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-- this query outputs a data visualisation to explore how many unique products people
-- see on your website, and the conversion rate

-- it uses Big Query SQL on a GA4Dataform events table - check https://github.com/superformlabs/ga4dataform-community

with products_per_user as (
  SELECT
    user_pseudo_id,
    count( distinct if(event_name = 'view_item', i.item_name, NULL) ) as n_items_viewed,
    max( if(event_name = 'purchase', 1, 0)) as purchaser 

zjuul

library(tidyverse)

richmondway <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-09-26/richmondway.csv')


ggplot(
  richmondway,
  aes(
    x = (max(richmondway$Episode) + 1) * Season + Episode,
    y = F_count_RK

zjuul

library(httr)
library(jsonlite)
library(lubridate)
library(tidyverse)



########### config
#
# my info - replace this with your enphase api key, app id, user_id

zjuul

(function() {
  var oldPush = window.dataLayer.push;
  window.dataLayer.push = function() {
    var states = [].slice.call(arguments, 0);
    states.forEach(function(s) {
      if (!s.hasOwnProperty('event')) {
        s.event = 'default';
      }
    });
    return oldPush.apply(window.dataLayer, states);

zjuul

create or replace table `PROJECTNAME.DATASETNAME.performance_long` partition by DATE(timestamp) as 

with base as (
	select
		timestamp, date, event_timestamp, clientid, user_id, device_category, country, url, hostname,
		page_path, page_title, page_referrer, type
	from `PROJECTNAME.DATASETNAME.performance_wide`
)

,intervals as (

zjuul

create or replace table `PROJECTNAME.DATASETNAME.performance_wide` partition by DATE(timestamp) as 
/*
	wide version of nested events table
*/



SELECT
  TIMESTAMP_MICROS(event_timestamp) as timestamp,
  DATE(TIMESTAMP_MICROS(event_timestamp)) as date,

zjuul

library(httr)
library(jsonlite)
library(ggplot2)
library(dplyr)

# url with TDF standings
url <- "https://sportapi.widgets.sports.gracenote.com/cycling/getresult/classificationid/2148837/languagecode/1.json?c=64&module=cycling&type=classification"

standings <- fromJSON(content(GET(url), "text")[[1]])

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// GTM custom HTML tag for tracking incomplete searches
// logic: if user starts typing, start a timer. Stop the timer when user is idle 2.5 seconds
// then push the event on the DL

(function() {
	var timer, firstResult, searchText;
	var waiting = false;

	var waitTime = 2500; // milliseconds

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-- create two tables: L and R
-- content of tables a "val" column with two rows.
-- rows in L: "left only" and "both"
-- rows in R: "right only" and "both"

with l as (
	select 'both' as val
	union
	select 'left_only' as val
), r as (

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# From: https://stuifbergen.com/2018/03/cohort-analysis-with-snowplow-and-r/

########## 1: get data 
appname <- "NAME" # used as selection criterium, replace that with the name of your app
q <- paste0("
  SELECT
  user_id,
  TO_CHAR(CONVERT_TIMEZONE('UTC', 'Europe/Amsterdam', derived_tstamp),'YYYY ww') AS yw
  FROM atomic.events
  WHERE app_id = '", appname, "' AND derived_tstamp >= '2018-01-01'