For backward compatibility dplyr provides three srcs for popular open source databases:

However, modern best practice is to use tbl() directly on an DBIConnection.

src_mysql(dbname, host = NULL, port = 0L, username = "root",
  password = "", ...)

src_postgres(dbname = NULL, host = NULL, port = NULL, user = NULL,
  password = NULL, ...)

src_sqlite(path, create = FALSE)

Arguments

dbname

Database name

host, port

Host name and port number of database

...

for the src, other arguments passed on to the underlying database connector, DBI::dbConnect(). For the tbl, included for compatibility with the generic, but otherwise ignored.

user, username, password

User name and password.

Generally, you should avoid saving username and password in your scripts as it is easy to accidentally expose valuable credentials. Instead, retrieve them from environment variables, or use database specific credential scores. For example, with MySQL you can set up my.cnf as described in RMySQL::MySQL().

path

Path to SQLite database. You can use the special path ":memory:" to create a temporary in memory database.

create

if FALSE, path must already exist. If TRUE, will create a new SQlite3 database at path if path does not exist and connect to the existing database if path does exist.

Value

An S3 object with class src_dbi, src_sql, src.

Details

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().

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 excpetion: do() is not lazy since it must pull the data into R.

Examples

# Basic connection using DBI ------------------------------------------- if (require(dbplyr, quietly = TRUE)) { con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") copy_to(con, mtcars) DBI::dbListTables(con) # To retrieve a single table from a source, use `tbl()` con %>% tbl("mtcars") # You can also use pass raw SQL if you want a more sophisticated query con %>% tbl(sql("SELECT * FROM mtcars WHERE cyl == 8")) # 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. lahman_p <- lahman_sqlite() batting <- lahman_p %>% tbl("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() }
#> <SQL> #> SELECT `playerID`, COUNT() AS `n` #> FROM `Batting` #> WHERE (`G` > 0.0) #> GROUP BY `playerID`