Intermediate Power BI (session 3)

This is part of our intermediate-level Power BI desktop course. The course structure is as follows:

• session 1 (this session): a simple build-a-dashboard session as a warmup, reminder about core Power BI techniques, and sharing some suggestions for good workflow practices
• session 2: DAX language foundations, including FILTER and CALCULATE
• session 3 (this session): core PowerQuery functions to manage sources, join data sets, pivot, and tidy values
• session 4: PowerQuery M language foundations, including a basic overview of syntax, a core function toolkit, advanced mode, and reproducibility
• session 5: a second, more ambitious, build-a-dashboard session to put all that PowerQuery and DAX knowledge into practice

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Forthcoming session(s)

Booking link	Date
Intermediate Power BI (session 3)	13:00-15:00 Wed 19th November 2025
Intermediate Power BI (session 3)	14:00-16:00 Thu 29th January 2026

Session aim

• to cover core Power Query (PQ) functions and techniques

Session outline

• understanding PQ queries - sources, connections, code folding, duplicating/referencing
• data formats for Power BI - tidy data, and key-value pairs, and interconverting with pivots
• joins and relationships
• normalising and the star schema

Data for this session

• we’ll use the NRS vital events data for Q3 2024
• that’s a complex Excel spreadsheet containing national birth, death, and marriage information

Getting started

• open Power BI
• create a blank report
• then click Tranform data to open PQ

Loading data from Power Query

• all the Power BI connectors are available in PQ
• New query > Web
• supply the link for the NRS vital events data for Q3 2024: https://www.nrscotland.gov.uk/media/arkfqyyr/q3-2024-data.xlsx

Tables and sheets

• we want table 6 (TableQ6_Deaths_by_sex_cause_and_NHS_Board_area)
• please import that from the navigator

Tidying up

• check the data types
• fix the query name to cause_death
• rename ICD 10 Summary List to ICD10
• rename Cause of death to cause_death
• remove the three All rows

This data isn’t quite tidy

Tidy data

Shaping your data

• two main approaches found in Power BI:
  • Excel-like tidy data
  • SQL-like relational data
• tidy data is conceptually easier to build
  • joins/merges
  • splitting columns
• but really Power BI prefers relational data
• more performant
• easier to manage complex datasets

Tidy data

• largely covered in session one, but a quick reminder about splitting and merging

Key-value pair data

Key-value pair data

Key	Value
Pneumonia	144
Asthma	20
Influenza 3	2

Relational data in two minutes

• break your data into multiple tables
• give each item a key
• build relationships between those keys

Take some wide data

NHS Board	AED count	AED model	Manufacturer	Cost
NHS A&A	3	AED Plus Fully Automatic Defibrillator	ZOLL	1305
NHS A&A	4	AED Plus Fully Automatic Defibrillator	ZOLL	975
NHS A&A	5	Philips HeartStart FRx Semi-Automatic Defibrillator with Carry Case	Philips	1200

Data from sja.org.uk

Break into tables

NHS Board	AED count
NHS A&A	3
NHS A&A	4
NHS A&A	5

AED model	Manufacturer	Cost
AED Plus Fully Automatic Defibrillator	ZOLL	1305
AED Plus Fully Automatic Defibrillator	ZOLL	975
Philips HeartStart FRx Semi-Automatic Defibrillator with Carry Case	Philips	1200

Add unique keys

NHS Board	AED count	AED key
NHS A&A	3	ZOLL-1
NHS A&A	4	ZOLL-2
NHS A&A	5	PHIL-1

AED key	AED model	Manufacturer	Cost
ZOLL-1	AED Plus Fully Automatic Defibrillator	ZOLL	1305
ZOLL-2	AED Plus Fully Automatic Defibrillator	ZOLL	975
PHIL-1	Philips HeartStart FRx Semi-Automatic Defibrillator with Carry Case	Philips	1200

Star schema

• you might also see Power BI data described as a star schema image: Wikimedia

Reshaping our data

• we’ll break out the cause of death parts of the data into a new table
• we’ll use the ICD 10 codes as a key
• duplicate the cause_death query and rename to ICD
• remove everything but ICD10 and cause_death from ICD
• remove duplicates via Remove Rows > Remove Duplicates

Duplicating vs referencing queries

• a duplicate query is an independent copy of a query that can be tweaked/extended
• a referenced query takes the output of a query, and uses it as the starting-point for additional work

Reshape cause_death to key-value

• remove the cause_death column
• then unpivot everything but ICD10 and Sex
• rename your new Attribute/Value columns to board and n
• filter out the Scotland values from your board column

Create national data for benchmarking

• duplicate cause_death
• name that new query national
• remove everything but the national data with = Table.SelectColumns(#"Filtered Rows",{"ICD 10 Summary List", "Scotland", "Sex"})
• then go back to the earlier filter, where you removed the total rows, and re-add them to your dataset

More data, same source

• go back to source step of the cause_death query
• find the table data in the row TableQ2b_Vital_events_by_NHS_Board_area
• right-click the Table, and select Add as New Query
• rename that new query population

Make a population table

• remove the top three rows
• then Use First Row as Headers
• remove everything but the females and males columns
• remove the null row
• replace SCOTLAND with Scotland
• rename NHS Board area to Board
• clean and trim the board values
• unpivot the population columns to Sex and Population
• replace values and change case in the Sex column to leave Male and Female values

Relationships

• Power BI works best when single columns are used for relationships
• create merged board-sex columns for relationships

Additional data sources

• you might like to add in a second data source at this point for practice
• e.g. population estimates, which could be added as a web source
• or second quarter 2024 NRS vital data as an Excel file