SQL for Ecology

Databases using SQL


Teaching: 60 min
Exercises: 5 min
  • What is a relational database and why should I use it?

  • What is SQL?

  • Understand the benefits of using a relational database

  • Set up a small database from csv files using SQLite

  • Understand SQLite data types


Note: this should have been done by participants before the start of the workshop.

We use SQLite Manager and the Portal Project dataset throughout this lesson. See Setup for instructions on how to download the data, and also how to install and open SQLite Manager.


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.

Dataset Description

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 just a little bit for the workshop, but you can download the full dataset and work with it using exactly the same tools we’ll learn about today.


First, let’s download and look at some of the cleaned spreadsheets from the Portal Project dataset.
We’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:

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:

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.

Definition: Relational Database

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.


Why use relational databases

Using a relational database serves several purposes.

Database Management Systems

There are a number of different database management systems for working with relational data. We’re going to use SQLite today, but basically everything we teach you will apply to the other database systems as well (e.g. MySQL, PostgreSQL, MS Access, MS SQL Server, Oracle Database and Filemaker Pro). The only things that will differ are the details of exactly how to import and export data and the details of data types.

Relational databases

Let’s look at a pre-existing database, the portal_mammals.sqlite file from the Portal Project dataset that we downloaded during Setup. Clicking on the “open file” icon, then find that file and clicking on it will open the database.

You can see the tables in the database by looking at the left hand side of the screen under Tables, where each table corresponds to one of the csv files we were exploring earlier. To see the contents of any table, click on it, and then click the “Browse and Search” tab in the right panel. This will give us a view that we’re used to - just 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 database”).

The leftmost tab, “Structure”, provides some metadata about each table. It describes the columns, often called fields. (The rows of a database table are called records.) If you scroll down in the Structure view, you’ll see a list of fields, their labels, and their data type. Each field contains one variety or type of data, often numbers or text. You can see in the surveys table that most fields contain numbers (integers) while the species table is nearly all text.

The “Execute SQL” tab is blank now - this is where we’ll be typing our queries to retrieve information from the database tables.

To summarize:

Database Design


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 and then follow these instructions:

  1. Start a New Database
    • Database -> New Database
    • Give a name Ok -> Open. Creates the database in the opened folder
  2. Start the import Database -> Import
  3. Select the surveys.csv file to import
  4. Give the table a name that matches the file name (surveys), or use the default
  5. If the first row has column headings, check the appropriate box
  6. Make sure the delimiter and quotation options are appropriate for the CSV files. Ensure ‘Ignore trailing Separator/Delimiter’ is left unchecked.
  7. Press OK
  8. When asked if you want to modify the table, click OK
  9. Set the data types for each field using the suggestions in the table below (this includes fields from plots and species tables also):
Field Data Type Motivation Table(s)
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

Finally, click OK one more time to confirm the operation.


You can also use this same approach to append new data to an existing table.

Adding data to existing tables

  1. “Browse and Search” tab -> Add
  2. Enter data into a csv file and append

Data types

Data type Description
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.
REAL Approximate numerical
FLOAT Approximate numerical
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

SQL Data Type Quick Reference

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:

Data type Access SQLServer Oracle MySQL PostgreSQL
boolean Yes/No Bit Byte N/A Boolean
integer Number (integer) Int Number Int / Integer Int / Integer
float Number (single) Float / Real Number Float Numeric
currency Currency Money N/A N/A Money
string (fixed) N/A Char Char Char Char
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

Key Points