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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

#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) )
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