Databases using SQL

Learning Objectives

Create and populate a database from a text file.
Describe why relational databases are useful.
Define SQLite data types.
Select, group, add to, and analyze subsets of data.
Combine data across multiple tables.

Setup

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

Install Firefox
Install the SQLite Manager add on: Menu (the three horizontal lines near the top right corner of Firefox) -> Add-ons -> Search -> SQLite Manager -> Install -> Restart now
Add SQLite Manager to the menu: Menu -> Customize, then drag the SQLite Manager icon to one of the empty menu squares on the right, Exit Customize
Open SQLite Manager: Menu -> SQLite Manager

Motivation

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.

Questions

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

surveys.csv
species.csv
plots.csv

Challenge

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?

Goals

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.

Definition: Relational database

A relational database is a digital database where all data is stored in relations (or tables) containing rows and columns. All tables contain records and all records are identified by a field contiaing a unique value. Every table shares at least one field with another table in one-to-one, one-to-many or many-to-many relationships. This allows the user to access the data in many ways

Databases

Why use relational databases

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 just 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 Access, Filemaker, etc.)
The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python.

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, 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 download and look at a pre-existing database, the portal_mammals.sqlite file. Clicking on the “open file” icon and then that file 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:

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

Database Design

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 (foreign key).

Import

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:

Start a New Database Database -> New Database
Fill in a proper name for the new database in the pop-up window, e.g. ProtalProject, click OK
Navigate to and select the folder where to store the new database
Start the import Database -> Import
Select the file to import by clicking the “Select File” button and selecting the appropriate CSV-file
Give the table a name that matches the file name (surveys, species, plots), or use the default
If the first row has column headings, check the appropriate box
Make sure the delimiter and quotation options are appropriate for the CSV files. Ensure ‘Ignore trailing Separator/Delimiter’ is left unchecked.
Click OK
When asked if you want to modify the table, click OK
Set the data types for each field: choose TEXT for fields with text (species_id, genus, sex, etc.) and INT for fields with numbers (day, month, year, weight, etc.), click *OK
Click *OK one more time to confirm the operation.

Challenge

Import the plots and species tables

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

Adding data to existing tables

Browse & Search -> Add
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	Varchar / Varchar2	Varchar	Varchar
binary object OLE Object Memo Binary (fixed up to 8K)	Varbinary (<8K)	Image (<2GB) Long	Raw Blob	Text Binary	Varbinary

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