Welcome to the tidyverse

Keynote presentation at ASA CSP conference.

The tidyverse is a language for solving data science challenges with R code. This talk discusses how the tidyverse is a language that you can use to work with data with little programming knowledge; my model of data science; and why coding is so important.

Transcript

1. Hadley Wickham 

   @hadleywickham

   Chief Scientist, RStudio
   Welcome to the 

   tidyverse
   February 2019
   

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2. The tidyverse is a
   language for solving
   data science challenges
   with R code.
   

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3. Your turn: What data do we
   need to recreate this plot?
   

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4. # A tibble: 193 x 6
   country four_regions year income life_exp pop
   
   1 Afghanistan asia 2015 1750 57.9 33700000
   2 Albania europe 2015 11000 77.6 2920000
   3 Algeria africa 2015 13700 77.3 39900000
   4 Andorra europe 2015 46600 82.5 78000
   5 Angola africa 2015 6230 64 27900000
   6 Antigua and Barbuda americas 2015 20100 77.2 99900
   7 Argentina americas 2015 19100 76.5 43400000
   8 Armenia europe 2015 8180 75.4 2920000
   9 Australia asia 2015 43800 82.6 23800000
   10 Austria europe 2015 44100 81.4 8680000
   # … with 183 more rows
   We need five variables
   

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5. # A tibble: 193 x 6
   country four_regions year income life_exp pop
   
   1 Afghanistan asia 2015 1750 57.9 33700000
   2 Albania europe 2015 11000 77.6 2920000
   3 Algeria africa 2015 13700 77.3 39900000
   4 Andorra europe 2015 46600 82.5 78000
   5 Angola africa 2015 6230 64 27900000
   6 Antigua and Barbuda americas 2015 20100 77.2 99900
   7 Argentina americas 2015 19100 76.5 43400000
   8 Armenia europe 2015 8180 75.4 2920000
   9 Australia asia 2015 43800 82.6 23800000
   10 Austria europe 2015 44100 81.4 8680000
   # … with 183 more rows
   This is “tidy” data
   Variable
   Observation
   

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6. # A tibble: 193 x 220
   country `1800` `1801` `1802` `1803` `1804` `1805` `1806` `1807` `1808` `1809` `1810` `1811` `1812` `1813`
   
   1 Afghan… 603 603 603 603 603 603 603 603 603 603 604 604 604 604
   2 Albania 667 667 667 667 667 668 668 668 668 668 668 668 668 668
   3 Algeria 715 716 717 718 719 720 721 722 723 724 725 726 727 728
   4 Andorra 1200 1200 1200 1200 1210 1210 1210 1210 1220 1220 1220 1220 1220 1230
   5 Angola 618 620 623 626 628 631 634 637 640 642 645 648 651 654
   6 Antigu… 757 757 757 757 757 757 757 758 758 758 758 758 758 758
   7 Argent… 1510 1510 1510 1510 1510 1510 1510 1510 1510 1510 1510 1510 1510 1510
   8 Armenia 514 514 514 514 514 514 514 514 514 514 514 515 515 515
   9 Austra… 814 816 818 820 822 824 825 827 829 831 833 835 837 839
   10 Austria 1850 1850 1860 1870 1880 1880 1890 1900 1910 1920 1920 1930 1940 1950
   # … with 183 more rows, and 205 more variables: `1814` , `1815` , `1816` , `1817` ,
   # `1818` , `1819` , `1820` , `1821` , `1822` , `1823` , `1824` ,
   # `1825` , `1826` , `1827` , `1828` , `1829` , `1830` , `1831` ,
   # `1832` , `1833` , `1834` , `1835` , `1836` , `1837` , `1838` ,
   # `1839` , `1840` , `1841` , `1842` , `1843` , `1844` , `1845` ,
   # `1846` , `1847` , `1848` , `1849` , `1850` , `1851` , `1852` , ...
   The original data looked like this
   

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7. gapminder %>%
   filter(year == 2015) ->
   gapminder15
   Start with single year
   Called the pipe;
   pronounced “then”
   Called the reverse assignment
   operator; pronounced “creates”
   

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8. gapminder %>%
   filter(year == 2015) ->
   gapminder15
   Phonics are important!
   filter rows where year equals 2015, creating
   Take the gapminder data, then
   gapminder15 variable
   

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9. 50
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   income
   life_exp
   gapminder15 %>%
   ggplot(aes(income, life_exp))
   

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    Your turn: What’s missing?
    

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16. gapminder15 %>%
    ggplot(aes(income, life_exp)) +
    geom_point(aes(fill = four_regions, size = pop), shape = 21) +
    scale_x_log10(breaks = 2^(-1:7) * 1000) +
    scale_size(range = c(1, 20), guide = FALSE) +
    scale_fill_manual(
    guide = FALSE,
    values = c(
    africa = "#60D2E6",
    americas = "#9AE847",
    asia = "#EC6475",
    europe = "#FBE84D"
    )
    ) +
    labs(
    x = "Income (GDP / capita)",
    y = "Life expectancy (years)"
    )
    It’s a lot more work to make a expository graphic
    

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    Income (GDP / capita)
    Life expectancy (years)
    Your turn: Can you spot
    the subtle problem?
    

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19. data %>%
    arrange(desc(pop)) %>%
    ggplot(aes(income, life_exp)) +
    ...
    A little motivation
    

    View Slide

20. gap_plot <- function(data) {
    data %>%
    arrange(desc(pop)) %>%
    ggplot(aes(income, life_exp)) +
    geom_point(aes(fill = four_regions, size = pop), shape = 21) +
    scale_x_log10(breaks = 2^(-1:7) * 1000) +
    scale_size(range = c(1, 20), guide = FALSE) +
    scale_fill_manual(values = c(
    africa = "#60D2E6",
    americas = "#9AE847",
    asia = "#EC6475",
    europe = "#FBE84D"
    )) +
    labs(
    x = "Income (GDP / capita)",
    y = "Life expectancy",
    fill = "Region"
    )
    }
    You can also turn your code into a function
    

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    ● asia
    gapminder %>%
    filter(country == "New Zealand") %>%
    gap_plot()
    

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    Income (GDP / capita)
    Life expectancy
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    gapminder %>%
    filter(year == 1900) %>%
    gap_plot()
    

    View Slide

23. ●
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    500 1000 2000 4000 8000
    Income (GDP / capita)
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    gapminder %>%
    filter(year == 1905) %>%
    gap_plot()
    

    View Slide

24. by_year <- gapminder %>%
    filter(year %% 5 == 0) %>%
    group_split(year)
    plots <- map(by_year, ~ gap_plot(.x))
    How can we do this with every plot?
    

    View Slide

25. View Slide

26. Datasets Plots
    

    View Slide

27. Demo
    

    View Slide

28. The tidyverse is a
    language for solving
    data science challenges
    with R code.
    

    View Slide

29. Tidy
    Import Visualise
    Transform
    Model
    Program
    tibble
    tidyr
    purrr
    magrittr
    dplyr
    forcats
    hms
    ggplot2
    recipes
    parsnip
    readr
    readxl
    haven
    xml2
    lubridate
    stringr
    tidyverse.org
    r4ds.had.co.nz
    

    View Slide

30. The tidyverse is a
    language for solving
    data science challenges
    with R code.
    

    View Slide

31. The disadvantages of code are obvious
    

    View Slide

32. 1. Code is text
    2. Code is read-able
    3. Code is reproducible
    Why code?
    

    View Slide

33. ⌘C
    ⌘V
    Copy
    Paste
    

    View Slide

34. View Slide

35. 1. Code is text
    2. Code is read-able
    3. Code is reproducible
    Why code?
    

    View Slide

36. What have you done?
    

    View Slide

37. 1. Code is text
    2. Code is read-able
    3. Code is reproducible
    Why code?
    

    View Slide

38. .Rmd Prose and code
    .md Prose and results
    .html Human shareable
    

    View Slide

39. .Rmd
    .md
    .html
    Prose and code
    Prose and results
    .doc .tex
    .pdf
    .ppt
    ...
    

    View Slide

40. View Slide

41. Other skills
    

    View Slide

42. SQL
    git(hub)
    marketing
    

    View Slide

43. Conclusions
    

    View Slide

44. We wanted users to be able to begin in an interactive
    environment, where they did not consciously think of
    themselves as programming. Then as their needs became
    clearer and their sophistication increased, they should be
    able to slide gradually into programming, when the
    language and system aspects would become more
    important.
    — John Chambers, “Stages in the Evolution of S”
    

    View Slide

45. https://unsplash.com/photos/8HyrGTYPQ68 — Eric Muhr
    Pit of success
    

    View Slide

46. https://simplystatistics.org/2018/07/12/use-r-keynote-2018/
    

    View Slide

47. # A tibble: 153 x 6
    Ozone Solar.R Wind Temp Month Day
    
    1 41 190 7.4 67 5 1
    2 36 118 8 72 5 2
    3 12 149 12.6 74 5 3
    4 18 313 11.5 62 5 4
    5 NA NA 14.3 56 5 5
    6 28 NA 14.9 66 5 6
    7 23 299 8.6 65 5 7
    8 19 99 13.8 59 5 8
    9 8 19 20.1 61 5 9
    10 NA 194 8.6 69 5 10
    # … with 143 more rows
    

    View Slide

48. library(dplyr)
    airquality %>%
    group_by(Month) %>%
    summarize(o3 = mean(Ozone, na.rm = TRUE))
    

    View Slide

49. aggregate(
    airquality[“Ozone”],
    airquality[“Month”],
    mean, na.rm = TRUE
    )
    

    View Slide

50. aggregate(
    airquality[“Ozone”],
    airquality[“Month”],
    mean, na.rm = TRUE
    )
    Why doesn’t
    airquality$Ozone work?
    

    View Slide

51. aggregate(
    airquality[“Ozone”],
    airquality[“Month”],
    mean, na.rm = TRUE
    ) Passing a function to another function
    

    View Slide

52. aggregate(
    airquality[“Ozone”],
    airquality[“Month”],
    mean, na.rm = TRUE
    ) Argument to mean, not to aggregate()
    

    View Slide

53. Tidy
    Import Visualise
    Transform
    Model
    Program
    tibble
    tidyr
    purrr
    magrittr
    dplyr
    forcats
    hms
    ggplot2
    recipes
    parsnip
    readr
    readxl
    haven
    xml2
    lubridate
    stringr
    tidyverse.org
    r4ds.had.co.nz
    

    View Slide

54. This work is licensed as
    Creative Commons

    Attribution-ShareAlike 4.0 

    International
    To view a copy of this license, visit 

    https://creativecommons.org/licenses/by-sa/4.0/
    

    View Slide