zdenekzed · August 27, 2019 17:04 · Jul 20, 2018 · Jul 20, 2018 · Jul 20, 2018 · Oct 26, 2017
diff --git a/0_BigQuery_Recipes.md b/0_BigQuery_Recipes.md
@@ -1,8 +1,7 @@
 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,]
-(https://support.google.com/firebase/answer/7029846) which was rolled out to Analytics products in July of 2018.
+Please note that these scripts have been updated to make sure of the [new Firebase schema,](https://support.google.com/firebase/answer/7029846) 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

diff --git a/0_BigQuery_Recipes.md b/0_BigQuery_Recipes.md
@@ -1,7 +1,8 @@
 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],(https://support.google.com/firebase/answer/7029846) which was rolled out to Analytics products in July of 2018.
+Please note that these scripts have been updated to make sure of the [new Firebase schema,]
+(https://support.google.com/firebase/answer/7029846) 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

diff --git a/3_filter_one_event_parameter_by_another.sql b/3_filter_one_event_parameter_by_another.sql
@@ -13,7 +13,7 @@ FROM (
     (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>`,
+    `<dataset>.events_<date>`
   WHERE
     event_name = "round_completed" )
 GROUP BY

diff --git a/4_filter_by_range_of_userprop_values.sql b/4_filter_by_range_of_userprop_values.sql
@@ -8,18 +8,17 @@ SELECT
   AVG(score)
 FROM (
   SELECT
-    event.name,
+    event_name,
     param.value.int_value AS score,
-    CAST(user_prop.value.value.string_value AS int64) AS xp
+    CAST(user_prop.value.string_value AS int64) AS xp
   FROM
-    `<dataset>.app_events_<date>`,
-    UNNEST(event_dim) AS event,
-    UNNEST(event.params) AS param,
-    UNNEST(user_dim.user_properties) AS user_prop
+    `<dataset>.events_<date>`,
+    UNNEST(event_params) AS param,
+    UNNEST(user_properties) AS user_prop
   WHERE
-    event.name = "round_completed"
+    event_name = "round_completed"
     AND param.key = "score"
     AND user_prop.key = "xp" )
 WHERE
   xp > 15000
-  OR xp < 25000
+  AND xp < 25000
diff --git a/5_filter_by_two_user_properties_at_once.sql b/5_filter_by_two_user_properties_at_once.sql
@@ -4,6 +4,7 @@
 # (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,
@@ -13,18 +14,17 @@ SELECT
   END AS powers_txt
 FROM (
   SELECT
-    event.name,
-    (SELECT value.int_value FROM UNNEST(event.params) WHERE key = "score") AS score,
-    (SELECT value.value.string_value FROM UNNEST(user_dim.user_properties) WHERE key = "is_spender") AS spender,
-    (SELECT CAST(value.value.string_value AS int64) FROM  UNNEST(user_dim.user_properties) WHERE key = "powers") AS powers
+    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>.app_events_<date>`,
-    UNNEST(event_dim) AS event
+    `<dataset>.events_<date>`
   WHERE
-    event.name = "round_completed" )
+    event_name = "round_completed" )
 GROUP BY
   spender,
   powers_txt
 ORDER BY
   spender,
-  powers_txt
+  powers_txt
diff --git a/6_spend_currency.sql b/6_spend_currency.sql
@@ -4,24 +4,22 @@
 # Make sure you replace "spend_virtual_currency" down near the bottom there with the actual 
 # event you're trying to target
 
-#standardSQL
 SELECT
   s0,
   s1,
   s2,
   COUNT(*) AS count
 FROM (
   SELECT
-    user_dim.app_info.app_instance_id AS app_instance,
-    event.timestamp_micros AS event_timestamp,
-    event.name AS s0,
-    LEAD (event.name, 1) OVER (PARTITION BY user_dim.app_info.app_instance_id ORDER BY event.timestamp_micros) AS s1,
-    LEAD (event.name, 2) OVER (PARTITION BY user_dim.app_info.app_instance_id ORDER BY event.timestamp_micros) AS s2
+    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
-    `<my_dataset_.app_events_<date>`,
-    UNNEST(event_dim) AS event
+    `<dataset>.events_<date>`
   ORDER BY
-    app_instance,
+    user_pseudo_id,
     event_timestamp )
 WHERE
   s2 = "spend_virtual_currency"

diff --git a/7_common_screen_patterns.sql b/7_common_screen_patterns.sql
@@ -10,20 +10,19 @@ SELECT
   COUNT(*) AS count
 FROM (
   SELECT
-    user_dim.app_info.app_instance_id AS app_instance,
-    event.timestamp_micros AS event_timestamp,
+    user_pseudo_id,
+    event_timestamp,
     param.value.string_value AS screen_0,
-    LEAD (param.value.string_value, 1) OVER (PARTITION BY user_dim.app_info.app_instance_id ORDER BY event.timestamp_micros) AS screen_1,
-    LEAD (param.value.string_value, 2) OVER (PARTITION BY user_dim.app_info.app_instance_id ORDER BY event.timestamp_micros) AS screen_2
+    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>.app_events_<date>`,
-    UNNEST(event_dim) AS event,
-    UNNEST(event.params) AS param
+    `<dataset>.events_<date>`,
+    UNNEST(event_params) AS param
   WHERE
-    event.name = "screen_view"
+    event_name = "screen_view"
     AND param.key = "firebase_screen_class"
   ORDER BY
-    app_instance,
+    user_pseudo_id,
     event_timestamp )
 WHERE
   screen_1 IS NOT NULL

diff --git a/8_closed_funnel_with_time_constraints.sql b/8_closed_funnel_with_time_constraints.sql
@@ -2,34 +2,34 @@
 
 # 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 pre
+# 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 app_instance_id ORDER BY event_time) AS funnel_end_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_micros,
+      event_name,
+      IF (event_name = "start_event",
+        event_timestamp,
         NULL) AS funnel_start_time,
-      IF (event.name = "end_event",
-        event.timestamp_micros,
+      IF (event_name = "end_event",
+        event_timestamp,
         NULL) AS funnel_end_time,
-      user_dim.app_info.app_instance_id,
-      event.timestamp_micros AS event_time
+      user_pseudo_id,
+      event_timestamp
     FROM
-      `<dataset>.app_events_*`, 
-      UNNEST(event_dim) AS event
+      #  `<dataset>.events_<date>`
     WHERE
-      event.name = "start_event"
-      OR event.name = "end_event"
-      AND _TABLE_SUFFIX BETWEEN '<date1>'
-      AND '<date2>'
+      event_name = "start_event"
+      OR event_name = "end_event"
+      AND _TABLE_SUFFIX BETWEEN '<yyyymmdd>'
+      AND '<yyyymmdd>'
     ORDER BY
-      app_instance_id,
-      event.timestamp_micros ) )
+      user_pseudo_id,
+      event_timestamp) )
diff --git a/0_BigQuery_Recipes.md b/0_BigQuery_Recipes.md
@@ -1,9 +1,11 @@
 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 my presentation from the Firebase Developer Conference in 
-Amsterdam (video to be added soon).  
+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],(https://support.google.com/firebase/answer/7029846) 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](https://www.apache.org/licenses/LICENSE-2.0)
+The contents of this gist are licensed under the [Apache License, version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
diff --git a/1_analyze_event_param.sql b/1_analyze_event_param.sql
@@ -8,9 +8,8 @@ SELECT
   AVG(param.value.int_value) AS avg_score,
   stddev(param.value.int_value) AS stddev
 FROM
-  `<dataset>.app_events_<date>`,
-  UNNEST(event_dim) AS event,
-  UNNEST(event.params) AS param
+ `<dataset>.events_<date>`,
+  UNNEST(event_params) AS param
 WHERE
-  event.name = "round_completed"
-  AND param.key = "score"
+  event_name = "round_completed"
+  AND param.key = "score"
diff --git a/2_coorelate_event_param_and_userprop.sql b/2_coorelate_event_param_and_userprop.sql
@@ -5,18 +5,18 @@
 # from a string to an int).
 
 SELECT
-  corr(score, xp)
+  corr(score,
+    xp)
 FROM (
   SELECT
-    event.name,
+    event_name,
     param.value.int_value AS score,
-    CAST(user_prop.value.value.string_value AS int64) AS xp
+    CAST(user_prop.value.string_value AS int64) AS xp
   FROM
-    `<dataset>.app_events_<date>`,
-    UNNEST(event_dim) AS event,
-    UNNEST(event.params) AS param,
-    UNNEST(user_dim.user_properties) AS user_prop
+    `<dataset>.events_<date>`,
+    UNNEST(event_params) AS param,
+    UNNEST(user_properties) AS user_prop
   WHERE
-    event.name = "round_completed"
+    event_name = "round_completed"
     AND param.key = "score"
     AND user_prop.key = "xp" )
diff --git a/3_filter_one_event_parameter_by_another.sql b/3_filter_one_event_parameter_by_another.sql
@@ -9,13 +9,13 @@ SELECT
   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
+    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>.app_events_<date>`,
-    UNNEST(event_dim) AS event
+    `<dataset>.events_<date>`,
   WHERE
-    event.name = "round_completed" )
+    event_name = "round_completed" )
 GROUP BY
-  type_of_game
+  type_of_game
+
diff --git a/3_filter_one_event_parameter_by_another.sql b/3_filter_one_event_parameter_by_another.sql
@@ -10,20 +10,8 @@ SELECT
 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
+    (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>.app_events_<date>`,
     UNNEST(event_dim) AS event

diff --git a/4_filter_by_range_of_userprop_values.sql b/4_filter_by_range_of_userprop_values.sql
@@ -0,0 +1,25 @@
+#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.value.string_value AS int64) AS xp
+  FROM
+    `<dataset>.app_events_<date>`,
+    UNNEST(event_dim) AS event,
+    UNNEST(event.params) AS param,
+    UNNEST(user_dim.user_properties) AS user_prop
+  WHERE
+    event.name = "round_completed"
+    AND param.key = "score"
+    AND user_prop.key = "xp" )
+WHERE
+  xp > 15000
+  OR xp < 25000
diff --git a/5_filter_by_two_user_properties_at_once.sql b/5_filter_by_two_user_properties_at_once.sql
@@ -14,27 +14,9 @@ SELECT
 FROM (
   SELECT
     event.name,
-    (
-    SELECT
-      value.int_value
-    FROM
-      UNNEST(event.params)
-    WHERE
-      key = "score") AS score,
-    (
-    SELECT
-      value.value.string_value
-    FROM
-      UNNEST(user_dim.user_properties)
-    WHERE
-      key = "is_spender") AS spender,
-    (
-    SELECT
-      CAST(value.value.string_value AS int64)
-    FROM
-      UNNEST(user_dim.user_properties)
-    WHERE
-      key = "powers") AS powers
+    (SELECT value.int_value FROM UNNEST(event.params) WHERE key = "score") AS score,
+    (SELECT value.value.string_value FROM UNNEST(user_dim.user_properties) WHERE key = "is_spender") AS spender,
+    (SELECT CAST(value.value.string_value AS int64) FROM  UNNEST(user_dim.user_properties) WHERE key = "powers") AS powers
   FROM
     `<dataset>.app_events_<date>`,
     UNNEST(event_dim) AS event

diff --git a/LICENSE b/LICENSE
@@ -1,4 +1,3 @@
-KB
 
                                  Apache License
                            Version 2.0, January 2004

diff --git a/BigQuery Recipes.md → 0_BigQuery_Recipes.md b/BigQuery Recipes.md → 0_BigQuery_Recipes.md
diff --git a/1_analyze_event_param.sql b/1_analyze_event_param.sql
@@ -0,0 +1,16 @@
+#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>.app_events_<date>`,
+  UNNEST(event_dim) AS event,
+  UNNEST(event.params) AS param
+WHERE
+  event.name = "round_completed"
+  AND param.key = "score"
diff --git a/coorelate_event_param_and_userprop.sql → 2_coorelate_event_param_and_userprop.sql b/coorelate_event_param_and_userprop.sql → 2_coorelate_event_param_and_userprop.sql
diff --git a/filter_one_event_parameter_by_another.sql → 3_filter_one_event_parameter_by_another.sql b/filter_one_event_parameter_by_another.sql → 3_filter_one_event_parameter_by_another.sql
diff --git a/filter_by_two_user_properties_at_once.sql → 5_filter_by_two_user_properties_at_once.sql b/filter_by_two_user_properties_at_once.sql → 5_filter_by_two_user_properties_at_once.sql
diff --git a/spend_currency.sql → 6_spend_currency.sql b/spend_currency.sql → 6_spend_currency.sql
diff --git a/common_screen_patterns.sql → 7_common_screen_patterns.sql b/common_screen_patterns.sql → 7_common_screen_patterns.sql
diff --git a/closed_funnel_with_time_constraints.sql → 8_closed_funnel_with_time_constraints.sql b/closed_funnel_with_time_constraints.sql → 8_closed_funnel_with_time_constraints.sql
diff --git a/BigQuery Recipes.md b/BigQuery Recipes.md
@@ -2,4 +2,8 @@ A handful of BigQuery SQL queries that you can use to analyze your own Google An
 To find out more about how they work, check out my presentation from the Firebase Developer Conference in 
 Amsterdam (video to be added soon).  
 
-The contents of this gist is licensed under the [Apache License, version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+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](https://www.apache.org/licenses/LICENSE-2.0)
diff --git a/analyze_event_param.sql b/analyze_event_param.sql
@@ -1,44 +0,0 @@
-#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>.app_events_<date>`,
-  UNNEST(event_dim) AS event,
-  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.value.string_value AS int64) AS xp
-  FROM
-    `<dataset>.app_events_<date>`,
-    UNNEST(event_dim) AS event,
-    UNNEST(event.params) AS param,
-    UNNEST(user_dim.user_properties) AS user_prop
-  WHERE
-    event.name = "round_completed"
-    AND param.key = "score"
-    AND user_prop.key = "xp" )
- @ToddKerpelman
-
-
-
-Write

diff --git a/_BigQuery Recipes.md → BigQuery Recipes.md b/_BigQuery Recipes.md → BigQuery Recipes.md
diff --git a/_BigQuery Recipes.md b/_BigQuery Recipes.md
@@ -0,0 +1,5 @@
+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 my presentation from the Firebase Developer Conference in 
+Amsterdam (video to be added soon).  
+
+The contents of this gist is licensed under the [Apache License, version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
diff --git a/LICENSE b/LICENSE
@@ -0,0 +1,203 @@
+KB
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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diff --git a/analyze_event_param.sql b/analyze_event_param.sql
@@ -0,0 +1,44 @@
+#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>.app_events_<date>`,
+  UNNEST(event_dim) AS event,
+  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.value.string_value AS int64) AS xp
+  FROM
+    `<dataset>.app_events_<date>`,
+    UNNEST(event_dim) AS event,
+    UNNEST(event.params) AS param,
+    UNNEST(user_dim.user_properties) AS user_prop
+  WHERE
+    event.name = "round_completed"
+    AND param.key = "score"
+    AND user_prop.key = "xp" )
+ @ToddKerpelman
+
+
+
+Write
diff --git a/closed_funnel_with_time_constraints.sql b/closed_funnel_with_time_constraints.sql
@@ -0,0 +1,35 @@
+#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 pre
+
+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 app_instance_id ORDER BY event_time) AS funnel_end_time
+  FROM (
+    SELECT
+      event.name,
+      IF (event.name = "start_event",
+        event.timestamp_micros,
+        NULL) AS funnel_start_time,
+      IF (event.name = "end_event",
+        event.timestamp_micros,
+        NULL) AS funnel_end_time,
+      user_dim.app_info.app_instance_id,
+      event.timestamp_micros AS event_time
+    FROM
+      `<dataset>.app_events_*`, 
+      UNNEST(event_dim) AS event
+    WHERE
+      event.name = "start_event"
+      OR event.name = "end_event"
+      AND _TABLE_SUFFIX BETWEEN '<date1>'
+      AND '<date2>'
+    ORDER BY
+      app_instance_id,
+      event.timestamp_micros ) )
diff --git a/common_screen_patterns.sql b/common_screen_patterns.sql
@@ -0,0 +1,36 @@
+#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_dim.app_info.app_instance_id AS app_instance,
+    event.timestamp_micros AS event_timestamp,
+    param.value.string_value AS screen_0,
+    LEAD (param.value.string_value, 1) OVER (PARTITION BY user_dim.app_info.app_instance_id ORDER BY event.timestamp_micros) AS screen_1,
+    LEAD (param.value.string_value, 2) OVER (PARTITION BY user_dim.app_info.app_instance_id ORDER BY event.timestamp_micros) AS screen_2
+  FROM
+    `<dataset>.app_events_<date>`,
+    UNNEST(event_dim) AS event,
+    UNNEST(event.params) AS param
+  WHERE
+    event.name = "screen_view"
+    AND param.key = "firebase_screen_class"
+  ORDER BY
+    app_instance,
+    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
diff --git a/coorelate_event_param_and_userprop.sql b/coorelate_event_param_and_userprop.sql
@@ -0,0 +1,22 @@
+#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.value.string_value AS int64) AS xp
+  FROM
+    `<dataset>.app_events_<date>`,
+    UNNEST(event_dim) AS event,
+    UNNEST(event.params) AS param,
+    UNNEST(user_dim.user_properties) AS user_prop
+  WHERE
+    event.name = "round_completed"
+    AND param.key = "score"
+    AND user_prop.key = "xp" )
diff --git a/filter_by_two_user_properties_at_once.sql b/filter_by_two_user_properties_at_once.sql
@@ -0,0 +1,48 @@
+# 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.value.string_value
+    FROM
+      UNNEST(user_dim.user_properties)
+    WHERE
+      key = "is_spender") AS spender,
+    (
+    SELECT
+      CAST(value.value.string_value AS int64)
+    FROM
+      UNNEST(user_dim.user_properties)
+    WHERE
+      key = "powers") AS powers
+  FROM
+    `<dataset>.app_events_<date>`,
+    UNNEST(event_dim) AS event
+  WHERE
+    event.name = "round_completed" )
+GROUP BY
+  spender,
+  powers_txt
+ORDER BY
+  spender,
+  powers_txt
diff --git a/filter_one_event_parameter_by_another.sql b/filter_one_event_parameter_by_another.sql
@@ -0,0 +1,33 @@
+#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>.app_events_<date>`,
+    UNNEST(event_dim) AS event
+  WHERE
+    event.name = "round_completed" )
+GROUP BY
+  type_of_game
diff --git a/spend_currency.sql b/spend_currency.sql
@@ -0,0 +1,33 @@
+#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
+
+#standardSQL
+SELECT
+  s0,
+  s1,
+  s2,
+  COUNT(*) AS count
+FROM (
+  SELECT
+    user_dim.app_info.app_instance_id AS app_instance,
+    event.timestamp_micros AS event_timestamp,
+    event.name AS s0,
+    LEAD (event.name, 1) OVER (PARTITION BY user_dim.app_info.app_instance_id ORDER BY event.timestamp_micros) AS s1,
+    LEAD (event.name, 2) OVER (PARTITION BY user_dim.app_info.app_instance_id ORDER BY event.timestamp_micros) AS s2
+  FROM
+    `<my_dataset_.app_events_<date>`,
+    UNNEST(event_dim) AS event
+  ORDER BY
+    app_instance,
+    event_timestamp )
+WHERE
+  s2 = "spend_virtual_currency"
+GROUP BY
+  s0,
+  s1,
+  s2
+ORDER BY
+  count DESC