SQL for dplyr users

Remember to

• Install DBI, RSQLite, and dbplyr
• Download portal_mammals.sqlite.

Why Databases

• Good place to store data
• Lots of existing data is stored in them (which you might want to use)
• Support real-time collaboration on adding and updating data
• Handle out of memory computation

Database Queries

• Data is stored in tables - equivalent of data frames
• Queries store questions about the data - equivalent of dplyr pipelines
• These queries are written in “Structured Query Language” or SQL for short
• Some folks also pronounce this “sequel”

Lots of SQL Databases

• MySQL
• PostgreSQL

• MS Access

• We’ll use SQLite
• Simple - each database is just a file
• Works basically anywhere with no setup
• Great for relatively small (million row) projects that don’t require simultaneous data entry by multiple folks
• Recommend PostgreSQL if you need something more powerful

Using RStudio with SQLite

• We can RStudio to connect to SQL databases and run queries in them
• We’ll start by doing this directly to learn SQL

• Then we’ll learn how to work with databases inside our R scripts

• First let’s install some packages that RStudio needs

install.packages(c('DBI', 'RSQLite'))

• Then download the SQLite database we’re going to work with

download.file("https://ndownloader.figshare.com/files/11188550",
              "portal.sqlite",
              mode = "wb")

• The mode = "wb" makes sure that this binary file downloads properly on all operating systems
• Finally connect to the database so that we can work with it
• New File -> SQL Script
• The special comment on Line 1 is RStudio’s way of connecting to the database
• Add , dbname = "portal.sqlite" to get

-- !preview conn=DBI::dbConnect(RSQLite::SQLite(), dbname = "portal.sqlite")

• Save file
• You should see the number 1 as output

Selecting columns

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

SELECT *
FROM surveys;

• To select specific columns we list them by name
• Just like dplyr, but with different formatting

SELECT year, month, day
FROM surveys;

• We can also do calculations and use functions
• Just like in dplyr

SELECT year, month, day, species_id, hindfoot_length/10
FROM surveys;

SELECT year, month, day, species_id, ROUND(hindfoot_length/10)
FROM surveys;

Filtering

• The equivalent of filter in SQL is WHERE
  • Follow WHERE with a conditional statement
  • Unlike in R = not == for equality

SELECT year, month, day, species_id, ROUND(hindfoot_length/10)
FROM surveys
WHERE species_id = 'DS';

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

SELECT year, month, day, species_id, ROUND(hindfoot_length/10)
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, ROUND(hindfoot_length/10)
FROM surveys
WHERE species_id = 'DS' AND year > 1990 AND hindfoot_length IS NOT NULL;

Style

• SQL generally doesn’t care about capitalization or line breaks. So it will run a query like this.
• To make the code readable follow style rules for writing SQL code
  • Capitalize SQL commands
  • Lowercase variable names
  • One clause/line

Do the Simple WHERE exercise.

Sorting

• Use ORDER BY to sort data.
• Equivalent of arrange in dplyr is ORDER BY.

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;

Aggregation

• Like in dplyr we use GROUP BY to calculate values for different groups.

SELECT species_id, AVG(weight), COUNT(species_id)
FROM surveys
GROUP BY species_id;

• We can group by multiple columns as well.

SELECT species_id, plot_id, AVG(weight), COUNT(species_id)
FROM surveys
GROUP BY species_id, plot_id;

• Aggregation functions remove null values from the calculations.
• COUNT(species_id) counts the number of individuals identified to species
• To count the number of individuals with weights

SELECT species_id, plot_id, AVG(weight), COUNT(weight)
FROM surveys
GROUP BY species_id, plot_id;

• Using * counts any row with at least one non-null value
• We can name aggregated columns using as

SELECT species_id, plot_id, AVG(weight) as avg_weight, COUNT(weight) as num_indiv
FROM surveys
GROUP BY species_id, plot_id;

Do the COUNT exercise.

Order matters

Nemonic from: https://twitter.com/statsnam/status/1149431249511075840

Select - So
From - Few
Where - Workers
Group by - Go
Having - Home
Order by - On time

Basic join

• Add genus and species data to surveys table
• inner_join is JOIN or INNER JOIN
• USING specifies the columns to join on if the tables share column names (like by in dplyr)

SELECT * 
FROM surveys
JOIN species USING (species_id)

• If the column names don’t match you can use ON instead of USING

• Unlike in dplyr you must specify the columns to join on (or things go badly)

SELECT * 
FROM surveys
JOIN species

• This just combines every row in surveys with every row in species, which isn’t what we want

Multi-table join

• To join multiple tables do multiple joins

SELECT year, month, day, taxa, plot_type
FROM surveys
JOIN species USING (species_id)
JOIN plots USING (plot_id)

Do Basic Join.

Do Multi-table Join.