etl is an R package to facilitate Extract - Transform - Load (ETL) operations for medium data. The end result is generally a populated SQL database, but the user interaction takes place solely within R.

etl is on CRAN, so you can install it in the usual way, then load it.

install.packages("etl")

library(etl)

Instantiate an etl object using a string that determines the class of the resulting object, and the package that provides access to that data. The trivial mtcars database is built into etl.

cars <- etl("mtcars")

## No database was specified so I created one for you at:

## /tmp/Rtmpw1T0Oo/file44856c2958c2.sqlite3

class(cars)

## [1] "etl_mtcars" "etl"        "src_dbi"    "src_sql"    "src"

etl works with a local or remote database to store your data. Every etl object extends a dplyr::src_dbi object. If, as in the example above, you do not specify a SQL source, a local RSQLite database will be created for you. However, you can also specify any source that inherits from dplyr::src_dbi.

Note: If you want to use a database other than a local RSQLite, you must create the mtcars database and have permission to write to it first!

# For PostgreSQL
library(RPostgreSQL)
db <- src_postgres(dbname = "mtcars", user = "postgres", host = "localhost")

# Alternatively, for MySQL
library(RMySQL)
db <- src_mysql(dbname = "mtcars", user = "r-user", password = "mypass", host = "localhost")
cars <- etl("mtcars", db)

At the heart of etl are three functions: etl_extract(), etl_transform(), and etl_load().

The first step is to acquire data from an online source.

cars %>%
  etl_extract()

## Extracting raw data...

This creates a local store of raw data.

These data may need to be transformed from their raw form to files suitable for importing into SQL (usually CSVs).

cars %>%
  etl_transform()

## Transforming raw data...

Populate the SQL database with the transformed data.

cars %>%
  etl_load()

## Loading 1 file(s) into the database...

To populate the whole database from scratch, use etl_create.

cars %>%
  etl_create()

## Initializing DB using SQL script init.sqlite

## Extracting raw data...

## Transforming raw data...

## Loading 1 file(s) into the database...

You can also update an existing database without re-initializing, but watch out for primary key collisions.

cars %>%
  etl_update()

Now that your database is populated, you can work with it as a src data table just like any other dplyr source.

cars %>%
  tbl("mtcars") %>%
  group_by(cyl) %>%
  summarise(N = n(), mean_mpg = mean(mpg))

## Warning: Missing values are always removed in SQL.
## Use `AVG(x, na.rm = TRUE)` to silence this warning

## # Source:   lazy query [?? x 3]
## # Database: sqlite 3.22.0 [/tmp/Rtmpw1T0Oo/file44856c2958c2.sqlite3]
##     cyl     N mean_mpg
##   <int> <int>    <dbl>
## 1     4    11     26.7
## 2     6     7     19.7
## 3     8    14     15.1

Suppose you want to create your own ETL package called pkgname. All you have to do is write a package that requires etl, and then you have to write two S3 methods:

etl_extract.etl_pkgname()
etl_load.etl_pkgname()

Please see the "Extending etl" vignette for more information.

• macleish : Weather and spatial data from the MacLeish Field Station in Whately, MA.
• airlines : On-time flight arrival data from the Bureau of Transportation Statistics
• citibike : Municipal bike-sharing system in New York City
• nyc311 : Phone calls to New York City's feedback hotline
• fec : Campaign contribution data from the Federal Election Commission
• imdb : Mirror of the Internet Movie Database

Please see the full manuscript for additional details.

citation("etl")

## 
## To cite etl in publications use:
## 
##   Benjamin S. Baumer (2017). A Grammar for Reproducible and
##   Painless Extract-Transform-Load Operations on Medium Data.
##   arXiv, 8(23), 1-24. URL https://arxiv.org/abs/1708.07073.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {A Grammar for Reproducible and Painless Extract-Transform-Load Operations on Medium Data},
##     author = {Benjamin S. Baumer},
##     journal = {arXiv},
##     year = {2017},
##     volume = {8},
##     number = {23},
##     pages = {1--24},
##     url = {https://arxiv.org/abs/1708.07073},
##   }