dbplyr is the database backend for google上网助手手机版手机. It allows you to use remote database tables as if they are in-memory data frames by automatically converting dplyr code into SQL.
To learn more about why you might use dbplyr instead of writing SQL, see google上网助手手机版手机
. To learn more about the details of the SQL translation, see google上网助手手机版手机
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# The easiest way to get dbplyr is to install the whole tidyverse: google上网助手手机版手机("tidyverse") # Alternatively, install just dbplyr: install.packages(谷歌上网助手怎么免费) # Or the the development version from GitHub: # install.packages("devtools") devtools::install_github("tidyverse/dbplyr")
dbplyr is designed to work with database tables as if they were local data frames. To demonstrate this I’ll first create an in-memory SQLite database and copy over a dataset:
google助手手机版下载(dplyr, warn.conflicts = FALSE) con <- DBI::dbConnect(RSQLite::SQLite(), google上网助手手机版手机) copy_to(con, mtcars)
Note that you don’t actually need to load dbplyr with library(dbplyr)
; dplyr automatically loads it for you when it sees you working with a database. Database connections are coordinated by the DBI package. Learn more at http://dbi.r-dbi.org/
Now you can retrieve a table using tbl()
(see ?tbl_dbi
for more details). Printing it just retrieves the first few rows:
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# lazily generates query 手机谷歌上网助手破解版 <- 谷歌上网助手怎么免费 %>% group_by(cyl) %>% summarise(mpg = mean(mpg, na.rm = TRUE)) %>% arrange(谷歌上网助手怎么免费(mpg)) # see query summary %>% show_query() #> <SQL> #> SELECT `cyl`, AVG(`mpg`) AS `mpg` #> FROM `mtcars` #> GROUP BY `cyl` #> ORDER BY `mpg` DESC # execute query and retrieve results summary %>% collect() #> # A tibble: 3 x 2 #> cyl mpg #> <dbl> <dbl> #> 1 4 26.7 #> 2 6 19.7 #> 3 8 15.1
Please note that the dbplyr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.