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Doing data science
with Clojure
@sbelak
simon@goopti.com
The analytics chasm
Ideal. Almost real-time, can
be done during brainstorming
without disrupting flow
< 2min < 20min project
squeeze in
somewhere
in the day
fail
roadmap

ahoy!
Easy things should be easy,
and hard things should be
possible.
— L. Wall
Data frames considered
harmful
• Data frame (=table) conflates representation and
abstraction
• Clojure excels in structure manipulation/encoding
github.com/sbelak/huri
• No data structures, just functions over collections
• Composable (even DSLs — no macros!)
• Reasonably fast (transducers <3)
• Do-what-I-mean (auto-sort, liberal with inputs, …)
• Minimal buy-in
• Support reaching into nested structures everywhere
Composability is key to
quick iterating
• Curried versions where possible
• ->> and partial friendly
• Side benefit: consistent API
“This is possibly Clojure’s most important
property: the syntax expresses the code’s
semantic layers. An experienced reader of
Clojure can skip over most of the code and
have a lossless understanding of its high-
level intent.”
— Z. Tellman, Elements of Clojure
Live programming
Catching errors early more context
easier debugging faster iterating
clojure.spec
Queryable data
descriptions
<3 Bret Victor
Think in distributions,
not numbers
The power of
sharing runtime
Notebooks as
dashboards
The ecosystem
What about machine learning?
farm it out to
sklearn
Mini compilers targeting
a specific library in
another language
huri.plot
• DSL that compiles to ggplot2
• Targets Gorilla REPL
• Follows the rest of Huri’s design philosophy
• bar chart, scatter plot, line chart, box & violin plot,
heatmap, histogram
Takeouts
• Speed-of-answer matters
• Data science is about communication
• We don’t have to reinvent every wheel in Clojure
• Clojure is fantastic at structure manipulation, play
to its strengths
• Blurring the line between environment and work is
a powerful idea
Questions
@sbelak
github.com/sbelak/huri

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