Pipelines for data analysis in R

7ba164f40a50bc23dbb2aa825fb7bc16?s=47 Hadley Wickham
September 20, 2015
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Pipelines for data analysis in R

7ba164f40a50bc23dbb2aa825fb7bc16?s=128

Hadley Wickham

September 20, 2015
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  1. 2.

    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
  2. 3.

    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
  3. 4.

    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
  4. 10.

    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)
  5. 11.

    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)
  6. 12.

    # 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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  8. 15.

    Storage Meaning Table / File Data set Rows Observations Columns

    Variables Tidy data = data that makes data analysis easy
  9. 16.

    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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    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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    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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    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)
  18. 35.

    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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    # 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)
  22. 43.

    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!
  23. 44.

    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%)
  25. 46.

    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.
  28. 50.

    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.