npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

pauls-embedded-analytics

v2.0.2

Published

A sqlite-backed embedded analytics module for nodejs.

Downloads

5

Readme

pauls-embedded-analytics

A sqlite-backed embedded analytics module for nodejs.

Usage

const PEAnalytics = require('pauls-embedded-analytics')

// open/create an instance
var analytics = new PEAnalytics({
  db: './analytics.db',
  domain: 'mysite.com'
})

// event tracking
// =

// add visits to the DB
await analytics.logEvent({
  event: 'visit', // (optional) set the event type, defaults to 'visit'
  url: '/index.html', // where did they visit
  domain: 'myothersite.com', // (optional) override the default domain
  session: '12345', // (optional) user ID, should be set using a cookie or similar
  referer: 'google.com', // (optional) referer header
  userAgent: 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.79..', // (optional) user agent, lets us parse the browser, os, etc
  ip: '255.255.255.255', // (optional) user IP address
  extra: {foo: 'bar'}, // (optional) additional data that should be stored
  note: 'This is important!' // (optional) a note to be attached to the event
})

// query
await analytics.listEvents({
  where: `date BETWEEN date('now', '-1 day') AND date('now')`, // WHERE clause
  limit: 100,
  offset: 0
})
await analytics.listVisits({
  // same params as listEvents but filters to event='visit'
})
await analytics.countEvents({
  unique: false, // unique visits only (according to session)? default false
  groupBy: 'url' | 'date' | false, // outputs as eg [{url:, count}], [{date:, count:}], or just a number
  where: `date BETWEEN date('now', '-1 day') AND date('now')` // WHERE clause
})
await analytics.countVisits({
  // same params as countEvents but filters to event='visit'
})

// cohort tracking
// =

// track state in the active_users campaign
// (in this example, cohort 2 might mean '2nd week since launch', and state 3 might me 'is active')
await analytics.updateCohort('active_users', {
  cohort: 2, // 2nd week since launch
  subject: user.id,
  state: 3 // is active
})

// query
await analytics.countCohortStates('active_users')

Note, the WHERE clause in any query have the following fields available:

  • event
  • date
  • url
  • domain
  • session
  • referer
  • ip
  • isMobile
  • isDesktop
  • isBot
  • browser
  • version
  • os
  • platform
  • events_extra.key / events_extra.value

These are also the fields output by a list call.

Cohorts

Cohort tracking is designed to help questions about user retention. A cohort should be identified by a value specified by the application. Each cohort specifies a grouping of users. A simple example cohort id would be the year + the week of the year, eg 201701 for the first week of 2017.

The update method tracks the state of a subject within the cohort. State may be anything, but a recommended scheme would be a set of enums. In hashbase's active_users campaign, we use 1 for registered, 2 for "activated," and 3 for active in the last 2 weeks.

When a user registers, we run:

analytics.updateCohort('active_users', {
  cohort: getUserCohort(user),
  subject: user.id,
  state: 1 // is registered
})

When they upload an archive, we run:

analytics.updateCohort('active_users', {
  cohort: getUserCohort(user),
  subject: user.id,
  state: 3 // is active
})

And every week we do a full sweep of the users table, where we set the state to 1, 2, or 3 based on the state of their archives.

The countCohortStates method outputs an array which looks like this:

[ { cohort: '201701', state: '1', count: 3 },
  { cohort: '201701', state: '2', count: 2 },
  { cohort: '201701', state: '3', count: 1 },
  { cohort: '201702', state: '1', count: 3 },
  { cohort: '201702', state: '2', count: 2 },
  { cohort: '201702', state: '3', count: 1 } ]

For hashbase, these numbers are actually cumulative. 50 users are in the registered state, but activated and active users are also registered, so there's actually 50+30+15 = 95 registered users.

Why?

SaaS solutions like Google Analytics track users around the Web (which isn't cool) and that's prompted users to block tracker scripts, which screws up your results. So: SaaS options are unethical and a bit broken.

We needed a solution for hashbase so we made this.