9 Practice Datasets

9.1 Example Datasets

Two small, clean datasets are provided for practice exercises in Sessions 1-2. From Session 3 onwards, analysts work with their own real indicator data.

9.1.1 Bus Ridership

File: bus_ridership.csv

Description: Annual bus ridership data for the WECA region

Columns:

year - Year (2015-2024)
ridership - Annual ridership in millions
routes - Number of bus routes
region - Geographic area

Use cases:

Session 1: Creating first visualisation
Session 2: Line charts and trends with ggplot2

9.1.2 Area Employment

File: area_employment.csv

Description: Employment rate statistics by WECA local authority (2019-2023)

Columns:

area - Local authority name (Bath and North East Somerset, Bristol, North Somerset, South Gloucestershire)
year - Year (2019-2023)
employment_rate - Proportion employed, 16-64 age group (0-1 scale)
population - Total population

Use cases:

Session 2: Filtering, grouping, summarising with dplyr verbs
Session 2: Bar charts and comparisons with ggplot2

9.2 Real Indicator Data

From Session 3 onwards, analysts bring their own real WECA datasets. These will typically be messier than the example data above – see Data Cleaning Notes below for common issues and solutions.

9.3 Data Cleaning Notes

Common issues to watch for:

Column naming:
- Mixed case: “Area Name”, “area_name”, “AREA_NAME”
- Special characters: “Year (2023)”, “Percentage (%)”
- Solution: Use janitor::clean_names()
Missing values:
- Coded as: “N/A”, “n/a”, “-”, “*“, empty cells
- Solution: Explicit is.na() checks and replace_na()
Date formats:
- “31/12/2023”, “2023-12-31”, “December 2023”
- Solution: Use as.Date() with appropriate format strings
Numeric values as text:
- “1,234”, “£50.00”, “12.5%”
- Solution: str_remove_all() non-numeric characters, then as.numeric()
Excel header rows:
- Title rows, merged cells, footnotes
- Solution: Use skip = parameter in read_excel()

9.4 Dataset Documentation Template

When adding new practice datasets, document them using this template:

### Dataset Name

**File:** `filename.csv`

**Description:** Brief description of what the data contains

**Columns:**

- `column1` - Description
- `column2` - Description

**Source:** Data source and URL

**Date range:** YYYY-YYYY

**Use cases:** Which sessions/exercises use this data

**Known issues:** Any data quality notes

--- title: "Practice Datasets" --- ## Example Datasets Two small, clean datasets are provided for practice exercises in Sessions 1-2. From Session 3 onwards, analysts work with their own real indicator data. ### Bus Ridership **File:** `bus_ridership.csv` **Description:** Annual bus ridership data for the WECA region **Columns:** - `year` - Year (2015-2024) - `ridership` - Annual ridership in millions - `routes` - Number of bus routes - `region` - Geographic area **Use cases:** - Session 1: Creating first visualisation - Session 2: Line charts and trends with ggplot2 ### Area Employment **File:** `area_employment.csv` **Description:** Employment rate statistics by WECA local authority (2019-2023) **Columns:** - `area` - Local authority name (Bath and North East Somerset, Bristol, North Somerset, South Gloucestershire) - `year` - Year (2019-2023) - `employment_rate` - Proportion employed, 16-64 age group (0-1 scale) - `population` - Total population **Use cases:** - Session 2: Filtering, grouping, summarising with dplyr verbs - Session 2: Bar charts and comparisons with ggplot2 ## Real Indicator Data From Session 3 onwards, analysts bring their own real WECA datasets. These will typically be messier than the example data above -- see Data Cleaning Notes below for common issues and solutions. ## Data Cleaning Notes **Common issues to watch for:** 1. **Column naming:** - Mixed case: "Area Name", "area_name", "AREA_NAME" - Special characters: "Year (2023)", "Percentage (%)" - Solution: Use `janitor::clean_names()` 2. **Missing values:** - Coded as: "N/A", "n/a", "-", "*", empty cells - Solution: Explicit `is.na()` checks and `replace_na()` 3. **Date formats:** - "31/12/2023", "2023-12-31", "December 2023" - Solution: Use `as.Date()` with appropriate format strings 4. **Numeric values as text:** - "1,234", "£50.00", "12.5%" - Solution: `str_remove_all()` non-numeric characters, then `as.numeric()` 5. **Excel header rows:** - Title rows, merged cells, footnotes - Solution: Use `skip =` parameter in `read_excel()` ## Dataset Documentation Template When adding new practice datasets, document them using this template: ```markdown ### Dataset Name **File:** `filename.csv` **Description:** Brief description of what the data contains **Columns:** - `column1` - Description - `column2` - Description **Source:** Data source and URL **Date range:** YYYY-YYYY **Use cases:** Which sessions/exercises use this data **Known issues:** Any data quality notes ```