Hadley Wickham 
 @hadleywickham
 Chief Scientist, RStudio Welcome to the 
 tidyverse February 2019

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

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

# 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

# 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

# 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

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

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

50 60 70 80 0 25000 50000 75000 100000 12500 income life_exp gapminder15 %>% ggplot(aes(income, life_exp))

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

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

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 40 50 60 70 80 1000 2000 4000 8000 16000 32000 Income (GDP / capita) Life expectancy Region ● asia gapminder %>% filter(country == "New Zealand") %>% gap_plot()

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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10 20 30 40 50 500 1000 2000 4000 8000 Income (GDP / capita) Life expectancy Region ● ● ● ● africa americas asia europe gapminder %>% filter(year == 1905) %>% gap_plot()

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?

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

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SQL git(hub) marketing

Conclusions

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”

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

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

# 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

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

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

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

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

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

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

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