Package 'etl' reference manual

Title:	Extract-Transform-Load Framework for Medium Data
Description:	A predictable and pipeable framework for performing ETL (extract-transform-load) operations on publicly-accessible medium-sized data set. This package sets up the method structure and implements generic functions. Packages that depend on this package download specific data sets from the Internet, clean them up, and import them into a local or remote relational database management system.
Authors:	Benjamin S. Baumer [aut, cre] , Carson Sievert [ctb], Natalia Iannucci [ctb]
Maintainer:	Benjamin S. Baumer <[email protected]>
License:	CC0
Version:	0.4.1
Built:	2024-06-08 02:59:37 UTC
Source:	https://github.com/beanumber/etl

Create an ETL package skeleton

Description

Create an ETL package skeleton

Usage

create_etl_package(...)
create_etl_package(...)

Arguments

...

arguments passed to create_package

Details

Extends create_package and places a template source file in the R subdirectory of the new package. The file has a working stub of etl_extract. The new package can be built immediately and run.

New S3 methods for etl_transform and etl_load can be added if necessary, but the default methods may suffice.

Examples

## Not run: 
path <- file.path(tempdir(), "scorecard")
create_etl_package(path)

## End(Not run)
# Now switch projects, and "Install and Restart"
## Not run: 
path <- file.path(tempdir(), "scorecard")
create_etl_package(path)

## End(Not run)
# Now switch projects, and "Install and Restart"

Return the database type for an ETL or DBI connection

Description

Return the database type for an ETL or DBI connection

Usage

db_type(obj, ...)

## S3 method for class 'src_dbi'
db_type(obj, ...)

## S3 method for class 'DBIConnection'
db_type(obj, ...)
db_type(obj, ...)

## S3 method for class 'src_dbi'
db_type(obj, ...)

## S3 method for class 'DBIConnection'
db_type(obj, ...)

Arguments

`obj`	and `etl` or `DBIConnection-class` object
`...`	currently ignored

Examples

if (require(RMySQL) && mysqlHasDefault()) {
  # connect to test database using rs-dbi
  db <- src_mysql_cnf()
  class(db)
  db
  # connect to another server using the 'client' group
  db_type(db)
  db_type(db$con)
}
if (require(RMySQL) && mysqlHasDefault()) {
  # connect to test database using rs-dbi
  db <- src_mysql_cnf()
  class(db)
  db
  # connect to another server using the 'client' group
  db_type(db)
  db_type(db$con)
}

Execute an SQL script

Description

Execute an SQL script

Usage

dbRunScript(conn, script, echo = FALSE, ...)
dbRunScript(conn, script, echo = FALSE, ...)

Arguments

`conn`	a `DBIConnection-class` object
`script`	Either a filename pointing to an SQL script or a character vector of length 1 containing SQL.
`echo`	print the SQL commands to the output?
`...`	arguments passed to `dbExecute`

Details

The SQL script file must be ; delimited.

Value

a list of results from dbExecute for each of the individual SQL statements in script.

Examples

sql <- "SHOW TABLES; SELECT 1+1 as Two;"
sql2 <- system.file("sql", "mtcars.mysql", package = "etl")
sql3 <- "SELECT * FROM user WHERE user = 'mysql';SELECT * FROM user WHERE 't' = 't';"

if (require(RSQLite)) {
  con <- dbConnect(RSQLite::SQLite())
  dbRunScript(con, "SELECT 1+1 as Two; VACUUM; ANALYZE;")
}
## Not run: 
if (require(RMySQL)) {
 con <- dbConnect(RMySQL::MySQL(), default.file = path.expand("~/.my.cnf"),
   group = "client", user = NULL, password = NULL, dbname = "mysql", host = "127.0.0.1")
 dbRunScript(con, script = sql)
 dbRunScript(con, script = sql2)
 dbRunScript(con, script = sql3)
 dbDisconnect(con)
}

## End(Not run)

sql <- "SHOW TABLES; SELECT 1+1 as Two;"
sql2 <- system.file("sql", "mtcars.mysql", package = "etl")
sql3 <- "SELECT * FROM user WHERE user = 'mysql';SELECT * FROM user WHERE 't' = 't';"

if (require(RSQLite)) {
  con <- dbConnect(RSQLite::SQLite())
  dbRunScript(con, "SELECT 1+1 as Two; VACUUM; ANALYZE;")
}
## Not run: 
if (require(RMySQL)) {
 con <- dbConnect(RMySQL::MySQL(), default.file = path.expand("~/.my.cnf"),
   group = "client", user = NULL, password = NULL, dbname = "mysql", host = "127.0.0.1")
 dbRunScript(con, script = sql)
 dbRunScript(con, script = sql2)
 dbRunScript(con, script = sql3)
 dbDisconnect(con)
}

## End(Not run)

Wipe out all tables in a database

Description

Wipe out all tables in a database

Usage

dbWipe(conn, ...)
dbWipe(conn, ...)

Arguments

`conn`	A DBIConnection object, as returned by `dbConnect()`.
`...`	Other parameters passed on to methods.

Details

Finds all tables within a database and removes them

Initialize an `etl` object

Description

Initialize an etl object

Usage

etl(x, db = NULL, dir = tempdir(), ...)

## Default S3 method:
etl(x, db = NULL, dir = tempdir(), ...)

## S3 method for class 'etl'
summary(object, ...)

is.etl(object)

## S3 method for class 'etl'
print(x, ...)
etl(x, db = NULL, dir = tempdir(), ...)

## Default S3 method:
etl(x, db = NULL, dir = tempdir(), ...)

## S3 method for class 'etl'
summary(object, ...)

is.etl(object)

## S3 method for class 'etl'
print(x, ...)

Arguments

`x`	the name of the `etl` package that you wish to populate with data. This determines the class of the resulting `etl` object, which determines method dispatch of `etl_*()` functions. There is no default, but you can use `mtcars` as an test example.
`db`	a database connection that inherits from `src_dbi`. It is NULL by default, which results in a `SQLite` connection being created in `dir`.
`dir`	a directory to store the raw and processed data files
`...`	arguments passed to methods (currently ignored)
`object`	an object for which a summary is desired.

Details

A constructor function that instantiates an etl object. An etl object extends a src_dbi object. It also has attributes for:

pkg: the name of the etl package corresponding to the data source
dir: the directory where the raw and processed data are stored
raw_dir: the directory where the raw data files are stored
load_dir: the directory where the processed data files are stored

Just like any src_dbi object, an etl object is a data source backed by an SQL database. However, an etl object has additional functionality based on the presumption that the SQL database will be populated from data files stored on the local hard disk. The ETL functions documented in etl_create provide the necessary functionality for extracting data from the Internet to raw_dir, transforming those data and placing the cleaned up data (usually in CSV format) into load_dir, and finally loading the clean data into the SQL database.

Value

For etl, an object of class etl_x and etl that inherits from src_dbi

For is.etl, TRUE or FALSE, depending on whether x has class etl

Examples


# Instantiate the etl object
cars <- etl("mtcars")
str(cars)
is.etl(cars)
summary(cars)

## Not run: 
# connect to a PostgreSQL server
if (require(RPostgreSQL)) {
  db <- src_postgres("mtcars", user = "postgres", host = "localhost")
  cars <- etl("mtcars", db)
}

## End(Not run)

# Do it step-by-step
cars %>%
  etl_extract() %>%
  etl_transform() %>%
  etl_load()
src_tbls(cars)
cars %>%
  tbl("mtcars") %>%
  group_by(cyl) %>%
  summarize(N = n(), mean_mpg = mean(mpg))

# Do it all in one step
cars2 <- etl("mtcars")
cars2 %>%
  etl_update()
src_tbls(cars2)


# generic summary function provides information about the object
cars <- etl("mtcars")
summary(cars)
cars <- etl("mtcars")
# returns TRUE
is.etl(cars)

# returns FALSE
is.etl("hello world")
cars <- etl("mtcars") %>%
  etl_create()
cars
# Instantiate the etl object
cars <- etl("mtcars")
str(cars)
is.etl(cars)
summary(cars)

## Not run: 
# connect to a PostgreSQL server
if (require(RPostgreSQL)) {
  db <- src_postgres("mtcars", user = "postgres", host = "localhost")
  cars <- etl("mtcars", db)
}

## End(Not run)

# Do it step-by-step
cars %>%
  etl_extract() %>%
  etl_transform() %>%
  etl_load()
src_tbls(cars)
cars %>%
  tbl("mtcars") %>%
  group_by(cyl) %>%
  summarize(N = n(), mean_mpg = mean(mpg))

# Do it all in one step
cars2 <- etl("mtcars")
cars2 %>%
  etl_update()
src_tbls(cars2)


# generic summary function provides information about the object
cars <- etl("mtcars")
summary(cars)
cars <- etl("mtcars")
# returns TRUE
is.etl(cars)

# returns FALSE
is.etl("hello world")
cars <- etl("mtcars") %>%
  etl_create()
cars

ETL functions for working with medium sized data

Description

These generic functions provide a systematic approach for performing ETL (exchange-transform-load) operations on medium sized data.

Usage

etl_cleanup(obj, ...)

## Default S3 method:
etl_cleanup(
  obj,
  delete_raw = FALSE,
  delete_load = FALSE,
  pattern = "\\.(csv|zip)$",
  ...
)

etl_create(obj, ...)

## Default S3 method:
etl_create(obj, ...)

etl_update(obj, ...)

## Default S3 method:
etl_update(obj, ...)

etl_extract(obj, ...)

## Default S3 method:
etl_extract(obj, ...)

## S3 method for class 'etl_mtcars'
etl_extract(obj, ...)

## S3 method for class 'etl_cities'
etl_extract(obj, ...)

etl_load(obj, ...)

## Default S3 method:
etl_load(obj, ...)

etl_transform(obj, ...)

## Default S3 method:
etl_transform(obj, ...)

## S3 method for class 'etl_cities'
etl_transform(obj, ...)
etl_cleanup(obj, ...)

## Default S3 method:
etl_cleanup(
  obj,
  delete_raw = FALSE,
  delete_load = FALSE,
  pattern = "\\.(csv|zip)$",
  ...
)

etl_create(obj, ...)

## Default S3 method:
etl_create(obj, ...)

etl_update(obj, ...)

## Default S3 method:
etl_update(obj, ...)

etl_extract(obj, ...)

## Default S3 method:
etl_extract(obj, ...)

## S3 method for class 'etl_mtcars'
etl_extract(obj, ...)

## S3 method for class 'etl_cities'
etl_extract(obj, ...)

etl_load(obj, ...)

## Default S3 method:
etl_load(obj, ...)

etl_transform(obj, ...)

## Default S3 method:
etl_transform(obj, ...)

## S3 method for class 'etl_cities'
etl_transform(obj, ...)

Arguments

`obj`	an `etl` object
`...`	arguments passed to methods
`delete_raw`	should files be deleted from the `raw_dir`?
`delete_load`	should files be deleted from the `load_dir`?
`pattern`	regular expression matching file names to be deleted. By default, this matches filenames ending in `.csv` and `.zip`.

Details

The purposes of these functions are to download data from a particular data source from the Internet, process it, and load it into a SQL database server.

There are five primary functions:

etl_init: Initialize the database schema.
etl_extract: Download data from the Internet and store it locally in its raw form.
etl_transform: Manipulate the raw data such that it can be loaded into a database table. Usually, this means converting the raw data to (a series of) CSV files, which are also stored locally.
etl_load: Load the transformed data into the database.
etl_cleanup: Perform housekeeping, such as deleting unnecessary raw data files.

Additionally, two convenience functions chain these operations together:

etl_create: Run all five functions in succession. This is useful when you want to create the database from scratch.
etl_update: Run the etl_extract-etl_transform-etl_load functions in succession. This is useful when the database already exists, but you want to insert some new data.

Value

Each one of these functions returns an etl object, invisibly.

Examples


## Not run: 
if (require(RPostgreSQL)) {
  db <- src_postgres(dbname = "mtcars", user = "postgres", host = "localhost")
  cars <- etl("mtcars", db)
}
if (require(RMySQL) && mysqlHasDefault()) {
  db <- src_mysql(dbname = "mtcars", user = "r-user",
                  host = "localhost", password = "mypass")
  cars <- etl("mtcars", db)
}

## End(Not run)
cars <- etl("mtcars")
cars %>%
 etl_extract() %>%
 etl_transform() %>%
 etl_load() %>%
 etl_cleanup()
cars

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

 # do it all in one step, and peek at the SQL creation script
 cars %>%
   etl_create(echo = TRUE)
 # specify a directory for the data
 ## Not run: 
 cars <- etl("mtcars", dir = "~/dumps/mtcars/")
 str(cars)
 
## End(Not run)
cars <- etl("mtcars")
# Do it step-by-step
cars %>%
  etl_extract() %>%
  etl_transform() %>%
  etl_load()

# Note the somewhat imprecise data types for the columns. These are the default.
tbl(cars, "mtcars")

# But you can also specify your own schema if you want
schema <- system.file("sql", "init.sqlite", package = "etl")
cars %>%
  etl_init(schema) %>%
  etl_load()
## Not run: 
if (require(RPostgreSQL)) {
  db <- src_postgres(dbname = "mtcars", user = "postgres", host = "localhost")
  cars <- etl("mtcars", db)
}
if (require(RMySQL) && mysqlHasDefault()) {
  db <- src_mysql(dbname = "mtcars", user = "r-user",
                  host = "localhost", password = "mypass")
  cars <- etl("mtcars", db)
}

## End(Not run)
cars <- etl("mtcars")
cars %>%
 etl_extract() %>%
 etl_transform() %>%
 etl_load() %>%
 etl_cleanup()
cars

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

 # do it all in one step, and peek at the SQL creation script
 cars %>%
   etl_create(echo = TRUE)
 # specify a directory for the data
 ## Not run: 
 cars <- etl("mtcars", dir = "~/dumps/mtcars/")
 str(cars)
 
## End(Not run)
cars <- etl("mtcars")
# Do it step-by-step
cars %>%
  etl_extract() %>%
  etl_transform() %>%
  etl_load()

# Note the somewhat imprecise data types for the columns. These are the default.
tbl(cars, "mtcars")

# But you can also specify your own schema if you want
schema <- system.file("sql", "init.sqlite", package = "etl")
cars %>%
  etl_init(schema) %>%
  etl_load()

Initialize a database using a defined schema

Description

Initialize a database using a defined schema

Usage

etl_init(
  obj,
  script = NULL,
  schema_name = "init",
  pkg = attr(obj, "pkg"),
  ext = NULL,
  ...
)

## Default S3 method:
etl_init(
  obj,
  script = NULL,
  schema_name = "init",
  pkg = attr(obj, "pkg"),
  ext = NULL,
  ...
)

find_schema(obj, schema_name = "init", pkg = attr(obj, "pkg"), ext = NULL, ...)
etl_init(
  obj,
  script = NULL,
  schema_name = "init",
  pkg = attr(obj, "pkg"),
  ext = NULL,
  ...
)

## Default S3 method:
etl_init(
  obj,
  script = NULL,
  schema_name = "init",
  pkg = attr(obj, "pkg"),
  ext = NULL,
  ...
)

find_schema(obj, schema_name = "init", pkg = attr(obj, "pkg"), ext = NULL, ...)

Arguments

`obj`	An `etl` object
`script`	either a vector of SQL commands to be executed, or a file path as a character vector containing an SQL initialization script. If `NULL` (the default), then the appropriate built-in schema will be fetched by `find_schema`, if it exists. Note that the flavor of SQL in this file must match the type of the source. That is, if your object is of type `src_mysql`, then make sure that the schema you specify here is written in MySQL (and not PostgreSQL). Please note that SQL syntax is not, in general, completely portable. Use with caution, as this may clobber any existing data you have in an existing database.
`schema_name`	The name of the schema. Default is `init`.
`pkg`	The package defining the schema. Should be set in `etl`.
`ext`	The file extension used for the SQL schema file. If NULL (the default) it be inferred from the `src_*` class of `con`. For example, if `con` has class `SQLite` then `ext` will be `sqlite`.
`...`	Currently ignored

Details

If the table definitions are at all non-trivial, you may wish to include a pre-defined table schema. This function will retrieve it.

Examples

cars <- etl("mtcars")
cars %>%
  etl_init()
cars %>%
  etl_init(script = sql("CREATE TABLE IF NOT EXISTS mtcars_alt (id INTEGER);"))
cars %>%
  etl_init(schema_name = "init")
init_script <- find_schema(cars, schema_name = "init")
cars %>%
  etl_init(script = init_script, echo = TRUE)
src_tbls(cars)

cars <- etl("mtcars")
find_schema(cars)
find_schema(cars, "init", "etl")
find_schema(cars, "my_crazy_schema", "etl")

cars <- etl("mtcars")
cars %>%
  etl_init()
cars %>%
  etl_init(script = sql("CREATE TABLE IF NOT EXISTS mtcars_alt (id INTEGER);"))
cars %>%
  etl_init(schema_name = "init")
init_script <- find_schema(cars, schema_name = "init")
cars %>%
  etl_init(script = init_script, echo = TRUE)
src_tbls(cars)

cars <- etl("mtcars")
find_schema(cars)
find_schema(cars, "init", "etl")
find_schema(cars, "my_crazy_schema", "etl")

Match year and month vectors to filenames

Description

Match year and month vectors to filenames

Extracts a date from filenames

Usage

match_files_by_year_months(
  files,
  pattern,
  years = as.numeric(format(Sys.Date(), "%Y")),
  months = 1:12,
  ...
)

extract_date_from_filename(files, pattern, ...)
match_files_by_year_months(
  files,
  pattern,
  years = as.numeric(format(Sys.Date(), "%Y")),
  months = 1:12,
  ...
)

extract_date_from_filename(files, pattern, ...)

Arguments

`files`	a character vector of filenames
`pattern`	a regular expression to be passed to `fast_strptime`
`years`	a numeric vector of years
`months`	a numeric vector of months
`...`	arguments passed to `fast_strptime`

Value

a character vector of files that match the pattern, year, and month arguments

a vector of POSIXct dates matching the pattern

Examples

## Not run: 
if (require(airlines)) {
  airlines <- etl("airlines", dir = "~/Data/airlines") %>%
    etl_extract(year = 1987)
  summary(airlines)
  match_files_by_year_months(list.files(attr(airlines, "raw_dir")),
    pattern = "On_Time_On_Time_Performance_%Y_%m.zip", year = 1987)
}

## End(Not run)
## Not run: 
if (require(airlines)) {
  airlines <- etl("airlines", dir = "~/Data/airlines") %>%
    etl_extract(year = 1987)
  summary(airlines)
  match_files_by_year_months(list.files(attr(airlines, "raw_dir")),
    pattern = "On_Time_On_Time_Performance_%Y_%m.zip", year = 1987)
}

## End(Not run)

Download only those files that don't already exist

Description

Download only those files that don't already exist

Usage

smart_download(obj, src, new_filenames = basename(src), clobber = FALSE, ...)
smart_download(obj, src, new_filenames = basename(src), clobber = FALSE, ...)

Arguments

`obj`	an `etl` object
`src`	a character vector of URLs that you want to download
`new_filenames`	an optional character vector of filenames for the new (local) files. Defaults to having the same filenames as those in `src`.
`clobber`	do you want to clobber any existing files?
`...`	arguments passed to `download`

Details

Downloads only those files in src that are not already present in the directory specified by the raw_dir attribute of obj.

Author(s)

idiom courtesy of Hadley Wickham

Examples

## Not run: 
cars <- etl("mtcars")
urls <- c("https://raw.githubusercontent.com/beanumber/etl/master/etl.Rproj",
"https://www.reddit.com/robots.txt")
smart_download(cars, src = urls)
# won't download again if the files are already there
smart_download(cars, src = urls)
# use clobber to overwrite
smart_download(cars, src = urls, clobber = TRUE)

## End(Not run)
## Not run: 
cars <- etl("mtcars")
urls <- c("https://raw.githubusercontent.com/beanumber/etl/master/etl.Rproj",
"https://www.reddit.com/robots.txt")
smart_download(cars, src = urls)
# won't download again if the files are already there
smart_download(cars, src = urls)
# use clobber to overwrite
smart_download(cars, src = urls, clobber = TRUE)

## End(Not run)

Upload a list of files to the DB

Description

Upload a list of files to the DB

Usage

smart_upload(obj, src = NULL, tablenames = NULL, ...)
smart_upload(obj, src = NULL, tablenames = NULL, ...)

Arguments

`obj`	An `etl` object
`src`	a list of CSV files to upload. If `NULL`, will return all CSVs in the load directory
`tablenames`	a list the same length as `src` of tablenames in the database corresponding to each of the files in `src`. If `NULL`, will default to the same name as `src`, without paths or file extensions.
`...`	arguments passed to `dbWriteTable`

Examples

## Not run: 
if (require(RMySQL)) {
  # must have pre-existing database "fec"
  # if not, try
  system("mysql -e 'CREATE DATABASE IF NOT EXISTS fec;'")
  db <- src_mysql_cnf(dbname = "mtcars")
}

## End(Not run)
## Not run: 
if (require(RMySQL)) {
  # must have pre-existing database "fec"
  # if not, try
  system("mysql -e 'CREATE DATABASE IF NOT EXISTS fec;'")
  db <- src_mysql_cnf(dbname = "mtcars")
}

## End(Not run)

Connect to local MySQL Server using ~/.my.cnf

Description

Connect to local MySQL Server using ~/.my.cnf

Usage

src_mysql_cnf(dbname = "test", groups = "rs-dbi", ...)
src_mysql_cnf(dbname = "test", groups = "rs-dbi", ...)

Arguments

`dbname`	name of the local database you wish to connect to. Default is `test`, as in `mysqlHasDefault`.
`groups`	section of `~/.my.cnf` file. Default is `rs-dbi` as in `mysqlHasDefault`
`...`	arguments passed to `src_mysql`

Examples

if (require(RMySQL) && mysqlHasDefault()) {
  # connect to test database using rs-dbi
  db <- src_mysql_cnf()
  class(db)
  db
  # connect to another server using the 'client' group
  src_mysql_cnf(groups = "client")
}
if (require(RMySQL) && mysqlHasDefault()) {
  # connect to test database using rs-dbi
  db <- src_mysql_cnf()
  class(db)
  db
  # connect to another server using the 'client' group
  src_mysql_cnf(groups = "client")
}

Ensure that years and months are within a certain time span

Description

Ensure that years and months are within a certain time span

Usage

valid_year_month(years, months, begin = "1870-01-01", end = Sys.Date())
valid_year_month(years, months, begin = "1870-01-01", end = Sys.Date())

Arguments

`years`	a numeric vector of years
`months`	a numeric vector of months
`begin`	the earliest valid date, defaults to the UNIX epoch
`end`	the most recent valid date, defaults to today

Details

Often, a data source will begin and end at known points in time. At the same time, many data sources are divided into monthly archives. Given a set of years and months, any combination of which should be considered valid, this function will return a data.frame in which each row is one of those valid year-month pairs. Further, if the optional begin and end arguments are specified, the rows will be filter to lie within that time interval. Furthermore, the first and last day of each month are computed.

Value

a data.frame with four variables: year, month, month_begin (the first day of the month), and month_end (the last day of the month).

Examples


valid_year_month(years = 1999:2001, months = c(1:3, 7))

# Mets in the World Series since the UNIX epoch
mets_ws <- c(1969, 1973, 1986, 2000, 2015)
valid_year_month(years = mets_ws, months = 10)

# Mets in the World Series during the Clinton administration
if (require(ggplot2)) {
  clinton <- filter(presidential, name == "Clinton")
  valid_year_month(years = mets_ws, months = 10,
    begin = clinton$start, end = clinton$end)
}

valid_year_month(years = 1999:2001, months = c(1:3, 7))

# Mets in the World Series since the UNIX epoch
mets_ws <- c(1969, 1973, 1986, 2000, 2015)
valid_year_month(years = mets_ws, months = 10)

# Mets in the World Series during the Clinton administration
if (require(ggplot2)) {
  clinton <- filter(presidential, name == "Clinton")
  valid_year_month(years = mets_ws, months = 10,
    begin = clinton$start, end = clinton$end)
}

Package 'etl'

Help Index

Create an ETL package skeleton

Description

Usage

Arguments

Details

See Also

Examples

Return the database type for an ETL or DBI connection

Description

Usage

Arguments

Examples

Execute an SQL script

Description

Usage

Arguments

Details

Value

Examples

Wipe out all tables in a database

Description

Usage

Arguments

Details

Initialize an etl object

Description

Usage

Arguments

Details

Value

See Also

Examples

ETL functions for working with medium sized data

Description

Usage

Arguments

Details

Value

See Also

Examples

Initialize a database using a defined schema

Description

Usage

Arguments

Details

Examples

Match year and month vectors to filenames

Description

Usage

Arguments

Value

Examples

Download only those files that don't already exist

Description

Usage

Arguments

Details

Author(s)

Examples

Upload a list of files to the DB

Description

Usage

Arguments

Examples

Connect to local MySQL Server using ~/.my.cnf

Description

Usage

Arguments

See Also

Examples

Ensure that years and months are within a certain time span

Description

Usage

Arguments

Details

Value

Examples

Initialize an `etl` object