Introducing Databases and SQL
OverviewTeaching: 60 min
Exercises: 5 minQuestions
What is a relational database and why should I use it?
What is SQL?Objectives
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.
What is SQL?
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.
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.
- 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.
Database Management Systems
There are different database management systems to work with relational databases such as SQLite, MySQL, Potsgresql, 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
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
we were exploring earlier. To see the contents of any table, click on it, and
then click the “Browse Data” tab next to the “Database Structure” tab. 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 database”).
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
surveys table that most fields contain numbers (BIGINT, or big integer, and FLOAT, or floating point numbers/decimals) while the
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
we downloaded earlier. Close the currently open database (File > Close Database) and then
follow these instructions:
- 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
|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. Then click the Write Changes button to save the database.
- Import the
You can also use this same approach to append new fields to an existing table.
Adding fields to existing tables
- 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.
Accessing Data With Queries
OverviewTeaching: 30 min
Exercises: 5 minQuestions
How do I write a basic query in SQL?Objectives
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 *.
SELECT * 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 *.
SELECT year 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;
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 the
LIMIT command. In particular, you would want to do this if you were working with large databases.
SELECT * FROM surveys LIMIT 10;
If we want only the unique values so that we can quickly see what species have
been sampled we use
SELECT DISTINCT species_id FROM surveys;
If we select more than one column, then the distinct pairs of values are returned
SELECT DISTINCT year, species_id FROM surveys;
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 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:
SELECT * 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
year >= '2000'.
We can use more sophisticated conditions by combining tests
OR. For example, suppose we want the data on Dipodomys merriami
starting in the year 2000:
SELECT * 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
in the way that we intend.
If we wanted to get data for any of the Dipodomys species, which have
DS, we could combine the tests using OR:
SELECT * 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:
SELECT * 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
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.
SELECT * FROM species;
Now let’s order it by taxa.
SELECT * FROM species ORDER BY taxa ASC;
ASC tells us to order it in ascending order.
We could alternately use
DESC to get descending order.
SELECT * FROM species ORDER BY taxa DESC;
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.
SELECT * 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 BY weight DESC;
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:
- Filtering rows according to WHERE
- Sorting results according to ORDER BY
- Displaying requested columns or expressions.
Clauses are written in a fixed order:
BY. It is possible to write a query as a single line, but for readability,
we recommend to put each clause on its own line.
- 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
- 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;
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.
Aggregating and Grouping Data
OverviewTeaching: 50 min
Exercises: 10 minQuestions
How can I summarize my data by aggregating, filtering, or ordering query results?
How can I make sure column names from my queries make sense and aren’t too long?Objectives
Apply aggregation functions to group records together.
Filter and order results of a query based on aggregate functions.
Employ aliases to assign new names to items in a query.
Save a query to make a new table.
Apply filters to find missing values in SQL.
COUNT and GROUP BY
Aggregation allows us to combine results by grouping records based on value. It is also useful for calculating combined values in groups.
Let’s go to the surveys table and find out how many individuals there are. Using the wildcard * counts the number of records (rows):
SELECT COUNT(*) FROM surveys;
We can also find out how much all of those individuals weigh:
SELECT COUNT(*), SUM(weight) FROM surveys;
We can output this value in kilograms (dividing the value by 1000.00), then rounding to 3 decimal places: (Notice the divisor has numbers after the decimal point, which forces the answer to have a decimal fraction)
SELECT ROUND(SUM(weight)/1000.00, 3) FROM surveys;
There are many other aggregate functions included in SQL, for example:
Write a query that returns: the total weight, average weight, minimum and maximum weights for all animals caught over the duration of the survey. Can you modify it so that it outputs these values only for weights between 5 and 10?
-- All animals SELECT SUM(weight), AVG(weight), MIN(weight), MAX(weight) FROM surveys; -- Only weights between 5 and 10 SELECT SUM(weight), AVG(weight), MIN(weight), MAX(weight) FROM surveys WHERE (weight > 5) AND (weight < 10);
Now, let’s see how many individuals were counted in each species. We do this
GROUP BY clause
SELECT species_id, COUNT(*) FROM surveys GROUP BY species_id;
GROUP BY tells SQL what field or fields we want to use to aggregate the data.
If we want to group by multiple fields, we give
GROUP BY a comma separated list.
Write queries that return:
- How many individuals were counted in each year in total
- How many were counted each year, for each different species
- The average weights of each species in each year
Can you get the answer to both 2 and 3 in a single query?
Solution of 1
SELECT year, COUNT(*) FROM surveys GROUP BY year;
Solution of 2 and 3
SELECT year, species_id, COUNT(*), AVG(weight) FROM surveys GROUP BY year, species_id;
Ordering Aggregated Results
We can order the results of our aggregation by a specific column, including the aggregated column. Let’s count the number of individuals of each species captured, ordered by the count:
SELECT species_id, COUNT(*) FROM surveys GROUP BY species_id ORDER BY COUNT(species_id);
As queries get more complex, the expressions we use can get long and unwieldy. To help make things clearer in the query and in its output, we can use aliases to assign new names to things in the query.
We can use aliases in column names using
SELECT MAX(year) AS last_surveyed_year FROM surveys;
AS isn’t technically required, so you could do
SELECT MAX(year) last_surveyed_year FROM surveys;
AS is much clearer so it is good style to include it.
We can not only alias column names, but also table names in the same way:
SELECT * FROM surveys AS surv;
And again, the
AS keyword is not required, so this works, too:
SELECT * FROM surveys surv;
Aliasing table names can be helpful when working with queries that involve multiple tables; you will learn more about this later.
In the previous episode, we have seen the keyword
WHERE, allowing to
filter the results according to some criteria. SQL offers a mechanism to
filter the results based on aggregate functions, through the
For example, we can request to only return information about species with a count higher than 10:
SELECT species_id, COUNT(species_id) FROM surveys GROUP BY species_id HAVING COUNT(species_id) > 10;
HAVING keyword works exactly like the
WHERE keyword, but uses
aggregate functions instead of database fields to filter.
You can use the
AS keyword to assign an alias to a column or table, and refer
to that alias in the
For example, in the above query, we can call the
another name, like
occurrences. This can be written this way:
SELECT species_id, COUNT(species_id) AS occurrences FROM surveys GROUP BY species_id HAVING occurrences > 10;
Note that in both queries,
HAVING comes after
GROUP BY. One way to
think about this is: the data are retrieved (
SELECT), which can be filtered
WHERE), then joined in groups (
GROUP BY); finally, we can filter again based on some
of these groups (
Write a query that returns, from the
speciestable, the number of
taxa, only for the
taxawith more than 10
SELECT taxa, COUNT(*) AS n FROM species GROUP BY taxa HAVING n > 10;
Saving Queries for Future Use
It is not uncommon to repeat the same operation more than once, for example for monitoring or reporting purposes. SQL comes with a very powerful mechanism to do this by creating views. Views are a form of query that is saved in the database, and can be used to look at, filter, and even update information. One way to think of views is as a table, that can read, aggregate, and filter information from several places before showing it to you.
Creating a view from a query requires us to add
CREATE VIEW viewname AS
before the query itself. For example, imagine that our project only covers
the data gathered during the summer (May - September) of 2000. That
query would look like:
SELECT * FROM surveys WHERE year = 2000 AND (month > 4 AND month < 10);
But we don’t want to have to type that every time we want to ask a question about that particular subset of data. Hence, we can benefit from a view:
CREATE VIEW summer_2000 AS SELECT * FROM surveys WHERE year = 2000 AND (month > 4 AND month < 10);
Using a view we will be able to access these results with a much shorter notation:
SELECT * FROM summer_2000 WHERE species_id = 'PE';
What About NULL?
From the last example, there should only be five records. If you look at the
weight column, it’s
easy to see what the average weight would be. If we use SQL to find the
average weight, SQL behaves like we would hope, ignoring the NULL values:
SELECT AVG(weight) FROM summer_2000 WHERE species_id = 'PE';
But if we try to be extra clever, and find the average ourselves, we might get tripped up:
SELECT SUM(weight), COUNT(*), SUM(weight)/COUNT(*) FROM summer_2000 WHERE species_id = 'PE';
COUNT command includes all five records (even those with NULL
values), but the
SUM only includes the three records with data in the
weight field, giving us an incorrect average. However,
our strategy will work if we modify the
COUNT command slightly:
SELECT SUM(weight), COUNT(weight), SUM(weight)/COUNT(weight) FROM summer_2000 WHERE species_id = 'PE';
When we count the weight field specifically, SQL ignores the records with data
missing in that field. So here is one example where NULLs can be tricky:
COUNT(field) can return different values.
Another case is when we use a “negative” query. Let’s count all the non-female animals:
SELECT COUNT(*) FROM summer_2000 WHERE sex != 'F';
Now let’s count all the non-male animals:
SELECT COUNT(*) FROM summer_2000 WHERE sex != 'M';
But if we compare those two numbers with the total:
SELECT COUNT(*) FROM summer_2000;
We’ll see that they don’t add up to the total! That’s because SQL doesn’t automatically include NULL values in a negative conditional statement. So if we are querying “not x”, then SQL divides our data into three categories: ‘x’, ‘not NULL, not x’ and NULL; then, returns the ‘not NULL, not x’ group. Sometimes this may be what we want - but sometimes we may want the missing values included as well! In that case, we’d need to change our query to:
SELECT COUNT(*) FROM summer_2000 WHERE sex != 'M' OR sex IS NULL;
GROUP BYkeyword to aggregate data.
COUNT, etc. operate on aggregated data.
Aliases can help shorten long queries. To write clear and readible queries, use the
ASkeyword when creating aliases.
HAVINGkeyword to filter on aggregate properties.
VIEWto access the result of a query as though it was a new table.
Combining Data With Joins
OverviewTeaching: 15 min
Exercises: 10 minQuestions
How do I bring data together from separate tables?Objectives
Employ joins to combine data from two tables.
Apply functions to manipulate individual values.
Employ aliases to assign new names to tables and columns in a query.
To combine data from two tables we use the SQL
JOIN command, which comes after
Database tables are used to organize and group data by common characteristics or principles.
Often, we need to combine elements from separate tables into a single tables or queries for analysis and visualization. A JOIN is a means for combining columns from multiple tables by using values common to each.
The JOIN command combined with ON is used to combine fields from separate tables.
JOIN command on its own will result in a cross product, where each row in
the first table is paired with each row in the second table. Usually this is not
what is desired when combining two tables with data that is related in some way.
For that, we need to tell the computer which columns provide the link between the two
tables using the word
ON. What we want is to join the data with the same
SELECT * FROM surveys JOIN species ON surveys.species_id = species.species_id;
ON is like
WHERE. It filters things out according to a test condition. We use
table.colname format to tell the manager what column in which table we are
The output of the
JOIN command will have columns from the first table plus the
columns from the second table. For the above command, the output will be a table
that has the following column names:
Alternatively, we can use the word
USING, as a short-hand.
works on columns which share the same name. In this case we are
telling the manager that we want to combine
species and that
the common column is
SELECT * FROM surveys JOIN species USING (species_id);
The output will only have one species_id column
We often won’t want all of the fields from both tables, so anywhere we would
have used a field name in a non-join query, we can use
For example, what if we wanted information on when individuals of each species were captured, but instead of their species ID we wanted their actual species names.
SELECT surveys.year, surveys.month, surveys.day, species.genus, species.species FROM surveys JOIN species ON surveys.species_id = species.species_id;
Many databases, including SQLite, also support a join through the
WHERE clause of a query.
For example, you may see the query above written without an explicit JOIN.
SELECT surveys.year, surveys.month, surveys.day, species.genus, species.species FROM surveys, species WHERE surveys.species_id = species.species_id;
For the remainder of this lesson, we’ll stick with the explicit use of the
JOIN keyword for
joining tables in SQL.
- Write a query that returns the genus, the species name, and the weight of every individual captured at the site
SELECT species.genus, species.species, surveys.weight FROM surveys JOIN species ON surveys.species_id = species.species_id;
Different join types
We can count the number of records returned by our original join query.
SELECT COUNT(*) FROM surveys JOIN species USING (species_id);
Notice that this number is smaller than the number of records present in the survey data.
SELECT COUNT(*) FROM surveys;
This is because, by default, SQL only returns records where the joining value
is present in the joined columns of both tables (i.e. it takes the intersection
of the two join columns). This joining behaviour is known as an
In fact the
JOIN command is simply shorthand for
INNER JOIN and the two
terms can be used interchangably as they will produce the same result.
We can also tell the computer that we wish to keep all the records in the first
table by using the command
LEFT OUTER JOIN, or
LEFT JOIN for short.
- Re-write the original query to keep all the entries present in the
surveystable. How many records are returned by this query?
SELECT * FROM surveys LEFT JOIN species USING (species_id);
- Count the number of records in the
surveystable that have a
NULLvalue in the
SELECT COUNT(*) FROM surveys WHERE species_id IS NULL;
Remember: In SQL a
NULL value in one table can never be joined to a
NULL value in a
second table because
NULL is not equal to anything, not even itself.
Combining joins with sorting and aggregation
Joins can be combined with sorting, filtering, and aggregation. So, if we wanted average mass of the individuals on each different type of treatment, we could do something like
SELECT plots.plot_type, AVG(surveys.weight) FROM surveys JOIN plots ON surveys.plot_id = plots.plot_id GROUP BY plots.plot_type;
- Write a query that returns the number of animals caught of each genus in each plot. Order the results by plot number (ascending) and by descending number of individuals in each plot.
SELECT surveys.plot_id, species.genus, COUNT(*) AS number_indiv FROM surveys JOIN species ON surveys.species_id = species.species_id GROUP BY species.genus, surveys.plot_id ORDER BY surveys.plot_id ASC, number_indiv DESC;
- Write a query that finds the average weight of each rodent species (i.e., only include species with Rodent in the taxa field).
SELECT surveys.species_id, AVG(surveys.weight) FROM surveys JOIN species ON surveys.species_id = species.species_id WHERE species.taxa = 'Rodent' GROUP BY surveys.species_id;
NULLIF and more
SQL includes numerous functions for manipulating data. You’ve already seen some
of these being used for aggregation (
COUNT) but there are functions
that operate on individual values as well. Probably the most important of these
COALESCE allows us to specify a value to use in
We can represent unknown sexes with
'U' instead of
SELECT species_id, sex, COALESCE(sex, 'U') FROM surveys;
The lone “sex” column is only included in the query above to illustrate where
COALESCE has changed values; this isn’t a usage requirement.
- Write a query that returns 30 instead of
NULLfor values in the
SELECT hindfoot_length, COALESCE(hindfoot_length, 30) FROM surveys;
- Write a query that calculates the average hind-foot length of each species, assuming that unknown lengths are 30 (as above).
SELECT species_id, AVG(COALESCE(hindfoot_length, 30)) FROM surveys GROUP BY species_id;
COALESCE can be particularly useful in
JOIN. When joining the
surveys tables earlier, some results were excluded because the
NULL in the surveys table. We can use
COALESCE to include them again, re-writing the
a valid joining value:
SELECT surveys.year, surveys.month, surveys.day, species.genus, species.species FROM surveys JOIN species ON COALESCE(surveys.species_id, 'AB') = species.species_id;
- Write a query that returns the number of animals caught of each genus in each plot, assuming that unknown species are all of the genus “Rodent”.
SELECT plot_id, COALESCE(genus, 'Rodent') AS genus2, COUNT(*) FROM surveys LEFT JOIN species ON surveys.species_id=species.species_id GROUP BY plot_id, genus2;
The inverse of
NULLIF. This returns
NULL if the first argument
is equal to the second argument. If the two are not equal, the first argument
is returned. This is useful for “nulling out” specific values.
We can “null out” plot 7:
SELECT species_id, plot_id, NULLIF(plot_id, 7) FROM surveys;
Some more functions which are common to SQL databases are listed in the table below:
||Returns the absolute (positive) value of the numeric expression n|
||Returns the first of its parameters that is not NULL|
||Returns the length of the string expression s|
||Returns the string expression s converted to lowercase|
||Returns NULL if x is equal to y, otherwise returns x|
||Returns the numeric expression n rounded to x digits after the decimal point (0 by default)|
||Returns the string expression s without leading and trailing whitespace characters|
||Returns the string expression s converted to uppercase|
Finally, some useful functions which are particular to SQLite are listed in the table below:
||Returns a random integer between -9223372036854775808 and +9223372036854775807.|
||Returns the string expression s in which every occurrence of f has been replaced with r|
||Returns the portion of the string expression s starting at the character position x (leftmost position is 1), y characters long (or to the end of s if y is omitted)|
Write a query that returns genus names (no repeats), sorted from longest genus name down to shortest.
SELECT DISTINCT genus FROM species ORDER BY LENGTH(genus) DESC;
As we saw before, aliases make things clearer, and are especially useful when joining tables.
SELECT surv.year AS yr, surv.month AS mo, surv.day AS day, sp.genus AS gen, sp.species AS sp FROM surveys AS surv JOIN species AS sp ON surv.species_id = sp.species_id;
To practice we have some optional challenges for you.
SQL queries help us ask specific questions which we want to answer about our data. The real skill with SQL is to know how to translate our scientific questions into a sensible SQL query (and subsequently visualize and interpret our results).
Have a look at the following questions; these questions are written in plain English. Can you translate them to SQL queries and give a suitable answer?
How many plots from each type are there?
How many specimens are of each sex are there for each year, including those whose sex is unknown?
How many specimens of each species were captured in each type of plot, excluding specimens of unknown species?
What is the average weight of each taxa?
What are the minimum, maximum and average weight for each species of Rodent?
What is the average hindfoot length for male and female rodent of each species? Is there a Male / Female difference?
What is the average weight of each rodent species over the course of the years? Is there any noticeable trend for any of the species?
SELECT plot_type, COUNT(*) AS num_plots FROM plots GROUP BY plot_type;
SELECT year, sex, COUNT(*) AS num_animal FROM surveys GROUP BY sex, year;
SELECT species_id, plot_type, COUNT(*) FROM surveys JOIN plots USING(plot_id) WHERE species_id IS NOT NULL GROUP BY species_id, plot_type;
SELECT taxa, AVG(weight) FROM surveys JOIN species ON species.species_id = surveys.species_id GROUP BY taxa;
SELECT surveys.species_id, MIN(weight), MAX(weight), AVG(weight) FROM surveys JOIN species ON surveys.species_id = species.species_id WHERE taxa = 'Rodent' GROUP BY surveys.species_id;
SELECT surveys.species_id, sex, AVG(hindfoot_length) FROM surveys JOIN species ON surveys.species_id = species.species_id WHERE (taxa = 'Rodent') AND (sex IS NOT NULL) GROUP BY surveys.species_id, sex;
SELECT surveys.species_id, year, AVG(weight) as mean_weight FROM surveys JOIN species ON surveys.species_id = species.species_id WHERE taxa = 'Rodent' GROUP BY surveys.species_id, year;
JOINcommand to combine data from two tables—the
USINGkeywords specify which columns link the tables.
JOINreturns only matching rows. Other join commands provide different behavior, e.g.,
LEFT JOINretains all rows of the table on the left side of the command.
COALESCEallows you to specify a value to use in place of
NULL, which can help in joins
NULLIFcan be used to replace certain values with
Many other functions like
NULLIFcan operate on individual values.