Accessing Data With Queries


Teaching: 30 min
Exercises: 5 min
  • How do I write a basic query in SQL?

  • Write and build queries.

  • Filter data given various criteria.

  • Sort the results of a query.

Writing my first query

Let’s start by using the surveys table. Here we have data on every individual that was captured at the site, including when they were captured, what plot they were captured on, their species ID, sex and weight in grams.

Let’s write an SQL query that selects all of the columns in the surveys table. SQL queries can be written in the box located under the “Execute SQL” tab. Click on the right arrow above the query box to execute the query. (You can also use the keyboard shortcut “Cmd-Enter” on a Mac or “Ctrl-Enter” on a Windows machine to execute a query.) The results are displayed in the box below your query. If you want to display all of the columns in a table, use the wildcard *.

FROM surveys;

We have capitalized the words SELECT and FROM because they are SQL keywords. SQL is case insensitive, but it helps for readability, and is good style.

If we want to select a single column, we can type the column name instead of the wildcard *.

FROM surveys;

If we want more information, we can add more columns to the list of fields, right after SELECT:

SELECT year, month, day
FROM surveys;

Limiting results

Sometimes you don’t want to see all the results, you just want to get a sense of what’s being returned. In that case, you can use a LIMIT clause. In particular, you would want to do this if you were working with large databases.

FROM surveys

Unique values

If we want only the unique values so that we can quickly see what species have been sampled we use DISTINCT

FROM surveys;

If we select more than one column, then the distinct pairs of values are returned

SELECT DISTINCT year, species_id
FROM surveys;

Calculated values

We can also do calculations with the values in a query. For example, if we wanted to look at the mass of each individual on different dates, but we needed it in kg instead of g we would use

SELECT year, month, day, weight/1000
FROM surveys;

When we run the query, the expression weight / 1000 is evaluated for each row and appended to that row, in a new column. If we used the INTEGER data type for the weight field then integer division would have been done, to obtain the correct results in that case divide by 1000.0. Expressions can use any fields, any arithmetic operators (+, -, *, and /) and a variety of built-in functions. For example, we could round the values to make them easier to read.

SELECT plot_id, species_id, sex, weight, ROUND(weight / 1000, 2)
FROM surveys;


  • Write a query that returns the year, month, day, species_id and weight in mg.


SELECT day, month, year, species_id, weight * 1000
FROM surveys;


Databases can also filter data – selecting only the data meeting certain criteria. For example, let’s say we only want data for the species Dipodomys merriami, which has a species code of DM. We need to add a WHERE clause to our query:

FROM surveys
WHERE species_id='DM';

We can do the same thing with numbers. Here, we only want the data since 2000:

SELECT * FROM surveys
WHERE year >= 2000;

If we used the TEXT data type for the year, the WHERE clause should be year >= '2000'.

We can use more sophisticated conditions by combining tests with AND and OR. For example, suppose we want the data on Dipodomys merriami starting in the year 2000:

FROM surveys
WHERE (year >= 2000) AND (species_id = 'DM');

Note that the parentheses are not needed, but again, they help with readability. They also ensure that the computer combines AND and OR in the way that we intend.

If we wanted to get data for any of the Dipodomys species, which have species codes DM, DO, and DS, we could combine the tests using OR:

FROM surveys
WHERE (species_id = 'DM') OR (species_id = 'DO') OR (species_id = 'DS');


  • Produce a table listing the data for all individuals in Plot 1 that weighed more than 75 grams, telling us the date, species id code, and weight (in kg).


SELECT day, month, year, species_id, weight / 1000
FROM surveys
WHERE (plot_id = 1) AND (weight > 75);

Building more complex queries

Now, let’s combine the above queries to get data for the 3 Dipodomys species from the year 2000 on. This time, let’s use IN as one way to make the query easier to understand. It is equivalent to saying WHERE (species_id = 'DM') OR (species_id = 'DO') OR (species_id = 'DS'), but reads more neatly:

FROM surveys
WHERE (year >= 2000) AND (species_id IN ('DM', 'DO', 'DS'));

We started with something simple, then added more clauses one by one, testing their effects as we went along. For complex queries, this is a good strategy, to make sure you are getting what you want. Sometimes it might help to take a subset of the data that you can easily see in a temporary database to practice your queries on before working on a larger or more complicated database.

When the queries become more complex, it can be useful to add comments. In SQL, comments are started by --, and end at the end of the line. For example, a commented version of the above query can be written as:

-- Get post 2000 data on Dipodomys' species
-- These are in the surveys table, and we are interested in all columns
SELECT * FROM surveys
-- Sampling year is in the column `year`, and we want to include 2000
WHERE (year >= 2000)
-- Dipodomys' species have the `species_id` DM, DO, and DS
AND (species_id IN ('DM', 'DO', 'DS'));

Although SQL queries often read like plain English, it is always useful to add comments; this is especially true of more complex queries.


We can also sort the results of our queries by using ORDER BY. For simplicity, let’s go back to the species table and alphabetize it by taxa.

First, let’s look at what’s in the species table. It’s a table of the species_id and the full genus, species and taxa information for each species_id. Having this in a separate table is nice, because we didn’t need to include all this information in our main surveys table.

FROM species;

Now let’s order it by taxa.

FROM species

The keyword ASC tells us to order it in ascending order. We could alternately use DESC to get descending order.

FROM species

ASC is the default.

We can also sort on several fields at once. To truly be alphabetical, we might want to order by genus then species.

FROM species
ORDER BY genus ASC, species ASC;


  • Write a query that returns year, species_id, and weight in kg from the surveys table, sorted with the largest weights at the top.


SELECT year, species_id, weight / 1000
FROM surveys

Order of execution

Another note for ordering. We don’t actually have to display a column to sort by it. For example, let’s say we want to order the birds by their species ID, but we only want to see genus and species.

SELECT genus, species
FROM species
WHERE taxa = 'Bird'
ORDER BY species_id ASC;

We can do this because sorting occurs earlier in the computational pipeline than field selection.

The computer is basically doing this:

  1. Filtering rows according to WHERE
  2. Sorting results according to ORDER BY
  3. Displaying requested columns or expressions.

Clauses are written in a fixed order: SELECT, FROM, WHERE, then ORDER BY.

Multiple statements

It is possible to write a query as a single line, but for readability, we recommend to put each clause on its own line. The standard way to separate a whole SQL statement is with a semicolon. This allows more than one SQL statement to be executed together.


  • Let’s try to combine what we’ve learned so far in a single query. Using the surveys table, write a query to display the three date fields, species_id, and weight in kilograms (rounded to two decimal places), for individuals captured in 1999, ordered alphabetically by the species_id.
  • Write the query as a single line, then put each clause on its own line, and see how more legible the query becomes!


SELECT year, month, day, species_id, ROUND(weight / 1000, 2)
FROM surveys
WHERE year = 1999
ORDER BY species_id;

Key Points

  • It is useful to apply conventions when writing SQL queries to aid readability.

  • Use logical connectors such as AND or OR to create more complex queries.

  • Calculations using mathematical symbols can also be performed on SQL queries.

  • Adding comments in SQL helps keep complex queries understandable.