Spark: Group RDD Sql Query

I have 3 RDDs that I need to join.

val event1001RDD: schemaRDD = [eventtype,id,location,date1]

[1001,4929102,LOC01,2015-01-20 10:44:39]
[1001,4929103,LOC02,2015-01-20 10:44:39]
[1001,4929104,LOC03,2015-01-20 10:44:39]

val event2009RDD: schemaRDD = [eventtype,id,celltype,date1] (not grouped by id since I need 4 dates from this depending on celltype)

[2009,4929101,R01,2015-01-20 20:44:39]
[2009,4929102,R02,2015-01-20 14:00:00] (RPM)
[2009,4929102,P01,2015-01-20 12:00:00] (PPM)
[2009,4929102,R03,2015-01-20 15:00:00] (RPM)
[2009,4929102,C01,2015-01-20 13:00:00] (RPM)
[2009,4929103,R01,2015-01-20 14:44:39]
[2009,4929105,R01,2015-01-20 12:44:39]
[2009,4929105,V01,2015-01-20 11:44:39]
[2009,4929106,R01,2015-01-20 13:44:39]

val cellLookupRDD: [celltype, cellname] (cellname has 4 values)

[R01,RPM]
[R02,RPM]
[R03,RPM]
[C01,RPM]
[P01,PPM]
[V01,PPM]

Expected result: [id,1001's location,1001's date1,2009's first RPM date,2009's last RPM date, 2009's first PPM date,2009's last PPM date]

4929101,NULL,NULL,2015-01-20 20:44:39,NULL,NULL,NULL
4929102,LOC01,2015-01-20 10:44:39,2015-01-20 13:00:00,2015-01-20 15:00:00,2015-01-20 12:00:00,NULL
4929103,LOC02,2015-01-20 10:44:39,2015-01-20 14:44:39,NULL,NULL,NULL
4929104,LOC03,2015-01-20 10:44:39,NULL,NULL,NULL,NULL
4929105,NULL,NULL,2015-01-20 12:44:39,NULL,2015-01-20 11:44:39,NULL
4929106,NULL,NULL,2015-01-20 13:44:39,NULL,NULL,NULL

This is my current query (where I am also indicating an optional eventtype as the first column; but in my previous event2009RDD, I am selecting a min and max date which is wrong since I need the four dates determined via cellLookupRDD - RPM and PPM):

select if(event1001Table.eventtype is not null, event1001Table.eventtype,
          event2009Table.eventtype), 
       if(event1001Table.id is not null, event1001Table.id, 
          event2009Table.id), 
       event1001Table.date1, event2009Table.minDate, event2009Table.maxDate  
       from event1001Table full outer join event2009Table  
       on event1001Table.id=event2009Table.id")

EDITED to show result after applying answer:

  " min(if(l.cn = 'RPM' or l.cn = 'RPM2', r.date1, 'NULL')) as rpmmn, " +
  " max(if(l.cn = 'RPM' or l.cn = 'RPM2', r.date1, 'NULL')) as rpmmx, " +
  " min(if(l.cn = 'PPM' or l.cn = 'PPM2', r.date1, 'NULL')) as ppmmn, " +
  " max(if(l.cn = 'PPM' or l.cn = 'PPM2', r.date1, 'NULL')) as ppmmx " +


[2009,4929102,R01,2015-01-20 13:00:00] min_rpm
[2009,4929102,C01,2015-01-20 14:00:00] max_rpm
---res: [2009,4929102,NULL,NULL,2015-01-20 13:00:00,2015-01-20 14:00:00] 
--- CORRECT

[2009,4929102,R01,2015-01-20 13:00:00] min_rpm
[2009,4929102,P01,2015-01-20 14:00:00] min_ppm
---res: [2009,4929102,2015-01-20 13:00:00,NULL,2015-01-20 14:00:00,NULL] 
--- INCORRECT (max should be equal to MIN although NULL is preferred if possible but I could just check in the code later on if min=max)

[2009,4929102,R01,2015-01-20 13:00:00] min_rpm
[2009,4929102,C01,2015-01-20 14:00:00] max_rpm
[2009,4929102,P01,2015-01-20 09:00:00] min_ppm
---res: [2009,4929102,2015-01-20 13:00:00,NULL,2015-01-20 09:00:00,NULL] 
--- INCORRECT (max is not working)

Lets work it step by step. Lets first construct the 2009 part

event2009RDD.registerTempTable("base2009")
cellLookupRDD.registerTempTable("lookup")

trns2009 = ssc.sql("select eventtype, id, \
                          min(case when l.cn = 'RPM' then r.date1 else null end) rpmmn, \
max(case when l.cn = 'RPM' then r.date1 else null end) rpmmx, \
min(case when l.cn = 'PPM' then r.date1 else null end) ppmmn, \
max(case when l.cn = 'PPM' then r.date1 else null end) ppmmx, \
from base2009 r inner join lookup l on r.celltype=l.celltype \
group by eventtype,id "

trns2009 .registerTempTable("transformed2009")

Now you can do a full outer join with 1001 data set and get output.

Note: you should not have

4929101,NULL,NULL,2015-01-20 20:44:39,NULL,NULL,NULL

instead, you should have

4929101,NULL,NULL,2015-01-20 20:44:39,2015-01-20 20:44:39,NULL,NULL

Because, if 2009 event have occurred once, then it should have both first and last date. NULL should represent an event never occurred, like for id=4929101, celltype=PPM.

Please let me know if this works (or not). I do not have access to spark right this moment, but should be able to debug, if needed, tonight.