Glue offers interpreted string literals that are small, fast, and dependency-free. Glue does this by embedding R expressions in curly braces which are then evaluated and inserted into the argument string.
# Install released version from CRAN
install.packages("glue")# Install development version from GitHub
pak::pak("tidyverse/glue")library(glue)
name <- "Fred"
glue('My name is {name}.')
#> My name is Fred.Note that glue::glue() is also made available via
stringr::str_glue(). So if you’ve already attached stringr (or perhaps
the whole tidyverse), you can access glue() like so:
library(stringr) # or library(tidyverse)
stringr_fcn <- "`stringr::str_glue()`"
glue_fcn <- "`glue::glue()`"
str_glue('{stringr_fcn} is essentially an alias for {glue_fcn}.')
#> `stringr::str_glue()` is essentially an alias for `glue::glue()`.library(glue)
name <- "Fred"
age <- 50
anniversary <- as.Date("1991-10-12")
glue('My name is {name},',
' my age next year is {age + 1},',
' my anniversary is {format(anniversary, "%A, %B %d, %Y")}.')
#> My name is Fred, my age next year is 51, my anniversary is Saturday, October 12, 1991.glue('My name is {name},',
' my age next year is {age + 1},',
' my anniversary is {format(anniversary, "%A, %B %d, %Y")}.',
name = "Joe",
age = 40,
anniversary = as.Date("2001-10-12"))
#> My name is Joe, my age next year is 41, my anniversary is Friday, October 12, 2001.glue_data() is useful with magrittr pipes.
`%>%` <- magrittr::`%>%`
head(mtcars) %>% glue_data("{rownames(.)} has {hp} hp")
#> Mazda RX4 has 110 hp
#> Mazda RX4 Wag has 110 hp
#> Datsun 710 has 93 hp
#> Hornet 4 Drive has 110 hp
#> Hornet Sportabout has 175 hp
#> Valiant has 105 hplibrary(dplyr)
head(iris) %>%
mutate(description = glue("This {Species} has a petal length of {Petal.Length}"))
#> 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
#> description
#> 1 This setosa has a petal length of 1.4
#> 2 This setosa has a petal length of 1.4
#> 3 This setosa has a petal length of 1.3
#> 4 This setosa has a petal length of 1.5
#> 5 This setosa has a petal length of 1.4
#> 6 This setosa has a petal length of 1.7This lets you indent the strings naturally in code.
glue("
A formatted string
Can have multiple lines
with additional indention preserved
")
#> A formatted string
#> Can have multiple lines
#> with additional indention preservedglue("
leading or trailing newlines can be added explicitly
")
#>
#> leading or trailing newlines can be added explicitlyglue("
A formatted string \\
can also be on a \\
single line
")
#> A formatted string can also be on a single linename <- "Fred"
glue("My name is {name}, not {{name}}.")
#> My name is Fred, not {name}.one <- "1"
glue("The value of $e^{2\\pi i}$ is $<<one>>$.", .open = "<<", .close = ">>")
#> The value of $e^{2\pi i}$ is $1$.Backslashes do need to be doubled just like in all R strings.
`foo}\`` <- "foo"
glue("{
{
'}\\'' # { and } in comments, single quotes
\"}\\\"\" # or double quotes are ignored
`foo}\\`` # as are { in backticks
}
}")
#> fooUse backticks to quote identifiers, normal strings and numbers are quoted appropriately for your backend.
library(glue)
con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
colnames(iris) <- gsub("[.]", "_", tolower(colnames(iris)))
DBI::dbWriteTable(con, "iris", iris)
var <- "sepal_width"
tbl <- "iris"
num <- 2
val <- "setosa"
glue_sql("
SELECT {`var`}
FROM {`tbl`}
WHERE {`tbl`}.sepal_length > {num}
AND {`tbl`}.species = {val}
", .con = con)
#> <SQL> SELECT `sepal_width`
#> FROM `iris`
#> WHERE `iris`.sepal_length > 2
#> AND `iris`.species = 'setosa'
# `glue_sql()` can be used in conjunction with parameterized queries using
# `DBI::dbBind()` to provide protection for SQL Injection attacks
sql <- glue_sql("
SELECT {`var`}
FROM {`tbl`}
WHERE {`tbl`}.sepal_length > ?
", .con = con)
query <- DBI::dbSendQuery(con, sql)
DBI::dbBind(query, list(num))
DBI::dbFetch(query, n = 4)
#> sepal_width
#> 1 3.5
#> 2 3.0
#> 3 3.2
#> 4 3.1
DBI::dbClearResult(query)
# `glue_sql()` can be used to build up more complex queries with
# interchangeable sub queries. It returns `DBI::SQL()` objects which are
# properly protected from quoting.
sub_query <- glue_sql("
SELECT *
FROM {`tbl`}
", .con = con)
glue_sql("
SELECT s.{`var`}
FROM ({sub_query}) AS s
", .con = con)
#> <SQL> SELECT s.`sepal_width`
#> FROM (SELECT *
#> FROM `iris`) AS s
# If you want to input multiple values for use in SQL IN statements put `*`
# at the end of the value and the values will be collapsed and quoted appropriately.
glue_sql("SELECT * FROM {`tbl`} WHERE sepal_length IN ({vals*})",
vals = 1, .con = con)
#> <SQL> SELECT * FROM `iris` WHERE sepal_length IN (1)
glue_sql("SELECT * FROM {`tbl`} WHERE sepal_length IN ({vals*})",
vals = 1:5, .con = con)
#> <SQL> SELECT * FROM `iris` WHERE sepal_length IN (1, 2, 3, 4, 5)
glue_sql("SELECT * FROM {`tbl`} WHERE species IN ({vals*})",
vals = "setosa", .con = con)
#> <SQL> SELECT * FROM `iris` WHERE species IN ('setosa')
glue_sql("SELECT * FROM {`tbl`} WHERE species IN ({vals*})",
vals = c("setosa", "versicolor"), .con = con)
#> <SQL> SELECT * FROM `iris` WHERE species IN ('setosa', 'versicolor')Some other implementations of string interpolation in R (although not using identical syntax).
String templating is closely related to string interpolation, although not exactly the same concept. Some packages implementing string templating in R include.
Please note that the glue project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.