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Joachim Zuckarelli
xml2relational
doesxml2relational is designed to convert XML documents with
nested object hierarchies into a set of R dataframes. These dataframes
represent the different tables in a relational data model and are
connected amongst each other by foreign keys. Essentially,
xml2relational flattens an object-oriented data structure
into a relational data structure.
Once the relational structure is created (and that is basically a
list of dataframes representing the different tables) you can export
both the data model (as SQL CREATE statements) and the data
(either as SQL INSERT statements or as CSV files) to get
the data easily into a relational database.
xml2relationalYou can install the xml2relational package from CRAN by
executing the following code in your script or in the R console:
install.packages("xml2relational", dependencies = TRUE)After having installed the package you need to load it (attach it to
the search path) by calling library():
library(xml2relational)To demonstrate how xml2relational works, we will use a
small sample dataset that is shipped together with the
xml2relational package: the customer
dataset.
Here is how it looks like:
<xml>
<customer>
<customerno>C0023751</customerno>
<givenname>Sarah</givenname>
<surname>Durbin</surname>
<email>sarah.durbin@absolutelynowhere.com</email>
<address>
<street>139 W Jackson Blvd</street>
<postalcode>60604</postalcode>
<city>
<name>Chicago</name>
<state>Illinois</state>
</city>
<country>
<name>United States of America</name>
<isocode>US</isocode>
</country>
</address>
<username>queenofqueens</username>
</customer>
<customer>
<customerno>C0017439</customerno>
<givenname>Mark</givenname>
<surname>Durbin</surname>
<email>mark@durbinshome.net</email>
<address>
<street>139 W Jackson Blvd</street>
<postalcode>60604</postalcode>
<city>
<name>Chicago</name>
<state>Illinois</state>
</city>
<country>
<name>United States of America</name>
<isocode>US</isocode>
</country>
</address>
<username>durby82</username>
</customer>
<customer>
<customerno>C0248538</customerno>
<givenname>Max</givenname>
<surname>Brunner</surname>
<email>mbrunner@winetasting-brunner.de</email>
<address>
<street>Rotkreuzplatz 5</street>
<postalcode>80634</postalcode>
<city>
<name>Munich</name>
<state>Bavaria</state>
</city>
<country>
<name>Germany</name>
<isocode>DE</isocode>
</country>
</address>
<username>brunnermax_69</username>
</customer>
<customer>
<customerno>C0271182</customerno>
<givenname>Urs</givenname>
<surname>Richli</surname>
<email>urs.richli@richli-design.ch</email>
<address>
<street>Seestrasse 43</street>
<postalcode>6052</postalcode>
<city>
<name>Hergiswil</name>
<state>Luzern</state>
</city>
<country>
<name>Switzerland</name>
<isocode>CH</isocode>
</country>
</address>
<username>ursrichli</username>
</customer>
<customer>
<customerno>C0019935</customerno>
<givenname>Clara-Sophie</givenname>
<surname>Dr. Hellmann</surname>
<email>clara-sophie@ginternetpost.de</email>
<address>
<street>Brienner Strasse 11</street>
<postalcode>80333</postalcode>
<city>
<name>Munich</name>
<state>Bavaria</state>
</city>
<country>
<name>Germany</name>
<isocode>DE</isocode>
</country>
</address>
<username>helli</username>
</customer>
<customer>
<customerno>C0019935</customerno>
<givenname>Thomas</givenname>
<surname>Chang</surname>
<email>chang-thomas@sf-foryou.com</email>
<address>
<street>539 Lombard St</street>
<postalcode>94133</postalcode>
<city>
<name>San Francisco</name>
<state>California</state>
</city>
<country>
<name>United States of America</name>
<isocode>US</isocode>
</country>
</address>
<username>tchango123</username>
</customer>
</xml>In this dataset we have a nested object structure. Specifically, each customer has an address consisting of several elements. Among those elements is the city which is again an object of its own, with a city name and state. The same applies to the country which is included with its name and its ISO country code. When you look at the (completely made-up) customers here, you will notice that the customers Sarah Durbin and Mark Durbin (the first two customers) share the same address. Also, Max Brunner and Clara-Sophie Hellmann both live in Munich, Germany (although at different addresses). Thomas Chang of San Francisco lives in the USA, as do the Durbins.
When we now process the data and derive the relational data model,
xml2relational will take care of these ‘duplicates’.
Deriving the relational data model from this XML data is fairly simple:
customer.data <- toRelational("customers.xml")The toRelational() function flattens the hierarchical
structure of the XML data and distributes the data to a set of
dataframes representing the tables of our relational data model. It
returns these dataframes as a list (customer.data). We can
now inspect this list to see the tables that have been generated:
class(customer.data)## [1] "list"names(customer.data)## [1] "xml" "customer" "address" "city" "country"class(customer.data$customer)## [1] "data.frame"Let us have a closer look at the customer dataframe:
customer.data$customer## ID_customer customerno givenname surname
## 1 263023 C0023751 Sarah Durbin
## 2 597336 C0017439 Mark Durbin
## 3 59960 C0248538 Max Brunner
## 4 159381 C0271182 Urs Richli
## 5 83969 C0019935 Clara-Sophie Dr. Hellmann
## 6 465004 C0019935 Thomas Chang
## email FKID_address username
## 1 sarah.durbin@absolutelynowhere.com 674038 queenofqueens
## 2 mark@durbinshome.net 674038 durby82
## 3 mbrunner@winetasting-brunner.de 149765 brunnermax_69
## 4 urs.richli@richli-design.ch 718252 ursrichli
## 5 clara-sophie@ginternetpost.de 977313 helli
## 6 chang-thomas@sf-foryou.com 112551 tchango123As you can see, each customer record has been assigned a primary key,
ID_customer. The argument prefix.primary of
the toRelational() function lets you change the prefix that
is used to identify primary key fields. Its default value is
"ID_". Similiarly, using the prefix.foreign
argument you can change the prefix used for the names of foreign key
fields from its default value "FKID_" to whatever you like.
The name of the key fields always consists of the prefix and the name of
the table.
In the customer table we have a foreign key that relates
to the address. You may have noticed that, as expected, the data records
of Sarah and Mark Durbin point to the same address record
as they live in the same place.
Let us now look into the address table:
customer.data$address## ID_address street postalcode FKID_city FKID_country
## 1 674038 139 W Jackson Blvd 60604 735977 495268
## 2 149765 Rotkreuzplatz 5 80634 2299 352009
## 3 718252 Seestrasse 43 6052 448761 817914
## 4 977313 Brienner Strasse 11 80333 2299 352009
## 5 112551 539 Lombard St 94133 70561 495268Again, the address points to other tables, namely the
city and the country table. As we would have
expected, the two Munich addresses point to the same city and the same
country, and the two US addresses point to the same record in the
country table.
You see how easy it is to flatten a hierarchical, objected-oriented
XML data structure to a relational data model using the
toRelational() function.
In the next step, we want to export our results. That can mean two things:
For the first task, xml2relational provides the
getCreateSQL() function. This function returns
ready-to-excecute SQL CREATE statements. It supports three
built-in SQL flavors, MySQL, TransactSQL and
Oracle. You add additional SQL flavors, if you like. In
this case, you would use sql.style argument to provide a
special dataframe containing the required definitions for the new SQL
dialect. Please consult the online help texts for more information on
how this is done.
In order to generate proper SQL CREATE statements,
getCreateSQL() guesses the data types of the table fields
from the data. If you do not like the results, you can provide your own
function to derive the data types as datatype.func
argument. This function would need to accept exactly one argument, a
vector with the field vales of the field for which a datatype needs to
be guessed. It then must return the datatype as a one-element character
vector.
If you are not going to change the behavior of
getCreateSQL() using these options, generating the SQL
CREATE statements is very straightforward:
create.sql <- getCreateSQL(customer.data, "MySQL")
cat(create.sql, sep="\n\n")## CREATE TABLE xml (
## PRIMARY KEY (ID_xml)
## , ID_xml BIGINT
## , FOREIGN KEY (FKID_customer) REFERENCES customer(ID_customer)
## , FKID_customer BIGINT
## );
##
## CREATE TABLE customer (
## PRIMARY KEY (ID_customer)
## , ID_customer BIGINT
## , customerno VARCHAR(8) NOT NULL
## , givenname VARCHAR(12) NOT NULL
## , surname VARCHAR(12) NOT NULL
## , email VARCHAR(34) NOT NULL
## , FOREIGN KEY (FKID_address) REFERENCES address(ID_address)
## , FKID_address BIGINT
## , username VARCHAR(13) NOT NULL
## );
##
## CREATE TABLE address (
## PRIMARY KEY (ID_address)
## , ID_address BIGINT
## , street VARCHAR(19) NOT NULL
## , postalcode BIGINT NOT NULL
## , FOREIGN KEY (FKID_city) REFERENCES city(ID_city)
## , FKID_city BIGINT
## , FOREIGN KEY (FKID_country) REFERENCES country(ID_country)
## , FKID_country BIGINT
## );
##
## CREATE TABLE city (
## PRIMARY KEY (ID_city)
## , ID_city BIGINT
## , name VARCHAR(13) NOT NULL
## , state VARCHAR(10) NOT NULL
## );
##
## CREATE TABLE country (
## PRIMARY KEY (ID_country)
## , ID_country BIGINT
## , name VARCHAR(24) NOT NULL
## , isocode VARCHAR(2) NOT NULL
## );xml2relational tries to guess the datatype from the
actual data. When you are working with the MySQL,
Transact SQL (T-SQL) and Oracle
dialects/flavors of SQL, this should be alright. Nevertheless, using the
datatype.func argument of getcreateSQL() you
can also provide your own function to determine the data type. This
function would need to take exactly one argument, a data vector from a
data table, and return the appropriate SQL data type as a one-element
character vector. Alternatively, you can also use the built-in mechanism
for determining the data type and just supply additional information on
the SQL flavor that you use. Please consult the online help with
?getCreateSQL to learn more on providing the necessary
information.
By setting the logical one.statement argument to
TRUE you can let getcreateSQL() return the
CREATE statements in one character value instead of a
vector with one element per CREATE statement. In this case
you can use the line.break argument to define how the
different CREATE statement are to be separated (apart from
a semicolon that is added by default).
To export the data as such you have two options:
INSERT statements using
getInsertSQL() functionsavetofiles().Producing SQL INSERT statements for the data in one of
the tables is very easy with getInsertSQL():
insert.sql <- getInsertSQL(customer.data, table.name = "city")
cat(insert.sql, sep="\n")## INSERT INTO city(ID_city, name, state) VALUES (735977, 'Chicago', 'Illinois');
## INSERT INTO city(ID_city, name, state) VALUES (2299, 'Munich', 'Bavaria');
## INSERT INTO city(ID_city, name, state) VALUES (448761, 'Hergiswil', 'Luzern');
## INSERT INTO city(ID_city, name, state) VALUES (70561, 'San Francisco', 'California');You can also export all the tables of your relational model with
savetofiles():
savetofiles(customer.data)This will save as many CSV files to your current working directory as
you have tables in your model (customer.data). Each file is
named for the name of the dataframe connected to the respective table,
so city.csv will store the data from the city
table.
More optional arguments for most of the functions discussed here are available. Please check the online help for more details.
I appreciate your questions, issues and feature requests. Contact me on joachim@zuckarelli.de, visit the GitHub repository on https://github.com/jsugarelli/xml2relational for the package source and follow me on Twitter to stay up-to-date!
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.