nolansingroy/0_BigQuery_Recipes.md

## 0_BigQuery_Recipes.md

      
    Raw
  

              0_BigQuery_Recipes.md
            
          
    A handful of BigQuery SQL queries that you can use to analyze your own Google Analytics for Firebase data.
To find out more about how they work, check out our presentation from Cloud Next 2018 (Video link to be added soon)
Please note that these scripts have been updated to make sure of the new Firebase schema, which was rolled out to Analytics products in July of 2018.
Note that none of these scripts will work out of the box -- you'll need to update the values in brackets
with the names of your actual datasets, and you'll most likely also need to replace the names of events
and event properties with ones that are appropriate for your app.
The contents of this gist are licensed under the Apache License, version 2.0

  
## 1_analyze_event_param.sql
#standardSQL

# This query will get the average and standard deviation of a "score" parameter that's part of a
# "round_completed" event. Feel free to replace these values to match events and parameters that are
# already in your app.

SELECT
  AVG(param.value.int_value) AS avg_score,
  stddev(param.value.int_value) AS stddev
FROM
 `<dataset>.events_<date>`,
  UNNEST(event_params) AS param
WHERE
  event_name = "round_completed"
  AND param.key = "score"

## 2_coorelate_event_param_and_userprop.sql
#standardSQL

# Get the correlation between one event parameter (in this case, the "score" parameter of the
# "round_completed" event and a user property (in this case, the "xp" user property, converted
# from a string to an int).

SELECT
  corr(score,
    xp)
FROM (
  SELECT
    event_name,
    param.value.int_value AS score,
    CAST(user_prop.value.string_value AS int64) AS xp
  FROM
    `<dataset>.events_<date>`,
    UNNEST(event_params) AS param,
    UNNEST(user_properties) AS user_prop
  WHERE
    event_name = "round_completed"
    AND param.key = "score"
    AND user_prop.key = "xp" )

## 3_filter_one_event_parameter_by_another.sql
#standardSQL

# Let's group one event parameter (in this case, the "score" parameter of the round_completed event)
# with another event parameter (in this case, the "type_of_game" parameter) through the magic of
# SELECT FROM UNNEST !

SELECT
  AVG(score),
  MAX(type_of_game)
FROM (
  SELECT
    event_name,
    (SELECT value.int_value FROM UNNEST(event_params) WHERE key = "score") AS score,
    (SELECT value.string_value FROM UNNEST(event_params) WHERE key = "type_of_game") AS type_of_game
  FROM
    `<dataset>.events_<date>`
  WHERE
    event_name = "round_completed" )
GROUP BY
  type_of_game


## 4_filter_by_range_of_userprop_values.sql
#standardSQL

# Normally in the Firebase console, you can only filter your events by a single user property value. This allows you to
# filter the property of an event (in this case the "score" value of the "round_completed" event) by a range of user
# property values

SELECT
  AVG(score)
FROM (
  SELECT
    event_name,
    param.value.int_value AS score,
    CAST(user_prop.value.string_value AS int64) AS xp
  FROM
    `<dataset>.events_<date>`,
    UNNEST(event_params) AS param,
    UNNEST(user_properties) AS user_prop
  WHERE
    event_name = "round_completed"
    AND param.key = "score"
    AND user_prop.key = "xp" )
WHERE
  xp > 15000
  AND xp < 25000

## 5_filter_by_two_user_properties_at_once.sql
# standardSQL

# Suppose we want a side-by-side comparison of an event parameter (in this case "score") by two different user properties
# (in this case, is_spender and how many power-ups they have). This is how you'd do it. Note that the case statement
# at the top isn't really necessary if you have a user property that doesn't have too many values.


SELECT
  AVG(score) AS avg_score,
  spender,
  CASE
    WHEN powers < 20 THEN 'few_powers'
    ELSE 'many_powers'
  END AS powers_txt
FROM (
  SELECT
    event_name,
    (SELECT value.int_value FROM UNNEST(event_params) WHERE key = "score") AS score,
    (SELECT value.string_value FROM UNNEST(user_properties) WHERE key = "is_spender") AS spender,
    (SELECT CAST(value.string_value AS int64) FROM  UNNEST(user_properties) WHERE key = "powers") AS powers
  FROM
    `<dataset>.events_<date>`
  WHERE
    event_name = "round_completed" )
GROUP BY
  spender,
  powers_txt
ORDER BY
  spender,
  powers_txt

## 6_spend_currency.sql
#standardSQL

# See what trio of events are leading up to a desired event within your app!
# Make sure you replace "spend_virtual_currency" down near the bottom there with the actual
# event you're trying to target

SELECT
  s0,
  s1,
  s2,
  COUNT(*) AS count
FROM (
  SELECT
    user_pseudo_id,
    event_timestamp,
    event_name AS s0,
    LEAD (event_name, 1) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS s1,
    LEAD (event_name, 2) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS s2
  FROM
    `<dataset>.events_<date>`
  ORDER BY
    user_pseudo_id,
    event_timestamp )
WHERE
  s2 = "spend_virtual_currency"
GROUP BY
  s0,
  s1,
  s2
ORDER BY
  count DESC

## 7_common_screen_patterns.sql
#standardSQL

# Using the new "screen_view" event being tracked by Google Analytics for Firebase, can we figure out what the most
# common triplets of "screen progressions" are through our app?

SELECT
  screen_0,
  screen_1,
  screen_2,
  COUNT(*) AS count
FROM (
  SELECT
    user_pseudo_id,
    event_timestamp,
    param.value.string_value AS screen_0,
    LEAD (param.value.string_value, 1) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp ) AS screen_1,
    LEAD (param.value.string_value, 2) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp ) AS screen_2
  FROM
    `<dataset>.events_<date>`,
    UNNEST(event_params) AS param
  WHERE
    event_name = "screen_view"
    AND param.key = "firebase_screen_class"
  ORDER BY
    user_pseudo_id,
    event_timestamp )
WHERE
  screen_1 IS NOT NULL
  AND screen_2 IS NOT NULL
GROUP BY
  screen_0,
  screen_1,
  screen_2
ORDER BY
  count DESC

## 8_closed_funnel_with_time_constraints.sql
#standardSQL

# A closed funnel with time constraints!
# Count the number of occurrences a user encountered a "start_event" event, and then the number of times
# they encountered an "end_event" event after encountering the start event within a certain time window (4
# hours, in this example.)

SELECT
  COUNTIF(funnel_start_time IS NOT NULL) AS funnel_begin_count,
  COUNTIF(funnel_end_time - funnel_start_time < 4 * 60 * 60 * 1000 * 1000) AS funnel_end_count
FROM (
  SELECT
    funnel_start_time,
    LEAD(funnel_end_time, 1) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS funnel_end_time
  FROM (
    SELECT
      event_name,
      IF (event_name = "start_event",
        event_timestamp,
        NULL) AS funnel_start_time,
      IF (event_name = "end_event",
        event_timestamp,
        NULL) AS funnel_end_time,
      user_pseudo_id,
      event_timestamp
    FROM
      #  `<dataset>.events_<date>`
    WHERE
      event_name = "start_event"
      OR event_name = "end_event"
      AND _TABLE_SUFFIX BETWEEN '<yyyymmdd>'
      AND '<yyyymmdd>'
    ORDER BY
      user_pseudo_id,
      event_timestamp) )

## LICENSE

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	#standardSQL

	# This query will get the average and standard deviation of a "score" parameter that's part of a
	# "round_completed" event. Feel free to replace these values to match events and parameters that are
	# already in your app.

	SELECT
	AVG(param.value.int_value) AS avg_score,
	stddev(param.value.int_value) AS stddev
	FROM
	`<dataset>.events_<date>`,
	UNNEST(event_params) AS param
	WHERE
	event_name = "round_completed"
	AND param.key = "score"
	#standardSQL

	# Get the correlation between one event parameter (in this case, the "score" parameter of the
	# "round_completed" event and a user property (in this case, the "xp" user property, converted
	# from a string to an int).

	SELECT
	corr(score,
	xp)
	FROM (
	SELECT
	event_name,
	param.value.int_value AS score,
	CAST(user_prop.value.string_value AS int64) AS xp
	FROM
	`<dataset>.events_<date>`,
	UNNEST(event_params) AS param,
	UNNEST(user_properties) AS user_prop
	WHERE
	event_name = "round_completed"
	AND param.key = "score"
	AND user_prop.key = "xp" )
	#standardSQL

	# Let's group one event parameter (in this case, the "score" parameter of the round_completed event)
	# with another event parameter (in this case, the "type_of_game" parameter) through the magic of
	# SELECT FROM UNNEST !

	SELECT
	AVG(score),
	MAX(type_of_game)
	FROM (
	SELECT
	event_name,
	(SELECT value.int_value FROM UNNEST(event_params) WHERE key = "score") AS score,
	(SELECT value.string_value FROM UNNEST(event_params) WHERE key = "type_of_game") AS type_of_game
	FROM
	`<dataset>.events_<date>`
	WHERE
	event_name = "round_completed" )
	GROUP BY
	type_of_game
	#standardSQL

	# Normally in the Firebase console, you can only filter your events by a single user property value. This allows you to
	# filter the property of an event (in this case the "score" value of the "round_completed" event) by a range of user
	# property values

	SELECT
	AVG(score)
	FROM (
	SELECT
	event_name,
	param.value.int_value AS score,
	CAST(user_prop.value.string_value AS int64) AS xp
	FROM
	`<dataset>.events_<date>`,
	UNNEST(event_params) AS param,
	UNNEST(user_properties) AS user_prop
	WHERE
	event_name = "round_completed"
	AND param.key = "score"
	AND user_prop.key = "xp" )
	WHERE
	xp > 15000
	AND xp < 25000
	# standardSQL

	# Suppose we want a side-by-side comparison of an event parameter (in this case "score") by two different user properties
	# (in this case, is_spender and how many power-ups they have). This is how you'd do it. Note that the case statement
	# at the top isn't really necessary if you have a user property that doesn't have too many values.


	SELECT
	AVG(score) AS avg_score,
	spender,
	CASE
	WHEN powers < 20 THEN 'few_powers'
	ELSE 'many_powers'
	END AS powers_txt
	FROM (
	SELECT
	event_name,
	(SELECT value.int_value FROM UNNEST(event_params) WHERE key = "score") AS score,
	(SELECT value.string_value FROM UNNEST(user_properties) WHERE key = "is_spender") AS spender,
	(SELECT CAST(value.string_value AS int64) FROM UNNEST(user_properties) WHERE key = "powers") AS powers
	FROM
	`<dataset>.events_<date>`
	WHERE
	event_name = "round_completed" )
	GROUP BY
	spender,
	powers_txt
	ORDER BY
	spender,
	powers_txt
	#standardSQL

	# See what trio of events are leading up to a desired event within your app!
	# Make sure you replace "spend_virtual_currency" down near the bottom there with the actual
	# event you're trying to target

	SELECT
	s0,
	s1,
	s2,
	COUNT(*) AS count
	FROM (
	SELECT
	user_pseudo_id,
	event_timestamp,
	event_name AS s0,
	LEAD (event_name, 1) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS s1,
	LEAD (event_name, 2) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS s2
	FROM
	`<dataset>.events_<date>`
	ORDER BY
	user_pseudo_id,
	event_timestamp )
	WHERE
	s2 = "spend_virtual_currency"
	GROUP BY
	s0,
	s1,
	s2
	ORDER BY
	count DESC
	#standardSQL

	# Using the new "screen_view" event being tracked by Google Analytics for Firebase, can we figure out what the most
	# common triplets of "screen progressions" are through our app?

	SELECT
	screen_0,
	screen_1,
	screen_2,
	COUNT(*) AS count
	FROM (
	SELECT
	user_pseudo_id,
	event_timestamp,
	param.value.string_value AS screen_0,
	LEAD (param.value.string_value, 1) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp ) AS screen_1,
	LEAD (param.value.string_value, 2) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp ) AS screen_2
	FROM
	`<dataset>.events_<date>`,
	UNNEST(event_params) AS param
	WHERE
	event_name = "screen_view"
	AND param.key = "firebase_screen_class"
	ORDER BY
	user_pseudo_id,
	event_timestamp )
	WHERE
	screen_1 IS NOT NULL
	AND screen_2 IS NOT NULL
	GROUP BY
	screen_0,
	screen_1,
	screen_2
	ORDER BY
	count DESC
	#standardSQL

	# A closed funnel with time constraints!
	# Count the number of occurrences a user encountered a "start_event" event, and then the number of times
	# they encountered an "end_event" event after encountering the start event within a certain time window (4
	# hours, in this example.)

	SELECT
	COUNTIF(funnel_start_time IS NOT NULL) AS funnel_begin_count,
	COUNTIF(funnel_end_time - funnel_start_time < 4 * 60 * 60 * 1000 * 1000) AS funnel_end_count
	FROM (
	SELECT
	funnel_start_time,
	LEAD(funnel_end_time, 1) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS funnel_end_time
	FROM (
	SELECT
	event_name,
	IF (event_name = "start_event",
	event_timestamp,
	NULL) AS funnel_start_time,
	IF (event_name = "end_event",
	event_timestamp,
	NULL) AS funnel_end_time,
	user_pseudo_id,
	event_timestamp
	FROM
	# `<dataset>.events_<date>`
	WHERE
	event_name = "start_event"
	OR event_name = "end_event"
	AND _TABLE_SUFFIX BETWEEN '<yyyymmdd>'
	AND '<yyyymmdd>'
	ORDER BY
	user_pseudo_id,
	event_timestamp) )

	Apache License
	Version 2.0, January 2004
	http://www.apache.org/licenses/

	TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

	1. Definitions.

	"License" shall mean the terms and conditions for use, reproduction,
	and distribution as defined by Sections 1 through 9 of this document.

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	the copyright owner that is granting the License.

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	other entities that control, are controlled by, or are under common
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