# Translations

This article describes the translations provided by duckplyr for different data types, verbs, and functions within verbs. If a translation is not provided, duckplyr falls back to dplyr, see `vignette("fallback")` for details. The translation layer can be bypassed, see `vignette("duckdb")` for details.

    library(conflicted)
    library(dplyr)
    conflict_prefer("filter", "dplyr")
    #> [conflicted] Removing existing preference.
    #> [conflicted] Will prefer dplyr::filter over any other package.
    conflict_prefer("lag", "dplyr")
    #> [conflicted] Will prefer dplyr::lag over any other package.

## Data types

duckplyr supports the following data types:

- `is.logical()`
- `is.integer()`
- `is.numeric()`
- `is.character()`
- `is.Date()`
- `is.POSIXct()` (with UTC time zone)
- `is.difftime()`

    duckplyr::duckdb_tibble(
      logical = TRUE,
      integer = 1L,
      numeric = 1.1,
      character = "a",
      Date = as.Date("2025-01-11"),
      POSIXct = as.POSIXct("2025-01-11 19:23:00", tz = "UTC"),
      difftime = as.difftime(1, units = "secs"),
    ) |>
      compute()
    #> # A duckplyr data frame: 7 variables
    #>   logical integer numeric character Date       POSIXct             difftime
    #>   <lgl>     <int>   <dbl> <chr>     <date>     <dttm>              <drtn>
    #> 1 TRUE          1     1.1 a         2025-01-11 2025-01-11 19:23:00 1 secs

Generally, zero-column tibbles are not supported by duckplyr, neither as input nor as a result.

    duckplyr::duckdb_tibble()
    #> Error in `duckdb_rel_from_df()`:
    #> ! rel_from_df: Can't convert empty data frame to relational.
    duckplyr::duckdb_tibble(a = 1, .prudence = "stingy") |>
      select(-a)
    #> Error in `select()`:
    #> ! This operation cannot be carried out by DuckDB, and the input is a
    #>   stingy duckplyr frame.
    #> • Zero-column result set not supported.
    #> ℹ Use `compute(prudence = "lavish")` to materialize to temporary storage and
    #>   continue with duckplyr.
    #> ℹ See `vignette("prudence")` for other options.

Support for more data types, and passthrough of unknown data types, is planned. Let’s discuss any additional data types you would like to see supported.

## Verbs

Not all dplyr verbs are implemented within duckplyr. For unsupported verbs, duckplyr automatically falls back to dplyr. See `?unsupported` for a list of verbs for which duckplyr does not provide a method.

See the reference index for a list of verbs with corresponding duckplyr methods.

Let’s discuss any additional verbs you would like to see supported.

## Functions within verbs

For all functions used in dplyr verbs, translations must be provided. If an expression contains a function for which no translation is provided, duckplyr falls back to dplyr. With some exceptions, only positional matching is implemented.

As of now, here are the translations provided:

### Parentheses

Implemented: `(`.

Reference: `?Paren`.

    duckplyr::duckdb_tibble(a = 1, b = 2, c = 3, .prudence = "stingy") |>
      mutate((a + b) * c)
    #> # A duckplyr data frame: 4 variables
    #>       a     b     c `(a + b) * c`
    #>   <dbl> <dbl> <dbl>         <dbl>
    #> 1     1     2     3             9

### Comparison operators

Implemented: `>`, `>=`, `<`, `<=`, `==`, `!=`.

Reference: `?Comparison`.

    duckplyr::duckdb_tibble(
      a = c(1, 2, NA),
      b = c(2, NA, 3),
      c = c(NA, 3, 4),
      .prudence = "stingy"
    ) |>
      mutate(a > b, b != c, c < a, a >= b, b <= c)
    #> # A duckplyr data frame: 8 variables
    #>       a     b     c `a > b` `b != c` `c < a` `a >= b` `b <= c`
    #>   <dbl> <dbl> <dbl> <lgl>   <lgl>    <lgl>   <lgl>    <lgl>
    #> 1     1     2    NA FALSE   NA       NA      FALSE    NA
    #> 2     2    NA     3 NA      NA       FALSE   NA       NA
    #> 3    NA     3     4 NA      TRUE     NA      NA       TRUE

### Basic arithmetics

Implemented: `+`, `-`, `*`, `/`.

Reference: `?Arithmetic`.

    duckplyr::duckdb_tibble(a = 1, b = 2, c = 3, .prudence = "stingy") |>
      mutate(a + b, a / b, a - b, a * b)
    #> # A duckplyr data frame: 7 variables
    #>       a     b     c `a + b` `a/b` `a - b` `a * b`
    #>   <dbl> <dbl> <dbl>   <dbl> <dbl>   <dbl>   <dbl>
    #> 1     1     2     3       3   0.5      -1       2

### Math functions

Implemented: `log()`, `log10()`, `abs()`.

Reference: `?Math`.

    duckplyr::duckdb_tibble(a = 1, b = 2, c = -3, .prudence = "stingy") |>
      mutate(log10(a), log(b), abs(c))
    #> # A duckplyr data frame: 6 variables
    #>       a     b     c `log10(a)` `log(b)` `abs(c)`
    #>   <dbl> <dbl> <dbl>      <dbl>    <dbl>    <dbl>
    #> 1     1     2    -3          0    0.693        3

### Logical operators

Implemented: `!`, `&`, `|`.

Reference: `?Logic`.

    duckplyr::duckdb_tibble(a = FALSE, b = TRUE, c = NA, .prudence = "stingy") |>
      mutate(!a, a & b, b | c)
    #> # A duckplyr data frame: 6 variables
    #>   a     b     c     `!a`  `a & b` `b | c`
    #>   <lgl> <lgl> <lgl> <lgl> <lgl>   <lgl>
    #> 1 FALSE TRUE  NA    TRUE  FALSE   TRUE

### Branching and conversion

Implemented:

- `is.na()`, `as.integer()`
- `dplyr::if_else()`, `dplyr::coalesce()`
- `strftime(x, format)`

    duckplyr::duckdb_tibble(a = 1, b = NA, .prudence = "stingy") |>
      mutate(is.na(b), if_else(is.na(b), 0, 1), as.integer(b))
    #> # A duckplyr data frame: 5 variables
    #>       a b     `is.na(b)` `if_else(is.na(b), 0, 1)` `as.integer(b)`
    #>   <dbl> <lgl> <lgl>                          <dbl>           <int>
    #> 1     1 NA    TRUE                               0              NA

    duckplyr::duckdb_tibble(
      a = as.POSIXct("2025-01-11 19:23:46", tz = "UTC"),
      .prudence = "stingy") |>
      mutate(strftime(a, "%H:%M:%S"))
    #> # A duckplyr data frame: 2 variables
    #>   a                   `strftime(a, "%H:%M:%S")`
    #>   <dttm>              <chr>
    #> 1 2025-01-11 19:23:46 19:23:46

### String manipulation

Implemented: `grepl()`, `substr()`, `sub()`, `gsub()`.

    duckplyr::duckdb_tibble(a = "abbc", .prudence = "stingy") |>
      mutate(grepl("b", a), substr(a, 2L, 3L), sub("b", "B", a), gsub("b", "B", a))
    #> # A duckplyr data frame: 5 variables
    #>   a     `grepl("b", a)` `substr(a, 2L, 3L)` `sub("b", "B", a)`
    #>   <chr> <lgl>           <chr>               <chr>
    #> 1 abbc  TRUE            bbc                 aBbc
    #> # ℹ 1 more variable: `gsub("b", "B", a)` <chr>

### Date manipulation

Implemented: `lubridate::hour()`, `lubridate::minute()`, `lubridate::second()`, `lubridate::wday()`.

    duckplyr::duckdb_tibble(
      a = as.POSIXct("2025-01-11 19:23:46", tz = "UTC"),
      .prudence = "stingy"
    ) |>
      mutate(
        hour = lubridate::hour(a),
        minute = lubridate::minute(a),
        second = lubridate::second(a),
        wday = lubridate::wday(a)
      )
    #> # A duckplyr data frame: 5 variables
    #>   a                    hour minute second  wday
    #>   <dttm>              <dbl>  <dbl>  <dbl> <int>
    #> 1 2025-01-11 19:23:46    19     23     46     7

### Aggregation

Implemented:

- `sum(x, na.rm)`, `dplyr::n()`, `dplyr::n_distinct()`
- `mean(x, na.rm)`, `median(x, na.rm)`, `sd(x, na.rm)`
- `min()`, `max()`, `any()`, `all()`

    duckplyr::duckdb_tibble(a = 1:3, b = c(1, 2, 2), .prudence = "stingy") |>
      summarize(
        sum(a),
        n(),
        n_distinct(b),
      )
    #> # A duckplyr data frame: 3 variables
    #>   `sum(a)` `n()` `n_distinct(b)`
    #>      <dbl> <int>           <dbl>
    #> 1        6     3               2

    duckplyr::duckdb_tibble(a = 1:3, b = c(1, 2, NA), .prudence = "stingy") |>
      summarize(
        mean(b, na.rm = TRUE),
        median(a),
        sd(b),
      )
    #> # A duckplyr data frame: 3 variables
    #>   `mean(b, na.rm = TRUE)` `median(a)` `sd(b)`
    #>                     <dbl>       <dbl>   <dbl>
    #> 1                     1.5           2      NA

    duckplyr::duckdb_tibble(a = 1:3, .prudence = "stingy") |>
      summarize(
        min(a),
        max(a),
        any(a > 1),
        all(a > 1),
      )
    #> # A duckplyr data frame: 4 variables
    #>   `min(a)` `max(a)` `any(a > 1)` `all(a > 1)`
    #>      <int>    <int> <lgl>        <lgl>
    #> 1        1        3 TRUE         FALSE

### Shifting

All optional arguments to `dplyr::lag()` and `dplyr::lead()` are supported.

    duckplyr::duckdb_tibble(a = 1:3, .prudence = "stingy") |>
      mutate(lag(a), lead(a))
    #> # A duckplyr data frame: 3 variables
    #>       a `lag(a)` `lead(a)`
    #>   <int>    <int>     <int>
    #> 1     1       NA         2
    #> 2     2        1         3
    #> 3     3        2        NA
    duckplyr::duckdb_tibble(a = 1:3, .prudence = "stingy") |>
      mutate(lag(a, 2), lead(a, n = 2))
    #> # A duckplyr data frame: 3 variables
    #>       a `lag(a, 2)` `lead(a, n = 2)`
    #>   <int>       <int>            <int>
    #> 1     1          NA                3
    #> 2     2          NA               NA
    #> 3     3           1               NA
    duckplyr::duckdb_tibble(a = 1:3, .prudence = "stingy") |>
      mutate(lag(a, default = 0), lead(a, default = 4))
    #> # A duckplyr data frame: 3 variables
    #>       a `lag(a, default = 0)` `lead(a, default = 4)`
    #>   <int>                 <int>                  <int>
    #> 1     1                     0                      2
    #> 2     2                     1                      3
    #> 3     3                     2                      4
    duckplyr::duckdb_tibble(a = 1:3, b = c(2, 3, 1), .prudence = "stingy") |>
      mutate(lag(a, order_by = b), lead(a, order_by = b))
    #> # A duckplyr data frame: 4 variables
    #>       a     b `lag(a, order_by = b)` `lead(a, order_by = b)`
    #>   <int> <dbl>                  <int>                   <int>
    #> 1     1     2                      3                       2
    #> 2     2     3                      1                      NA
    #> 3     3     1                     NA                       1

### Ranking

Ranking in DuckDB is very different from dplyr. Most functions in DuckDB rank only by the current row number, whereas in dplyr, ranking is done by a column. It will be difficult to provide translations for the following ranking functions.

- `rank()`, `dplyr::min_rank()`, `dplyr::dense_rank()`
- `dplyr::percent_rank()`, `dplyr::cume_dist()`

Implementing `dplyr::ntile()` is feasible for the `n` argument. The only ranking function currently implemented is `dplyr::row_number()`.

    duckplyr::duckdb_tibble(a = c(1, 2, 2, 3), .prudence = "stingy") |>
      mutate(row_number())
    #> # A duckplyr data frame: 2 variables
    #>       a `row_number()`
    #>   <dbl>          <int>
    #> 1     1              1
    #> 2     2              2
    #> 3     2              3
    #> 4     3              4

### Special cases

`$` (`?Extract`) is implemented if the LHS is `.data` or `.env`:

    b <- 4
    duckplyr::duckdb_tibble(a = 1, b = 2, .prudence = "stingy") |>
      mutate(.data$a + .data$b, .env$b)
    #> # A duckplyr data frame: 4 variables
    #>       a     b `.data$a + .data$b` `.env$b`
    #>   <dbl> <dbl>               <dbl>    <dbl>
    #> 1     1     2                   3        4

`%in%` (`?match`) is implemented if the RHS is a constant with up to 100 values:

    duckplyr::duckdb_tibble(a = 1:3, .prudence = "stingy") |>
      mutate(a %in% c(1, 3)) |>
      collect()
    #> # A tibble: 3 × 2
    #>       a `a %in% c(1, 3)`
    #>   <int> <lgl>
    #> 1     1 TRUE
    #> 2     2 FALSE
    #> 3     3 TRUE
    duckplyr::last_rel()
    #> DuckDB Relation:
    #> ---------------------
    #> --- Relation Tree ---
    #> ---------------------
    #> Projection [a as a, ___coalesce("|"("r_base::=="(a, 1.0), "r_base::=="(a, 3.0)), false) as a %in% c(1, 3)]
    #>   r_dataframe_scan(0xdeadbeef)
    #>
    #> ---------------------
    #> -- Result Columns  --
    #> ---------------------
    #> - a (INTEGER)
    #> - a %in% c(1, 3) (BOOLEAN)

`dplyr::desc()` is only implemented in the context of `dplyr::arrange()`:

    duckplyr::duckdb_tibble(a = 1:3, .prudence = "stingy") |>
      arrange(desc(a)) |>
      explain()
    #> ┌---------------------------┐
    #> │          ORDER_BY         │
    #> │    --------------------   │
    #> │      dataframe_42_42      │
    #> │        5265.a DESC        │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │     R_DATAFRAME_SCAN      │
    #> │    --------------------   │
    #> │      Text: data.frame     │
    #> │       Projections: a      │
    #> │                           │
    #> │          ~3 rows          │
    #> └---------------------------┘

`suppressWarnings()` is a no-op:

    duckplyr::duckdb_tibble(a = 1, .prudence = "stingy") |>
      mutate(suppressWarnings(a + 1))
    #> # A duckplyr data frame: 2 variables
    #>       a `suppressWarnings(a + 1)`
    #>   <dbl>                     <dbl>
    #> 1     1                         2

### Contributing

Refer to our contributing guide to learn how to contribute new translations to the package. Ideally, duckplyr will also support adding custom translations for functions for the duration of the current R session.

## Known incompatibilities

This section tracks known incompatibilities between dplyr and duckplyr. Changing these is likely to require substantial effort, and might be best addressed by providing new functions with consistent behavior in both dplyr and DuckDB.

### Output order stability

DuckDB does not guarantee order stability for the output. For performance reasons, duckplyr does not enable output order stability by default.

    duckplyr::flights_df() |>
      duckplyr::as_duckdb_tibble() |>
      distinct(day) |>
      summarize(paste(day, collapse = " ")) # fallback
    #> # A duckplyr data frame: 1 variable
    #>   `paste(day, collapse = " ")`
    #>   <chr>
    #> 1 7 8 13 19 27 11 15 16 22 26 31 1 21 25 5 9 14 17 30 4 23 24 2 10 18 28 29 3 6…

    duckplyr::flights_df() |>
      distinct(day) |>
      summarize(paste(day, collapse = " "))
    #> # A tibble: 1 × 1
    #>   `paste(day, collapse = " ")`
    #>   <chr>
    #> 1 2 10 18 28 29 11 15 16 22 26 31 1 21 25 5 9 14 17 30 4 23 24 7 8 13 19 27 3 6…

This can be changed globally with the `DUCKPLYR_OUTPUT_ORDER` environment variable, see `?config` for details. With this setting, the output order is stable, but the plans are more complicated, and DuckDB needs to do more work.

    duckplyr::flights_df() |>
      duckplyr::as_duckdb_tibble() |>
      distinct(day) |>
      explain()
    #> ┌---------------------------┐
    #> │       HASH_GROUP_BY       │
    #> │    --------------------   │
    #> │         Groups: #0        │
    #> │                           │
    #> │       ~336,776 rows       │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │         PROJECTION        │
    #> │    --------------------   │
    #> │            day            │
    #> │                           │
    #> │       ~336,776 rows       │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │     R_DATAFRAME_SCAN      │
    #> │    --------------------   │
    #> │      Text: data.frame     │
    #> │      Projections: day     │
    #> │                           │
    #> │       ~336,776 rows       │
    #> └---------------------------┘

    withr::with_envvar(
      c(DUCKPLYR_OUTPUT_ORDER = "TRUE"),
      duckplyr::flights_df() |>
        duckplyr::as_duckdb_tibble() |>
        distinct(day) |>
        explain()
    )
    #> ┌---------------------------┐
    #> │          ORDER_BY         │
    #> │    --------------------   │
    #> │      dataframe_42_42      │
    #> │   42.___row_number ASC   │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │         PROJECTION        │
    #> │    --------------------   │
    #> │             #0            │
    #> │             #1            │
    #> │                           │
    #> │        ~67,355 rows       │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │           FILTER          │
    #> │    --------------------   │
    #> │   (___row_number_by = 1)  │
    #> │                           │
    #> │        ~67,355 rows       │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │         PROJECTION        │
    #> │    --------------------   │
    #> │             #0            │
    #> │             #1            │
    #> │             #2            │
    #> │                           │
    #> │       ~336,776 rows       │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │           WINDOW          │
    #> │    --------------------   │
    #> │        Projections:       │
    #> │     ROW_NUMBER() OVER     │
    #> │ (PARTITION BY day ORDER BY│
    #> │   ___row_number ASC NULLS │
    #> │            LAST)          │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │         PROJECTION        │
    #> │    --------------------   │
    #> │             #0            │
    #> │             #1            │
    #> │                           │
    #> │       ~336,776 rows       │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │      STREAMING_WINDOW     │
    #> │    --------------------   │
    #> │        Projections:       │
    #> │    ROW_NUMBER() OVER ()   │
    #> └-------------┬-------------┘
    #> ┌-------------┴-------------┐
    #> │     R_DATAFRAME_SCAN      │
    #> │    --------------------   │
    #> │      Text: data.frame     │
    #> │      Projections: day     │
    #> │                           │
    #> │       ~336,776 rows       │
    #> └---------------------------┘

### `sum()`

In duckplyr, this function returns a numeric value also for integers, due to DuckDB’s type stability requirement.

    duckplyr::duckdb_tibble(a = 1:100) |>
      summarize(sum(a))
    #> # A duckplyr data frame: 1 variable
    #>   `sum(a)`
    #>      <dbl>
    #> 1     5050

    duckplyr::duckdb_tibble(a = 1:1000000) |>
      summarize(sum(a))
    #> # A duckplyr data frame: 1 variable
    #>       `sum(a)`
    #>          <dbl>
    #> 1 500000500000

    tibble(a = 1:100) |>
      summarize(sum(a))
    #> # A tibble: 1 × 1
    #>   `sum(a)`
    #>      <dbl>
    #> 1     5050

    tibble(a = 1:1000000) |>
      summarize(sum(a))
    #> # A tibble: 1 × 1
    #>       `sum(a)`
    #>          <dbl>
    #> 1 500000500000

### Empty vectors in aggregate functions

At the time of writing, empty vectors only occur when summarizing an empty table without grouping. In all cases, duckplyr returns `NA`, and the behavior of dplyr is different:

- `sum()` for an empty vector returns `0`
- `any()` and `all()` return `FALSE`
- `min()` and `max()` return infinity values (with a warning)

    duckplyr::duckdb_tibble(a = integer(), b = logical()) |>
      summarize(sum(a), any(b), all(b), min(a), max(a))
    #> # A duckplyr data frame: 5 variables
    #>   `sum(a)` `any(b)` `all(b)` `min(a)` `max(a)`
    #>      <dbl> <lgl>    <lgl>       <int>    <int>
    #> 1       NA NA       NA             NA       NA
    tibble(a = integer(), b = logical()) |>
      summarize(sum(a), any(b), all(b), min(a), max(a))
    #> # A tibble: 1 × 5
    #>   `sum(a)` `any(b)` `all(b)` `min(a)` `max(a)`
    #>      <dbl> <lgl>    <lgl>       <int>    <int>
    #> 1       NA NA       NA             NA       NA

### `min()` and `max()` for logical input

For completeness, duckplyr returns a logical for `min()` and `max()` when the input is logical, while dplyr returns an integer.

    duckplyr::duckdb_tibble(a = c(TRUE, FALSE)) |>
      summarize(min(a), max(a))
    #> # A duckplyr data frame: 2 variables
    #>   `min(a)` `max(a)`
    #>   <lgl>    <lgl>
    #> 1 FALSE    TRUE

    tibble(a = c(TRUE, FALSE)) |>
      summarize(min(a), max(a))
    #> # A tibble: 1 × 2
    #>   `min(a)` `max(a)`
    #>   <lgl>    <lgl>
    #> 1 FALSE    TRUE

### `n_distinct()` and multiple arguments

This function needs exactly one argument besides the optional `na.rm`. Multiple arguments is not supported.

### `is.na()` and `NaN` values

This function returns `FALSE` for `NaN` values in duckplyr, while it returns `TRUE` in dplyr.

    duckplyr::duckdb_tibble(a = c(NA, NaN)) |>
      mutate(is.na(a))
    #> # A duckplyr data frame: 2 variables
    #>       a `is.na(a)`
    #>   <dbl> <lgl>
    #> 1    NA TRUE
    #> 2   NaN FALSE

    tibble(a = c(NA, NaN)) |>
      mutate(is.na(a))
    #> # A tibble: 2 × 2
    #>       a `is.na(a)`
    #>   <dbl> <lgl>
    #> 1    NA TRUE
    #> 2   NaN FALSE

### Row names

DuckDB does not support data frames with row names. When converting a data frame with row names to a duckplyr data frame, the row names are silently stripped. This is relevant when working with data frames that have row names, such as `mtcars`.

    # mtcars has character row names
    head(rownames(mtcars))
    #> [1] "Mazda RX4"         "Mazda RX4 Wag"     "Datsun 710"
    #> [4] "Hornet 4 Drive"    "Hornet Sportabout" "Valiant"
    # After conversion, the row names are lost
    mtcars |>
      duckplyr::as_duckdb_tibble() |>
      head()
    #> # A duckplyr data frame: 11 variables
    #>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
    #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    #> 1  21       6   160   110  3.9   2.62  16.5     0     1     4     4
    #> 2  21       6   160   110  3.9   2.88  17.0     0     1     4     4
    #> 3  22.8     4   108    93  3.85  2.32  18.6     1     1     4     1
    #> 4  21.4     6   258   110  3.08  3.22  19.4     1     0     3     1
    #> 5  18.7     8   360   175  3.15  3.44  17.0     0     0     3     2
    #> 6  18.1     6   225   105  2.76  3.46  20.2     1     0     3     1

To preserve row names, convert them to a column before using duckplyr:

    mtcars |>
      tibble::rownames_to_column("name") |>
      duckplyr::as_duckdb_tibble() |>
      head()
    #> # A duckplyr data frame: 12 variables
    #>   name           mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
    #>   <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    #> 1 Mazda RX4     21       6   160   110  3.9   2.62  16.5     0     1     4     4
    #> 2 Mazda RX4 W…  21       6   160   110  3.9   2.88  17.0     0     1     4     4
    #> 3 Datsun 710    22.8     4   108    93  3.85  2.32  18.6     1     1     4     1
    #> 4 Hornet 4 Dr…  21.4     6   258   110  3.08  3.22  19.4     1     0     3     1
    #> 5 Hornet Spor…  18.7     8   360   175  3.15  3.44  17.0     0     0     3     2
    #> 6 Valiant       18.1     6   225   105  2.76  3.46  20.2     1     0     3     1

### Other differences

Does the same pipeline give different results with `tibble()` and `duckdb_tibble()`? We would love to hear about it, please file an issue.