Hadley Wickham 
 @hadleywickham
 Chief Scientist, RStudio Expressing 
 yourself with R July 2017

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

@aaronwolen, @aghaynes, @ajdamico, @ajschumacher, @alberthkcheng, @alyst, @andrew, @andrewjlm, @apjanke, @arneschillert, @artemklevtsov, @arunsrinivasan, @asnr, @astamm, @austenhead, @baptiste, @bbolker, @bearloga, @benmarwick, @bhive01, @BioStatMatt, @bpbond, @bquast, @BrianDiggs, @briatte, @burchill, @casallas, @cb4ds, @cboettig, @cderv, @christophergandrud, @cmartin, @colinbrislawn, @coolbutuseless, @cosinequanon, @craigcitro, @csgillespie, @ctbrown, @daattali, @dandermotj, @danliIDEA, @DanRuderman, @davharris, @davidmorrison, @dchiu911, @dchudz, @dewittpe, @dgromer, @dgrtwo, @dhimmel, @dickoa, @diogocp, @djmurphy420, @dlebauer, @dmedri, @dmenne, @dougmitarotonda, @dpastoor, @dpocock, @dtelad11, @earino, @echasnovski, @ecortens, @eddelbuettel, @edgararuiz, @edwindj, @egnha, @ehrlinger, @eibanez, @eipi10, @ekstroem, @emojiencoding, @etiennebr, @evanmiller, @fpinter, @FvD, @gaborcsardi, @gagolews, @garrettgman, @gavinsimpson, @gergness, @gnustats, @gorcha, @goyalmunish, @gregmacfarlane, @guillett, @gvelasq2, @hannesmuehleisen, @has2k1, @helix123, @hmalmedal, @hoehleatsu, @hoesler, @holstius, @hrbrmstr, @ianmcook, @ijlyttle, @ilarischeinin, @imanuelcostigan, @Ironholds, @ismayc, @isomorphisms, @itsdalmo, @JakeRuss, @janschulz, @jasonelaw, @javierluraschi, @jayhesselberth, @jcheng5, @jdnewmil, @jefferis, @jennybc, @jenzopr, @jeremystan, @jeroen, @jgabry, @jhuovari, @jiho, @jimhester, @jirkalewandowski, @jjallaire, @jmarshallnz, @jmi5, @joethorley, @JoFrhwld, @jonboiser, @jonmcalder, @joranE, @joshkatz, @jrnold, @juba, @junkka, @justmarkham, @kalibera, @karawoo, @karthik, @Katiedaisey, @kbenoit, @Kevin-M-Smith, @kevinushey, @kmillar, @kohske, @krlmlr, @kwenzig, @kwstat, @KZARCA, @l-d-s, @LaDilettante, @larmarange, @leondutoit, @lepennec, @lindbrook, @lionel-, @lmullen, @lorenzwalthert, @lselzer, @luckyrandom, @LucyMcGowan, @lwjohnst86, @MarcusWalz, @markdly, @markriseley, @matthieugomez, @maurolepore, @mdlincoln, @mgacc0, @mgirlich, @michaelquinn32, @mikelove, @mkcor, @mkuehn10, @mkuhn, @mmparker, @msonnabaum, @ncarchedi, @NoahMarconi, @noamross, @npjc, @nutterb, @paternogbc, @paul-buerkner, @PedramNavid, @PeteHaitch, @pierucci, @pimentel, @pitakakariki, @pkq, @r2evans, @rbdixon, @richierocks, @RiRam, @rmsharp, @robertzk, @rohan-shah, @romainfrancois, @RoyalTS, @rsaporta, @rtaph, @rudazhan, @ruderphilipp, @s-fleck, @seaaan, @setempler, @sfirke, @shabbybanks, @sjackman, @sjPlot, @smbache, @statisfactions, @steromano, @t-kalinowski, @tareefk, @tdhock, @terrytangyuan, @thomasp85, @tjmahr, @tklebel, @tmshn, @tonytonov, @tuttinator, @tverbeke, @uribo, @vspinu, @wch, @webbedfeet, @wibeasley, @wligtenberg, @x0rshift, @xiaodaigh, @Yeedle, @yutannihilation, @zeehio, @zhaoy, and @zhilongjia

My goal is to make a pit of success http://blog.codinghorror.com/falling-into-the-pit-of-success/

Solve complex problems by combining simple pieces that have a consistent structure

Pieces

df$a[df$a == -99] <- NA df$b[df$b == -99] <- NA df$e[df$e == -99] <- NA df$f[df$f == -99] <- NA df$g[df$g == -98] <- NA df$h[df$h == -99] <- NA df$i[df$i == -99] <- NA df$i[df$j == -99] <- NA df$k[df$k == -99] <- NA df$l[df$l == -99] <- NA df$m[df$m == -99] <- NA df$n[df$n == -99] <- NA What’s the point of this code? What’s wrong?

df$a[df$a == -99] <- NA df$b[df$b == -99] <- NA # c & d are character variables df$e[df$e == -99] <- NA df$f[df$f == -99] <- NA df$g[df$g == -98] <- NA df$h[df$h == -99] <- NA df$i[df$i == -99] <- NA df$i[df$j == -99] <- NA df$k[df$k == -99] <- NA df$l[df$l == -99] <- NA df$m[df$m == -99] <- NA df$n[df$n == -99] <- NA Duplicated code hides intent & errors

fix_missing <- function(x) { x[x == -99] <- NA x } df$a <- fix_missing(df$a) df$b <- fix_missing(df$b) df$e <- fix_missing(df$e) df$f <- fix_missing(df$f) df$g <- fix_missing(df$g) df$h <- fix_missing(df$h) df$i <- fix_missing(df$i) df$j <- fix_missing(df$j) df$k <- fix_missing(df$k) Create a function whenever you’ve pasted >3 times

fix_missing <- function(x) { x[x == -99] <- NA x } df <- purrr::modify_if(df, is.numeric, fix_missing) Learn FP tools to remove even more duplication

Simple pieces

Generally, want functions like legos

https://unsplash.com/photos/0VNVxhEnkII Not like playmobil

What is a simple function? Does one thing well Needs minimal context to be understood

Computes a value / Changes the world One thing well Minimal context Type stable Obey scoping rules No hidden arguments Evocative 
 name

Computes a value / Changes the world One thing well Minimal context Type stable Obey scoping rules No hidden arguments Evocative 
 name

Computes a value / Changes the world

print() mean() mutate() write_csv() + geom_line() <- runif() Which is which?

# Computes a value mean() mutate() + geom_line() # Changes the world print() write_csv() <- Which is which?

runif(5) #> [1] 0.5530 0.0138 0.8774 0.9225 0.0606 runif(5) #> [1] 0.8210 0.0459 0.6008 0.4323 0.3644 Some functions must do both

.Random.seed[2] #> [1] 624 runif(5) #> [1] 0.0808 0.8343 0.6008 0.1572 0.0074 .Random.seed[2] #> [1] 5 Some functions must do both

mod <- lm(mpg ~ wt, data = mtcars) summary(mod) #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 37.285 1.878 19.86 < 2e-16 *** #> wt -5.344 0.559 -9.56 1.3e-10 *** #> --- #> #> Residual standard error: 3.05 on 30 degrees of freedom #> Multiple R-squared: 0.753, Adjusted R-squared: 0.745 #> F-statistic: 91.4 on 1 and 30 DF, p-value: 1.29e-10 Base R generally does this well

So the exceptions are extra frustrating 10 15 20 25 30 −4 0 2 4 6 8 Fitted values Residuals ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Residuals vs Fitted Fiat 128 Toyota Corolla Chrysler Imperial ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −2 −1 0 1 2 −1 0 1 2 Theoretical Quantiles Standardized residuals Normal Q−Q Fiat 128 Toyota Corolla Chrysler Imperial 10 15 20 25 30 0.0 0.5 1.0 1.5 Fitted values Standardized residuals ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Scale−Location Fiat 128 Toyota Corolla Chrysler Imperial 0.00 0.05 0.10 0.15 0.20 −2 −1 0 1 2 Leverage Standardized residuals ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Cook's distance 0.5 0.5 1 Residuals vs Leverage Chrysler Imperial Toyota Corolla Fiat 128

Type stability

“Mama always said type- unstable functions are like a box of chocolates. You never know what you’re gonna get.”
 — Hadley Gump

Type-stable functions f f f g g g Regardless of the input, a type-stable function gives the same type of output It’s harder to predict the result of a type-unstable function

# A tibble: 150 x 5 Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa 7 4.6 3.4 1.4 0.3 setosa 8 5.0 3.4 1.5 0.2 setosa 9 4.4 2.9 1.4 0.2 setosa 10 4.9 3.1 1.5 0.1 setosa # ... with 140 more rows iris

find_vars <- function(df, predicate) { vars <- sapply(df, predicate) df[, vars] } find_vars(iris, is.numeric) find_vars(iris, is.factor) # For experts only: find_vars(iris[, 0], is.numeric) What will this function return? iris has four numeric variables and one factor

class(find_vars(iris, is.numeric)) #> [1] "data.frame" class(find_vars(iris, is.factor)) #> [1] "factor" find_vars(iris[, 0], is.numeric) #> Error in .subset(x, j): #> invalid subscript type 'list'

find_vars <- function(df, predicate) { vars <- sapply(df, predicate) df[, vars] } sapply() & [.data.frame are type-unstable Returns vector or data frame Returns vector, matrix, or list

find_vars <- function(df, predicate) { vars <- purrr::map_lgl(df, predicate) df[, vars, drop = FALSE] } Two changes make it much more predictable

Combining 
 simple pieces

by_dest <- group_by(flights, dest) dest_delay <- summarise(by_dest, delay = mean(dep_delay, na.rm = TRUE), n = n() ) big_dest <- filter(dest_delay, n > 100) arrange(big_dest, desc(delay)) Base R has two ways to combine functions

foo <- group_by(flights, dest) foo <- summarise(foo, delay = mean(dep_delay, na.rm = TRUE), n = n() ) foo <- filter(foo, n > 100) arrange(foo, desc(delay)) But naming is hard work

foo1 <- group_by(flights, dest) foo2 <- summarise(foo1, delay = mean(dep_delay, na.rm = TRUE), n = n() ) foo3 <- filter(foo2, n > 100) arrange(foo2, desc(delay)) But naming is hard work

arrange( filter( summarise( group_by(flights, dest), delay = mean(dep_delay, na.rm = TRUE), n = n() ), n > 100 ), desc(delay) ) Alternatively, you could nest function calls

magrittr provides a third option %>%

x %>% f() # Is the same as f(x) x %>% f() %>% g(y) # Is the same as g(f(x), y) The pipe

flights %>% group_by(dest) %>% summarise( delay = mean(dep_delay, na.rm = TRUE), n = n() ) %>% filter(n > 100) %>% arrange(desc(delay)) This is easy to read & doesn’t require naming

library(tidyverse) library(magick) dir(pattern = ".png") %>% map(image_read) %>% image_join() %>% image_animate(fps = 1, loop = 25) %>% image_write("my_animation.gif") Makes it easy to read unfamiliar code https://twitter.com/ricardokriebel/status/849626401611411458 What does this code do?

https://twitter.com/ricardokriebel/status/849626401611411458

Read 
 left-to-right Omits intermediate values Non-linear y <- f(x) g(y) ✅ ✅ g(f(x)) ✅ ✅ x %>% 
 f() %>% 
 g() ✅ ✅

flights %>% group_by(date) %>% summarise(n = n()) %>% ggplot(aes(date, n)) + geom_line() What happens if your pieces aren’t simple functions?

ggsave( flights %>% group_by(date) %>% summarise(n = n()) %>% ggplot(aes(date, n)) + geom_line(), "my-plot.pdf" ) Which makes it quite inconsistent

# https://github.com/hadley/ggplot1 library(ggplot1) flights %>% group_by(date) %>% summarise(n = n()) %>% ggplot(aes(date, n)) %>% ggpoint() %>% ggsave("my-plot.pdf") Interestingly, ggplot did not have this problem

flights %>% group_by(dest) %>% summarise( delay = mean(dep_delay, na.rm = TRUE), n = n() ) %>% filter(n > 100) %>% arrange(desc(delay)) -> dest_delays Another interesting connection is ->

dest_delays <- flights %>% group_by(dest) %>% summarise( delay = mean(dep_delay, na.rm = TRUE), n = n() ) %>% filter(n > 100) %>% arrange(desc(delay)) But leading with assignment improves readability

Consistent structure

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Simple and have a consistent structure

http://brickartist.com/gallery/pc-magazine-computer/. CC-BY-NC

Tidy data is a consistent way of storing data 1. Each dataset goes 
 in a data frame. 2. Each variable goes 
 in a column.

Tidy datasets are all alike; 
 every messy dataset is 
 messy in its own way — Hadley Tolstoy

# A tibble: 5,769 × 22 iso2 year m04 m514 m014 m1524 m2534 m3544 m4554 m5564 m65 mu f04 f514 f014 f1524 1 AD 1989 NA NA 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 NA NA 3 AD 1991 NA NA 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 NA NA 5 AD 1993 NA NA 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 NA NA 7 AD 1996 NA NA 0 0 0 4 1 0 0 NA NA NA 0 1 8 AD 1997 NA NA 0 0 1 2 2 1 6 NA NA NA 0 1 9 AD 1998 NA NA 0 0 0 1 0 0 0 NA NA NA NA NA 10 AD 1999 NA NA 0 0 0 1 1 0 0 NA NA NA 0 0 11 AD 2000 NA NA 0 0 1 0 0 0 0 NA NA NA NA NA 12 AD 2001 NA NA 0 NA NA 2 1 NA NA NA NA NA NA NA 13 AD 2002 NA NA 0 0 0 1 0 0 0 NA NA NA 0 1 14 AD 2003 NA NA 0 0 0 1 2 0 0 NA NA NA 0 1 15 AD 2004 NA NA 0 0 0 1 1 0 0 NA NA NA 0 0 16 AD 2005 0 0 0 0 1 1 0 0 0 0 0 0 0 1 17 AD 2006 0 0 0 1 1 2 0 1 1 0 0 0 0 0 # ... with 5,752 more rows, and 6 more variables: f2534 , f3544 , f4554 , # f5564 , f65 , fu Messy data has a varied shape What are the variables in this dataset? (Hint: f = female, u = unknown, 1524 = 15-24)

# A tibble: 35,750 × 5 country year sex age n 1 AD 1996 f 014 0 2 AD 1996 f 1524 1 3 AD 1996 f 2534 1 4 AD 1996 f 3544 0 5 AD 1996 f 4554 0 6 AD 1996 f 5564 1 7 AD 1996 f 65 0 8 AD 1996 m 014 0 9 AD 1996 m 1524 0 10 AD 1996 m 2534 0 # ... with 35,740 more rows Tidy data has a uniform shape

tidytext by Julia Silge & David Robinson
 http://tidytextmining.com

The family of Dashwood had long been settled in Sussex. Their estate was large, and their residence was at Norland Park, in the centre of their property, where, for many generations, they had lived in so respectable a manner as to engage the general good opinion of their surrounding acquaintance. — Sense & Sensibility, Jane Austen

# A tibble: 724,880 × 4 book linenumber chapter word 1 Sense & Sensibility 10 1 chapter 2 Sense & Sensibility 10 1 1 3 Sense & Sensibility 13 1 the 4 Sense & Sensibility 13 1 family 5 Sense & Sensibility 13 1 of 6 Sense & Sensibility 13 1 dashwood 7 Sense & Sensibility 13 1 had 8 Sense & Sensibility 13 1 long 9 Sense & Sensibility 13 1 been 10 Sense & Sensibility 13 1 settled # ... with 724,870 more rows tidytext provides an answer

Emma Northanger Abbey Persuasion Sense & Sensibility Pride & Prejudice Mansfield Park 0 50 100 150 0 20 40 60 80 0 20 40 60 80 0 50 100 0 50 100 0 50 100 150 −50 −25 0 25 50 −50 −25 0 25 50 sentiment Sentiment of Jane Austen books

sfby Edzer Pebesma
 http://r-spatial.github.io/sf/

34°N 34.5°N 35°N 35.5°N 36°N 36.5°N 84°W 82°W 80°W 78°W 76°W 84°W 82°W 80°W 78°W 76°W 34°N 34.5°N 35°N 35.5°N 36°N 36.5°N 0.05 0.10 0.15 0.20 AREA

nc <- sf::st_read(system.file("shape/nc.shp", package = "sf")) nc %>% as_tibble() %>% select(NAME, FIPS, AREA, geometry) #> # A tibble: 100 × 4 #> NAME FIPS AREA geometry #> #> 1 Ashe 37009 0.114 #> 2 Alleghany 37005 0.061 #> 3 Surry 37171 0.143 #> 4 Currituck 37053 0.070 #> 5 Northampton 37131 0.153 #> 6 Hertford 37091 0.097 #> 7 Camden 37029 0.062 #> 8 Gates 37073 0.091 #> 9 Warren 37185 0.118 #> 10 Stokes 37169 0.124 #> # ... with 90 more rows Store complex geometries in a list-column

What if you have complex data? 1. Each dataset goes 
 in a tibble. 2. Each variable goes 
 in a column.

df <- tibble(xyz = "a") df$x #> Warning: Unknown column 'x' #> NULL df$xyz #> [1] "a" Tibbles are data frames that are lazy & surly

data.frame(x = list(1:2, 3:5)) #> Error: arguments imply differing number #> of rows: 2, 3 tibble(x = list(1:2, 3:5)) #> # A tibble: 2 x 1 #> x #> #> 1 #> 2 But also have better support for list-cols

List-columns keep related things together Anything can go in a list & a list can go in a data frame

Conclusion

Solve complex problems by combining simple pieces that have a consistent structure

Solve complex problems by combining simple pieces that have a consistent structure Functions that do one thing well & can be understood with minimal context

Solve complex problems by combining simple pieces that have a consistent structure With assignment, composition, or the pipe

Solve complex problems by combining simple pieces that have a consistent structure Tidy tibbles have variables in columns and cases in rows.
 List-cols can store richer data structures

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

This work is licensed under the 
 Creative Commons Attribution-Noncommercial 3.0 
 United States License. To view a copy of this license, visit 
 http://creativecommons.org/licenses/by-nc/3.0/us/