Data wrangling transforms, cleans, and reshapes data so it can support analysis and visualization. In JavaScript, it commonly starts with arrays of objects loaded from datasets such as CSV files. section 1.2.5
A practical JavaScript workflow includes:
- Iterate through each datum with forEach and convert values such as string-formatted IDs into numbers. section 1.2.5
- Use map to reshape a dataset into a simpler array, such as a list of employee names. section 1.2.5
- Use filter to isolate every datum matching a condition, or find to retrieve one unique matching datum. section 1.2.5
- Clean, format, and prepare data before analysis, then prototype visualizations before committing to a refined D3 implementation. section 1.1.2
Preparing employee data
Given an array of employee objects, `data.forEach(d => { d.id = +d.id; });` converts each ID from a string to a number. `data.map(d => d.name)` produces names for labeling a visualization, while `data.filter(d => d.works_with_d3)` selects matching employees and `data.find(d => d.id === 3)` locates one employee. section 1.2.5
const data = [
{id:"1", name:"Zoe", position:"Data analyst", works_with_d3:false},
{id:"2", name:"James", position:"Frontend developer", works_with_d3:true},
{id:"3", name:"Alice", position:"Fullstack developer",works_with_d3:true},
{id:"4", name:"Hubert", position:"Designer", works_with_d3:false}
];
Inspect the compact employee dataset used for the wrangling operations. section 1.2.5
