# Reprexes for dbplyr

If you’re reporting a bug in dbplyr, it is much easier for me to help you if you can supply a reprex that I can run on my computer. Creating reprexes for dbplyr is particularly challenging because you are probably using a database that you can’t share with me. Fortunately, in many cases you can still demonstrate the problem even if I don’t have the complete dataset, or even access to the database system that you’re using.

This vignette outlines three approaches for creating reprexes that will work anywhere:

- Use `memdb_frame()`/`tbl_memdb()` to easily create datasets that live in an in-memory SQLite database.

- Use `lazy_frame()`/`tbl_lazy()` to simulate SQL generation of dplyr pipelines.

- Use `translate_sql()` to simulate SQL generation of columnar expression.

    library(dplyr)
    library(dbplyr)

## Using `memdb_frame()`

The first place to start is with SQLite. SQLite is particularly appealing because it’s completely embedded instead an R package so doesn’t have any external dependencies. SQLite is designed to be small and simple, so it can’t demonstrate all problems, but it’s easy to try out and a great place to start.

You can easily create a SQLite in-memory database table using `memdb_frame()`:

    mf <- memdb_frame(g = c(1, 1, 2, 2, 2), x = 1:5, y = 5:1)
    mf
    #> # Source:   table<dbplyr_001> [?? x 3]
    #> # Database: sqlite 3.34.1 [:memory:]
    #>       g     x     y
    #>   <dbl> <int> <int>
    #> 1     1     1     5
    #> 2     1     2     4
    #> 3     2     3     3
    #> 4     2     4     2
    #> # … with more rows

    mf %>%
      group_by(g) %>%
      summarise_all(mean, na.rm = TRUE)
    #> # Source:   lazy query [?? x 3]
    #> # Database: sqlite 3.34.1 [:memory:]
    #>       g     x     y
    #>   <dbl> <dbl> <dbl>
    #> 1     1   1.5   4.5
    #> 2     2   4     2

Reprexes are easiest to understand if you create very small custom data, but if you do want to use an existing data frame you can use `tbl_memdb()`:

    mtcars_db <- tbl_memdb(mtcars)
    mtcars_db %>%
      group_by(cyl) %>%
      summarise(n = n()) %>%
      show_query()
    #> <SQL>
    #> SELECT `cyl`, COUNT(*) AS `n`
    #> FROM `mtcars`
    #> GROUP BY `cyl`

## Translating verbs

Many problems with dbplyr come down to incorrect SQL generation. Fortunately, it’s possible to generate SQL without a database using `lazy_frame()` and `tbl_lazy()`. Both take an `con` argument which takes a database “simulator” like `simulate_postgres()`, `simulate_sqlite()`, etc.

    x <- c("abc", "def", "ghif")

    lazy_frame(x = x, con = simulate_postgres()) %>%
      head(5) %>%
      show_query()
    #> <SQL>
    #> SELECT *
    #> FROM `df`
    #> LIMIT 5

    lazy_frame(x = x, con = simulate_mssql()) %>%
      head(5) %>%
      show_query()
    #> <SQL>
    #> SELECT TOP 5 *
    #> FROM `df`

If you isolate the problem to incorrect SQL generation, it would be very helpful if you could also suggest more appropriate SQL.

## Translating individual expressions

In some cases, you might be able to track the problem down to incorrect translation for a single column expression. In that case, you can make your reprex even simpler with `translate_sql()`:

    translate_sql(substr(x, 1, 2), con = simulate_postgres())
    #> <SQL> SUBSTR(`x`, 1, 2)
    translate_sql(substr(x, 1, 2), con = simulate_sqlite())
    #> <SQL> SUBSTR(`x`, 1, 2)