Overview

dbtools provides helpers to insert, update, or delete in a database table with the rows of a data frame. The main functions are:

• db_insert_data() to insert rows,
• db_update_data() to update rows
• db_insert_missing_data() to insert new rows,
• db_upsert_data() to insert new and update existing rows,
• db_delete_data() to delete rows.

While they are simple to use they also provide these features:

• batch operations to increase the speed,
• custom SQL code to have even better control,
• nesting transactions with the help of with_transaction(),
• returning the inserted/updated rows,
• upserts/insert missing also possible without a unique index.

And of course they can easily be used with the pipe %>%.

Installation

dbtools is not yet on CRAN and can only be installed from GitHub.

# install.packages("devtools")
devtools::install_github("mgirlich/dbtools")

Insert (Missing) Data

Insert records

library(dbtools)

if (DBI::dbExistsTable(con_memdb(), "dbtools")) {
  DBI::dbRemoveTable(con_memdb(), "dbtools")
}

dbplyr::db_copy_to(
  con = con_memdb(),
  table = "dbtools",
  values = data.frame(
    id = integer(),
    value = character(),
    update_counter = integer(),
    updated_at = character()
  )[0, ],
  overwrite = TRUE,
  unique_indexes = "id"
)
#> [1] "dbtools"

db_insert_data(
  data.frame(
    id = 1,
    value = c("a"),
    update_counter = 0,
    updated_at = Sys.time()
  ),
  table = "dbtools",
  con = con_memdb()
)
#> [1] 1

DBI::dbReadTable(con_memdb(), "dbtools")
#>   id value update_counter           updated_at
#> 1  1     a              0 2020-11-26T11:43:47Z

If you want to insert data where the id is already in the table you get an error because of the unique constraint on id:

db_insert_data(
  data.frame(
    id = 1,
    value = c("a"),
    update_counter = 0,
    updated_at = Sys.time()
  ),
  table = "dbtools",
  con = con_memdb()
)
#> Error: UNIQUE constraint failed: dbtools.id

To only insert data where the id is not yet found in the table use db_insert_missing_data()

db_insert_missing_data(
  data = data.frame(
    id = 2:3,
    value = c("b", "c"),
    updated_at = Sys.time(),
    update_counter = c(2, 3)
  ),
  table = "dbtools",
  con = con_memdb(),
  conflict_target = "id"
)
#> [1] 2

DBI::dbReadTable(con_memdb(), "dbtools")
#>   id value update_counter           updated_at
#> 1  1     a              0 2020-11-26T11:43:47Z
#> 2  2     b              2 2020-11-26T11:43:48Z
#> 3  3     c              3 2020-11-26T11:43:48Z

For more information on how to handle conflicts see the Conflicts and Unique Columns section.

Update Data

db_update_data(
  data = data.frame(
    id = 1:2,
    value = c("x", "y"),
    updated_at = Sys.time()
  ),
  table = "dbtools",
  con = con_memdb(),
  update = c("value", "updated_at"),
  where = "id"
)
#> [1] 2

DBI::dbReadTable(con_memdb(), "dbtools")
#>   id value update_counter           updated_at
#> 1  1     x              0 2020-11-26T11:43:48Z
#> 2  2     y              2 2020-11-26T11:43:48Z
#> 3  3     c              3 2020-11-26T11:43:48Z

Custom SQL

Let’s say you only want to update rows with an update_counter of at most 2 and then you also want to increase the update counter by 1 for the updated rows. This can easily be done by passing SQL code generated with sql():

db_update_data(
# sql_update(
  data = data.frame(
    id = 1:3,
    value = "z",
    updated_at = Sys.time()
  ),
  table = "dbtools",
  con = con_memdb(),
  update = list(
    "value",
    update_counter = sql("update_counter + 1"),
    "updated_at"
  ),
  where = list(
    "id",
    sql("update_counter <= 2")
  )
)
#> [1] 2

DBI::dbReadTable(con_memdb(), "dbtools")
#>   id value update_counter           updated_at
#> 1  1     z              1 2020-11-26T11:43:48Z
#> 2  2     z              3 2020-11-26T11:43:48Z
#> 3  3     c              3 2020-11-26T11:43:48Z

Data From Another Database Table

Instead of using local data to update/insert/upsert/delete you can also another database table by providing its name to the data argument:

if (DBI::dbExistsTable(con_memdb(), "dbtools2")) {
  DBI::dbRemoveTable(con_memdb(), "dbtools2")
}

dbplyr::db_copy_to(
  con = con_memdb(),
  table = "dbtools2",
  values = data.frame(
    dbtools_id = 1
  ),
  overwrite = TRUE
)
#> [1] "dbtools2"

DBI::dbReadTable(con_memdb(), "dbtools")
#>   id value update_counter           updated_at
#> 1  1     z              1 2020-11-26T11:43:48Z
#> 2  2     z              3 2020-11-26T11:43:48Z
#> 3  3     c              3 2020-11-26T11:43:48Z
DBI::dbReadTable(con_memdb(), "dbtools2")
#>   dbtools_id
#> 1          1

db_delete_data(
  "dbtools2",
  "dbtools",
  con_memdb(),
  where = c(id = "dbtools_id")
)
#> [1] 1

DBI::dbReadTable(con_memdb(), "dbtools")
#>   id value update_counter           updated_at
#> 1  2     z              3 2020-11-26T11:43:48Z
#> 2  3     c              3 2020-11-26T11:43:48Z

Conflicts and Unique Columns

When a table has a unique constraint on some columns (or it should have one but for some reason it doesn’t) and you try to insert data some rows might violate this unique constraint. This is referred to as conflict target. There are two types of conflict targets in dbtools:

• sql_unique_cols() to specify a set of unique columns,
• sql_constraint() to specify the name of an existing unique constraint.

Mind that sql_constraint() only works in newer versions of databases. If your database version does not yet support the ON CONFLICT clause you have to use sql_unique_cols() and use the mode = "old".

Generate SQL

You can also easily get the underlying SQL code with the corresponding sql_*() function:

• sql_insert()
• sql_insert_missing()
• sql_update()
• sql_delete()

There are also some helper functions you might find useful:

• sql_values() to generate a VALUES clause from the data frame