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R Stories from the Trenches Szilárd Pafka, PhD Chief Scientist, Epoch Budapest R Meetup August 2015

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T-25 ~ 1990

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T-20 ~ 1996

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T-15 ~ 2001

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T-10 ~ 2006 - cost was not an issue! - data.frame - 800 packages

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~ 2009 aka data mining, aka (today) data science

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T ~ 2014 Data Science

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1999 CRISP Data Mining

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2006

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5 yrs

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(2009)

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(2009)

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(382 RSVPs)

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high-level API fast environment reproducibility

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high-level API fast environment reproducibility

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high-level API fast environment reproducibility

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high-level API fast environment reproducibility

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high-level API fast environment reproducibility

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Data frames: “in-memory table” with (fast) bulk operations (“vectorized”) thousands of packages (providing high-level API) R, Python (pandas), Spark best way to work with structured data

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Data frames: “in-memory table” with (fast) bulk operations (“vectorized”) thousands of packages (providing high-level API) R, Python (pandas), Spark best way to work with structured data

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R data.table (on one server!) aggregation 100M rows 1M groups 1.3 sec join 100M rows x 1M rows 1.7sec

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aggregation 100M rows 1M groups join 100M rows x 1M rows

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A colleague from work, asked me to investigate about Spark and R. So the most obvious thing to was to investigate about SparkR I came across a piece of code that reads lines from a file and count how many lines have a "a" and how many lines have a "b". I prepared a file with 5 columns and 1 million records.

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A colleague from work, asked me to investigate about Spark and R. So the most obvious thing to was to investigate about SparkR I came across a piece of code that reads lines from a file and count how many lines have a "a" and how many lines have a "b". I prepared a file with 5 columns and 1 million records. Spark: 26.45734 seconds for a million records? Nice job -:) R: 48.31641 seconds? Look like Spark was almost twice as fast this time...and this is a pretty simple example...I'm sure that when complexity arises...the gap is even bigger…

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A colleague from work, asked me to investigate about Spark and R. So the most obvious thing to was to investigate about SparkR I came across a piece of code that reads lines from a file and count how many lines have a "a" and how many lines have a "b". I prepared a file with 5 columns and 1 million records. Spark: 26.45734 seconds for a million records? Nice job -:) R: 48.31641 seconds? Look like Spark was almost twice as fast this time...and this is a pretty simple example...I'm sure that when complexity arises...the gap is even bigger… HOLLY CRAP UPDATE! Markus gave me this code on the comments... [R: 0.1791632 seconds]. I just added a couple of things to make complaint...but...damn...I wish I could code like that in R

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