PRQL (Pipelined Relational Query Language, pronounced “Prequel”) is a modern language for transforming data, can be compiled to SQL.
This package provides a simple function to convert a PRQL query string to a SQL string.
For example, this is a PRQL query.
And, this is the SQL query that is compiled from the PRQL query.
To compile a PRQL string, just pass the query string to the
prql_compile() function, like this.
library(prqlr)
"
from mtcars
filter cyl > 6
select {cyl, mpg}
derive {mpg_int = math.round 0 mpg}
" |>
  prql_compile() |>
  cat()
#> SELECT
#>   cyl,
#>   mpg,
#>   ROUND(mpg, 0) AS mpg_int
#> FROM
#>   mtcars
#> WHERE
#>   cyl > 6
#> 
#> -- Generated by PRQL compiler version:0.13.4 (https://prql-lang.org)This output SQL query string can be used with already existing great packages that manipulate data with SQL.
Using it with the {DBI} package, we can execute PRQL
queries against the database.
library(DBI)
# Create an ephemeral in-memory SQLite database
con <- dbConnect(RSQLite::SQLite(), ":memory:")
# Create a table inclueds `mtcars` data
dbWriteTable(con, "mtcars", mtcars)
# Execute a PRQL query
"
from mtcars
filter cyl > 6
select {cyl, mpg}
derive {mpg_int = math.round 0 mpg}
take 3
" |>
  prql_compile("sql.sqlite") |>
  dbGetQuery(con, statement = _)
#>   cyl  mpg mpg_int
#> 1   8 18.7      19
#> 2   8 14.3      14
#> 3   8 16.4      16We can also use the sqldf::sqldf() function to
automatically register Data Frames to the database.
"
from mtcars
filter cyl > 6
select {cyl, mpg}
derive {mpg_int = math.round 0 mpg}
take 3
" |>
  prql_compile("sql.sqlite") |>
  sqldf::sqldf()
#>   cyl  mpg mpg_int
#> 1   8 18.7      19
#> 2   8 14.3      14
#> 3   8 16.4      16Since SQLite is used here via {RSQLite}, the
target option of prql_compile() is set to
"sql.sqlite".
Available target names can be found with the
prql_get_targets() function.
Using {prqlr} with the {tidyquery} package,
we can execute PRQL queries against R Data Frames via
{dplyr}.
{dplyr} is a very popular R package for manipulating
Data Frames, and the PRQL syntax is very similar to the
{dplyr} syntax.
Let’s run a query that aggregates a Data Frame flights,
contained in the {nycflights13} package.
library(tidyquery)
library(nycflights13)
"
from flights
filter (distance | in 200..300)
filter air_time != null
group {origin, dest} (
  aggregate {
    num_flts = count this,
    avg_delay = (average arr_delay | math.round 0)
  }
)
sort {-origin, avg_delay}
take 2
" |>
  prql_compile() |>
  query()
#> # A tibble: 2 × 4
#>   origin dest  num_flts avg_delay
#>   <chr>  <chr>    <int>     <dbl>
#> 1 LGA    BUF        122        -2
#> 2 LGA    PWM        273         2This query can be written with {dplyr}’s functions as
follows.
library(dplyr, warn.conflicts = FALSE)
library(nycflights13)
flights |>
  filter(
    distance |> between(200, 300),
    !is.na(air_time)
  ) |>
  group_by(origin, dest) |>
  summarise(
    num_flts = n(),
    avg_delay = mean(arr_delay, na.rm = TRUE) |> round(0),
    .groups = "drop"
  ) |>
  arrange(desc(origin), avg_delay) |>
  head(2)
#> # A tibble: 2 × 4
#>   origin dest  num_flts avg_delay
#>   <chr>  <chr>    <int>     <dbl>
#> 1 LGA    BUF        122        -2
#> 2 LGA    PWM        273         2Note that {dplyr} queries can be generated by the
tidyquery::show_dplyr() function!