如何用Ramda按custodianCenter分组并统计value总和与数量

需要将数组中按custodianCenter字段对对象分组，同时计算每组的value属性总和及元素数量。目前已通过Ramda的groupBy方法完成分组，但无法生成包含总和与数量的统计属性。

示例数据与当前代码 #

const data = [
  {"_id":"+UJIRvYOSXysumiDdQTfkA==","arrival_date":"2022-01-10T00:00:00.000Z","attached_invoice":"invoice","attachment_photo_receipt":"photo receipt","code":"2432341-A","custodianCenter":"Pastagens","date_received":"2020-01-10T00:00:00.000Z","description":"Description Financial Input 1","expense_origin":2,"installment_entry":true,"installment_payment":null,"invoice_number":123,"nivel":"Fazenda","nivelData":["71b292ed-8441-4d1e-a19f-b39fdad55d9a"],"status":"input","status_payment":null,"value":234,"property":"Fertilizantes"},
  {"_id":"XghoVK9uRke90xnsPZVjYw==","arrival_date":"2022-01-10T00:00:00.000Z","attached_invoice":"invoice","attachment_photo_receipt":"photo receipt","code":"2432341-A","comment":"Comentário 1","custodianCenter":"Pastagens","custodianCenterValue":22,"date_received":"2020-01-10T00:00:00.000Z","description":"Description Financial Input 1","expense_origin":2,"installment_entry":true,"installment_payment":null,"invoice_number":123,"nivel":null,"nivelData":["71b292ed-8441-4d1e-a19f-b39fdad55d9a"],"status":"input","status_payment":null,"value":234,"property":"Gratificações"},
  {"_id":"mK+YXWeiTpGa3alAOENbvg==","arrival_date":"2022-01-10T00:00:00.000Z","attached_invoice":"invoice","attachment_photo_receipt":"photo receipt","code":"2432341-A","comment":"Comentário 1","custodianCenter":"Pastagens","custodianCenterValue":22,"date_received":"2020-01-10T00:00:00.000Z","description":"Description Financial Input 1","expense_origin":2,"installment_entry":true,"installment_payment":null,"invoice_number":123,"nivel":null,"nivelData":["71b292ed-8441-4d1e-a19f-b39fdad55d9a"],"property":null,"status":"input","status_payment":null,"value":234},
  {"_id":"g7fVa41uRey+K+8OnKBO9g==","arrival_date":"2022-01-10T00:00:00.000Z","attached_invoice":"invoice","attachment_photo_receipt":"photo receipt","code":"2432341-A","comment":"Comentário 1","custodianCenter":"Pastagens","custodianCenterValue":22,"date_received":"2020-01-10T00:00:00.000Z","description":"Description Financial Input 1","expense_origin":2,"installment_entry":true,"installment_payment":null,"invoice_number":123,"nivel":null,"nivelData":["71b292ed-8441-4d1e-a19f-b39fdad55d9a"],"property":null,"status":"input","status_payment":null,"value":234},
  {"_id":"GTZbvPnVQZ+7V4dfUjIT/w==","arrival_date":"2022-01-10T00:00:00.000Z","attached_invoice":"invoice","attachment_photo_receipt":"photo receipt","category":"Gratificações","code":"2432341-A","comment":"Comentário 1","custodianCenter":"Mão de Obra","custodianCenterValue":22,"date_received":"2020-01-10T00:00:00.000Z","description":"Description Financial Input 1","expense_origin":2,"installment_entry":true,"installment_payment":null,"invoice_number":123,"nivel":null,"nivelData":["71b292ed-8441-4d1e-a19f-b39fdad55d9a"],"status":"input","status_payment":null,"value":234}
]

const res = R.compose(
  R.groupBy(R.prop("custodianCenter"))
)(data)

期望结果 #

[
  { custodianCenter: 'Pastagens', total: 936, quantity: 4 },
  { custodianCenter: 'Mão de Obra', total: 234, quantity: 1 }
]

通过Ramda的函数组合，在groupBy之后添加统计转换步骤即可实现需求：

1. 用R.toPairs将分组后的对象转为键值对数组（每个元素为[分组名称, 对应元素数组]）
2. 用R.map遍历每个键值对，生成包含统计信息的对象：
   • custodianCenter直接取键值对的第一个元素
   • quantity取对应元素数组的长度
   • total用R.sum结合R.pluck('value')计算数组中所有value的总和

完整代码（compose版） #

const res = R.compose(
  R.map(([custodianCenter, items]) => ({
    custodianCenter,
    quantity: items.length,
    total: R.sum(R.pluck('value', items))
  })),
  R.toPairs,
  R.groupBy(R.prop("custodianCenter"))
)(data)

更直观的pipe版 #

如果觉得compose的函数顺序反直觉，可以用R.pipe按执行顺序编写：

const res = R.pipe(
  R.groupBy(R.prop("custodianCenter")),
  R.toPairs,
  R.map(([custodianCenter, items]) => ({
    custodianCenter,
    quantity: items.length,
    total: R.sum(R.pluck('value', items))
  }))
)(data)

执行上述代码后，会直接返回期望的数组格式，每个元素包含分组名称、元素数量和value总和。

内容的提问来源于stack exchange，提问作者Denis Alayza