Use dplyr verbs to interact with the remote database table

Generic implementing of how to create a dplyr::tbl() connection in order to use dplyr verbs to interact with the remote database table. Mostly relevant for DBI connectors.

• connector_dbi: Uses dplyr::tbl() to create a table reference to a table in a DBI connection.

Usage

tbl_cnt(connector_object, name, ...)

# S3 method for class 'connector_dbi'
tbl_cnt(connector_object, name, ...)

Arguments

connector_object

connector The connector object to use.

name

character Name of the content to read, write, or remove. Typically the table name.

...

Additional arguments passed to the method for the individual connector.

Value

A dplyr::tbl object.

Examples

# Use dplyr verbs on a table in a DBI database
cnt <- connector_dbi$new(RSQLite::SQLite())

iris_cnt <- cnt |>
  write_cnt(iris, "iris") |>
  tbl_cnt("iris")

iris_cnt
#> # Source:   table<`iris`> [?? x 5]
#> # Database: sqlite 3.47.1 []
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#>  1          5.1         3.5          1.4         0.2 setosa 
#>  2          4.9         3            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           3.6          1.4         0.2 setosa 
#>  6          5.4         3.9          1.7         0.4 setosa 
#>  7          4.6         3.4          1.4         0.3 setosa 
#>  8          5           3.4          1.5         0.2 setosa 
#>  9          4.4         2.9          1.4         0.2 setosa 
#> 10          4.9         3.1          1.5         0.1 setosa 
#> # ℹ more rows

iris_cnt |>
  dplyr::collect()
#> # A tibble: 150 × 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
#>  1          5.1         3.5          1.4         0.2 setosa 
#>  2          4.9         3            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           3.6          1.4         0.2 setosa 
#>  6          5.4         3.9          1.7         0.4 setosa 
#>  7          4.6         3.4          1.4         0.3 setosa 
#>  8          5           3.4          1.5         0.2 setosa 
#>  9          4.4         2.9          1.4         0.2 setosa 
#> 10          4.9         3.1          1.5         0.1 setosa 
#> # ℹ 140 more rows

iris_cnt |>
  dplyr::group_by(Species) |>
  dplyr::summarise(
    n = dplyr::n(),
    mean.Sepal.Length = mean(Sepal.Length, na.rm = TRUE)
  ) |>
  dplyr::collect()
#> # A tibble: 3 × 3
#>   Species        n mean.Sepal.Length
#>   <chr>      <int>             <dbl>
#> 1 setosa        50              5.01
#> 2 versicolor    50              5.94
#> 3 virginica     50              6.59