Categorize website visitors starting from the first occasion, based on if condition





.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box;
}







0















Could you please help me with sql statement, preferreby it should work in big query. I have 3 columns userid, date, hostname. I need to create additional column - client_type on the following condition: when userid first time comes to hostname = "online-store.com" then from this date on client_type for this particular userid will be always "current_client" else "visitor".



For example, in the image (link attached) we have userid = 1 and 4 who had become "current client". User 4 was just a visitor, but after visiting hostname = "online-store.com" he will be always classified as "current client".enter image description here










share|improve this question

























  • Please edit your question to show a Minimal, Complete, and Verifiable example of the code and most importantly data that you are having problems with, then we can try to help with the your problem. You can also read How to Ask.

    – Mikhail Berlyant
    Nov 21 '18 at 16:14











  • take a closer look especially to that part - Not all questions benefit from including code. But if your problem is with code you've written, you should include some. But don't just copy in your entire code! ..., it likely includes a lot of irrelevant details that readers will need to ignore when trying to reproduce the problem. Here are some guidelines: ...

    – Mikhail Berlyant
    Nov 21 '18 at 16:16













  • Mikhail, thanks a lot for advice, I have rewritten my question and added a picture. Also my question has been modified a bit.

    – Andrey
    Nov 22 '18 at 8:03











  • see the answer. please in your next/new questions - avoid using images and rather provide data examples as plain text so we can use it while helping you :o)

    – Mikhail Berlyant
    Nov 22 '18 at 20:20











  • ok, =) understand

    – Andrey
    Nov 23 '18 at 8:42


















0















Could you please help me with sql statement, preferreby it should work in big query. I have 3 columns userid, date, hostname. I need to create additional column - client_type on the following condition: when userid first time comes to hostname = "online-store.com" then from this date on client_type for this particular userid will be always "current_client" else "visitor".



For example, in the image (link attached) we have userid = 1 and 4 who had become "current client". User 4 was just a visitor, but after visiting hostname = "online-store.com" he will be always classified as "current client".enter image description here










share|improve this question

























  • Please edit your question to show a Minimal, Complete, and Verifiable example of the code and most importantly data that you are having problems with, then we can try to help with the your problem. You can also read How to Ask.

    – Mikhail Berlyant
    Nov 21 '18 at 16:14











  • take a closer look especially to that part - Not all questions benefit from including code. But if your problem is with code you've written, you should include some. But don't just copy in your entire code! ..., it likely includes a lot of irrelevant details that readers will need to ignore when trying to reproduce the problem. Here are some guidelines: ...

    – Mikhail Berlyant
    Nov 21 '18 at 16:16













  • Mikhail, thanks a lot for advice, I have rewritten my question and added a picture. Also my question has been modified a bit.

    – Andrey
    Nov 22 '18 at 8:03











  • see the answer. please in your next/new questions - avoid using images and rather provide data examples as plain text so we can use it while helping you :o)

    – Mikhail Berlyant
    Nov 22 '18 at 20:20











  • ok, =) understand

    – Andrey
    Nov 23 '18 at 8:42














0












0








0








Could you please help me with sql statement, preferreby it should work in big query. I have 3 columns userid, date, hostname. I need to create additional column - client_type on the following condition: when userid first time comes to hostname = "online-store.com" then from this date on client_type for this particular userid will be always "current_client" else "visitor".



For example, in the image (link attached) we have userid = 1 and 4 who had become "current client". User 4 was just a visitor, but after visiting hostname = "online-store.com" he will be always classified as "current client".enter image description here










share|improve this question
















Could you please help me with sql statement, preferreby it should work in big query. I have 3 columns userid, date, hostname. I need to create additional column - client_type on the following condition: when userid first time comes to hostname = "online-store.com" then from this date on client_type for this particular userid will be always "current_client" else "visitor".



For example, in the image (link attached) we have userid = 1 and 4 who had become "current client". User 4 was just a visitor, but after visiting hostname = "online-store.com" he will be always classified as "current client".enter image description here







google-bigquery intervals between dateadd






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 22 '18 at 8:26







Andrey

















asked Nov 18 '18 at 18:39









AndreyAndrey

12




12













  • Please edit your question to show a Minimal, Complete, and Verifiable example of the code and most importantly data that you are having problems with, then we can try to help with the your problem. You can also read How to Ask.

    – Mikhail Berlyant
    Nov 21 '18 at 16:14











  • take a closer look especially to that part - Not all questions benefit from including code. But if your problem is with code you've written, you should include some. But don't just copy in your entire code! ..., it likely includes a lot of irrelevant details that readers will need to ignore when trying to reproduce the problem. Here are some guidelines: ...

    – Mikhail Berlyant
    Nov 21 '18 at 16:16













  • Mikhail, thanks a lot for advice, I have rewritten my question and added a picture. Also my question has been modified a bit.

    – Andrey
    Nov 22 '18 at 8:03











  • see the answer. please in your next/new questions - avoid using images and rather provide data examples as plain text so we can use it while helping you :o)

    – Mikhail Berlyant
    Nov 22 '18 at 20:20











  • ok, =) understand

    – Andrey
    Nov 23 '18 at 8:42



















  • Please edit your question to show a Minimal, Complete, and Verifiable example of the code and most importantly data that you are having problems with, then we can try to help with the your problem. You can also read How to Ask.

    – Mikhail Berlyant
    Nov 21 '18 at 16:14











  • take a closer look especially to that part - Not all questions benefit from including code. But if your problem is with code you've written, you should include some. But don't just copy in your entire code! ..., it likely includes a lot of irrelevant details that readers will need to ignore when trying to reproduce the problem. Here are some guidelines: ...

    – Mikhail Berlyant
    Nov 21 '18 at 16:16













  • Mikhail, thanks a lot for advice, I have rewritten my question and added a picture. Also my question has been modified a bit.

    – Andrey
    Nov 22 '18 at 8:03











  • see the answer. please in your next/new questions - avoid using images and rather provide data examples as plain text so we can use it while helping you :o)

    – Mikhail Berlyant
    Nov 22 '18 at 20:20











  • ok, =) understand

    – Andrey
    Nov 23 '18 at 8:42

















Please edit your question to show a Minimal, Complete, and Verifiable example of the code and most importantly data that you are having problems with, then we can try to help with the your problem. You can also read How to Ask.

– Mikhail Berlyant
Nov 21 '18 at 16:14





Please edit your question to show a Minimal, Complete, and Verifiable example of the code and most importantly data that you are having problems with, then we can try to help with the your problem. You can also read How to Ask.

– Mikhail Berlyant
Nov 21 '18 at 16:14













take a closer look especially to that part - Not all questions benefit from including code. But if your problem is with code you've written, you should include some. But don't just copy in your entire code! ..., it likely includes a lot of irrelevant details that readers will need to ignore when trying to reproduce the problem. Here are some guidelines: ...

– Mikhail Berlyant
Nov 21 '18 at 16:16







take a closer look especially to that part - Not all questions benefit from including code. But if your problem is with code you've written, you should include some. But don't just copy in your entire code! ..., it likely includes a lot of irrelevant details that readers will need to ignore when trying to reproduce the problem. Here are some guidelines: ...

– Mikhail Berlyant
Nov 21 '18 at 16:16















Mikhail, thanks a lot for advice, I have rewritten my question and added a picture. Also my question has been modified a bit.

– Andrey
Nov 22 '18 at 8:03





Mikhail, thanks a lot for advice, I have rewritten my question and added a picture. Also my question has been modified a bit.

– Andrey
Nov 22 '18 at 8:03













see the answer. please in your next/new questions - avoid using images and rather provide data examples as plain text so we can use it while helping you :o)

– Mikhail Berlyant
Nov 22 '18 at 20:20





see the answer. please in your next/new questions - avoid using images and rather provide data examples as plain text so we can use it while helping you :o)

– Mikhail Berlyant
Nov 22 '18 at 20:20













ok, =) understand

– Andrey
Nov 23 '18 at 8:42





ok, =) understand

– Andrey
Nov 23 '18 at 8:42












2 Answers
2






active

oldest

votes


















0














Below is for BigQuery Standard SQL



#standardSQL
SELECT
userid, date, hostname,
IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
), 'visitor', 'current_client') client_type
FROM `project.dataset.table`


You can test, play with above using dummy data you provided in your question



#standardSQL
WITH `project.dataset.table` AS (
SELECT 1 userid, DATE '2018-02-01' date, 'online-store.com' hostname UNION ALL
SELECT 2, '2018-02-01', 'other' UNION ALL
SELECT 3, '2018-02-01', 'other' UNION ALL
SELECT 4, '2018-02-01', 'other' UNION ALL
SELECT 1, '2018-02-01', 'other' UNION ALL
SELECT 1, '2018-04-07', 'other' UNION ALL
SELECT 4, '2018-04-08', 'online-store.com' UNION ALL
SELECT 5, '2018-04-08', 'other' UNION ALL
SELECT 6, '2018-04-08', 'other' UNION ALL
SELECT 4, '2018-04-08', 'other' UNION ALL
SELECT 8, '2018-04-08', 'other' UNION ALL
SELECT 1, '2018-07-07', 'other' UNION ALL
SELECT 1, '2018-11-22', 'online-store.com'
)
SELECT
userid, date, hostname,
IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
), 'visitor', 'current_client') client_type
FROM `project.dataset.table`
ORDER BY date


with result



Row userid  date        hostname            client_type  
1 1 2018-02-01 online-store.com current_client
2 1 2018-02-01 other current_client
3 2 2018-02-01 other visitor
4 3 2018-02-01 other visitor
5 4 2018-02-01 other visitor
6 1 2018-04-07 other current_client
7 4 2018-04-08 online-store.com current_client
8 4 2018-04-08 other current_client
9 5 2018-04-08 other visitor
10 6 2018-04-08 other visitor
11 8 2018-04-08 other visitor
12 1 2018-07-07 other current_client
13 1 2018-11-22 online-store.com current_client





share|improve this answer































    0














    This should be good:



    #standardSQL
    with userdates as (
    select userid, hostname, min(date) as mindate from `dataset.table` where hostname = 'online-store.com' group by userid, hostname
    )

    select u.userid, u.date, u.hostname, case when u.date >= ud.mindate then 'current_user' else 'visitor' end as client_type
    from `dataset.table` u
    left outer join userdates ud on u.userid = ud.userid
    order by 1, 2





    share|improve this answer
























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      2 Answers
      2






      active

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      2 Answers
      2






      active

      oldest

      votes









      active

      oldest

      votes






      active

      oldest

      votes









      0














      Below is for BigQuery Standard SQL



      #standardSQL
      SELECT
      userid, date, hostname,
      IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
      PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
      ), 'visitor', 'current_client') client_type
      FROM `project.dataset.table`


      You can test, play with above using dummy data you provided in your question



      #standardSQL
      WITH `project.dataset.table` AS (
      SELECT 1 userid, DATE '2018-02-01' date, 'online-store.com' hostname UNION ALL
      SELECT 2, '2018-02-01', 'other' UNION ALL
      SELECT 3, '2018-02-01', 'other' UNION ALL
      SELECT 4, '2018-02-01', 'other' UNION ALL
      SELECT 1, '2018-02-01', 'other' UNION ALL
      SELECT 1, '2018-04-07', 'other' UNION ALL
      SELECT 4, '2018-04-08', 'online-store.com' UNION ALL
      SELECT 5, '2018-04-08', 'other' UNION ALL
      SELECT 6, '2018-04-08', 'other' UNION ALL
      SELECT 4, '2018-04-08', 'other' UNION ALL
      SELECT 8, '2018-04-08', 'other' UNION ALL
      SELECT 1, '2018-07-07', 'other' UNION ALL
      SELECT 1, '2018-11-22', 'online-store.com'
      )
      SELECT
      userid, date, hostname,
      IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
      PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
      ), 'visitor', 'current_client') client_type
      FROM `project.dataset.table`
      ORDER BY date


      with result



      Row userid  date        hostname            client_type  
      1 1 2018-02-01 online-store.com current_client
      2 1 2018-02-01 other current_client
      3 2 2018-02-01 other visitor
      4 3 2018-02-01 other visitor
      5 4 2018-02-01 other visitor
      6 1 2018-04-07 other current_client
      7 4 2018-04-08 online-store.com current_client
      8 4 2018-04-08 other current_client
      9 5 2018-04-08 other visitor
      10 6 2018-04-08 other visitor
      11 8 2018-04-08 other visitor
      12 1 2018-07-07 other current_client
      13 1 2018-11-22 online-store.com current_client





      share|improve this answer




























        0














        Below is for BigQuery Standard SQL



        #standardSQL
        SELECT
        userid, date, hostname,
        IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
        PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
        ), 'visitor', 'current_client') client_type
        FROM `project.dataset.table`


        You can test, play with above using dummy data you provided in your question



        #standardSQL
        WITH `project.dataset.table` AS (
        SELECT 1 userid, DATE '2018-02-01' date, 'online-store.com' hostname UNION ALL
        SELECT 2, '2018-02-01', 'other' UNION ALL
        SELECT 3, '2018-02-01', 'other' UNION ALL
        SELECT 4, '2018-02-01', 'other' UNION ALL
        SELECT 1, '2018-02-01', 'other' UNION ALL
        SELECT 1, '2018-04-07', 'other' UNION ALL
        SELECT 4, '2018-04-08', 'online-store.com' UNION ALL
        SELECT 5, '2018-04-08', 'other' UNION ALL
        SELECT 6, '2018-04-08', 'other' UNION ALL
        SELECT 4, '2018-04-08', 'other' UNION ALL
        SELECT 8, '2018-04-08', 'other' UNION ALL
        SELECT 1, '2018-07-07', 'other' UNION ALL
        SELECT 1, '2018-11-22', 'online-store.com'
        )
        SELECT
        userid, date, hostname,
        IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
        PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
        ), 'visitor', 'current_client') client_type
        FROM `project.dataset.table`
        ORDER BY date


        with result



        Row userid  date        hostname            client_type  
        1 1 2018-02-01 online-store.com current_client
        2 1 2018-02-01 other current_client
        3 2 2018-02-01 other visitor
        4 3 2018-02-01 other visitor
        5 4 2018-02-01 other visitor
        6 1 2018-04-07 other current_client
        7 4 2018-04-08 online-store.com current_client
        8 4 2018-04-08 other current_client
        9 5 2018-04-08 other visitor
        10 6 2018-04-08 other visitor
        11 8 2018-04-08 other visitor
        12 1 2018-07-07 other current_client
        13 1 2018-11-22 online-store.com current_client





        share|improve this answer


























          0












          0








          0







          Below is for BigQuery Standard SQL



          #standardSQL
          SELECT
          userid, date, hostname,
          IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
          PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
          ), 'visitor', 'current_client') client_type
          FROM `project.dataset.table`


          You can test, play with above using dummy data you provided in your question



          #standardSQL
          WITH `project.dataset.table` AS (
          SELECT 1 userid, DATE '2018-02-01' date, 'online-store.com' hostname UNION ALL
          SELECT 2, '2018-02-01', 'other' UNION ALL
          SELECT 3, '2018-02-01', 'other' UNION ALL
          SELECT 4, '2018-02-01', 'other' UNION ALL
          SELECT 1, '2018-02-01', 'other' UNION ALL
          SELECT 1, '2018-04-07', 'other' UNION ALL
          SELECT 4, '2018-04-08', 'online-store.com' UNION ALL
          SELECT 5, '2018-04-08', 'other' UNION ALL
          SELECT 6, '2018-04-08', 'other' UNION ALL
          SELECT 4, '2018-04-08', 'other' UNION ALL
          SELECT 8, '2018-04-08', 'other' UNION ALL
          SELECT 1, '2018-07-07', 'other' UNION ALL
          SELECT 1, '2018-11-22', 'online-store.com'
          )
          SELECT
          userid, date, hostname,
          IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
          PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
          ), 'visitor', 'current_client') client_type
          FROM `project.dataset.table`
          ORDER BY date


          with result



          Row userid  date        hostname            client_type  
          1 1 2018-02-01 online-store.com current_client
          2 1 2018-02-01 other current_client
          3 2 2018-02-01 other visitor
          4 3 2018-02-01 other visitor
          5 4 2018-02-01 other visitor
          6 1 2018-04-07 other current_client
          7 4 2018-04-08 online-store.com current_client
          8 4 2018-04-08 other current_client
          9 5 2018-04-08 other visitor
          10 6 2018-04-08 other visitor
          11 8 2018-04-08 other visitor
          12 1 2018-07-07 other current_client
          13 1 2018-11-22 online-store.com current_client





          share|improve this answer













          Below is for BigQuery Standard SQL



          #standardSQL
          SELECT
          userid, date, hostname,
          IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
          PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
          ), 'visitor', 'current_client') client_type
          FROM `project.dataset.table`


          You can test, play with above using dummy data you provided in your question



          #standardSQL
          WITH `project.dataset.table` AS (
          SELECT 1 userid, DATE '2018-02-01' date, 'online-store.com' hostname UNION ALL
          SELECT 2, '2018-02-01', 'other' UNION ALL
          SELECT 3, '2018-02-01', 'other' UNION ALL
          SELECT 4, '2018-02-01', 'other' UNION ALL
          SELECT 1, '2018-02-01', 'other' UNION ALL
          SELECT 1, '2018-04-07', 'other' UNION ALL
          SELECT 4, '2018-04-08', 'online-store.com' UNION ALL
          SELECT 5, '2018-04-08', 'other' UNION ALL
          SELECT 6, '2018-04-08', 'other' UNION ALL
          SELECT 4, '2018-04-08', 'other' UNION ALL
          SELECT 8, '2018-04-08', 'other' UNION ALL
          SELECT 1, '2018-07-07', 'other' UNION ALL
          SELECT 1, '2018-11-22', 'online-store.com'
          )
          SELECT
          userid, date, hostname,
          IF(0 = COUNTIF(hostname = 'online-store.com') OVER(
          PARTITION BY userid ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
          ), 'visitor', 'current_client') client_type
          FROM `project.dataset.table`
          ORDER BY date


          with result



          Row userid  date        hostname            client_type  
          1 1 2018-02-01 online-store.com current_client
          2 1 2018-02-01 other current_client
          3 2 2018-02-01 other visitor
          4 3 2018-02-01 other visitor
          5 4 2018-02-01 other visitor
          6 1 2018-04-07 other current_client
          7 4 2018-04-08 online-store.com current_client
          8 4 2018-04-08 other current_client
          9 5 2018-04-08 other visitor
          10 6 2018-04-08 other visitor
          11 8 2018-04-08 other visitor
          12 1 2018-07-07 other current_client
          13 1 2018-11-22 online-store.com current_client






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 22 '18 at 20:19









          Mikhail BerlyantMikhail Berlyant

          63.3k43874




          63.3k43874

























              0














              This should be good:



              #standardSQL
              with userdates as (
              select userid, hostname, min(date) as mindate from `dataset.table` where hostname = 'online-store.com' group by userid, hostname
              )

              select u.userid, u.date, u.hostname, case when u.date >= ud.mindate then 'current_user' else 'visitor' end as client_type
              from `dataset.table` u
              left outer join userdates ud on u.userid = ud.userid
              order by 1, 2





              share|improve this answer




























                0














                This should be good:



                #standardSQL
                with userdates as (
                select userid, hostname, min(date) as mindate from `dataset.table` where hostname = 'online-store.com' group by userid, hostname
                )

                select u.userid, u.date, u.hostname, case when u.date >= ud.mindate then 'current_user' else 'visitor' end as client_type
                from `dataset.table` u
                left outer join userdates ud on u.userid = ud.userid
                order by 1, 2





                share|improve this answer


























                  0












                  0








                  0







                  This should be good:



                  #standardSQL
                  with userdates as (
                  select userid, hostname, min(date) as mindate from `dataset.table` where hostname = 'online-store.com' group by userid, hostname
                  )

                  select u.userid, u.date, u.hostname, case when u.date >= ud.mindate then 'current_user' else 'visitor' end as client_type
                  from `dataset.table` u
                  left outer join userdates ud on u.userid = ud.userid
                  order by 1, 2





                  share|improve this answer













                  This should be good:



                  #standardSQL
                  with userdates as (
                  select userid, hostname, min(date) as mindate from `dataset.table` where hostname = 'online-store.com' group by userid, hostname
                  )

                  select u.userid, u.date, u.hostname, case when u.date >= ud.mindate then 'current_user' else 'visitor' end as client_type
                  from `dataset.table` u
                  left outer join userdates ud on u.userid = ud.userid
                  order by 1, 2






                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Nov 22 '18 at 23:33









                  khankhan

                  2,17993153




                  2,17993153






























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