Lego Set Explorer 🧩

Table of Contents

• Overview
• Data Dictonary
• Data Cleaning
• Important KPIs
• Dashboard Preview

Overview

The objective of this challenge is to create an interactive visual that lets users explore the history and evolution of LEGO sets from the past 50 years. The dashboard uses data from LEGO sets released from 1970 to 2022, including details on each set’s theme, pieces, recommended age, retail price, and image.

Data Dictionary

set_id: Official LEGO item number

name: Name of the LEGO set

year: Release year

theme: LEGO theme the set belongs to

subtheme: Subtheme within the theme

themeGroup: Overall group the theme belongs to

category: Type of set pieces Number of pieces in the set

minifigs:Number of mini figures included in the set

agerange_min: Minimum age recommended

US_retailPrice: US retail price at launch

bricksetURL: URL for the set on brickset.com

thumbnailURL: Small image of the set

imageURL: Full size image of the set

Data Cleaning

Once the dataset is imported into Power BI, the next step is to clean it, as it often contains various inconsistencies. We will perform data cleaning using Power BI Query Editor, an inbuilt feature of Power BI that allows efficient data transformation and preparation.

Click on the Transform Data tab to access the Power Query Editor.

• Removing Irrelevant Columns: In our analysis, certain columns are not needed, so it's important to remove them to ensure an effective analysis.

  • Columns Removed: thumbnailURL, bricksetURL, minifigs

• Changing Type of columns: Our dataset includes columns with incorrect data types that need to be corrected for accurate analysis.

  • The agerange_min column, initially in text format, is converted to a whole number type
  • The US_retailPrice column, initially in text format is converted to a fixed decimal type

• Handling Missing Values: Our dataset consist of columns with missing values. For this analysis, we removed empty values.

  • Columns: pieces, agerange_min, US_retailPrice

• Renaming Columns: For better identification of columns, we renamed some columns.

  • agerange_min: age
  • US_retailPrice: price

Important KPIs

1.Total Sets

Total Sets = DISTINCTCOUNT(lego_sets[set_id])

2.Total Groups

Total Groups = DISTINCTCOUNT(lego_sets[themeGroup])

3. Average Age

Avg. Age = AVERAGE(lego_sets[age])

4. Average Price

Avg. Price = AVERAGE(lego_sets[price])

5. Average Pieces

Avg. Pieces = AVERAGE(lego_sets[pieces])

Dashboard Preview