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Clojure data manipulation DSL — composable query syntax built on tech.ml.dataset

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# Datajure v2

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**One function. Seven keywords. Two expression modes.**

Datajure is a Clojure data manipulation library built on [tech.ml.dataset](https://github.com/techascent/tech.ml.dataset). It provides a clean, composable query DSL for filtering, transforming, grouping, and aggregating tabular data.

```clojure
(require '[datajure.core :refer [dt nrow asc desc]])

;; Filter, group, aggregate — one call
(dt ds
  :where #dt/e (> :year 2008)
  :by [:species]
  :agg {:n nrow :avg #dt/e (mn :mass)})

;; Window functions — same keywords
(dt ds
  :by [:species]
  :within-order [(desc :mass)]
  :set {:rank #dt/e (win/rank :mass)})

;; Thread for multi-step pipelines
(-> ds
    (dt :set {:bmi #dt/e (/ :mass (sq :height))})
    (dt :by [:species] :agg {:avg-bmi #dt/e (mn :bmi)})
    (dt :order-by [(desc :avg-bmi)]))
```

Datajure is a **syntax layer**, not an engine — it compiles `#dt/e` expressions to vectorized operations and delegates all computation to `tech.v3.dataset`. Every result is a standard dataset. Full interop with tablecloth, Clerk, Clay, and the Scicloj ecosystem.

## Installation

Add to your `deps.edn`:

```clojure
{:deps {com.github.clojure-finance/datajure {:mvn/version "2.7.3"}}}
```

Datajure requires Clojure 1.12+ and Java 21+.

## Why Datajure

Datajure takes inspiration from whichever library got a given idea right:

- **R's `data.table`** — `DT[i, j, by]` maps onto `(dt ds :where i :set-or-agg j :by by)`. Immutable (no in-place mutation).
- **Python's Polars** — expressions as first-class values, nil-safe comparisons and arithmetic.
- **q/kdb+** — `win/*` gives you `deltas`, `ratios`, `mavg`, `msum`, `mdev`, `ema`, `fills`, `scan`, `grr`, plus `wavg`, `wsum`, `first-val`, `last-val`. `xbar` for time-series bars. As-of and window joins built in.
- **Julia's `DataFramesMeta.jl`** — `#dt/e` serves the same role as `@transform`/`@subset`; Clojure's reader tag mechanism makes it integrate naturally.

The distinctive feature: `#dt/e` expressions are first-class AST values. Store them in vars, compose across queries, build a shared vocabulary:

```clojure
(def ret     #dt/e (- (win/ratio :price) 1))
(def log-ret #dt/e (log (+ 1 ret)))
(def vol-20d #dt/e (win/mdev ret 20))
(def wealth  #dt/e (win/scan * (+ 1 ret)))

(dt prices :by [:permno] :within-order [(asc :date)]
    :set {:ret ret :log-ret log-ret :vol-20d vol-20d :wealth wealth})
```

## The Key Insight: `:by` × `:set`/`:agg`

Two orthogonal keywords produce four distinct operations with no new concepts:

|            | No `:by`            | With `:by`          |
|------------|---------------------|---------------------|
| **`:set`** | Column derivation (+ whole-dataset window if `win/*` present) | **Partitioned window** |
| **`:agg`** | Whole-table summary | Group aggregation   |

```clojure
;; Column derivation — add/update columns, keep all rows
(dt ds :set {:bmi #dt/e (/ :mass (sq :height))})

;; Group aggregation — collapse rows per group
(dt ds :by [:species] :agg {:n nrow :avg-mass #dt/e (mn :mass)})

;; Whole-table summary — collapse everything
(dt ds :agg {:total #dt/e (sm :mass) :n nrow})

;; Partitioned window — compute within groups, keep all rows
(dt ds
  :by [:species]
  :within-order [(desc :mass)]
  :set {:rank #dt/e (win/rank :mass)
        :cumul #dt/e (win/cumsum :mass)})

;; Whole-dataset window — no :by, entire dataset is one partition
(dt ds
  :within-order [(asc :date)]
  :set {:cumret #dt/e (win/cumsum :ret)
        :prev   #dt/e (win/lag :price 1)})
```

A window-mode `:set` **preserves the input row order** — with or without `:by` (2.7.0). `:within-order` sets only the *computation* order (so `win/lag`/`win/cumsum`/… walk by, say, date within each partition), and the derived values are scattered back to their original rows. Use `:order-by` if you want sorted output.

`:within-order` also combines with `:agg`, sorting rows within each group before the aggregation runs. This is the one-call OHLC pattern and the reason `first-val` / `last-val` are first-class helpers:

```clojure
(dt trades
    :by [:sym :date]
    :within-order [(asc :time)]
    :agg {:open  #dt/e (first-val :price)
          :close #dt/e (last-val :price)
          :hi    #dt/e (mx :price)
          :vol   #dt/e (sm :size)})

;; VWAP and weighted sum
(dt trades :by [:sym :date]
    :agg {:vwap #dt/e (wavg :size :price)
          :vol  #dt/e (wsum :size :price)})
```

## `dt` Dispatch Modes

`dt` runs a single fixed evaluation order: `:where` → `:set`-or-`:agg` → `:select` → `:order-by` → `:take`. What the middle step does depends on which other keywords are present:

| `:by`  | `:set`  | `:agg`  | `:within-order` | Mode                                                    |
|--------|---------|---------|-----------------|---------------------------------------------------------|
| —      | plain   | —       | —               | Derive columns over whole dataset                       |
| —      | `win/*` | —       | optional        | Whole-dataset window                                    |
| ✓      | plain   | —       | optional        | Per-group derivation                                    |
| ✓      | `win/*` | —       | optional        | Partitioned window                                      |
| —      | —       | ✓       | optional        | Whole-table aggregate (sorted first if `:within-order`) |
| ✓      | —       | ✓       | optional        | Group aggregate (sorted within group if `:within-order`)|

Disallowed: `:set` and `:agg` in the same call (use `->` threading); `:within-order` without `:set` or `:agg`. Unknown query keywords throw a structured error with a typo suggestion (`:wehre -> :where`) rather than being silently ignored.

`dt` also accepts its whole query as a **single map** — `(dt ds {:where [:> :mass 4000] :by [:species] :agg {:n [:nrow]}})` — equivalent to the kwargs form. Combined with data-form expressions (next sections), a query is pure EDN: storable, mergeable, and built programmatically without `apply` gymnastics.

## Expression Mode: `#dt/e`

`#dt/e` is a reader tag that rewrites bare keywords to column accessors. It returns an AST object that `dt` interprets — vectorized, pre-validated, and nil-literal-safe.

```clojure
;; With #dt/e — terse, keyword-lifted, vectorized
(dt ds :where #dt/e (> :mass 4000))
(dt ds :set {:bmi #dt/e (/ :mass (sq :height))})

;; Without — plain Clojure functions (always works)
(dt ds :where #(> (:mass %) 4000))
(dt ds :set {:bmi #(/ (:mass %) (Math/pow (:height %) 2))})
```

`#dt/e` is opt-in. Users who prefer plain Clojure functions can ignore it entirely. See *Expression Mode vs. Plain Functions* below for when to pick which.

### Nil handling

Datajure has a layered nil story rather than blanket "nil-safety". The rules:

| Situation                                             | Behaviour                |
|-------------------------------------------------------|--------------------------|
| Comparison op with a nil *literal* in `#dt/e`         | evaluates to `false` (nil is unambiguous in a predicate) |
| Arithmetic op with a nil *literal* in `#dt/e`         | clear read-time error — use `coalesce`/`div0` to handle nils explicitly |
| Column-level nils (nil values within a column)        | depends on the `dfn` op  |
| Aggregation helpers (`mn`/`sm`/`md`/`sd`/`nrow`/...)  | skip nil; `nil` if all missing (never `0`/`-Inf`/`NaN`) |
| `win/fills :col`                                      | forward-fill nils        |
| `coalesce :col default`                               | replace nils with fallback (first non-nil) |
| `coalesce-finite :col default` (alias `coalescef`)    | first **finite** value — also skips `NaN`/`±Inf` |
| `div0 num den`                                        | `nil` if denominator is `nil` or zero |
| `win/ratio :col`                                      | `nil` if previous value is `nil` or zero |
| `when-finite :g body`                                 | body where `:g` is finite; `nil` where it is nil/`NaN`/`±Inf` (2.7.0) |
| `year`/`month`/`day`/`dow`/`quarter` on a missing date | `nil` (compares `false` in predicates; stored by `:set` as a missing slot) |
| Plain Clojure functions                               | **not** automatic; wrap with `pass-nil` |

```clojure
(dt ds :where #dt/e (> :mass 4000))                  ;; nil-literal → false
(dt ds :set {:mass #dt/e (coalesce :mass 0)})         ;; nil → 0
(dt ds :set {:pe   #dt/e (div0 :price :earnings)})    ;; zero denom → nil
(dt ds :set {:pe   #(div0 (:price %) (:earnings %))})  ;; div0 is also a plain fn
(dt ds :set {:x (pass-nil #(parse-int (:x-str %)) :x-str)})  ;; nil if :x-str is nil
```

`div0` works both inside `#dt/e` and as a plain function, so it's usable in plain-fn `:set`/`:agg` and computed `:by`.

**`when-finite` — the NA-propagating guard (2.7.0).** A bare comparison collapses a nil operand to `false` *before* `if` sees it, so `#dt/e (if (>= :g 0) 1.0 0.0)` yields `0.0` at missing rows — R's `ifelse(NA → NA)` was inexpressible. `when-finite` guards on the *input*: the body value where the guard expression is finite, missing where it is nil/`NaN`/`±Inf`. This is the Piotroski-style binary-indicator primitive:

```clojure
;; 1/0 indicator that stays missing where the input is missing
(dt ds :set {:P3 #dt/e (when-finite :G.R4.ROA (if (>= :G.R4.ROA 0) 1.0 0.0))})

;; computed guard via let (indicator over a derived quantity)
(dt ds :set {:P4 #dt/e (let [d (- :R4.oancfq :R4.ibq)]
                         (when-finite d (if (> d 0) 1.0 0.0)))})
```

### Special forms

```clojure
;; Multi-branch conditional
(dt ds :set {:size #dt/e (cond
                           (> :mass 5000) "large"
                           (> :mass 3500) "medium"
                           :else "small")})

;; Local bindings
(dt ds :set {:adj #dt/e (let [bmi (/ :mass (sq :height))
                              base (if (> :year 2010) 1.1 1.0)]
                          (* base bmi))})

;; Boolean composition, membership, range
(dt ds :where #dt/e (and (> :mass 4000) (not (= :species "Adelie"))))
(dt ds :where #dt/e (in :species #{"Gentoo" "Chinstrap"}))
(dt ds :where #dt/e (between? :year 2007 2009))
```

### Reusable expressions

As shown in the "Why Datajure" section, `#dt/e` expressions are first-class AST values — store in vars, compose across queries:

```clojure
(def bmi   #dt/e (/ :mass (sq :height)))
(def obese #dt/e (> bmi 30))              ;; composition — bmi spliced in

(dt ds :set {:bmi bmi})
(dt ds :by [:species] :agg {:avg-bmi #dt/e (mn bmi)})
```

The mechanism: `#dt/e` returns an AST map, `(def ...)` captures it. When the symbol appears inside another `#dt/e`, Clojure evaluates it to its AST value before the outer reader sees it, and the compiler splices it in.

### Expression Mode vs. Plain Functions

|                       | `#dt/e` (column-wise)                  | Plain function (context-dependent)     |
|-----------------------|----------------------------------------|----------------------------------------|
| Operates on           | Whole column vectors via `dfn`         | Row map in `:set`/`:where`; group dataset in `:agg` |
| Column access         | Bare keywords: `:mass`                 | `(:mass %)`                            |
| Performance           | Fast — vectorized                      | Slower — per-row call in `:set`/`:where` |
| Nil handling          | Automatic (for literals and helpers)   | Manual (`pass-nil` or explicit checks) |
| Validation            | Pre-execution column checking; Damerau suggestions | Runtime errors only          |
| Best for              | Arithmetic, comparisons, aggregations  | Complex branching, Java interop, non-vectorizable logic |

Prefer `#dt/e` by default. Fall back to plain functions when the computation doesn't map to vectorized ops.

**Footgun to know about in `:agg`:** plain functions receive the *group dataset*, not a row, so `(:mass %)` returns a column vector rather than a scalar. Datajure detects this and throws a structured error since v2.0.6 — but this is why `#dt/e (mn :mass)` is safer than `#(mean (:mass %))`.

### Runtime values & programmatic queries — the data-form

`#dt/e` is a *read-time* reader tag, so it can't see a runtime local: you can't write `#dt/e (= :tic ticker)` and have `ticker` resolve. The escape isn't a plain-fn `:where` (that builds a row map per row — slow on a wide dataset). Instead, `:where`, `:agg`, and `:set` accept a **data-form vector**: a keyword is a column, a keyword-headed vector is an operation `[op-kw & args]` (a number-headed vector like `[0.2 0.5 0.8]` is a literal), anything else is a literal value, so runtime values flow straight in. It desugars to the same AST `#dt/e` compiles — same vectorized `dfn` path, no row map.

Since 2.7.0 the data-form covers the **entire** `#dt/e` vocabulary — one expression language, two spellings. `#dt/e (...)` is read-time sugar for static expressions; the keyword-vector is the same AST written as data, for anything built at runtime:

```clojure
(let [ticker "AAPL", lo 3700, hi 4900]
  (dt panel :where [:= :tic ticker])                       ;; runtime value
  (dt ds    :where [:and [:>= :mass lo] [:< :mass hi]])     ;; nested
  (dt ds    :where [:in :species #{"Adelie" "Gentoo"}])     ;; set membership
  (dt ds    :where [:> [:- :mass 100] 4000]))               ;; arithmetic

;; predicates assemble programmatically — ideal for parameterized screens
(dt ds :where (into [:and] (for [[c lo hi] thresholds] [:between? c lo hi])))

;; :agg / :set take data-forms too — generate aggregations over a column list
(dt panel :by [:gind]
    :agg (into {} (for [c benchmark-vars]
                    [(keyword (str (name c) "_q20")) [:qnt c 0.2]])))
(dt ds :set {:gross-margin [:div0 [:- :sales :cogs] :sales]})

;; window / row / stat ops and the special forms all have data-form spellings (2.7.0)
(dt ds :by [:gvkey] :within-order [[:asc :date]]
    :set (for [c [:saleq :cogsq]]                       ;; :set takes any seq of pairs
           [(keyword (str "L1." (name c))) [:win/lag c 1]]))
(dt ds :set {:cat  [:cond [:> :bmi 30] "obese" [:> :bmi 25] "over" :else "normal"]
             :size [:cut :mktcap 5 :from [:= :exchcd 1]]
             :w    [:win/scan :* [:+ 1 :ret]]})

;; a whole query as one EDN value
(dt ds {:where [:> :mass 3700]
        :by [:species]
        :within-order [[:desc :mass]]
        :agg {:top [:first-val :mass] :n [:nrow]}})
```

Every `#dt/e` op keyword works, including all full-name/concise aliases (`[:mean …]` == `[:mn …]`, `[:fst …]` == `[:first-val …]`), the special forms (`[:if …]`, `[:cond … :else …]`, `[:let [:name expr …] body]`, `[:coalesce-finite …]`, `[:cut …]`, `[:xbar …]`, `[:win/scan :op …]`, `[:win/each-prior :op …]`), and `[:nrow]` for row count. Use a set for `:in` (a non-number-headed vector denotes an operation). Context rules match `#dt/e` exactly — e.g. `win/*` outside `:set` throws; unknown ops throw `:unknown-data-op` with a suggestion.

## `:select` — Polymorphic Column Selection

```clojure
(dt ds :select [:species :mass])                    ;; explicit list
(dt ds :select :type/numerical)                     ;; all numeric columns
(dt ds :select :!type/numerical)                    ;; all non-numeric
(dt ds :select #"body-.*")                          ;; regex match
(dt ds :select [:not :id :timestamp])               ;; exclusion
(dt ds :select {:species :sp :mass :m})             ;; select + rename
(dt ds :select (between :month-01 :month-12))       ;; positional range (inclusive)
```

## Window Functions

Available via `win/*` inside `#dt/e`. Work in `:set` context — with `:by` for partitioned windows, or without `:by` for whole-dataset windows:

```clojure
;; Partitioned window — grouped by permno
(dt ds
  :by [:permno]
  :within-order [(asc :date)]
  :set {:rank    #dt/e (win/rank :ret)
        :lag-1   #dt/e (win/lag :ret 1)
        :cumret  #dt/e (win/cumsum :ret)
        :regime  #dt/e (win/rleid :sign-ret)})

;; Whole-dataset window — no :by, entire dataset is one partition
(dt ds
  :within-order [(asc :date)]
  :set {:cumret #dt/e (win/cumsum :ret)
        :prev   #dt/e (win/lag :price 1)})
```

Functions: `win/rank`, `win/dense-rank`, `win/row-number`, `win/lag`, `win/lead`, `win/tlag`, `win/cumsum`, `win/cummin`, `win/cummax`, `win/cummean`, `win/rleid`, `win/delta`, `win/ratio`, `win/differ`, `win/mavg`, `win/msum`, `win/mdev`, `win/mdowndev`, `win/mmin`, `win/mmax`, `win/ema`, `win/fills`, `win/bfill`, `win/scan`, `win/each-prior`, `win/grr`.

### Date-Aware Lag (`win/tlag`)

`win/lag` is positional — in a panel with gaps it silently reaches back to the wrong period. `win/tlag` lags by **date value**: row *i* gets the value at the row whose date equals `date - shift`, and a missing period yields `nil`:

```clojure
;; firm B has no 2016 → its 2017 row gets nil (win/lag would return the 2015 value)
(dt ds :by [:gvkey] :set {:l-at #dt/e (win/tlag :at :fyear)})       ;; shift defaults to 1
(dt ds :by [:gvkey] :set {:l4 #dt/e (win/tlag :saleq :qtr 4)})      ;; 4 periods back
(dt ds :by [:id] :set {:nxt #dt/e (win/tlag :x :year -1)})          ;; negative shift = lead
```

Dates may be numbers (years, `xbar` buckets, encoded periods — plain subtraction) or `java.time` temporals, shifted per an options map: `{:unit :day}` (default), `:week`, `:month`, `:quarter`, `:year`:

```clojure
(dt ds :by [:permno] :set {:l-ret #dt/e (win/tlag :ret :month-start 1 {:unit :month})})
```

Temporal matching is exact — calendar arithmetic must land on a date present in the data. For monthly panels keyed on month-*end* dates (last trading day), tlag on a normalised month column (e.g. an `xbar` bucket) instead of the raw date. Dates must be unique within each partition; duplicates throw a structured `:tlag-duplicate-dates` error (aggregate them first).

### Adjacent-Element Ops

Inspired by q's `deltas` and `ratios` — eliminate verbose lag patterns:

```clojure
(dt ds :by [:permno] :within-order [(asc :date)]
    :set {:ret       #dt/e (- (win/ratio :price) 1)    ;; simple return
          :price-chg #dt/e (win/delta :price)          ;; first differences
          :changed   #dt/e (win/differ :signal)})      ;; boolean change flag
```

`win/ratio` returns `nil` (not `Infinity`) when the previous value is zero or nil — the canonical simple-return idiom `(- (win/ratio :price) 1)` therefore produces `nil` after a zero-price row rather than contaminating downstream calculations.

### Rolling Windows & EMA

```clojure
(dt ds :by [:permno] :within-order [(asc :date)]
    :set {:ma-20   #dt/e (win/mavg :price 20)     ;; 20-day moving average
          :vol-20  #dt/e (win/mdev :ret 20)       ;; 20-day moving std dev
          :hi-52w  #dt/e (win/mmax :price 252)    ;; 52-week high
          :ema-10  #dt/e (win/ema :price 10)})    ;; 10-day EMA
```

The moving ops (`win/mavg`, `win/msum`, `win/mdev`, `win/mdowndev`, `win/mmin`, `win/mmax`) use an **expanding** window at the start (a value from the first row, q convention). Pass a trailing `{:min-periods n}` options map for a **non-expanding** window — `#dt/e (win/mavg :price 20 {:min-periods 20})` emits nil until a full 20-row window exists (R's `zoo::rollapplyr`). `win/mdev` also reads `:ddof` from the map (`{:ddof 0 :min-periods 20}`); the positional `(win/mdev :ret 20 0)` still works.

A few window ops take their own trailing options map: `win/lag`/`win/lead` accept `{:fill v}` to fill the boundary positions (no history/future) instead of nil — `#dt/e (win/lag :price 1 {:fill 0})` collapses the `(coalesce (win/lag …) 0)` two-step into one. `win/ema` accepts `{:alpha 0.18}` or `{:period 10}` as a self-documenting alternative to its numeric `>= 1` → period / `< 1` → alpha shorthand.

For a `:set` + keyword-only `:by` query, numeric derived columns are materialised in **off-heap native buffers by default** (freed on GC, Arrow-exportable, type-preserving int/float) rather than on the JVM heap — for wide per-group transforms this is the difference between gigabytes of heap and near-zero (a 270-column per-firm transform drops from ~6 GB to ~90 MB). Output is identical to on-heap by construction. Pass `:off-heap false` to keep derived columns on the JVM heap.

For a **multi-pass** per-entity transform (many sequential `:set :by` passes over the same grouping), `prepare-grouping` computes the grouping + within-order permutation once and reuses it, skipping the per-pass grouping/sort:

```clojure
(let [g (prepare-grouping ds [:gvkey] [(asc :datadate)])]
  (-> ds
      (dt :set {:R4.saleq #dt/e (win/mavg :saleq 4)} :by g)
      (dt :set {:G.R4.saleq #dt/e (win/grr :R4.saleq)} :by g)   ;; later passes can use earlier-derived cols
      ...))
```

It's valid for any dataset with the same rows in the same order (adding columns is fine). On a real 2.1M-row × 45k-firm 10-pass transform this cut the run ~2.6×.

### Fills (Forward / Backward)

```clojure
(dt ds :by [:permno] :within-order [(asc :date)]
    :set {:price #dt/e (win/fills :price)             ;; carry forward last known
          :capped #dt/e (win/fills :price {:limit 3}) ;; carry at most 3 rows into a gap
          :next  #dt/e (win/bfill :price)             ;; carry backward (NOCB)
          :both  #dt/e (win/fills (win/bfill :price))}) ;; NOCB then LOCF, by nesting
```

`:limit` (bare number shorthand: `(win/fills :price 3)`) is a **partial** fill — the value is carried up to n positions into each nil run and the rest of the run stays nil, matching pandas `ffill(limit=n)` (deliberately *not* zoo's all-or-nothing `maxgap`). The finance use case: quarterly data carried at most 4 periods, then it goes stale. Both directions take the same option; combos compose by nesting, no combo API needed.

### Cumulative Scan

Generalized cumulative operation inspired by APL/q's scan (`\`). Supports `+`, `*`, `max`, `min` — the killer use case is the wealth index:

```clojure
(dt ds :by [:permno] :within-order [(asc :date)]
    :set {:wealth  #dt/e (win/scan * (+ 1 :ret))   ;; cumulative compounding
          :cum-vol #dt/e (win/scan + :volume)       ;; = win/cumsum
          :runmax  #dt/e (win/scan max :price)})    ;; running maximum
```

### Generalized Adjacent-Element Ops (`win/each-prior`)

`win/each-prior` is the generalization of `win/delta` and `win/ratio` — applies any binary operator to `f(x[i], x[i-1])`. Supports `+`, `-`, `*`, `/`, `max`, `min`, and comparison operators. First element → nil; nil propagates.

```clojure
(dt ds :by [:permno] :within-order [(asc :date)]
    :set {;; subtract: same result as win/delta (without double-casting)
          :chg     #dt/e (win/each-prior - :price)
          ;; max with previous — running pairwise high
          :pw-hi   #dt/e (win/each-prior max :price)
          ;; boolean: did value increase?
          :up?     #dt/e (win/each-prior > :price)})
```

Use `win/delta` when you want the named function with its double-casting; use `win/ratio` when you need the zero-guard (nil instead of Infinity). Use `win/each-prior` when you need a different operator entirely.

## Row-wise Functions

Cross-column operations within a single row via `row/*`:

```clojure
(dt ds :set {:total  #dt/e (row/sum :q1 :q2 :q3 :q4)
             :avg-q  #dt/e (row/mean :q1 :q2 :q3 :q4)
             :n-miss #dt/e (row/count-nil :q1 :q2 :q3 :q4)})
```

Functions: `row/sum` (nil as 0), `row/mean`, `row/min`, `row/max` (skip nil), `row/count-nil`, `row/any-nil?`.

## Statistical Transforms

Column-level transforms via `stat/*` inside `#dt/e`. All are nil-safe — nil values are excluded from reference statistics and produce nil outputs.

```clojure
;; Standardize: (x - mean) / sd — returns all-nil if sd is zero
(dt ds :set {:z #dt/e (stat/standardize :ret)})

;; Demean: x - mean(x)
(dt ds :set {:dm #dt/e (stat/demean :ret)})

;; Winsorize at 1% tails — clips to [p, 1-p] percentile bounds
(dt ds :set {:wr #dt/e (stat/winsorize :ret 0.01)})

;; One-sided winsorization — clip only the top (or bottom) tail
(dt ds :set {:wr #dt/e (stat/winsorize :ret 0.01 {:tail :upper})})

;; Trim at 1% tails — outside values become nil (removed, not clipped)
(dt ds :set {:tr #dt/e (stat/trim :ret 0.01)})

;; Min-max rescale to [0,1] (or an explicit [lo hi])
(dt ds :set {:r01 #dt/e (stat/rescale :score)
             :r11 #dt/e (stat/rescale :score -1 1)})

;; Compose with arithmetic
(dt ds :set {:scaled #dt/e (* 2 (stat/demean :x))})

;; Cross-sectional standardization per group
(dt ds :by [:date] :set {:z #dt/e (stat/standardize :signal)})
```

Functions: `stat/standardize`, `stat/demean`, `stat/winsorize`, `stat/trim`, `stat/rescale`. A constant column has no defined scale, so `rescale` returns all nil (matching `standardize`'s zero-sd behavior).

## Joins

Standalone function with cardinality validation and merge diagnostics. Supports regular joins (`:inner`, `:left`, `:right`, `:outer`), filtering joins (`:semi`, `:anti`), the Cartesian product (`:cross`), as-of joins (`:asof`, with `:direction` and `:tolerance`), and window joins (`:window`, aggregates over matched sub-datasets).

```clojure
(require '[datajure.join :refer [join]])

(join X Y :on :id :how :left)
(join X Y :on [:firm :date] :how :inner :validate :m:1)
(join X Y :left-on :id :right-on :key :how :left :report true)
;; [datajure] join report: 150 matched, 3 left-only, 0 right-only

;; Thread with dt
(-> (join X Y :on :id :how :left :validate :m:1)
    (dt :where #dt/e (> :year 2008)
        :agg {:total #dt/e (sm :revenue)}))
```

Filtering joins (dplyr `semi_join`/`anti_join`) keep or drop left rows by key existence in the right dataset — left columns only, left row order, no duplication:

```clojure
(join X universe :on :permno :how :semi)      ;; keep rows in the universe
(join X blacklist :on :permno :how :anti)     ;; drop blacklisted rows

;; The keep-groups-with-≥-N-rows idiom: agg → where → semi-join back
(-> (dt X :by [:permno] :agg {:n [:nrow]})
    (dt :where [:>= :n 24])
    (as-> big (join X big :on :permno :how :semi)))
```

`:cross` takes no keys and pairs every left row with every right row (colliding right columns are prefixed `right.`):

```clojure
(join firms scenarios :how :cross)
```

## As-of Joins

Inspired by q's `aj`. For each left row, find the last right row where `right-key <= left-key` within an exact-match group. All left rows are always preserved; unmatched rows get nil for right columns.

The **last column** in `:on` (or `:left-on`/`:right-on`) is the asof column — preceding columns are exact-match keys.

```clojure
(require '[datajure.join :refer [join]])

;; Trade-quote matching: each trade gets the last prevailing bid/ask.
;; sym is exact-match, time is asof (last quote where quote-time <= trade-time)
(join trades quotes :on [:sym :time] :how :asof)

;; Asymmetric key names
(join trades quotes
      :left-on  [:sym :trade-time]
      :right-on [:sym :quote-time]
      :how :asof)

;; With cardinality validation (right side only)
(join trades quotes :on [:sym :time] :how :asof :validate :m:1)
```

**Result schema:** all left columns in original order, plus right non-key columns appended. Conflicting non-key column names are suffixed `:right.<n>` (same convention as regular joins).

**`:validate` for `:asof`:** only the right side is checked (`:1:1` and `:m:1` require unique right keys). The left side is never checked since all left rows always appear.

### Directional and Bounded As-of Joins

`:direction` controls which side of the asof key is matched (default `:backward`). `:tolerance` sets a maximum allowable distance — matches beyond it produce nil.

```clojure
;; :forward — first right row where right-key >= left-key
(join left right :on [:sym :time] :how :asof :direction :forward)

;; :nearest — closest right row by absolute distance; ties prefer :backward
(join left right :on [:sym :time] :how :asof :direction :nearest)

;; :tolerance — reject matches more than 5 time units away
(join trades quotes :on [:sym :time] :how :asof :tolerance 5)

;; Combine: nearest match within a 3-unit window
(join left right :on [:time] :how :asof :direction :nearest :tolerance 3)

;; Temporal asof key — :tolerance is a [n unit] duration (point-in-time staleness cap).
;; Match each daily date to the most recent report by rdq, but no older than 90 days.
(join crsp compustat :left-on [:gvkey :date] :right-on [:gvkey :rdq]
      :how :asof :tolerance [90 :days])
```

`:tolerance` is a plain number for a **numeric** asof key, or a `[n unit]` spec (`:seconds`/`:minutes`/`:hours`/`:days`/`:weeks`) for a **temporal** (date/time) one. Matches that exceed the tolerance produce nil for right columns — same as having no match.

## Window Joins

Inspired by q's `wj`. For each left row, finds **all** right rows whose asof-key falls within a window around the left row's asof-key, then aggregates them with `:agg`. All left rows are preserved.

The **last column** in `:on` is the asof column — preceding columns are exact-match keys. It must be **numeric** (the window bounds are computed by arithmetic on the asof value, and a `:minutes`/`:days`/… unit just scales the offsets); a date/`java.time` asof column throws a structured `:asof-non-numeric-asof-key` — store it as epoch days/millis to window over it. The same applies to `:direction :nearest`, which ranks by raw distance; `:backward`/`:forward` accept date/temporal keys directly.

```clojure
(require '[datajure.join :refer [join]])

;; 3-unit lookback: each left row aggregates right rows in [left-t - 3, left-t]
(join trades quotes
  :on [:sym :time]
  :how :window
  :window [-3 0]
  :agg {:avg-bid #dt/e (mn :bid)
        :n-quotes core/nrow})

;; 5-minute lookback using temporal units
(join trades quotes
  :on [:sym :time]
  :how :window
  :window [-5 0 :minutes]
  :agg {:avg-bid #dt/e (mn :bid)
        :avg-ask #dt/e (mn :ask)
        :n       core/nrow})

;; Symmetric window: 2 units either side
(join events signals
  :on [:sym :time]
  :how :window
  :window [-2 2]
  :agg {:mean-signal #dt/e (mn :value)})

;; Asymmetric key names
(join trades quotes
  :left-on  [:sym :trade-time]
  :right-on [:sym :quote-time]
  :how :window
  :window [-5 0 :minutes]
  :agg {:vwap #dt/e (wavg :size :bid)})
```

**Window spec formats** — all three are equivalent:
```clojure
[-5 0 :minutes]   ;; [lo hi unit]  — recommended
[-5 :minutes 0]   ;; [lo unit hi]  — also accepted
[-300000 0]       ;; [lo hi]       ;; raw (300000 ms = 5 min)
```
Supported units: `:seconds`, `:minutes`, `:hours`, `:days`, `:weeks`.

**`:agg` values:**
- `#dt/e` expressions — apply to the matched sub-dataset; return **nil** for empty windows (avoids NaN from `dfn/mean` on empty columns)
- Plain fns — receive the 0-row sub-dataset directly; `nrow` naturally returns **0** for empty windows

**Result schema:** all left columns preserved, plus one column per `:agg` entry.

```clojure
;; VWAP over 5-minute rolling window — thread into dt
(-> (join trades quotes
          :on [:sym :time]
          :how :window
          :window [-5 0 :minutes]
          :agg {:vwap  #dt/e (wavg :size :bid)
                :depth core/nrow})
    (core/dt :where #dt/e (> :depth 0)
             :order-by [(core/asc :time)]))
```

## Reshaping

```clojure
(require '[datajure.reshape :refer [melt cast]])

;; Wide → long
(-> ds
    (melt {:id [:species :year] :measure [:mass :flipper :bill]})
    (dt :by [:species :variable] :agg {:avg #dt/e (mn :value)}))

;; Long → wide (complement to melt)
(cast ds {:id [:species :year] :from :variable :value :value})

;; With aggregation for duplicate (id, from) cells
(cast ds {:id [:date :sym] :from :metric :value :val :agg dfn/mean})

;; Round-trip
(-> ds
    (melt {:id [:species :year] :measure [:mass :flipper]})
    (cast {:id [:species :year] :from :variable :value :value}))
```

`cast` options: `:id` (required), `:from` (required), `:value` (required), `:agg` (fn applied to a vector of values when multiple rows share the same id+from combination; default: first value), `:fill` (value for missing cells; default: nil).

### Panel Gap-Filling (`tsfill`)

Stata's `tsfill` — essentially tidyr's `complete` for panel time series. Expands each group to a regular date grid, inserting missing rows (nil everywhere except the keys), so positional windows and `win/fills`-style carrying become safe:

```clojure
(require '[datajure.reshape :refer [tsfill]])

(tsfill ds {:by [:gvkey] :date :fyear})                            ;; yearly, step 1
(tsfill ds {:by [:permno] :date :month-start :every :month
            :carry [:ticker]})                                     ;; carry IDs across inserted rows
(tsfill ds {:by [:permno] :date :date :grid trading-days})         ;; explicit grid (e.g. trading days)
```

Semantics: the grid spans each group's own min..max date (no rows before a group's first or after its last observation); **union** with existing rows — off-grid rows are kept, never dropped; nil-date rows are dropped (they can't be placed); nil group keys and duplicate (key, date) combinations throw structured errors; `:carry` columns are backward- then forward-filled per group (NOCB→LOCF). Output is sorted by [keys, date]. Temporal grids match exactly, so month-end-keyed panels should be normalised first (an `xbar` bucket) — same caveat as `win/tlag`.

## Utilities

```clojure
(require '[datajure.util :as du])

(du/describe ds)                                ;; summary stats → dataset
(du/describe ds [:mass :height])                ;; subset of columns
(du/clean-column-names messy-ds)                ;; "Some Ugly Name!" → :some-ugly-name (Unicode-aware)
(du/mark-duplicates ds [:id :date])             ;; adds :duplicate? column
(du/drop-constant-columns ds)                   ;; remove zero-variance
(du/coerce-columns ds {:year :int64 :mass :float64})
(du/blank->nil ds)                              ;; ""/whitespace-only strings → missing
(du/parse-numeric ds [:price :volume])          ;; "$1,234.50a" → 1234.5, "n/a" → missing
```

`clean-column-names` preserves non-ASCII characters (CJK, accented Latin, Cyrillic, Greek) — `"市值 (HKD millions)!"` becomes `:市值-hkd-millions`.

## Keyed Lookups

Repeated point-lookups into a big panel — "give me one firm's rows" on every UI tab switch — shouldn't rescan the whole dataset. Build an index once, then look up in O(1):

```clojure
(require '[datajure.index :as idx])

(def by-tic (idx/index-by panel :tic))      ;; build once; key on one or many columns
(idx/lookup by-tic "AAPL")                   ;; => sub-dataset of AAPL's rows, original order

;; multi-column keys use a tuple
(def by-firm-date (idx/index-by panel [:gvkey :datadate]))
(idx/lookup by-firm-date [1690 (java.time.LocalDate/parse "2020-03-31")])

;; compose with dt — the everyday "last 20 quarters for one firm"
(-> (idx/lookup by-tic "AAPL")
    (dt :order-by [(asc :datadate)] :take -20))

(idx/lookup-indices by-tic "AAPL")           ;; raw row indices, to gather yourself
```

An index is an immutable value that holds a reference to the dataset it was built from, so a lookup can never be applied to a mismatched table — this is data.table's `setindex()` (a secondary index), never a mutating `setkey()`. A key maps to many rows by default; an absent key yields an empty dataset.

## File I/O

```clojure
(require '[datajure.io :as dio])

(def ds (dio/read "data.csv"))
(def ds (dio/read "data.json"))       ;; native, no extra dep
(def ds (dio/read "data.jsonl"))      ;; JSON Lines (also .ndjson)
(def ds (dio/read "data.parquet"))    ;; needs tech.v3.libs.parquet
(def ds (dio/read "data.tsv.gz"))     ;; gzip auto-detected
(dio/write ds "output.csv")
(dio/write ds "output.jsonl")

;; Stream a large file in row batches (Parquet by row-group; JSON Lines by :batch-size)
(doseq [chunk (dio/read-seq "huge.jsonl" {:batch-size 50000})]
  (process chunk))
```

Supported: CSV, TSV, JSON, JSON Lines (`.jsonl`/`.ndjson`), Nippy (native), Parquet, Arrow, Excel (optional deps). Gzipped native-format variants auto-detected. `read-seq` streams Parquet (row groups) and JSON Lines (`:batch-size` row batches).

Columns are read as keywords by default. `:column-allowlist`/`:column-blocklist` accept keyword **or** string names on every format — CSV/TSV match raw headers before `:key-fn`, Parquet/Arrow match after, and both `dio/read` and `dio/read-seq` normalise either way so you don't have to care:

```clojure
(dio/read "data.csv"     {:column-allowlist [:a :b]})   ;; only columns a, b
(dio/read "data.csv"     {:column-blocklist [:id]})      ;; everything except id
(dio/read "data.parquet" {:column-allowlist [:a :b]})   ;; same, keyword or string
```

## Calendar Date Parts

Element-wise extraction of calendar fields from date / date-time columns — `year`, `month`, `day`, `dow` (alias `day-of-week`), `quarter`. The bread-and-butter of calendar-effect and seasonality queries (data.table's `year()`/`month()`, Polars' `.dt.*`):

```clojure
;; The april-effect shape: filter + group on calendar fields, all in-DSL
(-> rets
    (dt :set {:yr #dt/e (year :date) :month #dt/e (month :date)})
    (dt :where #dt/e (and (>= :yr 2010) (= :month 4)))
    (dt :agg {:median #dt/e (md :ret) :mean #dt/e (mn :ret) :n nrow}))

;; Inline in :where — no :set needed when the part is only a filter
(dt rets :where #dt/e (= (quarter :date) 2))

;; Data-form spelling
(dt rets :set {:m [:month :date]})
```

Semantics:

- A missing date yields nil (and compares `false` in predicates) — `long-temporal-field`'s raw sentinel never leaks.
- `dow` follows ISO / `java.time`: 1 = Monday … 7 = Sunday.
- `quarter` is 1–4, derived from the month.
- Works on date and date-time columns. Raw `:instant` columns have no calendar fields (they're zone-less) — convert to local dates first.
- Grouping goes through `:set` first: expressions directly in `:by` throw a structured `:expr-in-by` error pointing at the idiom above. Note that stored parts become proper typed `:int64` columns with missing bitmaps.

`xbar` (below) is complementary, not overlapping: `xbar` bins timestamps into consecutive intervals; date-parts extract calendar fields across periods ("all Aprils across decades" is inexpressible with `xbar`).

For data pulled from a SQL source (e.g. WRDS), extracting date parts server-side (`EXTRACT(MONTH FROM ...)`) remains a fine pattern — less data transferred — but it is now a choice, not a necessity.

## Bucketing with `xbar`

Floor-division bucketing inspired by q's `xbar`. Primary use case is computed `:by` for time-series bar generation:

```clojure
;; Numeric bucketing in :by — price buckets of width 10
(dt ds :by [(xbar :price 10)] :agg {:n nrow :avg #dt/e (mn :volume)})

;; 5-minute OHLCV bars (xbar + the OHLC pattern from above)
(dt trades
    :by [(xbar :time 5 :minutes) :sym]
    :within-order [(asc :time)]
    :agg {:open #dt/e (first-val :price) :close #dt/e (last-val :price)
          :vol  #dt/e (sm :size) :n nrow})

;; Also usable inside #dt/e as a column derivation
(dt ds :set {:bucket #dt/e (xbar :price 5)})
```

Supported temporal units: `:seconds`, `:minutes`, `:hours`, `:days`, `:weeks`. Returns nil for nil input.

## Quantile Binning with `cut`

Equal-count (quantile) binning inside `#dt/e`. The optional `:from` mask computes breakpoints from a **reference subpopulation** and applies them to **all rows** — the reference and binned populations can be different sizes. This directly models the NYSE-breakpoints pattern used in empirical finance:

```clojure
;; Basic: 5 equal-count bins across all rows
(dt ds :set {:size-q #dt/e (cut :mktcap 5)})

;; NYSE breakpoints: compute quintile breakpoints from NYSE stocks only,
;; apply to all stocks (NYSE + AMEX + NASDAQ)
(dt ds :set {:size-q #dt/e (cut :mktcap 5 :from (= :exchcd 1))})

;; :from accepts any #dt/e boolean expression
(dt ds :set {:size-q #dt/e (cut :mktcap 5 :from (and (= :exchcd 1) (> :year 2000)))})

;; Per-date NYSE breakpoints — the canonical CRSP usage
(-> crsp
    (dt :where #dt/e (= (month :date) 6))
    (dt :by [:date]
        :set {:size-q #dt/e (cut :mktcap 5 :from (= :exchcd 1))}))
```

## Quantile Grouping with `qtile`

`qtile` is the `:by`-friendly companion to `cut` — produces an equal-count bin assignment from a column's distribution. Use it when you want to *group by* quantile, rather than *derive a column of* quantile bins. Inspired by R's `cut` and Stata's `xtile`; named `qtile` to evoke quintile/decile:

```clojure
;; Global quintile buckets of market cap
(dt stocks :by [(qtile :mktcap 5)]
    :agg {:n nrow :mean-ret #dt/e (mn :ret)})
;; Result column is auto-named :mktcap-q5

;; Per-date size quintiles — the canonical CRSP / Fama-French pattern.
;; Each date gets its own breakpoints.
(dt stocks :by [:date (qtile :mktcap 5)]
    :agg {:mean-ret #dt/e (mn :ret)})

;; Per-date NYSE-style breakpoints applied to all stocks — Fama-French size sort.
;; For each date, breakpoints are computed from that date's NYSE stocks only,
;; then applied to all stocks (NYSE + AMEX + NASDAQ) on that date.
(dt stocks :by [:date (qtile :mktcap 5 :from #dt/e (= :exchcd 1))]
    :agg {:mean-ret #dt/e (mn :ret)})
```

**Breakpoint population depends on what else is in `:by`:**

| `:by` shape | Breakpoints |
|---|---|
| `qtile` alone | Global — computed once from the whole dataset |
| `qtile` + exact keys | Per-partition — computed within each exact-key combination (the data.table / dplyr default) |
| `qtile :from <mask>` | Reference-subpopulation — the mask selects rows for breakpoint computation, applied in whichever population (global or per-partition) the rest of `:by` implies |

For the same bucketing semantics inside `#dt/e` expressions (`:set` / `:where` / `:agg`) rather than `:by`, use `#dt/e (cut :col n)`.

| | `qtile` | `#dt/e (cut ...)` |
|---|---|---|
| Context | `:by` (grouping) | `:set` / `:where` / `:agg` (expression) |
| Result | Integer bin key (1..n, or nil for nil input) | Column of bin integers |
| Per-partition via | Exact keys in same `:by` | `:by` + `:set` window mode |
| `:from` option | Supported (reference subpopulation) | Supported (reference subpopulation) |
| Result column name | Auto `<col>-q<n>` (customise via `:datajure/col` metadata) | Whatever you name it in `:set` |

Pick `qtile` when the bins are a grouping key; pick `cut` when the bins are a column value you want to keep alongside the original rows.

**Note on small partitions.** If a partition has fewer than `n` non-nil values, breakpoints cannot be computed and all non-nil rows in that partition land in bin 1. Filter out thin partitions upstream or use fewer bins.

## Computed `:by` — Custom Grouping Functions

`:by` accepts a plain function of the row in addition to column keywords. Functions can attach `:datajure/col` metadata to control the result-column name:

```clojure
;; Simple computed :by
(dt ds :by (fn [row] {:heavy? (> (:mass row) 4000)})
    :agg {:n nrow})

;; Custom bucketing function with friendly result column name
(defn percentile-bucket [col pct]
  (with-meta
    (fn [row]
      (let [v (get row col)]
        (when (some? v)
          (int (* pct (/ v 100))))))
    {:datajure/col (keyword (str (name col) "-pct-bucket"))}))

(dt ds :by [(percentile-bucket :score 10)] :agg {:n nrow})
;; Result column is named :score-pct-bucket
```

`xbar` uses the same mechanism internally. If no metadata is attached, result columns get synthetic names (`:fn-0`, `:fn-1`, ...).

## Rename

```clojure
(rename ds {:mass :weight-kg :species :penguin-species})
```

## Concise Namespace

Short aliases for power users (q / data.table users in particular):

```clojure
(require '[datajure.concise :refer [mn sm md sd ct nuniq fst lst wa ws mx mi N between]])

(dt ds :by [:species] :agg {:n N :avg #dt/e (mn :mass)})
```

| Symbol | Full name |
|--------|-----------|
| `mn`   | mean |
| `sm`   | sum |
| `md`   | median (R type-7) |
| `qnt`  | type-7 p-quantile: `(qnt :col p)` / `(qnt :col p min-n)`; `p` may be a vector `[0.2 0.5 0.8]` → vector of quantiles, sorted once |
| `sd`   | stddev |
| `mx`   | max (column maximum) |
| `mi`   | min (column minimum) |
| `ct`   | element count |
| `nuniq`| count-distinct |
| `fst`  | first-val |
| `lst`  | last-val |
| `wa`   | wavg (weighted average) |
| `ws`   | wsum (weighted sum) |
| `N`    | row count (alias for `nrow`) |
| `standardize` | stat/stat-standardize |
| `demean`      | stat/stat-demean |
| `winsorize`   | stat/stat-winsorize |
| `trim`        | stat/stat-trim |
| `rescale`     | stat/stat-rescale |
| `between`     | positional range selector |

Both `nrow` (discoverable) and `N` (terse, q/data.table style) live in `datajure.core`; `N` is also re-exported from `datajure.concise`. Since 2.7.0 row count is also a real nullary op — `#dt/e (nrow)` / `#dt/e (N)` / data-form `[:nrow]` — so it composes in arithmetic: `#dt/e (- (nrow) 1)` for peer counts.

## Notebook Integration

### Clay (Scicloj ecosystem)

```clojure
(require '[datajure.clay :as dc])
(dc/install!)   ;; auto-renders datasets, #dt/e exprs, describe output

;; Or explicit wrapping:
(dc/view ds)
(dc/view-expr #dt/e (/ :mass (sq :height)))
(dc/view-describe (du/describe ds))
```

Start a Clay notebook:
```clojure
(require '[scicloj.clay.v2.api :as clay])
(clay/make! {:source-path "notebooks/datajure_clay_demo.clj"})
```

### Clerk

```clojure
(require '[datajure.clerk :as dc])
(dc/install!)   ;; registers custom Clerk viewers
```

## REPL

`*dt*` holds the last dataset result (like `*1`), bound by nREPL middleware:

```clojure
user=> (dt ds :by [:species] :agg {:n nrow})
;; => dataset...

user=> (dt datajure.core/*dt* :order-by [(desc :n)])
```

Enable in `.nrepl.edn`: `{:middleware [datajure.nrepl/wrap-dt]}`

## Error Messages

Structured `ex-info` with suggestions. All errors carry a `:dt/error` key in `ex-data` for programmatic dispatch.

**Unknown column — Damerau-Levenshtein suggestions catch transpositions:**

```clojure
(dt ds :set {:bmi #dt/e (/ :mass :hieght)})
;; => ExceptionInfo: Unknown column(s) #{:hieght} in :set :bmi expression
;;    Did you mean: :height (edit distance 1)
;;    Available: :species :year :mass :height :flipper
```

**Unknown op — namespace-aware suggestions at read time:**

```clojure
#dt/e (sqrt :x)
;; => ExceptionInfo: Unknown op `sqrt` in #dt/e expression. Did you mean: `sq`?

#dt/e (win/mvag :price 20)
;; => ExceptionInfo: Unknown op `win/mvag` in #dt/e expression. Did you mean: `win/mavg`?
```

**`:agg` plain-function footgun — detected and reported:**

```clojure
(dt ds :by [:species] :agg {:bad #(:mass %)})
;; => ExceptionInfo: :agg plain function for column :bad returned a column, not a scalar.
;;    In :agg, plain functions receive the group dataset, so `(:col %)` returns a column
;;    vector. Use `(dfn/mean (:col %))` or prefer `#dt/e (mn :col)` which handles both
;;    cases uniformly.
```

**Structural errors:**

```clojure
(dt ds :set {:a #dt/e (/ :x 1)} :agg {:n nrow})
;; => ExceptionInfo: Cannot combine :set and :agg. Use -> threading.

(dt ds :set {:bmi  #dt/e (/ :mass (sq :height))
             :obese #dt/e (> :bmi 30)})
;; => ExceptionInfo: Map-form :set cross-reference.
;;    :obese references #{:bmi}, which is being derived in the same map.
;;    Use vector-of-pairs [[:bmi ...] [:obese ...]] for sequential derivation.
```

## Evaluation Order

`dt` evaluates keywords in this fixed order, regardless of the order they appear in the call:

1. `:where` — filter rows
2. `:set` or `:agg` — derive or aggregate (mutually exclusive; see dispatch modes above)
3. `:select` — keep listed columns
4. `:order-by` — sort final output
5. `:take` — row limit (positive = head, negative = tail); e.g. `:order-by [(asc :date)] :take -20` = last 20 by date

## Architecture

```
#dt/e (/ :mass (sq :height))
              ↓
AST (pure data, serializable)
              ↓
compile-expr → fn [ds] → column vector
              ↓
tech.v3.datatype.functional (dfn)
              ↓
tech.v3.dataset (columnar, JVM, fast)
```

The DSL adds only parsing and dispatch overhead; all computation is delegated to tech.v3.dataset.

## Namespace Guide

| Namespace | Purpose |
|-----------|---------|
| `datajure.core` | `dt`, `N`, `nrow`, `mean`, `sum`, `median`, `qnt`, `stddev`, `variance`, `max*`, `min*`, `count*`, `prod`, `div0`, `asc`, `desc`, `pass-nil`, `rename`, `xbar`, `qtile`, `cut`, `between`, `*dt*` |
| `datajure.expr` | AST nodes, compiler, `#dt/e` reader tag |
| `datajure.concise` | Short aliases for power users |
| `datajure.window` | Window function implementations |
| `datajure.row` | Row-wise function implementations |
| `datajure.stat` | Statistical transforms: `stat/standardize`, `stat/demean`, `stat/winsorize` |
| `datajure.math` | Numeric primitives: `quantile-type7` (R type-7 quantiles, shared by `median`/`qnt`/`qtile`/`cut`/`winsorize`/`describe`) |
| `datajure.util` | `describe`, `clean-column-names`, `duplicate-rows`, etc. |
| `datajure.io` | Unified `read`/`write` dispatching on file extension |
| `datajure.reshape` | `melt` for wide→long, `cast` for long→wide |
| `datajure.join` | `join` with `:validate`, `:report`, `:how :asof` (`:direction`, `:tolerance`), and `:how :window` (`:window`, `:agg`) |
| `datajure.asof` | As-of/window join engine: `asof-search`, `asof-indices`, `asof-match`, `build-result`, `window-indices` |
| `datajure.index` | Keyed lookup index: `index-by`, `lookup`, `lookup-indices` (`:hash` and reusable `:asof` kinds) |
| `datajure.nrepl` | nREPL middleware for `*dt*` auto-binding |
| `datajure.clerk` | Rich Clerk notebook viewers |
| `datajure.clay` | Clay/Kindly notebook integration |

## Design Principles

1. **`dt` is a function** — not a macro. Debuggable, composable, predictable.
2. **`:where` always filters** — conditional updates go inside `:set` via `if`/`cond`.
3. **Keyword lifting requires `#dt/e`** — no implicit magic in plain Clojure forms.
4. **Layered nil story** — nil literals are safe in `#dt/e`, aggregation helpers skip nils, `coalesce`/`div0`/`win/fills` handle the rest, `pass-nil` wraps plain functions. Not a blanket "nil-safe" claim, but a coherent set of rules that eliminate the common NPE footguns.
5. **Expressions are values** — `#dt/e` returns an AST, not a function. Store in vars, compose freely, build shared vocabularies.
6. **One function, not dozens** — one `dt`, seven keywords, two expression modes. Threading for pipelines.
7. **Errors are data** — structured `ex-info` with `:dt/error` dispatch keys, Damerau-Levenshtein typo suggestions, extensible.
8. **Syntax layer, not engine** — delegate to tech.v3.dataset. Full interop with tablecloth, Clerk, Clay, and the Scicloj ecosystem.
9. **Steal the best ideas** — from data.table, q/kdb+, Polars, DataFramesMeta.jl, APL. The goal isn't to be any of them.

## Development

Tests run automatically on every push to `main` via GitHub Actions. CI runs the core test suites (core, concise, util, io, reshape, join, asof, stat) via `bin/run-tests.sh`. The nrepl, clerk, and clay test suites require optional deps and are run locally only. When adding a new core test namespace, add it to `bin/run-tests.sh` to include it in CI.

```bash
# Start nREPL
clj -A:nrepl

# Run core tests (same as CI)
bash bin/run-tests.sh

# Run all tests locally (including optional-dep suites)
clj -A:nrepl -e "
  (load-file \"test/datajure/core_test.clj\")
  (load-file \"test/datajure/concise_test.clj\")
  (load-file \"test/datajure/util_test.clj\")
  (load-file \"test/datajure/io_test.clj\")
  (load-file \"test/datajure/reshape_test.clj\")
  (load-file \"test/datajure/join_test.clj\")
  (load-file \"test/datajure/asof_test.clj\")
  (load-file \"test/datajure/nrepl_test.clj\")
  (load-file \"test/datajure/clerk_test.clj\")
  (load-file \"test/datajure/clay_test.clj\")
  (load-file \"test/datajure/stat_test.clj\")
  (clojure.test/run-tests
    'datajure.core-test 'datajure.concise-test 'datajure.util-test
    'datajure.io-test 'datajure.reshape-test 'datajure.join-test
    'datajure.asof-test 'datajure.nrepl-test 'datajure.clerk-test
    'datajure.clay-test 'datajure.stat-test)"
```

## Prior Work

Datajure v1 was a routing layer across three backends (tablecloth, clojask, geni/Spark). v2 takes a different approach: a single, opinionated syntax layer directly on tech.v3.dataset, stealing good ideas from data.table (query form), q/kdb+ (time-series primitives), Polars (expressions as values), and DataFramesMeta.jl (one function, keyword arguments).

- v1 repo: https://github.com/clojure-finance/datajure/tree/v1

Special thanks to [YANG Ming-Tian](https://github.com/skylee03) for the original v1 implementation.

## License

Distributed under the Apache License, Version 2.0 (since 2.7.1; versions up to 2.7.0 were published under the Eclipse Public License 2.0).

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