R dbplyr tbl.src_dbi 對遠程數據庫表使用 dplyr 動詞

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

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)
#> [1] "mtcars"       "sqlite_stat1" "sqlite_stat4"

# To retrieve a single table from a source, use `tbl()`
con %>% tbl("mtcars")
#> # Source:   table<mtcars> [?? x 11]
#> # Database: sqlite 3.41.2 [:memory:]
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # ℹ more rows

# Use `in_schema()` for fully qualified table names
con %>% tbl(in_schema("temp", "mtcars")) %>% head(1)
#> # Source:   SQL [1 x 11]
#> # Database: sqlite 3.41.2 [:memory:]
#>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1    21     6   160   110   3.9  2.62  16.5     0     1     4     4

# You can also use pass raw SQL if you want a more sophisticated query
con %>% tbl(sql("SELECT * FROM mtcars WHERE cyl = 8"))
#> # Source:   SQL [?? x 11]
#> # Database: sqlite 3.41.2 [:memory:]
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  2  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  3  16.4     8  276.   180  3.07  4.07  17.4     0     0     3     3
#>  4  17.3     8  276.   180  3.07  3.73  17.6     0     0     3     3
#>  5  15.2     8  276.   180  3.07  3.78  18       0     0     3     3
#>  6  10.4     8  472    205  2.93  5.25  18.0     0     0     3     4
#>  7  10.4     8  460    215  3     5.42  17.8     0     0     3     4
#>  8  14.7     8  440    230  3.23  5.34  17.4     0     0     3     4
#>  9  15.5     8  318    150  2.76  3.52  16.9     0     0     3     2
#> 10  15.2     8  304    150  3.15  3.44  17.3     0     0     3     2
#> # ℹ more rows

# 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()
}
#> <SQL>
#> SELECT `playerID`, COUNT(*) AS `n`
#> FROM `Lahman::Batting`
#> WHERE (`G` > 0.0)
#> GROUP BY `playerID`