How to use dplyr to work with remote databases?

That means as well as working with local data frames, you can also work with remote database tables, using exactly the same R code. Install the dbplyr package then read vignette ("databases", package = "dbplyr"). If you are new to dplyr, the best place to start is the data import chapter in R for data science.
Likewise, what kind of data does dplyr work with?
As well as working with local in-memory data stored in data frames, dplyr also works with remote on-disk data stored in databases. This is particularly useful in two scenarios:
Also Know, do you need to install dBi to use dplyr? To use databases with dplyr, you need to first install dbplyr: You’ll also need to install a DBI backend package. The DBI package provides a common interface that allows dplyr to work with many different databases using the same code.
Keeping this in consideration, which is database backend does dplyr connect to?
The DBI package provides a common interface that allows dplyr to work with many different databases using the same code. DBI is automatically installed with dbplyr, but you need to install a specific backend for the database that you want to connect to. Five commonly used backends are: RPostgreSQL connects to Postgres and Redshift.
In addition, how can I read data from dplyr into Spark?
For more information on dplyr, see the introduction , a guide for connecting to databases , and a variety of vignettes. You can read data into Spark DataFrames using the following functions: Regardless of the format of your data, Spark supports reading data from a variety of different data sources.

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