I found a interesting data from internet. In 2016 the total federal funding giving to science research.

 scala> dfs.agg(sum("2016_dollars").alias("total_usd")).withColumn("USD",$"total_usd".cast("bigint")).show()
 +-------------------+---------+                                                 
 |          total_usd|      USD|
 +-------------------+---------+
 |1.403136673080022E8|140313667|
 +-------------------+---------+

The funding in different subjects:

 scala> dfs.groupBy("cleanedoccupation").agg(sum("2016_dollars").alias("total_usd")).orderBy(desc("total_usd")).show(50,false)
 +--------------------------+--------------------+                               
 |cleanedoccupation         |total_usd           |
 +--------------------------+--------------------+
 |ENGINEER                  |5.6210328579001434E7|
 |ENGINEER (SOFTWARE)       |1.2404909456000067E7|
 |SCIENTIST                 |9626102.924999982   |
 |GEOLOGIST                 |4464040.507000001   |
 |CIVIL ENGINEER            |4310870.640999997   |
 |PHYSICIST                 |3884441.206999995   |
 |ELECTRICAL ENGINEER       |1872780.3499999999  |
 |ENGINEER PETROLEUM        |1801395.8540000003  |
 |RESEARCH SCIENTIST        |1465032.3850000012  |
 |(CHEMIST)                 |1377201.1840000015  |
 |ENGINEERING MANAGER       |984317.9560000005   |
 |COMPUTER SCIENTIST        |971759.3170000003   |
 |STATISTICIAN              |935231.7600000005   |
 |ENGINEER SYSTEMS          |923817.563000001    |
 |ENGINEER MECHANICAL       |893327.1470000002   |
 |CONSULTING ENGINEER       |891420.4819999998   |
 |PHYSICIAN & SCIENTIST     |778114.7870000006   |
 |ENGINEERING               |774313.1520000008   |
 |MATHEMATICIAN             |702732.044          |
 |CHEMICAL ENGINEER         |698136.0830000002   |
 |GEO PHYSICIST             |676961.9880000001   |
 |GEOLOGIST - PETROLEUM     |518072.99400000006  |
 |COMPUTER ENGINEER         |505745.18100000016  |
 |BIO-CHEMIST               |487593.8659999999   |
 |PROFESSIONAL ENGINEER     |471818.522          |
 |ENGINEER - SALES          |465968.792          |
 |ENGINEER NETWORK          |434904.4880000003   |
 |ENGINEER STRUCTURAL       |380760.5010000001   |
 |S.W. ENGINEER             |349827.36100000015  |
 |ENGINEER - MINING ENGINEER|348709.9329999999   |
 |CONSULTANT ENGINEERING    |336596.85199999996  |
 |ENGINEERING V.P.          |333680.348          |
 |CHEMISTRY PROFESSOR       |324671.82800000004  |
 |ENGINEER ( RETIRED )      |315816.797          |
 |CHIEF SCIENTIST           |296670.8009999999   |
 |MEDICAL PHYSICIST         |276801.33799999993  |
 |ENGINEER - ENVIRONMENTAL  |269114.5669999999   |
 |PROFESSOR OF PHYSICS      |267624.814          |
 |''DOMESTIC ENGINEER!''    |256155.36599999998  |
 |ENGINEER & MANAGER        |242135.45699999997  |
 |AEROSPACE ENGINEER        |238892.678          |
 |ENGINEER & PRESIDENT      |235359.60199999998  |
 |ENGINEER OWNER            |235189.19400000002  |
 |ENVIRONMENTAL /SCIENTIST  |233449.48299999995  |
 |RESEARCH ENGINEER         |225008.49199999997  |
 |ENGINEER & PRINCIPAL      |218452.696          |
 |ASTRONOMER                |213948.17700000003  |
 |SYSTEM ENGINEER           |198212.448          |
 |ENGINEER PROJECT          |181820.63699999996  |
 |DIRECTOR ENGINEERING      |180311.91499999998  |
 +--------------------------+--------------------+
 only showing top 50 rows

The total number of research fields:

 scala> dfs.select("cleanedoccupation").distinct().count()
 val res57: Long = 9266

The funding usage grouped by project names:

 scala> dfs.groupBy("cmte_nm").agg(sum("2016_dollars").alias("total_usd")).orderBy(desc("total_usd")).show(false)
 +------------------------------------------------------+--------------------+   
 |cmte_nm                                               |total_usd           |
 +------------------------------------------------------+--------------------+
 |OBAMA FOR AMERICA                                     |1.7751455284000028E7|
 |HILLARY FOR AMERICA                                   |7670062.769999982   |
 |REPUBLICAN NATIONAL COMMITTEE                         |7044682.358000069   |
 |BERNIE 2016                                           |5947401.752000537   |
 |DNC SERVICES CORPORATION/DEMOCRATIC NATIONAL COMMITTEE|4987350.171000016   |
 |ROMNEY FOR PRESIDENT INC.                             |4380457.562999998   |
 |DEMOCRATIC SENATORIAL CAMPAIGN COMMITTEE              |2962307.6000000057  |
 |JOHN MCCAIN 2008 INC.                                 |2528159.003999992   |
 |DEMOCRATIC CONGRESSIONAL CAMPAIGN COMMITTEE           |2420596.933         |
 |HILLARY CLINTON FOR PRESIDENT                         |2150429.051999999   |
 |CRUZ FOR PRESIDENT                                    |1856988.9939999753  |
 |NATIONAL REPUBLICAN SENATORIAL COMMITTEE              |1711146.4410000006  |
 |DCCC                                                  |1627752.27999995    |
 |RON PAUL 2008 PRESIDENTIAL CAMPAIGN COMMITTEE         |1564211.8560000015  |
 |RON PAUL 2012 PRESIDENTIAL CAMPAIGN COMMITTEE INC.    |1529315.4300000046  |
 |DSCC                                                  |1449962.9319999968  |
 |DONALD J. TRUMP FOR PRESIDENT, INC.                   |1291077.7140000004  |
 |BILL FOSTER FOR CONGRESS COMMITTEE                    |1242041.2119999994  |
 |NATIONAL REPUBLICAN CONGRESSIONAL COMMITTEE           |1085679.1910000003  |
 |NRCC                                                  |699361.8919999996   |
 +------------------------------------------------------+--------------------+
 only showing top 20 rows

The dataset is public for free downloading. Just for fun to analyse it with Apache Spark.