Remember to

• download portal_mammals.sqlite.
• connect portal_mammals.sqlite to SQLite Manager.

Introduce the Portal Project database

Database Queries

• Data is separate from manipulations of the data
• Tables - store the data
• Queries - store questions about the data
  • If we update the data, the query asks the same question of the new data.

Using SQLite Manager

• Use Execute SQL tab
• Run SQL w/ keyboard shortcut command + ;

Selecting columns

• Choose which columns to return.

SELECT year, month, day
FROM surveys;

• They can occur in any order.

SELECT month, day, year
FROM surveys;

• If we want to get all of the columns we can use *, which is a wildcard that means “all”.

SELECT *
FROM surveys;

• For unique values use DISTINCT.

SELECT DISTINCT year, month, day
FROM surveys;

• We can also do calculations in the SELECT.

SELECT species_id, hindfoot_length/1000.0
FROM surveys;

• We can also use functions.

SELECT species_id, ROUND(hindfoot_length/1000.0, 2)
FROM surveys;

Do Exercise 1 - SELECT. Don’t worry if you don’t know how to save it yet, we’ll cover that in a minute.

Saving queries for future use

• Views save queries to run again.
• Create them by using Create View in the View menu, or by adding CREATE VIEW *viewname* AS to the beginning of a query.

CREATE VIEW hindfoots_m AS
SELECT species_id, ROUND(hindfoot_length/1000.0, 2)
FROM surveys;

Save the results of Exercise 1 as a new view.

Filtering

• Use WHERE to select only the rows meeting certain criteria.
  • Follow WHERE with a conditional statement
    • General form: column, condition, value

    =	equals
    > <	greater / less than
    >= <=	greater / less than or equal to
    != <>	not equals (!= consistent with other languages)

SELECT hindfoot_length
FROM surveys
WHERE species_id = 'DS';

SELECT species_id
FROM surveys
WHERE hindfoot_length >= 30;

• To combine two or more conditions use AND and OR.

SELECT year, month, day, species_id, hindfoot_length
FROM surveys
WHERE species_id = 'DS' AND year > 1990;

• The red cells are NULL values, in this case instances were no hind foot measure was taken. We can use WHERE to remove them by asking SQL to only give us non-NULL values.

SELECT year, month, day, species_id, hindfoot_length
FROM surveys
WHERE species_id = 'DS' AND year > 1990 
  AND hindfoot_length IS NOT NULL;

• If you want only NULL values, use IS NULL instead

Do Exercise 2 - WHERE.

Style

• SQL generally doesn’t care about capitalization or line breaks. So it will run a query like this.

seLEcT year, MONTH, dAY, WEIght FrOm SURveyS wheRe hindfoot_LENGTH > 30 aND spECIes_ID = 'DM';

• This is difficult to read so we follow style rules for writing SQL code
  • Capitalize SQL commands
  • Lowercase variable names
  • One clause/line

Sorting

• Use ORDER BY to sort data.

SELECT genus, species
FROM species
ORDER BY genus;

• Use DESC to sort in descending order.

SELECT genus, species
FROM species
ORDER BY genus DESC;

• Use a list to sort by multiple columns.

SELECT genus, species
FROM species
ORDER BY taxa, genus, species;

Do Exercise 3 - ORDER BY.

Comments

• Even with good style it can quickly become difficult to remember exactly what a long query is doing. To help us remember/understand what the code is doing we can use comments.

-- Get post-2000 weight data on Kangaroo Rats
SELECT year, month, day, species_id, weight
FROM surveys
WHERE year > 2000 AND species_id IN ('DO', 'DM', 'DS');

• IN is a short way to check a single variable against multiple conditions. In this case species_id = 'DO' OR species_id = 'DM' OR species_id = 'DS'.