An introduction to SQL

SQL
beginner
Published

November 27, 2025

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Forthcoming session(s)
Booking link Date
An introduction to SQL 10:00-11:30 Tue 20th January 2026
  • This course is designed as a basic introduction to SQL delivered as live interactive sessions on Teams
  • It’s intended as a light introduction for users with good general digital skills (broadly level 2 in the Digital and Data Capability Framework)
  • It’s heavily based on the excellent W3 schools introductory SQL course
    • If you’re looking for a resource to use to support independent study, W3 schools is a better option than this page
    • this material is largely intended to support our live interactive training sessions
  • we use posit.cloud for this course. Although that platform is mainly meant for analysts writing R code, we can trick it to allow us to practice our SQL skills

About this session

This introductory session aims to give you a quick and easy introduction to SQL. That will include:

  • how data is held in SQL (as tables)
  • how you can investigate that data using SQL queries
  • and showing some of the basic tools that you can use to query and reshape data in SQL

Setup

  • You will need a free posit.cloud account. Please set this up and check that you have access before the session begins
  • Once you’ve logged-in to posit.cloud, please create a new Rstudio project New Rstudio project
  • In that project, create a new R script: Create a new R script from the file menu
  • Copy and paste the following R code into that script:
## install and attach R packages
pkg <- c("dbplyr", "RSQLite", "palmerpenguins")
install.packages(setdiff(pkg, rownames(installed.packages())))  
library(dbplyr)
library(dplyr)
library(palmerpenguins)

conn <- src_memdb() # create an sqlite db in memory
copy_to(conn, penguins, overwrite = T) # populate that db from R
  • save your script and click the Source button
    • that will create a simple SQL database in memory
  • now create a new SQL script (again, from the File menu)
  • delete all the pre-populated lines of code, as follows: Pre-populated code in new SQL script
  • to connect with our SQL db in memory, add the following line to the head of your SQL script:
-- !preview conn=src_memdb()$con
  • finally, save your SQL script (any file name is fine)

Getting started

Our db has one table, named penguins. A table is similar to an Excel sheet - a rectangular set of rows and columns containing our data. To preview our data, we’ll need to write a first line of SQL. Please add the following to the bottom of your SQL script:

SELECT * FROM penguins;
Displaying records 1 - 10
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
Adelie Torgersen 39.1 18.7 181 3750 male 2007
Adelie Torgersen 39.5 17.4 186 3800 female 2007
Adelie Torgersen 40.3 18.0 195 3250 female 2007
Adelie Torgersen NA NA NA NA NA 2007
Adelie Torgersen 36.7 19.3 193 3450 female 2007
Adelie Torgersen 39.3 20.6 190 3650 male 2007
Adelie Torgersen 38.9 17.8 181 3625 female 2007
Adelie Torgersen 39.2 19.6 195 4675 male 2007
Adelie Torgersen 34.1 18.1 193 3475 NA 2007
Adelie Torgersen 42.0 20.2 190 4250 NA 2007
  • SQL queries usually start with the SELECT keyword, which retrieves data from the database
  • * means “show me everything!”
  • FROM penguins specifies that we want everything from the penguins table. That specification is important, because most real-life SQL set-ups will have multiple tables of data
  • ; ends the line. Not essential, but good practice
  • Important: press Ctrl + Shift + Enter in the source code pane to run your SQL script

Playing with that query

  • We’ll now change that one query to do something slightly different
  • Unlike most other languages, you’ll write SQL queries one at a time
  • Add a new keyword LIMIT 5 at the end of your query to produce a shorter preview of our data:
SELECT * FROM penguins LIMIT 5;
5 records
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
Adelie Torgersen 39.1 18.7 181 3750 male 2007
Adelie Torgersen 39.5 17.4 186 3800 female 2007
Adelie Torgersen 40.3 18.0 195 3250 female 2007
Adelie Torgersen NA NA NA NA NA 2007
Adelie Torgersen 36.7 19.3 193 3450 female 2007
  • SQL is case insensitive - including for column names etc - but it’s customary to use capitals for the keywords. It makes your SQL easier to read, too.
Exercise
  • please could you modify your SQL statement to preview the first 10 rows of the data

SQL dialects

  • SQL has been around for a very long time, and has been used in lots of different ways
  • That means there are several slightly different dialects of SQL which are (unfortunately) incompatible
  • We’ll largely ignore those differences in this beginner’s training
  • But, just to illustrate, the LIMIT keyword is one that only works in some types of SQL
  • If you were using Microsoft’s dialect of SQL (which they refer to as Transact-SQL, or T-SQL), you’d need to write something slightly different (and reordered, thanks to John Mackintosh for the correction):
SELECT TOP 5 * from penguins;
  • Or if you were using Oracle’s newest dialect of SQL, you’d write either:
SELECT * FROM penguins FETCH FIRST 5 ROWS ONLY;
  • Or, an older - but possibly neater - way of writing Oracle SQL:
SELECT * FROM penguins WHERE ROWNUM <= 5;

Comments

  • If you want to turn off some of your SQL, or add notes, you can include comments
  • -- double-dash to comment a line, or part of a line
  • /* and then */ slash-star to comment a multi-line chunk
SELECT * FROM penguins LIMIT 5; -- comment in the line

/* or a longer chunk
  古池や蛙飛び込む水の音
  ふるいけやかわずとびこむみずのおと
*/
5 records
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
Adelie Torgersen 39.1 18.7 181 3750 male 2007
Adelie Torgersen 39.5 17.4 186 3800 female 2007
Adelie Torgersen 40.3 18.0 195 3250 female 2007
Adelie Torgersen NA NA NA NA NA 2007
Adelie Torgersen 36.7 19.3 193 3450 female 2007

Columns

  • you can select some of your columns by changing the SELECT * keyword
    • the * we’ve been using so far is a wildcard, which shows all the columns in your table
  • add column name(s) to retrieve just parts of your data
SELECT species, island FROM penguins;
Displaying records 1 - 10
species island
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
  • note that columns are returned in the order you ask for them
  • you can also rename selected columns using AS:
SELECT species, island AS landmass
    FROM penguins;
Displaying records 1 - 10
species landmass
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
Adelie Torgersen
  • note too that SQL is insensitive to whitespace, so you can generally split up longer queries over many lines
  • an alternative strategy (if you’re brave) is to use implicit renaming:
SELECT island landmass FROM penguins;
Displaying records 1 - 10
landmass
Torgersen
Torgersen
Torgersen
Torgersen
Torgersen
Torgersen
Torgersen
Torgersen
Torgersen
Torgersen
Exercise
  • please select the species and flipper_length_mm columns with new names of your choice

Distinct values

SELECT DISTINCT species FROM penguins;
3 records
species
Adelie
Gentoo
Chinstrap
  • DISTINCT gathers the unique values in a column, or unique combinations of values across multiple columns
  • this second query gives you all the combinations of penguins and islands that exist in our data:
SELECT DISTINCT island, species FROM penguins;
5 records
island species
Torgersen Adelie
Biscoe Adelie
Dream Adelie
Biscoe Gentoo
Dream Chinstrap

Acknowledgements

Like all our courses and sessions, this is a team effort, and I thank the network as a whole for their encouragements and contributions. Specific thanks in this case go to Amanda King (NHS GGC), Steven Knapman (NHS Fife), and James McMahon (Public Health Scotland), and to the members of the pilot cohort for this session.