You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 talkingdata
【Schema】
# Table: label_categories
[
(label_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(category:TEXT, Examples: [game-game type, game-Game themes, game-Art Style])
]
# Table: gender_age
[
(device_id:INTEGER, Primary Key, Examples: [-9221086586254644858, -9221079146476055829, -9221066489596332354]),
(gender:TEXT, Examples: [M, F]),
(age:INTEGER, Examples: [29, 31, 38]),
(group:TEXT, Examples: [M29-31, M32-38, F29-32])
]
# Table: events_relevant
[
(event_id:INTEGER, Primary Key, Examples: [2, 6, 7]),
(device_id:INTEGER),
(timestamp:DATETIME),
(longitude:REAL, Examples: [1.0]),
(latitude:REAL, Examples: [0.0, 1.0])
]
# Table: gender_age_train
[
(device_id:INTEGER, Primary Key, Examples: [-9223067244542181226, -9222956879900151005, -9222754701995937853]),
(gender:TEXT, Examples: [M, F]),
(age:INTEGER, Examples: [24, 36, 29]),
(group:TEXT, Examples: [M23-26, M32-38, M29-31])
]
# Table: app_all
[
(app_id:INTEGER, Primary Key, Examples: [-9223281467940916832, -9222877069545393219, -9222785464897897681])
]
# Table: app_events_relevant
[
(event_id:INTEGER, Primary Key, Examples: [2, 6, 7]),
(app_id:INTEGER, Primary Key, Examples: [-8942695423876075857, -8022267440849930066, -5720078949152207372]),
(is_installed:INTEGER, Examples: [1]),
(is_active:INTEGER, Examples: [0, 1])
]
# Table: phone_brand_device_model2
[
(device_id:INTEGER, Primary Key, Examples: [-9223321966609553846, -9223067244542181226, -9223042152723782980]),
(phone_brand:TEXT, Primary Key, Examples: [小米, vivo, 三星]),
(device_model:TEXT, Primary Key, Examples: [红米note, Y19T, MI 3])
]
# Table: sample_submission
[
(device_id:INTEGER, Primary Key, Examples: [-9223321966609553846, -9223042152723782980, -9222896629442493034]),
(F23-:REAL, Examples: [0.0833]),
(F24-26:REAL, Examples: [0.0833]),
(F27-28:REAL, Examples: [0.0833]),
(F29-32:REAL, Examples: [0.0833]),
(F33-42:REAL, Examples: [0.0833]),
(F43+:REAL, Examples: [0.0833]),
(M22-:REAL, Examples: [0.0833]),
(M23-26:REAL, Examples: [0.0833]),
(M27-28:REAL, Examples: [0.0833]),
(M29-31:REAL, Examples: [0.0833]),
(M32-38:REAL, Examples: [0.0833]),
(M39+:REAL, Examples: [0.0833])
]
# Table: gender_age_test
[
(device_id:INTEGER, Primary Key, Examples: [-9223321966609553846, -9223042152723782980, -9222896629442493034])
]
# Table: app_events
[
(event_id:INTEGER, Primary Key, Examples: [2, 6, 7]),
(app_id:INTEGER, Primary Key, Examples: [-8942695423876075857, -8022267440849930066, -5720078949152207372]),
(is_installed:INTEGER, Examples: [1]),
(is_active:INTEGER, Examples: [0, 1])
]
# Table: app_labels
[
(app_id:INTEGER, Examples: [7324884708820027918, -4494216993218550286, 6058196446775239644]),
(label_id:INTEGER, Examples: [251, 406, 407])
]
# Table: events
[
(event_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(device_id:INTEGER, Examples: [29182687948017175, -6401643145415154744, -4833982096941402721]),
(timestamp:DATETIME, Examples: [2016-05-01 00:55:25]),
(longitude:REAL, Examples: [121.0, 104.0, 107.0]),
(latitude:REAL, Examples: [31.0, 30.0, 23.0])
]
【Foreign keys】
gender_age.device_id=phone_brand_device_model2.device_id
events_relevant.device_id=gender_age.device_id
app_events_relevant.app_id=app_all.app_id
app_events_relevant.event_id=events_relevant.event_id
app_events.event_id=events.event_id
app_labels.app_id=app_all.app_id
app_labels.label_id=label_categories.label_id

    【Question】
    Among the female users that uses OPPO as their phone brand, what is the percentage of them of the user that uses R815T model to install the app? female refers to gender = 'F'; percentage = DIVIDE(SUM(IF(phone_brand = 'OPPO',1,0)), SUM(IF(device_id = 'R815T',1,0))); R815T refers to device_id = 'R815T';

    【Evidence】
    female refers to gender = 'F'; percentage = DIVIDE(SUM(IF(phone_brand = 'OPPO',1,0)), SUM(IF(device_id = 'R815T',1,0))); R815T refers to device_id = 'R815T';

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT SUM(IIF(T1.phone_brand = 'OPPO', 1, 0)) / SUM(IIF(T1.device_id = 'R815T', 1, 0)) AS num FROM phone_brand_device_model2 AS T1 INNER JOIN gender_age AS T2 ON T1.device_id = T2.device_id WHERE T2.gender = 'F'