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added income by category feature #175

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Merged
archa8 merged 3 commits into from
Oct 31, 2025
Merged

added income by category feature #175

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ca6fee3
added income by category feature
ayaz9616 Oct 24, 2025
856696a
feat(charts): income-by-category aggregation, optional date filters; …
ayaz9616 Oct 31, 2025
091c393
Merge branch 'master' into feature/income-by-category
ayaz9616 Oct 31, 2025
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20 changes: 18 additions & 2 deletions backend/controllers/transactionController.js
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Original file line number Diff line number Diff line change
Expand Up @@ -201,9 +201,25 @@ const getChartData = async (req, res) => {
const thirtyDaysAgo = new Date();
thirtyDaysAgo.setDate(thirtyDaysAgo.getDate() - 30);

// Optional date range from query for category aggregations
const { startDate, endDate } = req.query;
let dateMatch = {};
if (startDate || endDate) {
dateMatch.addedOn = {};
if (startDate) dateMatch.addedOn.$gte = new Date(startDate);
if (endDate) dateMatch.addedOn.$lte = new Date(endDate);
}

// Data for Expenses by Category (Pie Chart)
const expensesByCategory = await IncomeExpense.aggregate([
{ $match: { user: userId, isIncome: false, isDeleted: false } },
{ $match: { user: userId, isIncome: false, isDeleted: false, ...(dateMatch.addedOn ? { addedOn: dateMatch.addedOn } : {}) } },
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Copilot AI Oct 31, 2025

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The spread operator pattern ...(dateMatch.addedOn ? { addedOn: dateMatch.addedOn } : {}) adds complexity and could be simplified. Consider restructuring to build the match object conditionally before the aggregation, which improves readability and maintainability.

Copilot uses AI. Check for mistakes.
{ $group: { _id: '$category', total: { $sum: '$cost' } } },
{ $project: { name: '$_id', total: 1, _id: 0 } }
]);

// Data for Income by Category (Pie Chart)
const incomeByCategory = await IncomeExpense.aggregate([
{ $match: { user: userId, isIncome: true, isDeleted: false, ...(dateMatch.addedOn ? { addedOn: dateMatch.addedOn } : {}) } },
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Copilot AI Oct 31, 2025

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The spread operator pattern ...(dateMatch.addedOn ? { addedOn: dateMatch.addedOn } : {}) adds complexity and could be simplified. Consider restructuring to build the match object conditionally before the aggregation, which improves readability and maintainability.

Copilot uses AI. Check for mistakes.
{ $group: { _id: '$category', total: { $sum: '$cost' } } },
{ $project: { name: '$_id', total: 1, _id: 0 } }
]);
Expand Down Expand Up @@ -234,7 +250,7 @@ const getChartData = async (req, res) => {
{ $project: { date: '$_id', total: 1, _id: 0 } }
]);

res.json({ expensesByCategory, expensesOverTime, incomeOverTime });
res.json({ expensesByCategory, incomeByCategory, expensesOverTime, incomeOverTime });
} catch (error) {
// Also log the error to the backend console for easier debugging
console.error('Error in getChartData:', error);
Expand Down
6 changes: 6 additions & 0 deletions backend/models/IncomeExpense.js
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Expand Up @@ -41,4 +41,10 @@ const incomeExpenseSchema = new mongoose.Schema({
timestamps: true,
});

// Helpful indexes for common filters and aggregations
// Queries often filter by user, isIncome, isDeleted, date, and group by category
incomeExpenseSchema.index({ user: 1, isDeleted: 1, isIncome: 1, addedOn: -1 });
incomeExpenseSchema.index({ user: 1, isDeleted: 1, category: 1 });
incomeExpenseSchema.index({ user: 1, isDeleted: 1, addedOn: -1 });

module.exports = mongoose.model('IncomeExpense', incomeExpenseSchema);
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