Returns the contents of a database table given by name as a data frame.

# S4 method for SQLiteConnection,character
dbReadTable(conn, name, ...,
  row.names = pkgconfig::get_config("RSQLite::row.names.table", FALSE),
  check.names = TRUE, select.cols = NULL)

Arguments

conn

a SQLiteConnection object, produced by DBI::dbConnect()

name

a character string specifying a table name. SQLite table names are not case sensitive, e.g., table names ABC and abc are considered equal.

...

Needed for compatibility with generic. Otherwise ignored.

row.names

Either TRUE, FALSE, NA or a string.

If TRUE, always translate row names to a column called "row_names". If FALSE, never translate row names. If NA, translate rownames only if they're a character vector.

A string is equivalent to TRUE, but allows you to override the default name.

For backward compatibility, NULL is equivalent to FALSE.

check.names

If TRUE, the default, column names will be converted to valid R identifiers.

select.cols

Deprecated, do not use.

Value

A data frame.

Details

Note that the data frame returned by dbReadTable() only has primitive data, e.g., it does not coerce character data to factors.

See also

The corresponding generic function DBI::dbReadTable().

Examples

library(DBI) db <- RSQLite::datasetsDb() dbReadTable(db, "mtcars")
#> row_names mpg cyl disp hp drat wt qsec vs am gear carb #> 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
dbReadTable(db, "mtcars", row.names = FALSE)
#> row_names mpg cyl disp hp drat wt qsec vs am gear carb #> 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2