dbplyr (version 2.5.0)

tbl.src_dbi: Use dplyr verbs with a remote database table

Description

All data manipulation on SQL tbls are lazy: they will not actually run the query or retrieve the data unless you ask for it: they all return a new tbl_dbi object. Use compute() to run the query and save the results in a temporary in the database, or use collect() to retrieve the results to R. You can see the query with show_query().

Usage

# S3 method for src_dbi
tbl(src, from, ...)

Arguments

src

A DBIConnection object produced by DBI::dbConnect().

from

Either a table identifier or a literal sql() string.

Use a string to identify a table in the current schema/catalog. We recommend using I() to identify a table outside the default catalog or schema, e.g. I("schema.table") or I("catalog.schema.table"). You can also use in_schema()/in_catalog() or DBI::Id().

...

Passed on to tbl_sql()

Details

For best performance, the database should have an index on the variables that you are grouping by. Use explain() to check that the database is using the indexes that you expect.

There is one verb that is not lazy: do() is eager because it must pull the data into R.

Examples

library(dplyr)

# Connect to a temporary in-memory SQLite database
con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")

# Add some data
copy_to(con, mtcars)
DBI::dbListTables(con)

# To retrieve a single table from a source, use `tbl()`
con %>% tbl("mtcars")

# Use `I()` for qualified table names
con %>% tbl(I("temp.mtcars")) %>% head(1)

# You can also use pass raw SQL if you want a more sophisticated query
con %>% tbl(sql("SELECT * FROM mtcars WHERE cyl = 8"))

# If you just want a temporary in-memory database, use src_memdb()
src2 <- src_memdb()

# To show off the full features of dplyr's database integration,
# we'll use the Lahman database. lahman_sqlite() takes care of
# creating the database.

if (requireNamespace("Lahman", quietly = TRUE)) {
batting <- copy_to(con, Lahman::Batting)
batting

# Basic data manipulation verbs work in the same way as with a tibble
batting %>% filter(yearID > 2005, G > 130)
batting %>% select(playerID:lgID)
batting %>% arrange(playerID, desc(yearID))
batting %>% summarise(G = mean(G), n = n())

# There are a few exceptions. For example, databases give integer results
# when dividing one integer by another. Multiply by 1 to fix the problem
batting %>%
  select(playerID:lgID, AB, R, G) %>%
  mutate(
   R_per_game1 = R / G,
   R_per_game2 = R * 1.0 / G
 )

# All operations are lazy: they don't do anything until you request the
# data, either by `print()`ing it (which shows the first ten rows),
# or by `collect()`ing the results locally.
system.time(recent <- filter(batting, yearID > 2010))
system.time(collect(recent))

# You can see the query that dplyr creates with show_query()
batting %>%
  filter(G > 0) %>%
  group_by(playerID) %>%
  summarise(n = n()) %>%
  show_query()
}