Pipelines for data analysis in R

Transcript

1. 1.

   Hadley Wickham 
 @hadleywickham Chief Scientist, RStudio Pipelines for 


   data analysis in R September 2015
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   Data analysis is the process by which data becomes understanding,

   knowledge and insight Data analysis is the process by which data becomes understanding, knowledge and insight
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   Data analysis is the process by which data becomes understanding,

   knowledge and insight Data analysis is the process by which data becomes understanding, knowledge and insight
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   Transform Visualise Model Tidy Import Surprises, but doesn't scale Scales,

   but doesn't (fundamentally) surprise Create new variables & new summaries Consistent way of storing data
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   Transform Visualise Model tidyr dplyr Tidy Import readr readxl haven

   DBI httr broom ggplot2 ggvis
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   Pipelines

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   Think it Do it Describe it Cognitive Computational (precisely)

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   Cognition time ≫ Computation time http://www.flickr.com/photos/mutsmuts/4695658106

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   %>% Inspirations: unix, F#, haskell, clojure, method chaining magrittr::

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    foo_foo <- little_bunny() bop_on( scoop_up( hop_through(foo_foo, forest), field_mouse ), head

    ) # vs foo_foo %>% hop_through(forest) %>% scoop_up(field_mouse) %>% bop_on(head)
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    x %>% f(y) # f(x, y) x %>% f(z, .)

    # f(z, x) x %>% f(y) %>% g(z) # g(f(x, y), z) # Turns function composition (hard to read) # into sequence (easy to read)
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    # Any function can use it. Only needs a simple

    # property: the type of the first argument # needs to be the same as the type of the result. # tidyr: pipelines for messy -> tidy data # dplyr: pipelines for data manipulation # ggvis: pipelines for visualisations # rvest: pipelines for html # purrr: pipelines for lists # xml2: pipelines for xml # stringr: pipelines for strings
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    Tidy

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    Transform Visualise Model tidyr dplyr Tidy Import readr readxl haven

    DBI httr ggplot2 ggvis broom
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    Storage Meaning Table / File Data set Rows Observations Columns

    Variables Tidy data = data that makes data analysis easy
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    Source: local data frame [5,769 x 22] iso2 year m04

    m514 m014 m1524 m2534 m3544 m4554 m5564 m65 mu f04 f514 (chr) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) (int) 1 AD 1989 NA NA NA NA NA NA NA NA NA NA NA NA 2 AD 1990 NA NA NA NA NA NA NA NA NA NA NA NA 3 AD 1991 NA NA NA NA NA NA NA NA NA NA NA NA 4 AD 1992 NA NA NA NA NA NA NA NA NA NA NA NA 5 AD 1993 NA NA NA NA NA NA NA NA NA NA NA NA 6 AD 1994 NA NA NA NA NA NA NA NA NA NA NA NA 7 AD 1996 NA NA 0 0 0 4 1 0 0 NA NA NA 8 AD 1997 NA NA 0 0 1 2 2 1 6 NA NA NA 9 AD 1998 NA NA 0 0 0 1 0 0 0 NA NA NA 10 AD 1999 NA NA 0 0 0 1 1 0 0 NA NA NA 11 AD 2000 NA NA 0 0 1 0 0 0 0 NA NA NA 12 AD 2001 NA NA 0 NA NA 2 1 NA NA NA NA NA 13 AD 2002 NA NA 0 0 0 1 0 0 0 NA NA NA 14 AD 2003 NA NA 0 0 0 1 2 0 0 NA NA NA 15 AD 2004 NA NA 0 0 0 1 1 0 0 NA NA NA 16 AD 2005 0 0 0 0 1 1 0 0 0 0 0 0 .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... Variables not shown: f014 (int), f1524 (int), f2534 (int), f3544 (int), f4554 (int), f5564 (int), f65 (int), fu (int) What are the variables in this dataset? (Hint: f = female, 
 u = unknown, 1524 = 15-24)
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    # To convert this messy data into tidy data #

    we need two verbs. First we need to gather # together all the columns that aren't variables tb2 <- tb %>% gather(demo, n, -iso2, -year, na.rm = TRUE) tb2
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    # Then separate the demographic variable into # sex and

    age tb3 <- tb2 %>% separate(demo, c("sex", "age"), 1) tb3 # Many tidyr verbs come in pairs: # spread vs. gather # extract/separate vs. unite # nest vs. unnest
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    Google for “tidyr” & “tidy data”

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    Transform

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    Transform Visualise Model tidyr dplyr Tidy ggplot2 ggvis broom Import

    readr readxl haven DBI httr
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    Think it Do it Describe it Cognitive Computational (precisely)

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    One table verbs • select: subset variables by name •

    filter: subset observations by value • mutate: add new variables • summarise: reduce to a single obs • arrange: re-order the observations + group by
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    Demo

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    right_join() full_join() inner_join() left_join() Mutating semi_join() anti_join() Filtering Set intersect()

    setdiff() union()
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    dplyr sources • Local data frame (C++) • Local data

    table • Local data cube (experimental) • RDMS: Postgres, MySQL, SQLite, Oracle, MS SQL, JDBC, Impala • MonetDB, BigQuery
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    Google for “dplyr”

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    Visualise

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    Transform Visualise Model tidyr dplyr Tidy ggplot2 ggvis broom Import

    readr readxl haven DBI httr
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    What is ggvis? •A grammar of graphics 
 (like ggplot2)

    •Reactive (interactive & dynamic) 
 (like shiny) •A pipeline (a la dplyr) •Of the web (drawn with vega)
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    Demo 4-ggvis.R 4-ggvis.Rmd

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    Google for “ggvis”

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    Model with broom, by David Robinson

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    Transform Visualise Model tidyr dplyr Tidy ggplot2 ggvis broom Import

    readr readxl haven DBI httr
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    2.5 5.0 7.5 1990 1995 2000 2005 2010 2015 date

    log(sales) 46 TX cities, ~25 years of data What makes it hard to see the long term trend?
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    # Models are useful as tool for removing # known

    patterns tx <- tx %>% group_by(city) %>% mutate( resid = lm( log(sales) ~ factor(month), na.action = na.exclude ) %>% resid() )
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    −2 −1 0 1 1990 1995 2000 2005 2010 2015

    date resid
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    # Models are also useful in their own right models

    <- tx %>% group_by(city) %>% do(mod = lm( log(sales) ~ factor(month), data = ., na.action = na.exclude) )
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    Model summaries • Model level: one row per model •

    Coefficient level: one row per coefficient (per model) • Observation level: one row per observation (per model)
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    Demo 5-broom.R

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    Google for “broom r”

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    Big data and R

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    Big Can’t fit in memory on one computer: >5 TB

    Medium Fits in memory on a server: 10 GB-5 TB Small Fits in memory on a laptop: <10 GB R is great at this!
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    R • R provides an excellent environment for rapid interactive

    exploration of small data. • There is no technical reason why it can’t also work well with medium size data. (But the work mostly hasn’t been done) • What about big data?
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    1. Can be reduced to a small data problem with

    subsetting/sampling/ summarising (90%) 2. Can be reduced to a very large number of small data problems (9%) 3. Is irreducibly big (1%)
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    The right small data • Rapid iteration essential • dplyr

    supports this activity by avoiding cognitive costs of switching between languages.
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    Lots of small problems • Embarrassingly parallel (e.g. Hadoop) •

    R wrappers like foreach, rhipe, rhadoop • Challenging is matching architecture of computing to data storage
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    Irreducibly big • Computation must be performed by specialised system.

    • Typically C/C++, Fortran, Scala. • R needs to be able to talk to those systems.
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    Future work

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    End game Provide a fluent interface where you spent your

    mental energy on the specific data problem, not general data analysis process. The best tools become invisible with time! Still a lot of work to do, especially on the connection between modelling and visualisation.
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    Transform Visualise Model tidyr dplyr Tidy Import readr readxl haven

    DBI httr broom ggplot2 ggvis