ: 5.47, min: 32.10, max: 59.60
bill-depth-mm
n: 333, μ: 17.16, σ: 1.97, min: 13.10, max: 21.50
flipper-length-mm
n: 333, μ: 200.97, σ: 14.02, min: 172.00, max: 231.00
body-mass-g
n: 333, μ: 4207.06, σ: 805.22, min: 2700.00, max: 6300.00
sex
n: 333, card: 2
year
n: 333, μ: 2008.04, σ: 0.81, min: 2007.00, max: 2009.00
Matrix View: All Column Combinations
The next step: instead of showing each column separately, what if we show how every column relates to every other column? This is the idea behind the scatterplot matrix.
The matrix automatically chooses the right chart for each combination:
- Quantitative × Quantitative → scatter plot (reveal correlations)
- Temporal × Quantitative → line chart (show time series)
- Categorical × Anything → bar chart (show distribution by category)
- Single column (diagonal) → histogram/bar based on type
(defn matrix
"Create a scatterplot-matrix-style view of all column combinations.
Each cell uses an appropriate visualization based on the column types."
[ds]
(let [column-names (tc/column-names ds)
c (count column-names)]
^:kind/hiccup
[:svg {:width "100%"
:viewBox (str/join " " [0 0 (* plot-width c) (* plot-height c)])
:xmlns "http://www.w3.org/2000/svg"
:style {:border "solid 1px gray"}}
[:g {:stroke "gray", :fill "none"}
(for [[a-idx a] (map-indexed vector column-names)
[b-idx b] (map-indexed vector column-names)]
(let [col-a (ds a)
col-b (ds b)
geom (select-geometry-pair col-a col-b)]
[:g {:transform (str "translate(" (* a-idx plot-width) "," (* b-idx plot-height) ")")}
[:rect {:x 0 :y 0 :width plot-width :height plot-height
:fill "none" :stroke "gray" :stroke-width 1}]
(plot-basic [:graphic {:data ds
:mappings {:x a :y b}
:geometry [geom]}])]))]]))(matrix penguins)(defn labeled-matrix
"Create a scatterplot-matrix-style view with row/column labels.
Each cell uses an appropriate visualization based on the column types."
[ds]
(let [column-names (tc/column-names ds)
c (count column-names)
label-width 80
cell-size plot-width]
^:kind/hiccup
[:svg {:width "100%"
:viewBox (str/join " " [0 0 (+ label-width (* cell-size c)) (+ label-width (* cell-size c))])
:xmlns "http://www.w3.org/2000/svg"
:style {:border "solid 1px gray"}}
[:g
;; Column labels (top)
(for [[idx col-name] (map-indexed vector column-names)]
[:g {:key (str "col-label-" idx)}
[:text {:x (+ label-width (* idx cell-size) (/ cell-size 2))
:y 20
:text-anchor "middle"
:font-size "12"
:fill "#333"} (name col-name)]])
;; Row labels (left)
(for [[idx row-name] (map-indexed vector column-names)]
[:g {:key (str "row-label-" idx)}
[:text {:x 10
:y (+ label-width (* idx cell-size) (/ cell-size 2) 4)
:font-size "12"
:fill "#333"} (name row-name)]])
;; Grid cells
(for [[a-idx a] (map-indexed vector column-names)
[b-idx b] (map-indexed vector column-names)]
(let [col-a (ds a)
col-b (ds b)
geom (select-geometry-pair col-a col-b)]
[:g {:key (str "cell-" a-idx "-" b-idx)
:transform (str "translate(" (+ label-width (* a-idx cell-size)) "," (+ label-width (* b-idx cell-size)) ")")}
[:rect {:x 0 :y 0 :width cell-size :height cell-size
:fill "none" :stroke "gray" :stroke-width 1}]
(plot-basic [:graphic {:data ds
:mappings {:x a :y b}
:geometry [geom]}])]))]]))(labeled-matrix penguins)