Introducing Databases and SQL
Last updated on 2023-04-21 | Edit this page
- What is a relational database and why should I use it?
- What is SQL?
- Describe why relational databases are useful.
- Create and populate a database from a text file.
- Define SQLite data types.
Note: this should have been done by participants before the start of the workshop.
To start, let’s orient ourselves in our project workflow. Previously, we used Excel and OpenRefine to go from messy, human created data to cleaned, computer-readable data. Now we’re going to move to the next piece of the data workflow, using the computer to read in our data, and then use it for analysis and visualization.
SQL stands for Structured Query Language. SQL allows us to interact with relational databases through queries. These queries can allow you to perform a number of actions such as: insert, select, update and delete information in a database.
The data we will be using is a time-series for a small mammal community in southern Arizona. This is part of a project studying the effects of rodents and ants on the plant community that has been running for almost 40 years. The rodents are sampled on a series of 24 plots, with different experimental manipulations controlling which rodents are allowed to access which plots.
This is a real dataset that has been used in over 100 publications. We’ve simplified it for the workshop, but you can download the full dataset and work with it using exactly the same tools we’ll learn about today.
Let’s look at some of the cleaned spreadsheets you downloaded during Setup to complete this challenge. You’ll need the following three files:
Open each of these csv files and explore them. What information is contained in each file? Specifically, if I had the following research questions:
- How has the hindfoot length and weight of Dipodomys species changed over time?
- What is the average weight of each species, per year?
- What information can I learn about Dipodomys species in the 2000s, over time?
What would I need to answer these questions? Which files have the data I need? What operations would I need to perform if I were doing these analyses by hand?
In order to answer the questions described above, we’ll need to do the following basic data operations:
- select subsets of the data (rows and columns)
- group subsets of data
- do math and other calculations
- combine data across spreadsheets
In addition, we don’t want to do this manually! Instead of searching for the right pieces of data ourselves, or clicking between spreadsheets, or manually sorting columns, we want to make the computer do the work.
In particular, we want to use a tool where it’s easy to repeat our analysis in case our data changes. We also want to do all this searching without actually modifying our source data.
Putting our data into a relational database and using SQL will help us achieve these goals.
A relational database stores data in relations made up of records with fields. The relations are usually represented as tables; each record is usually shown as a row, and the fields as columns. In most cases, each record will have a unique identifier, called a key, which is stored as one of its fields. Records may also contain keys that refer to records in other tables, which enables us to combine information from two or more sources.
Using a relational database serves several purposes.
- It keeps your data separate from your analysis.
- This means there’s no risk of accidentally changing data when you analyze it.
- If we get new data we can rerun the query.
- It’s fast, even for large amounts of data.
- It improves quality control of data entry (type constraints and use of forms in MS Access, Filemaker, Oracle Application Express etc.)
- The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python.
There are different database management systems to work with relational databases such as SQLite, MySQL, PostgreSQL, MSSQL Server, and many more. Each of them differ mainly based on their scalability, but they all share the same core principles of relational databases. In this lesson, we use SQLite to introduce you to SQL and data retrieval from a relational database.
Let’s look at a pre-existing database, the
portal_mammals.sqlite file from the Portal Project dataset
that we downloaded during Setup. Click on the
“Open Database” button, select the portal_mammals.sqlite file, and click
“Open” to open the database.
You can see the tables in the database by looking at the left hand
side of the screen under Database Structure tab. Here you will see a
list under “Tables.” Each item listed here corresponds to one of the
csv files we were exploring earlier. To see the contents of
any table, right-click on it, and then click the “Browse Table” from the
menu, or select the “Browse Data” tab next to the “Database Structure”
tab and select the wanted table from the dropdown named “Table”. This
will give us a view that we’re used to - a copy of the table. Hopefully
this helps to show that a database is, in some sense, just a collection
of tables, where there’s some value in the tables that allows them to be
connected to each other (the “related” part of “relational
The “Database Structure” tab also provides some metadata about each
table. If you click on the down arrow next to a table name, you will see
information about the columns, which in databases are referred to as
“fields,” and their assigned data types. (The rows of a database table
are called records.) Each field contains one variety or type of
data, often numbers or text. You can see in the
table that most fields contain numbers (BIGINT, or big integer, and
FLOAT, or floating point numbers/decimals) while the
species table is entirely made up of text fields.
The “Execute SQL” tab is blank now - this is where we’ll be typing our queries to retrieve information from the database tables.
- Relational databases store data in tables with fields (columns) and records (rows)
- Data in tables has types, and all values in a field have the same type (list of data types)
- Queries let us look up data or make calculations based on columns
- Every row-column combination contains a single atomic value, i.e., not containing parts we might want to work with separately.
- One field per type of information
- No redundant information
- Split into separate tables with one table per class of information
- Needs an identifier in common between tables – shared column - to reconnect (known as a foreign key).
Before we get started with writing our own queries, we’ll create our
own database. We’ll be creating this database from the three
csv files we downloaded earlier. Close the currently open
database (File > Close Database) and then follow
- Start a New Database
- Click the New Database button
- Give a name and click Save to create the database in the opened folder
- In the “Edit table definition” window that pops up, click cancel as we will be importing tables, not creating them from scratch
- Select File >> Import >> Table from CSV file…
surveys.csvfrom the data folder we downloaded and click Open.
- Give the table a name that matches the file name
surveys), or use the default
- If the first row has column headings, be sure to check the box next to “Column names in first line”.
- Be sure the field separator and quotation options are correct. If you’re not sure which options are correct, test some of the options until the preview at the bottom of the window looks right.
- Press OK, you should subsequently get a message that the table was imported.
- Back on the Database Structure tab, you should now see the table listed. Right click on the table name and choose Modify Table, or click on the Modify Table button just under the tabs and above the table list.
- Click Save if asked to save all pending changes.
- In the center panel of the window that appears, set the data types
for each field using the suggestions in the table below (this includes
fields from the
- Finally, click OK one more time to confirm the operation. Then click the Write Changes button to save the database.
|day||INTEGER||Having data as numeric allows for meaningful arithmetic and comparisons||surveys|
|genus||TEXT||Field contains text data||species|
|hindfoot_length||REAL||Field contains measured numeric data||surveys|
|month||INTEGER||Having data as numeric allows for meaningful arithmetic and comparisons||surveys|
|plot_id||INTEGER||Field contains numeric data||plots, surveys|
|plot_type||TEXT||Field contains text data||plots|
|record_id||INTEGER||Field contains numeric data||surveys|
|sex||TEXT||Field contains text data||surveys|
|species_id||TEXT||Field contains text data||species, surveys|
|species||TEXT||Field contains text data||species|
|taxa||TEXT||Field contains text data||species|
|weight||REAL||Field contains measured numerical data||surveys|
|year||INTEGER||Allows for meaningful arithmetic and comparisons||surveys|
You can also use this same approach to append new fields to an existing table.
- Go to the “Database Structure” tab, right click on the table you’d like to add data to, and choose Modify Table, or click on the Modify Table just under the tabs and above the table.
- Click the Add Field button to add a new field and assign it a data type.
|CHARACTER(n)||Character string. Fixed-length n|
|VARCHAR(n) or CHARACTER VARYING(n)||Character string. Variable length. Maximum length n|
|BINARY(n)||Binary string. Fixed-length n|
|BOOLEAN||Stores TRUE or FALSE values|
|VARBINARY(n) or BINARY VARYING(n)||Binary string. Variable length. Maximum length n|
|INTEGER(p)||Integer numerical (no decimal).|
|SMALLINT||Integer numerical (no decimal).|
|INTEGER||Integer numerical (no decimal).|
|BIGINT||Integer numerical (no decimal).|
|DECIMAL(p,s)||Exact numerical, precision p, scale s.|
|NUMERIC(p,s)||Exact numerical, precision p, scale s. (Same as DECIMAL)|
|FLOAT(p)||Approximate numerical, mantissa precision p. A floating number in base 10 exponential notation.|
|DOUBLE PRECISION||Approximate numerical|
|DATE||Stores year, month, and day values|
|TIME||Stores hour, minute, and second values|
|TIMESTAMP||Stores year, month, day, hour, minute, and second values|
|INTERVAL||Composed of a number of integer fields, representing a period of time, depending on the type of interval|
|ARRAY||A set-length and ordered collection of elements|
|MULTISET||A variable-length and unordered collection of elements|
|XML||Stores XML data|
Different databases offer different choices for the data type definition.
The following table shows some of the common names of data types between the various database platforms:
|integer||Number (integer)||Int||Number||Int / Integer||Int / Integer|
|float||Number (single)||Float / Real||Number||Float||Numeric|
|string (variable)||Text (<256) / Memo (65k+)||Varchar||Varchar2||Varchar||Varchar|
|binary object OLE Object Memo Binary (fixed up to 8K)||Varbinary (<8K)||Image (<2GB) Long||Raw Blob||Text Binary||Varbinary|
- SQL allows us to select and group subsets of data, do math and other calculations, and combine data.
- A relational database is made up of tables which are related to each other by shared keys.
- Different database management systems (DBMS) use slightly different vocabulary, but they are all based on the same ideas.