# translate_sql

##### Translate an expression to sql.

Translate an expression to sql.

##### Usage

```
translate_sql(..., con = NULL, vars = character(), vars_group = NULL,
vars_order = NULL, window = TRUE)
```translate_sql_(dots, con = NULL, vars = character(), vars_group = NULL,
vars_order = NULL, window = TRUE)

##### Arguments

- ..., dots
Expressions to translate.

`sql_translate`

automatically quotes them for you.`sql_translate_`

expects a list of already quoted objects.- con
An optional database connection to control the details of the translation. The default,

`NULL`

, generates ANSI SQL.- vars
A character vector giving variable names in the remote data source. If this is supplied,

`translate_sql`

will call`partial_eval`

to interpolate in the values from local variables.- vars_group, vars_order
Grouping and ordering variables used for windowed functions.

- window
Use

`FALSE`

to suppress generation of the`OVER`

statement used for window functions. This is necessary when generating SQL for a grouped summary.

##### Base translation

The base translator, `base_sql`

,
provides custom mappings for `!`

(to NOT), `&&`

and `&`

to
`AND`

, `||`

and `|`

to `OR`

, `^`

to `POWER`

,
`%>%`

to `%`

, `ceiling`

to `CEIL`

, `mean`

to
`AVG`

, `var`

to `VARIANCE`

, `tolower`

to `LOWER`

,
`toupper`

to `UPPER`

and `nchar`

to `length`

.

`c`

and `:`

keep their usual R behaviour so you can easily create
vectors that are passed to sql.

All other functions will be preserved as is. R's infix functions
(e.g. `%like%`

) will be converted to their sql equivalents
(e.g. `LIKE`

). You can use this to access SQL string concatenation:
`||`

is mapped to `OR`

, but `%||%`

is mapped to `||`

.
To suppress this behaviour, and force errors immediately when dplyr doesn't
know how to translate a function it encounters, using set the
`dplyr.strict_sql`

option to `TRUE`

.

You can also use `sql`

to insert a raw sql string.

##### SQLite translation

The SQLite variant currently only adds one additional function: a mapping
from `sd`

to the SQL aggregation function `stdev`

.

##### Examples

`library(dplyr)`

```
# Regular maths is translated in a very straightforward way
translate_sql(x + 1)
translate_sql(sin(x) + tan(y))
# Note that all variable names are escaped
translate_sql(like == "x")
# In ANSI SQL: "" quotes variable _names_, '' quotes strings
# Logical operators are converted to their sql equivalents
translate_sql(x < 5 & !(y >= 5))
# xor() doesn't have a direct SQL equivalent
translate_sql(xor(x, y))
# If is translated into case when
translate_sql(if (x > 5) "big" else "small")
# Infix functions are passed onto SQL with % removed
translate_sql(first %like% "Had*")
translate_sql(first %is% NULL)
translate_sql(first %in% c("John", "Roger", "Robert"))
# And be careful if you really want integers
translate_sql(x == 1)
translate_sql(x == 1L)
# If you have an already quoted object, use translate_sql_:
x <- quote(y + 1 / sin(t))
translate_sql_(list(x))
# Translation with known variables ------------------------------------------
# If the variables in the dataset are known, translate_sql will interpolate
# in literal values from the current environment
x <- 10
translate_sql(mpg > x)
translate_sql(mpg > x, vars = names(mtcars))
# By default all computations happens in sql
translate_sql(cyl == 2 + 2, vars = names(mtcars))
# Use local to force local evaluation
translate_sql(cyl == local(2 + 2), vars = names(mtcars))
# This is also needed if you call a local function:
inc <- function(x) x + 1
translate_sql(mpg > inc(x), vars = names(mtcars))
translate_sql(mpg > local(inc(x)), vars = names(mtcars))
# Windowed translation --------------------------------------------
# Known window functions automatically get OVER()
translate_sql(mpg > mean(mpg))
# Suppress this with window = FALSE
translate_sql(mpg > mean(mpg), window = FALSE)
# vars_group controls partition:
translate_sql(mpg > mean(mpg), vars_group = "cyl")
# and vars_order controls ordering for those functions that need it
translate_sql(cumsum(mpg))
translate_sql(cumsum(mpg), vars_order = "mpg")
```

*Documentation reproduced from package dplyr, version 0.5.0, License: MIT + file LICENSE*