rstd.io/glue GLUE STRINGS TO DATA WITH GLUE STRINGS TO DATA WITH Jim Hester    @jimhester_ jimhester jim.hester@rstudio.com

GLUING STRINGS IS EVERYWHERE GLUING STRINGS IS EVERYWHERE EXCEL EXCEL =CONCATENATE(A1, " ", B1) BASH BASH dir=/tmp for file in $dir/*; do cat $dir/$file done PYTHON PYTHON apples = 4 print("I have {a} apples".format(a=apples)) # Python 3.6+ print(f"I have {apples} apples")

GLUING STRINGS IN R IS PAINFUL GLUING STRINGS IN R IS PAINFUL apples <- 3 bananas <- 2 paste0("Inventory", "\n", " Apples: ", apples, "\n", " Bananas: ", bananas, "\n", "Total: ", apples + bananas) ## Inventory ## Apples: 3 ## Bananas: 2 ## Total: 5 sprintf("Inventory\n Apples: %i\n Bananas: %i\nTotal: %i", apples, bananas, apples + bananas) ## Inventory ## Apples: 3 ## Bananas: 2 ## Total: 5 if (!file.exists(file)) { stop("'", file, "' not found") }

GLUE GLUE GLUE GLUE MAKES GLUING STRINGS EASY! MAKES GLUING STRINGS EASY! apples <- 3 bananas <- 2 glue(" Inventory Apples: {apples} Bananas: {bananas} Total: {apples + bananas}") ## Inventory ## Apples: 3 ## Bananas: 2 ## Total: 5

GLUE GLUE GLUE GLUE IS CONVIENENT IS CONVIENENT if (TRUE) { glue(" You can indent naturally \\ and break up long lines \\ if needed. ") } ## You can indent naturally and break up long lines if needed.

GLUE GLUE GLUE GLUE IS SAFE IS SAFE glue("{1:3} will recycle with a length of 1 {letters[1]}") ## 1 will recycle with a length of 1 a ## 2 will recycle with a length of 1 a ## 3 will recycle with a length of 1 a glue("{1:3} will recycle with the same length {letters[1:3]}") ## 1 will recycle with the same length a ## 2 will recycle with the same length b ## 3 will recycle with the same length c glue("{1:3} will not recycle with inconsistent length {letters[1:2]}") ## Error: Variables must be length 1 or 3

GLUE GLUE GLUE GLUE HANDLES MISSINGS HANDLES MISSINGS footies %>% mutate(glue("{first} {middle} {last}")) %>% pull() ## Tim Filiga Cahill ## Harry NA Kewell ## Mark NA Schwarzer footies %>% mutate(glue("{first} {middle} {last}", .na = NULL)) %>% pull() ## Tim Filiga Cahill ## NA ## NA footies %>% mutate(glue("{first} {middle} {last}", .na = "")) %>% pull() ## Tim Filiga Cahill ## Harry Kewell ## Mark Schwarzer

GLUE GLUE GLUE GLUE IS FOR PACKAGES IS FOR PACKAGES Zero dependencies, tested to R 3.1 Customizable Fast abort <- function(..., .sep = "", .envir = parent.frame()) { stop(glue(..., .envir = .envir), call. = FALSE) } if (actual != expected) { abort(" Expected content-type: * {expected} Actual content-type: * {actual} ") }

GLUE GLUE GLUE GLUE IS CUSTOMIZABLE IS CUSTOMIZABLE

.OPEN .OPEN AND AND .CLOSE .CLOSE CHANGE DELIMITERS CHANGE DELIMITERS j_glue <- function(..., .envir = parent.frame()) { glue(..., .open = "<", .close = ">", .envir = .envir) } apples <- 1; bananas <- 2 json <- j_glue(' { "fruits": { "apples": , "bananas": } }') jsonlite::fromJSON(json) ## $fruits ## $fruits$apples ## [1] 1 ## ## $fruits$bananas ## [1] 2

.TRANSFORMER .TRANSFORMER ARE ROBUST ARE ROBUST shell_transformer <- function(code, envir) { shQuote(eval(parse(text = code), envir)) } glue_sh <- function(..., .envir = parent.frame()) { glue(..., .envir = .envir, .transformer = shell_transformer) } filename <- "test" writeLines("hello!", filename) cmd <- glue_sh("cat {filename}") cmd ## cat 'test'

.TRANSFORMER .TRANSFORMER ARE USEFUL ARE USEFUL glue_fmt("π: {pi:.7}") ## π: 3.1415927 ratio <- sum(mtcars$gear == 3) / nrow(mtcars) glue_fmt("{ratio * 100:.2}% of mtcars have 3 gears") ## 46.88% of mtcars have 3 gears

.TRANSFORMER .TRANSFORMER ARE FUN ARE FUN glue_ji("If life gives you :lemon: make :tropical_drink:") ## If life gives you make glue_ji("Many :hands*: make :light: :work:") ## Many make

GLUE GLUE GLUE GLUE IS FAST IS FAST

TYPES OF SPEED TYPES OF SPEED Speed of writing Speed of recall Speed of execution

GLUE GLUE GLUE GLUE IS FAST ON SINGLE STRINGS IS FAST ON SINGLE STRINGS bar <- "bar" glue("foo{bar}")

GLUE GLUE GLUE GLUE IS FAST ON LOTS OF STRINGS IS FAST ON LOTS OF STRINGS bar <- rep("bar", 100000) glue("foo{bar}")

GLUE GLUE GLUE GLUE IS FAST IS FAST { } parsing in C performance dominated by parse() eval() and paste0(). still slow? Vectorize! https://glue.tidyverse.org/articles/speed.html

GLUE GLUE GLUE GLUE CONTAINS MORE THAN CONTAINS MORE THAN GLUE() GLUE()

GLUE_DATA() GLUE_DATA() BINDS STRINGS TO ANY LIST / BINDS STRINGS TO ANY LIST / ENVIRONMENT ENVIRONMENT library(magrittr) head(mtcars) %>% glue_data("{rownames(.)} has {cyl} cylinders and {hp}hp") ## Mazda RX4 has 6 cylinders and 110hp ## Mazda RX4 Wag has 6 cylinders and 110hp ## Datsun 710 has 4 cylinders and 93hp ## Hornet 4 Drive has 6 cylinders and 110hp ## Hornet Sportabout has 8 cylinders and 175hp ## Valiant has 6 cylinders and 105hp

GLUE_COLLAPSE() GLUE_COLLAPSE() COLLAPSES VECTORS COLLAPSES VECTORS glue_collapse(1:10, sep = " ") ## 1 2 3 4 5 6 7 8 9 10 glue_collapse(1:10, sep = " ", width = 10) ## 1 2 3 4... glue_collapse(backtick(1:10), sep = ", ", last = " and ") ## `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`, `9` and `10`

GLUE_SQL() GLUE_SQL() QUOTES SQL STRINGS QUOTES SQL STRINGS con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") tbl <- DBI::Id(schema = "xyz", table = "iris") glue_sql( "SELECT * FROM {`tbl`} WHERE species IN ({vals*})", vals = c("setosa", "versicolor"), .con = con) ## SELECT * FROM `xyz`.`iris` WHERE species IN ('setosa', 'versicol

STR_GLUE() STR_GLUE() IS IN IS IN tidyverse www.rstudio.com library(tidyverse) str_glue(" glue version: {packageVersion('glue')} tidyverse version: {packageVersion('tidyverse')} ") ## glue version: 1.2.0.9000 ## tidyverse version: 1.2.1

GLUE GLUE GLUE GLUE IS POPULAR??? IS POPULAR???

60 reverse dependencies Monthly downloads downloads downloads 419K/month 419K/month dplyr, stringr, tidyr dependency, maybe already installed

install.packages("glue") glue.tidyverse.org adjective <- c("luminous", "stylish", "super", "striking", "impressive", "fantastic") glue("Have a {sample(adjective, 1)} day!") ## Have a impressive day!    @jimhester_ jimhester jim.hester@rstudio.com