Member characteristics

Congressional record parsed by speaker

Directory structure of the data/txt folder:

cr_file <- list.files(here::here("data", "txt") %>% 
                        str_replace("redistricting", "Congressional records/cr"),
                      recursive = T) 

tibble(file = cr_file) %>% kablebox()
file
2018/14871/CREC-2018-10-03-pt1-PgS6492-000004-14871.txt
2018/14871/CREC-2018-10-11-pt1-PgS6809-2-000017-14871.txt
2018/14921/CREC-2018-10-05-pt2-PgS6698-2-000009-14921.txt
2018/14921/CREC-2018-10-05-pt2-PgS6698-2-000010-14921.txt
2018/14921/CREC-2018-10-11-pt1-PgS6880-3-000018-14921.txt
2018/14921/CREC-2018-10-11-pt1-PgS6880-3-000019-14921.txt
2018/14921/CREC-2018-10-11-pt1-PgS6880-3-000020-14921.txt
2018/15029/CREC-2018-11-13-pt1-PgH9508-4-000043-15029.txt
2018/15029/CREC-2018-11-13-pt1-PgH9508-4-000044-15029.txt
2018/15603/CREC-2018-11-09-pt1-PgE1501-2-000014-15603.txt
2018/15603/CREC-2018-11-09-pt1-PgE1501-2-000015-15603.txt
2018/20124/CREC-2018-10-23-pt1-PgE1441-3-000028-20124.txt
2018/20124/CREC-2018-10-23-pt1-PgE1441-3-000029-20124.txt
2018/20352/CREC-2018-11-14-pt1-PgH9525-000064-20352.txt
2018/20352/CREC-2018-11-14-pt1-PgH9525-000065-20352.txt
2018/20352/CREC-2018-11-14-pt1-PgH9525-000066-20352.txt
2018/20352/CREC-2018-11-14-pt1-PgH9525-000067-20352.txt
2018/20501/CREC-2018-10-16-pt1-PgE1417-000024-20501.txt
2018/20501/CREC-2018-10-16-pt1-PgE1417-000025-20501.txt
2018/20501/CREC-2018-11-09-pt1-PgE1506-2-000016-20501.txt
2018/20501/CREC-2018-11-09-pt1-PgE1506-2-000017-20501.txt
2018/20518/CREC-2018-11-02-pt1-PgE1485-2-000001-20518.txt
2018/20518/CREC-2018-11-02-pt1-PgE1485-2-000002-20518.txt
2018/20521/CREC-2018-11-13-pt1-PgH9474-000028-20521.txt
2018/20521/CREC-2018-11-13-pt1-PgH9474-000029-20521.txt
2018/20521/CREC-2018-11-13-pt1-PgH9507-000046-20521.txt
2018/20528/CREC-2018-11-02-pt1-PgE1491-7-000005-20528.txt
2018/20528/CREC-2018-11-02-pt1-PgE1491-7-000006-20528.txt
2018/20705/CREC-2018-10-30-pt1-PgE1481-4-000034-20705.txt
2018/20705/CREC-2018-10-30-pt1-PgE1481-4-000035-20705.txt
2018/20717/CREC-2018-11-13-pt1-PgS6910-2-000054-20717.txt
2018/20750/CREC-2018-10-26-pt1-PgH9449-13-000033-20750.txt
2018/20955/CREC-2018-10-26-pt1-PgE1462-2-000031-20955.txt
2018/20955/CREC-2018-10-26-pt1-PgE1462-2-000032-20955.txt
2018/20958/CREC-2018-11-02-pt1-PgE1487-3-000036-20958.txt
2018/20958/CREC-2018-11-02-pt1-PgE1487-3-000037-20958.txt
2018/20959/CREC-2018-10-12-pt1-PgE1401-4-000021-20959.txt
2018/20959/CREC-2018-10-12-pt1-PgE1401-4-000022-20959.txt
2018/21109/CREC-2018-11-09-pt1-PgE1502-2-000041-21109.txt
2018/21109/CREC-2018-11-09-pt1-PgE1502-2-000042-21109.txt
2018/21111/CREC-2018-11-13-pt1-PgE1513-3-000021-21111.txt
2018/21111/CREC-2018-11-13-pt1-PgE1513-3-000022-21111.txt
2018/21111/CREC-2018-11-13-pt1-PgE1513-3-000023-21111.txt
2018/21303/CREC-2018-11-13-pt1-PgE1509-000019-21303.txt
2018/21303/CREC-2018-11-13-pt1-PgE1509-000020-21303.txt
2018/21316/CREC-2018-11-06-pt1-PgE1495-2-000039-21316.txt
2018/21316/CREC-2018-11-06-pt1-PgE1495-2-000040-21316.txt
2018/21324/CREC-2018-11-14-pt1-PgH9514-000062-21324.txt
2018/21324/CREC-2018-11-14-pt1-PgH9514-000063-21324.txt
2018/21332/CREC-2018-11-06-pt1-PgE1495-3-000008-21332.txt
2018/21332/CREC-2018-11-06-pt1-PgE1495-3-000009-21332.txt
2018/21332/CREC-2018-11-06-pt1-PgE1498-000010-21332.txt
2018/21332/CREC-2018-11-06-pt1-PgE1498-000011-21332.txt
2018/21332/CREC-2018-11-14-pt1-PgE1520-000057-21332.txt
2018/21332/CREC-2018-11-14-pt1-PgE1520-000058-21332.txt
2018/21358/CREC-2018-11-13-pt1-PgE1511-2-000043-21358.txt
2018/21358/CREC-2018-11-13-pt1-PgE1511-2-000044-21358.txt
2018/21368/CREC-2018-10-05-pt1-PgH9420-11-000008-21368.txt
2018/21521/CREC-2018-11-02-pt1-PgE1489-2-000003-21521.txt
2018/21521/CREC-2018-11-02-pt1-PgE1489-2-000004-21521.txt
2018/21525/CREC-2018-11-14-pt1-PgH9526-8-000068-21525.txt
2018/21537/CREC-2018-10-09-pt1-PgH9425-9-000011-21537.txt
2018/21704/CREC-2018-11-13-pt1-PgE1516-000024-21704.txt
2018/21704/CREC-2018-11-13-pt1-PgE1516-000025-21704.txt
2018/21708/CREC-2018-10-23-pt1-PgH9443-5-000030-21708.txt
2018/21714/CREC-2018-11-13-pt1-PgH9510-000045-21714.txt
2018/21714/CREC-2018-11-13-pt1-PgH9510-000046-21714.txt
2018/21714/CREC-2018-11-13-pt1-PgH9510-000047-21714.txt
2018/21714/CREC-2018-11-13-pt1-PgH9510-000048-21714.txt
2018/21714/CREC-2018-11-13-pt1-PgH9510-000049-21714.txt
2018/21714/CREC-2018-11-13-pt1-PgH9510-000050-21714.txt
2018/21714/CREC-2018-11-13-pt1-PgH9510-000051-21714.txt
2018/21714/CREC-2018-11-13-pt1-PgH9510-000052-21714.txt
2018/21714/CREC-2018-11-13-pt1-PgH9510-000053-21714.txt
2018/21740/CREC-2018-10-16-pt1-PgH9436-9-000026-21740.txt
2018/29321/CREC-2018-11-13-pt1-PgH9495-000030-29321.txt
2018/29321/CREC-2018-11-13-pt1-PgH9495-000031-29321.txt
2018/29321/CREC-2018-11-13-pt1-PgH9495-000032-29321.txt
2018/29321/CREC-2018-11-13-pt1-PgH9495-000033-29321.txt
2018/29321/CREC-2018-11-13-pt1-PgH9495-000034-29321.txt
2018/29321/CREC-2018-11-13-pt1-PgH9495-000035-29321.txt
2018/29321/CREC-2018-11-13-pt1-PgH9495-000036-29321.txt
2018/29321/CREC-2018-11-13-pt1-PgH9495-000037-29321.txt
2018/29323/CREC-2018-11-14-pt1-PgE1523-000059-29323.txt
2018/29323/CREC-2018-11-14-pt1-PgE1523-000060-29323.txt
2018/29373/CREC-2018-10-10-pt1-PgS6767-2-000012-29373.txt
2018/29378/CREC-2018-10-12-pt1-PgH9431-7-000023-29378.txt
2018/29561/CREC-2018-11-13-pt1-PgH9500-2-000038-29561.txt
2018/29561/CREC-2018-11-13-pt1-PgH9500-2-000039-29561.txt
2018/29561/CREC-2018-11-13-pt1-PgH9500-2-000040-29561.txt
2018/29561/CREC-2018-11-13-pt1-PgH9500-2-000041-29561.txt
2018/29561/CREC-2018-11-13-pt1-PgH9500-2-000042-29561.txt
2018/29768/CREC-2018-10-05-pt1-PgE1372-4-000006-29768.txt
2018/29768/CREC-2018-10-05-pt1-PgE1372-4-000007-29768.txt
2018/41101/CREC-2018-10-01-pt1-PgS6414-2-000001-41101.txt
2018/41703/CREC-2018-10-11-pt1-PgS6793-2-000013-41703.txt
2018/41703/CREC-2018-10-11-pt1-PgS6793-2-000014-41703.txt
2018/41703/CREC-2018-10-11-pt1-PgS6793-2-000015-41703.txt
2018/41703/CREC-2018-10-11-pt1-PgS6793-2-000016-41703.txt
2018/NA/CREC-2018-10-02-pt1-PgH9411-3-000002-NA.txt

Merge in voteview data

by Congress and legislator ICPSR identifier linking to variables including:

  • DW-NOMINATE spatial ideology scores based on voting record (nominate.dim1, nominate.dim2)
  • party_size
d <- tibble(file = cr_file %>% str_remove(".*/"),
            speech_id = cr_file %>% str_extract("000[0-9]*"),
            icpsr = str_remove(cr_file, ".*-") %>% 
              str_remove(".txt") %>% 
              as.integer(),
            date = str_extract(cr_file, "CREC-[0-9]*-[0-9]*-[0-9]*") %>% 
              str_remove("CREC-") %>%
              as.Date) 

d %<>% filter(!is.na(icpsr))

# get congress from year 
d %<>% mutate(year = str_sub(date, 1,4) %>% as.integer()) # the 107th congress began in 2001

# get congress from year 
d %<>% mutate(congress = as.numeric(round((year - 2001.1)/2)) + 107) # the 107th congress began in 2001

# Devin's members data (expanded from voteview)
load(here("data", "members.Rdata"))

d %<>% left_join(members) 

# count speeches per member
d %<>% group_by(icpsr) %>% add_count(name = "speeches_n") 

# drop name varients
d %<>% select(-contains("first"), -contains("common"), -contains("middle"), -contains("last"), -contains("maiden"), -pattern)

speeches <- d %>% distinct(icpsr, file, speech_id, speeches_n, date, year, congress, chamber, bioname)

d %>% 
  select(bioname, cqlabel, icpsr, file) %>% 
  distinct() %>% 
  kablebox()
bioname cqlabel icpsr file
WYDEN, Ronald Lee (OR) 14871 CREC-2018-10-03-pt1-PgS6492-000004-14871.txt
WYDEN, Ronald Lee (OR) 14871 CREC-2018-10-11-pt1-PgS6809-2-000017-14871.txt
McCONNELL, Addison Mitchell (Mitch) (KY) 14921 CREC-2018-10-05-pt2-PgS6698-2-000009-14921.txt
McCONNELL, Addison Mitchell (Mitch) (KY) 14921 CREC-2018-10-05-pt2-PgS6698-2-000010-14921.txt
McCONNELL, Addison Mitchell (Mitch) (KY) 14921 CREC-2018-10-11-pt1-PgS6880-3-000018-14921.txt
McCONNELL, Addison Mitchell (Mitch) (KY) 14921 CREC-2018-10-11-pt1-PgS6880-3-000019-14921.txt
McCONNELL, Addison Mitchell (Mitch) (KY) 14921 CREC-2018-10-11-pt1-PgS6880-3-000020-14921.txt
KAPTUR, Marcia Carolyn (Marcy) (OH-09) 15029 CREC-2018-11-13-pt1-PgH9508-4-000043-15029.txt
KAPTUR, Marcia Carolyn (Marcy) (OH-09) 15029 CREC-2018-11-13-pt1-PgH9508-4-000044-15029.txt
ENGEL, Eliot Lance (NY-16) 15603 CREC-2018-11-09-pt1-PgE1501-2-000014-15603.txt
ENGEL, Eliot Lance (NY-16) 15603 CREC-2018-11-09-pt1-PgE1501-2-000015-15603.txt
GRAVES, Samuel (MO-06) 20124 CREC-2018-10-23-pt1-PgE1441-3-000028-20124.txt
GRAVES, Samuel (MO-06) 20124 CREC-2018-10-23-pt1-PgE1441-3-000029-20124.txt
HENSARLING, Jeb (TX-05) 20352 CREC-2018-11-14-pt1-PgH9525-000064-20352.txt
HENSARLING, Jeb (TX-05) 20352 CREC-2018-11-14-pt1-PgH9525-000065-20352.txt
HENSARLING, Jeb (TX-05) 20352 CREC-2018-11-14-pt1-PgH9525-000066-20352.txt
HENSARLING, Jeb (TX-05) 20352 CREC-2018-11-14-pt1-PgH9525-000067-20352.txt
COSTA, Jim (CA-16) 20501 CREC-2018-10-16-pt1-PgE1417-000024-20501.txt
COSTA, Jim (CA-16) 20501 CREC-2018-10-16-pt1-PgE1417-000025-20501.txt
COSTA, Jim (CA-16) 20501 CREC-2018-11-09-pt1-PgE1506-2-000016-20501.txt
COSTA, Jim (CA-16) 20501 CREC-2018-11-09-pt1-PgE1506-2-000017-20501.txt
FORTENBERRY, Jeff (NE-01) 20518 CREC-2018-11-02-pt1-PgE1485-2-000001-20518.txt
FORTENBERRY, Jeff (NE-01) 20518 CREC-2018-11-02-pt1-PgE1485-2-000002-20518.txt
FOXX, Virginia Ann (NC-05) 20521 CREC-2018-11-13-pt1-PgH9474-000028-20521.txt
FOXX, Virginia Ann (NC-05) 20521 CREC-2018-11-13-pt1-PgH9474-000029-20521.txt
FOXX, Virginia Ann (NC-05) 20521 CREC-2018-11-13-pt1-PgH9507-000046-20521.txt
POE, Ted (TX-02) 20528 CREC-2018-11-02-pt1-PgE1491-7-000005-20528.txt
POE, Ted (TX-02) 20528 CREC-2018-11-02-pt1-PgE1491-7-000006-20528.txt
PERLMUTTER, Ed (CO-07) 20705 CREC-2018-10-30-pt1-PgE1481-4-000034-20705.txt
PERLMUTTER, Ed (CO-07) 20705 CREC-2018-10-30-pt1-PgE1481-4-000035-20705.txt
DONNELLY, Joe (IN) 20717 CREC-2018-11-13-pt1-PgS6910-2-000054-20717.txt
WELCH, Peter (VT-01) 20750 CREC-2018-10-26-pt1-PgH9449-13-000033-20750.txt
CHU, Judy (CA-27) 20955 CREC-2018-10-26-pt1-PgE1462-2-000031-20955.txt
CHU, Judy (CA-27) 20955 CREC-2018-10-26-pt1-PgE1462-2-000032-20955.txt
GARAMENDI, John (CA-03) 20958 CREC-2018-11-02-pt1-PgE1487-3-000036-20958.txt
GARAMENDI, John (CA-03) 20958 CREC-2018-11-02-pt1-PgE1487-3-000037-20958.txt
DEUTCH, Theodore E. (Ted) (FL-22) 20959 CREC-2018-10-12-pt1-PgE1401-4-000021-20959.txt
DEUTCH, Theodore E. (Ted) (FL-22) 20959 CREC-2018-10-12-pt1-PgE1401-4-000022-20959.txt
DENHAM, Jeff (CA-10) 21109 CREC-2018-11-09-pt1-PgE1502-2-000041-21109.txt
DENHAM, Jeff (CA-10) 21109 CREC-2018-11-09-pt1-PgE1502-2-000042-21109.txt
TIPTON, Scott (CO-03) 21111 CREC-2018-11-13-pt1-PgE1513-3-000021-21111.txt
TIPTON, Scott (CO-03) 21111 CREC-2018-11-13-pt1-PgE1513-3-000022-21111.txt
TIPTON, Scott (CO-03) 21111 CREC-2018-11-13-pt1-PgE1513-3-000023-21111.txt
HUFFMAN, Jared (CA-02) 21303 CREC-2018-11-13-pt1-PgE1509-000019-21303.txt
HUFFMAN, Jared (CA-02) 21303 CREC-2018-11-13-pt1-PgE1509-000020-21303.txt
ESTY, Elizabeth (CT-05) 21316 CREC-2018-11-06-pt1-PgE1495-2-000039-21316.txt
ESTY, Elizabeth (CT-05) 21316 CREC-2018-11-06-pt1-PgE1495-2-000040-21316.txt
GABBARD, Tulsi (HI-02) 21324 CREC-2018-11-14-pt1-PgH9514-000062-21324.txt
GABBARD, Tulsi (HI-02) 21324 CREC-2018-11-14-pt1-PgH9514-000063-21324.txt
MESSER, Luke (IN-06) 21332 CREC-2018-11-06-pt1-PgE1495-3-000008-21332.txt
MESSER, Luke (IN-06) 21332 CREC-2018-11-06-pt1-PgE1495-3-000009-21332.txt
MESSER, Luke (IN-06) 21332 CREC-2018-11-06-pt1-PgE1498-000010-21332.txt
MESSER, Luke (IN-06) 21332 CREC-2018-11-06-pt1-PgE1498-000011-21332.txt
MESSER, Luke (IN-06) 21332 CREC-2018-11-14-pt1-PgE1520-000057-21332.txt
MESSER, Luke (IN-06) 21332 CREC-2018-11-14-pt1-PgE1520-000058-21332.txt
CARTWRIGHT, Matt (PA-17) 21358 CREC-2018-11-13-pt1-PgE1511-2-000043-21358.txt
CARTWRIGHT, Matt (PA-17) 21358 CREC-2018-11-13-pt1-PgE1511-2-000044-21358.txt
KILMER, Derek (WA-06) 21368 CREC-2018-10-05-pt1-PgH9420-11-000008-21368.txt
YOUNG, David (IA-03) 21521 CREC-2018-11-02-pt1-PgE1489-2-000003-21521.txt
YOUNG, David (IA-03) 21521 CREC-2018-11-02-pt1-PgE1489-2-000004-21521.txt
MOULTON, Seth (MA-06) 21525 CREC-2018-11-14-pt1-PgH9526-8-000068-21525.txt
MacARTHUR, Thomas Charles (NJ-03) 21537 CREC-2018-10-09-pt1-PgH9425-9-000011-21537.txt
BERGMAN, John (MI-01) 21704 CREC-2018-11-13-pt1-PgE1516-000024-21704.txt
BERGMAN, John (MI-01) 21704 CREC-2018-11-13-pt1-PgE1516-000025-21704.txt
BUDD, Theodore Paul (NC-13) 21708 CREC-2018-10-23-pt1-PgH9443-5-000030-21708.txt
DUNN, Neal Patrick (FL-02) 21714 CREC-2018-11-13-pt1-PgH9510-000045-21714.txt
DUNN, Neal Patrick (FL-02) 21714 CREC-2018-11-13-pt1-PgH9510-000046-21714.txt
DUNN, Neal Patrick (FL-02) 21714 CREC-2018-11-13-pt1-PgH9510-000047-21714.txt
DUNN, Neal Patrick (FL-02) 21714 CREC-2018-11-13-pt1-PgH9510-000048-21714.txt
DUNN, Neal Patrick (FL-02) 21714 CREC-2018-11-13-pt1-PgH9510-000049-21714.txt
DUNN, Neal Patrick (FL-02) 21714 CREC-2018-11-13-pt1-PgH9510-000050-21714.txt
DUNN, Neal Patrick (FL-02) 21714 CREC-2018-11-13-pt1-PgH9510-000051-21714.txt
DUNN, Neal Patrick (FL-02) 21714 CREC-2018-11-13-pt1-PgH9510-000052-21714.txt
DUNN, Neal Patrick (FL-02) 21714 CREC-2018-11-13-pt1-PgH9510-000053-21714.txt
PANETTA, James Varni (CA-20) 21740 CREC-2018-10-16-pt1-PgH9436-9-000026-21740.txt
ROYCE, Edward Randall (CA-39) 29321 CREC-2018-11-13-pt1-PgH9495-000030-29321.txt
ROYCE, Edward Randall (CA-39) 29321 CREC-2018-11-13-pt1-PgH9495-000031-29321.txt
ROYCE, Edward Randall (CA-39) 29321 CREC-2018-11-13-pt1-PgH9495-000032-29321.txt
ROYCE, Edward Randall (CA-39) 29321 CREC-2018-11-13-pt1-PgH9495-000033-29321.txt
ROYCE, Edward Randall (CA-39) 29321 CREC-2018-11-13-pt1-PgH9495-000034-29321.txt
ROYCE, Edward Randall (CA-39) 29321 CREC-2018-11-13-pt1-PgH9495-000035-29321.txt
ROYCE, Edward Randall (CA-39) 29321 CREC-2018-11-13-pt1-PgH9495-000036-29321.txt
ROYCE, Edward Randall (CA-39) 29321 CREC-2018-11-13-pt1-PgH9495-000037-29321.txt
CALVERT, Ken (CA-42) 29323 CREC-2018-11-14-pt1-PgE1523-000059-29323.txt
CALVERT, Ken (CA-42) 29323 CREC-2018-11-14-pt1-PgE1523-000060-29323.txt
MENENDEZ, Robert (NJ) 29373 CREC-2018-10-10-pt1-PgS6767-2-000012-29373.txt
VELÁZQUEZ, Nydia M. (NY-07) 29378 CREC-2018-10-12-pt1-PgH9431-7-000023-29378.txt
DOYLE, Michael F. (PA-14) 29561 CREC-2018-11-13-pt1-PgH9500-2-000038-29561.txt
DOYLE, Michael F. (PA-14) 29561 CREC-2018-11-13-pt1-PgH9500-2-000039-29561.txt
DOYLE, Michael F. (PA-14) 29561 CREC-2018-11-13-pt1-PgH9500-2-000040-29561.txt
DOYLE, Michael F. (PA-14) 29561 CREC-2018-11-13-pt1-PgH9500-2-000041-29561.txt
DOYLE, Michael F. (PA-14) 29561 CREC-2018-11-13-pt1-PgH9500-2-000042-29561.txt
SMITH, Adam (WA-09) 29768 CREC-2018-10-05-pt1-PgE1372-4-000006-29768.txt
SMITH, Adam (WA-09) 29768 CREC-2018-10-05-pt1-PgE1372-4-000007-29768.txt
BLUMENTHAL, Richard (CT) 41101 CREC-2018-10-01-pt1-PgS6414-2-000001-41101.txt
KENNEDY, John Neely (LA) 41703 CREC-2018-10-11-pt1-PgS6793-2-000013-41703.txt
KENNEDY, John Neely (LA) 41703 CREC-2018-10-11-pt1-PgS6793-2-000014-41703.txt
KENNEDY, John Neely (LA) 41703 CREC-2018-10-11-pt1-PgS6793-2-000015-41703.txt
KENNEDY, John Neely (LA) 41703 CREC-2018-10-11-pt1-PgS6793-2-000016-41703.txt
GRASSLEY, Charles Ernest (IA) 14226 CREC-2020-03-04-pt1-PgS1449-4-000033-14226.txt

Subset to Wisconsin and Texas House Reps

# House members from WI
d %<>% filter(state %in% c("wisconsin", "texas"),
             chamber == "House")




d %>% 
  select(bioname, cqlabel, icpsr, file) %>% 
  distinct() %>% 
  kablebox()
bioname cqlabel icpsr file
HENSARLING, Jeb (TX-05) 20352 CREC-2018-11-14-pt1-PgH9525-000064-20352.txt
HENSARLING, Jeb (TX-05) 20352 CREC-2018-11-14-pt1-PgH9525-000065-20352.txt
HENSARLING, Jeb (TX-05) 20352 CREC-2018-11-14-pt1-PgH9525-000066-20352.txt
HENSARLING, Jeb (TX-05) 20352 CREC-2018-11-14-pt1-PgH9525-000067-20352.txt
POE, Ted (TX-02) 20528 CREC-2018-11-02-pt1-PgE1491-7-000005-20528.txt
POE, Ted (TX-02) 20528 CREC-2018-11-02-pt1-PgE1491-7-000006-20528.txt
MOORE, Gwendolynne S. (Gwen) (WI-04) 20537 CREC-2020-07-09-pt1-PgH3078-6-000108-20537.txt
OLSON, Pete (TX-22) 20948 CREC-2020-04-22-pt1-PgE380-3-000031-20948.txt
OLSON, Pete (TX-22) 20948 CREC-2020-04-22-pt1-PgE380-3-000032-20948.txt
CASTRO, Joaquin (TX-20) 21362 CREC-2020-04-22-pt1-PgH1904-5-000033-21362.txt
CASTRO, Joaquin (TX-20) 21362 CREC-2020-04-22-pt1-PgH1904-5-000034-21362.txt
CASTRO, Joaquin (TX-20) 21362 CREC-2020-04-22-pt1-PgH1904-5-000035-21362.txt
CASTRO, Joaquin (TX-20) 21362 CREC-2020-04-22-pt1-PgH1904-5-000036-21362.txt
CASTRO, Joaquin (TX-20) 21362 CREC-2020-04-22-pt1-PgH1904-5-000037-21362.txt
CASTRO, Joaquin (TX-20) 21362 CREC-2020-04-22-pt1-PgH1904-5-000038-21362.txt
CASTRO, Joaquin (TX-20) 21362 CREC-2020-04-22-pt1-PgH1904-5-000039-21362.txt
BABIN, Brian (TX-36) 21551 CREC-2020-02-06-pt1-PgE140-4-000014-21551.txt
BABIN, Brian (TX-36) 21551 CREC-2020-02-06-pt1-PgE140-4-000015-21551.txt
ARRINGTON, Jodey Cook (TX-19) 21700 CREC-2020-06-11-pt1-PgH2389-7-000158-21700.txt
GOODEN, Lance (TX-05) 21925 CREC-2020-04-03-pt1-PgH1876-3-000004-21925.txt

TODO

  • identify WI House members who were up for reelection in 2020

  • measure gregariousness (frequency of speeches)

  • measure level of attention to their district (mentions of their home state, district, etc.)

  • measure ratio/log odds of “my district” vs “my conscience” etc.


District characteristics

Connectivity

We measure travel in two ways:

  • Flows between districts (flow_count, the top figure below)
  • Flows between districts to and from bordering census blocks, (flow_count_border, the bottom figure below)
include_graphics(here("figs", "flow.png"))
include_graphics(here("figs", "flow-CBG.png"))

There are three values for each measure of connectivity (facebook friendships, travel,and near-border travel):

  • within-district
  • out-of-district
  • the ratio of within-district to out-of-district
# facebook friend data
fb <- read_csv(here::here("data", "WI_congress_facebook_sci_pop.txt"))  %>% 
  mutate_all(as.integer) %>% 
  mutate(state = "wisconsin")

names(fb) %<>% str_remove("_C.*")

# cell phone mobility data
flow <- read_csv(here::here("data", "WI_DistrictFlows_January_2020.csv")) %>%
                   mutate(state = "wisconsin")  %>% 
  full_join(read_csv(here::here("data", "TX_DistrictFlows_January_2020.csv")) %>% 
              mutate(state = "texas"))

flow_border <- read_csv(here::here("data", "WI_DistrictFlows_BorderCBGs_January_2020.csv")) %>%
                   mutate(state = "wisconsin")  %>% 
  full_join(read_csv(here::here("data", "TX_DistrictFlows_BorderCBGs_January_2020.csv")) %>% 
              mutate(state = "texas")) %>% 
  rename(Flow_Count_Border = Flow_Count)

flow %<>% 
  full_join(flow_border) %>%
  mutate(OD_Pairs = OD_Pairs %>% str_remove_all("\\(|\\)")) %>% 
  separate(OD_Pairs, into = c("from", "to"), sep = ", ") %>% 
  mutate(across(c(from, to), as.integer))

# names to match facebook data
names(flow) %<>% str_to_lower()

c <- full_join(fb, flow) %>% select(state, everything())

within <- c %>% filter(from == to)

out <- c %>% filter(from != to) %>%
  select(-to) %>%
  group_by(state, from) %>% 
  summarise_all(sum) 

flows <- full_join(within, out, by = c("state", "from"), suffix = c("", "_out")) %>%
  mutate(facebook_sci_ratio = facebook_sci/facebook_sci_out,
         flow_ratio = flow_count/flow_count_out,
         flow_ratio_border = flow_count_border/flow_count_border_out
         ) %>% 
  rename(district_code = from) %>% 
  select(state, everything(), -to )

flows %>% kablebox()
state district_code facebook_sci flow_count flow_count_border facebook_sci_out flow_count_out flow_count_border_out facebook_sci_ratio flow_ratio flow_ratio_border
wisconsin 3 850188 3968910 536612 707114 882239 256509 1.2023351 4.4986789 2.0919812
wisconsin 7 803742 3637086 317339 640362 685039 211594 1.2551369 5.3093123 1.4997542
wisconsin 8 995091 5062438 427400 784984 848245 204504 1.2676577 5.9681318 2.0899347
wisconsin 6 933867 4024884 584179 1083714 1528513 446415 0.8617283 2.6332023 1.3086007
wisconsin 2 765383 4336161 304703 376776 878954 176815 2.0314006 4.9333196 1.7232871
wisconsin 5 515978 4686964 996047 794622 1733938 644484 0.6493377 2.7030747 1.5454953
wisconsin 1 626152 4452396 354047 628966 1558898 416712 0.9955260 2.8561176 0.8496204
wisconsin 4 1088428 4389604 667951 180506 2103750 620362 6.0298716 2.0865616 1.0767117
texas 1 NA 6786158 1034834 NA 1607086 374052 NA 4.2226477 2.7665512
texas 2 NA 3906385 1932209 NA 5103014 3012360 NA 0.7655054 0.6414270
texas 3 NA 6596739 730766 NA 2957487 660102 NA 2.2305217 1.1070501
texas 4 NA 5404387 944550 NA 2744095 984844 NA 1.9694606 0.9590859
texas 5 NA 4465371 1182073 NA 3951303 1514025 NA 1.1301009 0.7807487
texas 6 NA 5114884 1226365 NA 3775350 1470252 NA 1.3548105 0.8341189
texas 7 NA 4118559 1781930 NA 4413863 2268134 NA 0.9330962 0.7856370
texas 8 NA 5718536 1147474 NA 3722462 1550046 NA 1.5362241 0.7402838
texas 9 NA 3553740 1124302 NA 4314167 1854393 NA 0.8237372 0.6062911
texas 10 NA 3634491 1600028 NA 4602216 2660474 NA 0.7897263 0.6014071
texas 11 NA 6258935 753254 NA 2066954 639409 NA 3.0280959 1.1780472
texas 12 NA 5627826 1297504 NA 3691727 1286735 NA 1.5244426 1.0083692
texas 13 NA 6002440 464129 NA 1296193 387041 NA 4.6308227 1.1991727
texas 14 NA 5501517 650763 NA 2587887 603803 NA 2.1258722 1.0777737
texas 15 NA 5915005 1756487 NA 3199594 1532321 NA 1.8486736 1.1462918
texas 16 NA 5532866 700821 NA 552399 122707 NA 10.0160681 5.7113368
texas 17 NA 5675186 844024 NA 2790294 1051511 NA 2.0339025 0.8026773
texas 18 NA 3354833 1107211 NA 4439764 1866130 NA 0.7556332 0.5933193
texas 19 NA 7055603 469162 NA 1376549 187738 NA 5.1255734 2.4990252
texas 20 NA 5115287 1460108 NA 3604746 1269960 NA 1.4190423 1.1497276
texas 21 NA 4093907 1698184 NA 4092386 2258580 NA 1.0003717 0.7518813
texas 22 NA 4477506 1664592 NA 4356329 2132982 NA 1.0278163 0.7804060
texas 23 NA 3555943 1315660 NA 4182775 2150673 NA 0.8501397 0.6117434
texas 24 NA 4783658 981213 NA 3722186 1120792 NA 1.2851744 0.8754640
texas 25 NA 3689933 1398412 NA 4304929 2191617 NA 0.8571414 0.6380732
texas 26 NA 5561482 803567 NA 4156326 1050628 NA 1.3380765 0.7648445
texas 27 NA 6178512 617330 NA 1844275 561505 NA 3.3501034 1.0994203
texas 28 NA 5680399 1659209 NA 3332282 1484054 NA 1.7046573 1.1180247
texas 29 NA 4094408 1471072 NA 4487356 1950581 NA 0.9124322 0.7541712
texas 30 NA 3931457 870416 NA 3817296 1417626 NA 1.0299062 0.6139955
texas 31 NA 5855121 847128 NA 2546524 493872 NA 2.2992601 1.7152785
texas 32 NA 4571799 1327859 NA 3891268 1449991 NA 1.1748867 0.9157705
texas 33 NA 3333896 1260341 NA 4633828 2127076 NA 0.7194691 0.5925228
texas 34 NA 5812647 1032763 NA 2589182 1040056 NA 2.2449743 0.9929879
texas 35 NA 3586225 1892224 NA 4665742 2839085 NA 0.7686291 0.6664908
texas 36 NA 4982044 858822 NA 3677391 967782 NA 1.3547768 0.8874127

Merge in connectivity

d2 <- d %>% 
  left_join(flows) %>% 
  filter(!is.na(flow_count)) 

# number of speeches
d2 %>% distinct(bioname, cqlabel, 
                flow_count, flow_count_out, flow_count_border, flow_count, flow_count_border_out, flow_ratio_border, facebook_sci, facebook_sci_out,facebook_sci_ratio, speeches_n) %>% kablebox()
icpsr bioname cqlabel speeches_n facebook_sci flow_count flow_count_border facebook_sci_out flow_count_out flow_count_border_out facebook_sci_ratio flow_ratio_border
20352 HENSARLING, Jeb (TX-05) 4 NA 4465371 1182073 NA 3951303 1514025 NA 0.7807487
20528 POE, Ted (TX-02) 2 NA 3906385 1932209 NA 5103014 3012360 NA 0.6414270
20537 MOORE, Gwendolynne S. (Gwen) (WI-04) 1 1088428 4389604 667951 180506 2103750 620362 6.029872 1.0767117
20948 OLSON, Pete (TX-22) 2 NA 4477506 1664592 NA 4356329 2132982 NA 0.7804060
21362 CASTRO, Joaquin (TX-20) 7 NA 5115287 1460108 NA 3604746 1269960 NA 1.1497276
21551 BABIN, Brian (TX-36) 2 NA 4982044 858822 NA 3677391 967782 NA 0.8874127
21700 ARRINGTON, Jodey Cook (TX-19) 1 NA 7055603 469162 NA 1376549 187738 NA 2.4990252
21925 GOODEN, Lance (TX-05) 1 NA 4465371 1182073 NA 3951303 1514025 NA 0.7807487

Demographics

dem <- read_csv(here::here("data", "CD_DEMO_DATA", "115CD_DEMOGRAPHIC_SUMMARY_ACS_2010_CENSUS_1YR_EST.csv"))

# the first 4 rows are metadata for values
dem_names <- head(dem, 4) 

# pivot value names
dem_names %<>% 
  mutate(NAME = c("format", "type", "measure", "group") ) %>%
  group_by(NAME) %>% 
  pivot_longer(!NAME) %>% 
  ungroup() %>% 
  pivot_wider(names_from = NAME)

# pivot data
dem %<>% 
  group_by(NAME, GEO_ID) %>%
  pivot_longer(starts_with("SF")) %>% 
  mutate(state = str_remove(NAME, ".*, ") %>% str_to_lower(),
         district_code = str_extract(NAME, "District [0-9]*") %>% 
           str_remove("District ") %>% as.numeric()) %>% 
  # drop non-values 
  mutate(value = as.numeric(value)) %>% filter(!is.na(value)) %>%
  #join value names
  left_join(dem_names) %>% 
  rename(district_name = NAME)

# demographic variables
dem %>% 
  distinct(type, measure, group) %>%
  kablebox()
GEO_ID district_name type measure group
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population NA
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population Under 5 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 5 to 9 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 10 to 14 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 15 to 19 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 20 to 24 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 25 to 29 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 30 to 34 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 35 to 39 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 40 to 44 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 45 to 49 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 50 to 54 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 55 to 59 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 60 to 64 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 65 to 69 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 70 to 74 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 75 to 79 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 80 to 84 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 85 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population Median age (years)
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 16 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 18 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 21 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 62 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Total population 65 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population NA
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population Under 5 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 5 to 9 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 10 to 14 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 15 to 19 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 20 to 24 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 25 to 29 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 30 to 34 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 35 to 39 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 40 to 44 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 45 to 49 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 50 to 54 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 55 to 59 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 60 to 64 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 65 to 69 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 70 to 74 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 75 to 79 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 80 to 84 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 85 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population Median age (years)
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 16 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 18 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 21 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 62 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Male population 65 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population NA
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population Under 5 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 5 to 9 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 10 to 14 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 15 to 19 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 20 to 24 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 25 to 29 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 30 to 34 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 35 to 39 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 40 to 44 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 45 to 49 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 50 to 54 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 55 to 59 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 60 to 64 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 65 to 69 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 70 to 74 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 75 to 79 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 80 to 84 years
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 85 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population Median age (years)
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 16 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 18 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 21 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 62 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas SEX AND AGE Female population 65 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas RACE Total population NA
5001500US4823 Congressional District 23 (115th Congress), Texas RACE Total population One Race
5001500US4823 Congressional District 23 (115th Congress), Texas RACE Total population Two or More Races
5001500US4823 Congressional District 23 (115th Congress), Texas RACE Race alone or in combination with one or more other races: [4] White
5001500US4823 Congressional District 23 (115th Congress), Texas RACE Race alone or in combination with one or more other races: [4] Black or African American
5001500US4823 Congressional District 23 (115th Congress), Texas RACE Race alone or in combination with one or more other races: [4] American Indian and Alaska Native
5001500US4823 Congressional District 23 (115th Congress), Texas RACE Race alone or in combination with one or more other races: [4] Asian
5001500US4823 Congressional District 23 (115th Congress), Texas RACE Race alone or in combination with one or more other races: [4] Native Hawaiian and Other Pacific Islander
5001500US4823 Congressional District 23 (115th Congress), Texas RACE Race alone or in combination with one or more other races: [4] Some Other Race
5001500US4823 Congressional District 23 (115th Congress), Texas HISPANIC OR LATINO Total population NA
5001500US4823 Congressional District 23 (115th Congress), Texas HISPANIC OR LATINO Total population Hispanic or Latino (of any race)
5001500US4823 Congressional District 23 (115th Congress), Texas HISPANIC OR LATINO Total population Not Hispanic or Latino
5001500US4823 Congressional District 23 (115th Congress), Texas HISPANIC OR LATINO AND RACE Total population NA
5001500US4823 Congressional District 23 (115th Congress), Texas HISPANIC OR LATINO AND RACE Total population Hispanic or Latino
5001500US4823 Congressional District 23 (115th Congress), Texas HISPANIC OR LATINO AND RACE Total population Not Hispanic or Latino
5001500US4823 Congressional District 23 (115th Congress), Texas RELATIONSHIP Total population NA
5001500US4823 Congressional District 23 (115th Congress), Texas RELATIONSHIP Total population In households
5001500US4823 Congressional District 23 (115th Congress), Texas RELATIONSHIP Total population In group quarters
5001500US4823 Congressional District 23 (115th Congress), Texas HOUSEHOLDS BY TYPE Total households NA
5001500US4823 Congressional District 23 (115th Congress), Texas HOUSEHOLDS BY TYPE Total households Family households (families) [7]
5001500US4823 Congressional District 23 (115th Congress), Texas HOUSEHOLDS BY TYPE Total households Nonfamily households [7]
5001500US4823 Congressional District 23 (115th Congress), Texas HOUSEHOLDS BY TYPE Total households Households with individuals under 18 years
5001500US4823 Congressional District 23 (115th Congress), Texas HOUSEHOLDS BY TYPE Total households Households with individuals 65 years and over
5001500US4823 Congressional District 23 (115th Congress), Texas HOUSEHOLDS BY TYPE Total households Average household size
5001500US4823 Congressional District 23 (115th Congress), Texas HOUSEHOLDS BY TYPE Total households Average family size [7]

Merge in Census Demographics

# join demographics to speech and community data
d2 %<>% left_join(dem) 

d2 %>% 
  filter(format == "Percent"#, type == "RACE"
         ) %>% 
  select(bioname, cqlabel, type,  group, measure, value, format) %>% 
  distinct() %>%
  kablebox()
icpsr bioname cqlabel type group measure value format
20352 HENSARLING, Jeb (TX-05) SEX AND AGE NA Total population 100.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE Under 5 years Total population 7.6 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 5 to 9 years Total population 7.5 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 10 to 14 years Total population 7.3 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 15 to 19 years Total population 7.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 20 to 24 years Total population 6.1 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 25 to 29 years Total population 6.8 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 30 to 34 years Total population 7.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 35 to 39 years Total population 7.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 40 to 44 years Total population 6.8 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 45 to 49 years Total population 7.2 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 50 to 54 years Total population 6.9 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 55 to 59 years Total population 5.8 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 60 to 64 years Total population 5.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 65 to 69 years Total population 3.9 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 70 to 74 years Total population 2.9 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 75 to 79 years Total population 2.3 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 80 to 84 years Total population 1.6 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 85 years and over Total population 1.4 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 16 years and over Total population 76.2 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 18 years and over Total population 73.3 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 21 years and over Total population 69.4 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 62 years and over Total population 15.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 65 years and over Total population 12.1 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE NA Male population 50.2 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE Under 5 years Male population 3.9 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 5 to 9 years Male population 3.9 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 10 to 14 years Male population 3.7 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 15 to 19 years Male population 3.6 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 20 to 24 years Male population 3.2 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 25 to 29 years Male population 3.5 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 30 to 34 years Male population 3.6 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 35 to 39 years Male population 3.6 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 40 to 44 years Male population 3.6 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 45 to 49 years Male population 3.7 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 50 to 54 years Male population 3.5 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 55 to 59 years Male population 2.9 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 60 to 64 years Male population 2.4 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 65 to 69 years Male population 1.8 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 70 to 74 years Male population 1.4 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 75 to 79 years Male population 1.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 80 to 84 years Male population 0.6 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 85 years and over Male population 0.5 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 16 years and over Male population 38.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 18 years and over Male population 36.5 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 21 years and over Male population 34.5 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 62 years and over Male population 6.7 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 65 years and over Male population 5.3 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE NA Female population 49.8 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE Under 5 years Female population 3.7 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 5 to 9 years Female population 3.7 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 10 to 14 years Female population 3.6 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 15 to 19 years Female population 3.4 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 20 to 24 years Female population 3.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 25 to 29 years Female population 3.3 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 30 to 34 years Female population 3.3 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 35 to 39 years Female population 3.4 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 40 to 44 years Female population 3.3 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 45 to 49 years Female population 3.5 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 50 to 54 years Female population 3.4 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 55 to 59 years Female population 3.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 60 to 64 years Female population 2.6 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 65 to 69 years Female population 2.1 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 70 to 74 years Female population 1.5 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 75 to 79 years Female population 1.3 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 80 to 84 years Female population 1.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 85 years and over Female population 1.0 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 16 years and over Female population 38.1 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 18 years and over Female population 36.7 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 21 years and over Female population 34.9 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 62 years and over Female population 8.3 Percent
20352 HENSARLING, Jeb (TX-05) SEX AND AGE 65 years and over Female population 6.8 Percent
20352 HENSARLING, Jeb (TX-05) RACE NA Total population 100.0 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 97.5 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 70.0 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 14.2 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 0.8 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 1.8 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 0.5 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 0.1 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 0.3 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 0.0 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 0.6 Percent
20352 HENSARLING, Jeb (TX-05) RACE One Race Total population 10.7 Percent
20352 HENSARLING, Jeb (TX-05) RACE Two or More Races Total population 2.5 Percent
20352 HENSARLING, Jeb (TX-05) RACE Two or More Races Total population 0.5 Percent
20352 HENSARLING, Jeb (TX-05) RACE Two or More Races Total population 0.2 Percent
20352 HENSARLING, Jeb (TX-05) RACE Two or More Races Total population 0.8 Percent
20352 HENSARLING, Jeb (TX-05) RACE White Race alone or in combination with one or more other races: [4] 72.1 Percent
20352 HENSARLING, Jeb (TX-05) RACE Black or African American Race alone or in combination with one or more other races: [4] 14.9 Percent
20352 HENSARLING, Jeb (TX-05) RACE American Indian and Alaska Native Race alone or in combination with one or more other races: [4] 1.4 Percent
20352 HENSARLING, Jeb (TX-05) RACE Asian Race alone or in combination with one or more other races: [4] 2.2 Percent
20352 HENSARLING, Jeb (TX-05) RACE Native Hawaiian and Other Pacific Islander Race alone or in combination with one or more other races: [4] 0.1 Percent
20352 HENSARLING, Jeb (TX-05) RACE Some Other Race Race alone or in combination with one or more other races: [4] 11.8 Percent
20352 HENSARLING, Jeb (TX-05) HISPANIC OR LATINO NA Total population 100.0 Percent
20352 HENSARLING, Jeb (TX-05) HISPANIC OR LATINO Hispanic or Latino (of any race) Total population 25.2 Percent
20352 HENSARLING, Jeb (TX-05) HISPANIC OR LATINO Hispanic or Latino (of any race) Total population 21.4 Percent
20352 HENSARLING, Jeb (TX-05) HISPANIC OR LATINO Hispanic or Latino (of any race) Total population 0.3 Percent
20352 HENSARLING, Jeb (TX-05) HISPANIC OR LATINO Hispanic or Latino (of any race) Total population 0.1 Percent
20352 HENSARLING, Jeb (TX-05) HISPANIC OR LATINO Hispanic or Latino (of any race) Total population 3.3 Percent

Partisanship

partisanship <- read_csv(here::here("data", "WI_FULL_WARD_ELECTORAL_DATA_02_TO_18.csv")) %>% 
  mutate(district_code = CON %>% as.integer() ) %>% 
  select(-NAME) %>% 
  #FIXME these data are only for WI
  mutate(state = "wisconsin")

partisanship %>% kablebox()
GEOID CNTY_FIPS CNTY_NAME COUSUBFP MCD_FIPS MCD_NAME CTV LABEL LSAD DISTRICT ASM SEN CON STR_WARDS PERSONS PERSONS18 WHITE BLACK HISPANIC ASIAN AMINDIAN PISLAND OTHER OTHERMLT WHITE18 BLACK18 HISPANIC18 ASIAN18 AMINDIAN18 PISLAND18 OTHER18 OTHERMLT18 TOT_CUM_10 REP_CUM_10 DEM_CUM_10 TOT_CUM_18 REP_CUM_18 DEM_CUM_18 REP_PCT_PROJECTION DEM_PCT_PROJECTION REP_PCT_PROJ_18 DEM_PCT_PROJ_18 REP_PCT_DIF_GOV18 REP_PCT_DIF_PRE16 REP_PCT_DIF_GOV14 REP_PCT_DIF_PRE12 REP_PCT_DIF_USH18 REP_PCT_DIF_USH16 REP_PCT_DIF_USH14 REP_PCT_DIF_USH12 REP_PCT_DIF_WSA18 REP_PCT_DIF_WSA16 REP_PCT_DIF_WSA14 REP_PCT_DIF_WSA12 GOVREP18_P GOVDEM18_P PREREP16_P PREDEM16_P GOVREP14_P GOVDEM14_P PREREP12_P PREDEM12_P GOVREP10_P GOVDEM10_P PREREP08_P PREDEM08_P GOVREP06_P GOVDEM06_P PREREP04_P PREDEM04_P GOVREP02_P GOVDEM02_P USHREP18_P USHDEM18_P USHREP16_P USHDEM16_P USHREP14_P USHDEM14_P USHREP12_P USHDEM12_P USHREP10_P USHDEM10_P USHREP08_P USHDEM08_P USHREP06_P USHDEM06_P USHREP04_P USHDEM04_P USHREP02_P USHDEM02_P USSREP18_P USSDEM18_P USSREP16_P USSDEM16_P USSREP12_P USSDEM12_P USSREP10_P USSDEM10_P USSREP06_P USSDEM06_P USSREP04_P USSDEM04_P WSAREP18_P WSADEM18_P WSAREP16_P WSADEM16_P WSAREP14_P WSADEM14_P WSAREP12_P WSADEM12_P WSAREP10_P WSADEM10_P WSAREP08_P WSADEM08_P WSAREP06_P WSADEM06_P WSAREP04_P WSADEM04_P WSAREP02_P WSADEM02_P WAGREP18_P WAGDEM18_P WAGREP14_P WAGDEM14_P WAGREP10_P WAGDEM10_P WAGREP06_P WAGDEM06_P WAGREP02_P WAGDEM02_P WSSREP16_P WSSDEM16_P WSSREP12_P WSSDEM12_P WSSREP08_P WSSDEM08_P WSSREP06_P WSSDEM06_P WSSREP04_P WSSDEM04_P WSSREP02_P WSSDEM02_P GOVTOT18 GOVREP18 GOVDEM18 SOSTOT18 SOSREP18 SOSDEM18 TRSTOT18 TRSREP18 TRSDEM18 USHTOT18 USHREP18 USHDEM18 USSTOT18 USSREP18 USSDEM18 WAGTOT18 WAGREP18 WAGDEM18 WSATOT18 WSAREP18 WSADEM18 PRETOT16 PREDEM16 PREREP16 USHTOT16 USHDEM16 USHREP16 USSTOT16 USSDEM16 USSREP16 WSATOT16 WSADEM16 WSAREP16 WSSTOT16 WSSDEM16 WSSREP16 GOVTOT14 GOVDEM14 GOVREP14 SOSTOT14 SOSDEM14 SOSREP14 TRSTOT14 TRSDEM14 TRSREP14 USHTOT14 USHDEM14 USHREP14 WAGTOT14 WAGDEM14 WAGREP14 WSATOT14 WSADEM14 WSAREP14 GOVTOT12 GOVDEM12 GOVREP12 PRETOT12 PREDEM12 PREREP12 USHTOT12 USHDEM12 USHREP12 USSTOT12 USSDEM12 USSREP12 WSATOT12 WSADEM12 WSAREP12 WSSTOT12 WSSDEM12 WSSREP12 GOVTOT10 GOVDEM10 GOVREP10 SOSTOT10 SOSDEM10 SOSREP10 TRSTOT10 TRSDEM10 TRSREP10 USHTOT10 USHDEM10 USHREP10 USSTOT10 USSDEM10 USSREP10 WAGTOT10 WAGDEM10 WAGREP10 WSATOT10 WSADEM10 WSAREP10 PRETOT08 PREDEM08 PREREP08 USHTOT08 USHDEM08 USHREP08 WSATOT08 WSADEM08 WSAREP08 WSSTOT08 WSSDEM08 WSSREP08 GOVTOT06 GOVDEM06 GOVREP06 SOSTOT06 SOSDEM06 SOSREP06 TRSTOT06 TRSDEM06 TRSREP06 USHTOT06 USHDEM06 USHREP06 USSTOT06 USSDEM06 USSREP06 WAGTOT06 WAGDEM06 WAGREP06 WSATOT06 WSADEM06 WSAREP06 WSSTOT06 WSSDEM06 WSSREP06 PRETOT04 PREDEM04 PREREP04 USHTOT04 USHDEM04 USHREP04 USSTOT04 USSDEM04 USSREP04 WSATOT04 WSADEM04 WSAREP04 WSSTOT04 WSSDEM04 WSSREP04 GOVTOT02 GOVDEM02 GOVREP02 SOSTOT02 SOSDEM02 SOSREP02 TRSTOT02 TRSDEM02 TRSREP02 USHTOT02 USHDEM02 USHREP02 WAGTOT02 WAGDEM02 WAGREP02 WSATOT02 WSADEM02 WSAREP02 WSSTOT02 WSSDEM02 WSSREP02 district_code state
55001002750001 55001 Adams 275 5500100275 Adams C Adams - C 0001 25 41 41 14 3 0001 661 484 620 17 15 1 5 0 0 3 463 9 8 1 3 0 0 0 3020 1061 1906 3090 1392 1563 0.3513245 0.6311258 0.4504854 0.5058252 -0.1749913 -0.1409236 -0.0890900 -0.0199990 -0.1032210 0.3513245 -0.0397369 -0.0115447 -0.2168573 -0.2293207 -0.1293385 -0.0452272 0.5263158 0.4342105 0.4922481 0.4263566 0.4404145 0.5233161 0.3713235 0.6176471 0.4637681 0.5362319 0.2988048 0.7011952 0.3523316 0.6476684 0.4071146 0.5928854 0.3030303 0.5090909 0.4545455 0.5454545 0.0000000 0.9878049 0.3910615 0.6089385 0.3628692 0.6329114 0.5744681 0.4255319 0.4156379 0.5843621 1.0000000 0.0000000 0.6371308 0.3628692 0.9834711 0.0000000 0.4618834 0.5336323 0.4734694 0.4530612 0.3778626 0.5648855 0.4255319 0.5744681 0.1658031 0.8341969 0.3960000 0.6040000 0.5681818 0.4318182 0.5806452 0.0000000 0.4806630 0.5193370 0.3965517 0.6034483 0.4057971 0.5144928 0.2745902 0.7254098 0.2828283 0.7171717 0.3415638 0.6584362 0.2955975 0.7044025 0.5135135 0.4594595 0.4450549 0.5164835 0.5182482 0.4817518 0.4030612 0.5969388 0.3375796 0.6560510 0.4669604 0.5330396 0.4000000 0.5956522 0.2674897 0.7325103 0.0000000 0.0000000 0.2921811 0.7078189 0.0000000 0.0000000 228 120 99 218 110 108 217 106 102 220 100 120 223 103 119 222 114 102 220 125 95 258 110 127 164 162 0 245 111 116 217 0 126 227 121 106 193 101 85 180 106 65 174 91 70 179 109 70 182 94 81 181 94 87 216 104 95 272 168 101 237 150 86 262 148 99 232 140 92 230 137 92 138 74 64 136 86 50 137 83 54 141 60 81 141 81 60 137 66 71 138 71 56 251 176 75 243 142 101 244 177 67 243 178 65 193 125 68 192 138 51 178 120 55 130 0 130 193 161 32 196 117 79 198 142 56 0 0 0 253 150 103 237 86 151 250 151 99 243 160 83 243 172 71 165 84 50 152 106 43 151 85 54 121 0 119 157 103 53 159 112 47 0 0 0 3 wisconsin
55001002750002 55001 Adams 275 5500100275 Adams C Adams - C 0002 25 41 41 14 3 0002 652 463 599 6 23 10 12 0 2 0 435 4 7 7 8 0 2 0 1391 480 853 2942 1348 1513 0.3450755 0.6132279 0.4581917 0.5142760 -0.1937373 -0.1694473 -0.1089786 -0.0289703 -0.1107385 0.3450755 -0.0492102 -0.0157941 -0.2250180 -0.2351132 -0.1321972 -0.0487298 0.5388128 0.4429224 0.5145228 0.4439834 0.4540541 0.5297297 0.3740458 0.6259542 0.4545455 0.5454545 0.2916667 0.7083333 0.3703704 0.6111111 0.3875000 0.6125000 0.2954545 0.5056818 0.4558140 0.5441860 0.0000000 0.9937500 0.3942857 0.6057143 0.3608696 0.6391304 0.5555556 0.4444444 0.4328358 0.5671642 1.0000000 0.0000000 0.6447368 0.3421053 0.9843750 0.0000000 0.4633028 0.5366972 0.4728033 0.4560669 0.3794466 0.5731225 0.4347826 0.5434783 0.1864407 0.8135593 0.3750000 0.6250000 0.5700935 0.4299065 0.5801887 0.0000000 0.4772727 0.5227273 0.3938053 0.6061947 0.4222222 0.5111111 0.2676056 0.7323944 0.3090909 0.6909091 0.3333333 0.6666667 0.2934132 0.7065868 0.5186916 0.4626168 0.4463277 0.5141243 0.5111111 0.4888889 0.4259259 0.5740741 0.3393939 0.6545455 0.4660633 0.5339367 0.3973214 0.6026786 0.2571429 0.7428571 0.0000000 0.0000000 0.2987013 0.7012987 0.0000000 0.0000000 219 118 97 216 109 107 212 104 99 215 98 117 218 101 117 214 111 99 214 122 92 241 107 124 160 159 0 239 109 113 212 0 123 221 118 103 185 98 84 171 103 63 166 88 68 175 106 69 177 91 79 176 92 84 169 89 80 262 164 98 230 147 83 253 145 96 226 137 89 224 135 89 44 24 20 45 26 19 45 26 19 45 20 25 46 25 20 45 22 23 45 23 19 72 51 21 67 38 29 71 52 19 70 52 18 54 33 20 54 38 15 50 31 17 34 0 34 59 48 11 54 31 23 55 38 17 0 0 0 80 49 31 76 26 49 80 50 30 78 52 26 77 54 23 176 89 52 161 112 45 161 90 58 128 0 126 165 108 56 167 118 49 0 0 0 3 wisconsin
55001002750003 55001 Adams 275 5500100275 Adams C Adams - C 0003 25 41 41 14 3 0003 288 241 278 6 3 0 1 0 0 0 234 4 2 0 1 0 0 0 2032 743 1253 1359 615 690 0.3656496 0.6166339 0.4525386 0.5077263 -0.1492019 -0.1299256 -0.0748266 -0.0104188 -0.0926837 0.3656496 -0.0267555 0.0029045 -0.2027714 -0.2132978 -0.1153631 -0.0303900 0.5148515 0.4356436 0.4955752 0.4247788 0.4404762 0.5238095 0.3760684 0.6239316 0.4563107 0.5436893 0.3000000 0.7000000 0.3507463 0.6417910 0.4218750 0.5781250 0.3012048 0.4939759 0.4583333 0.5416667 0.0000000 0.9859155 0.3924051 0.6075949 0.3627451 0.6372549 0.5480769 0.4519231 0.4187500 0.5812500 1.0000000 0.0000000 0.6292135 0.3651685 0.9824561 0.0000000 0.4591837 0.5306122 0.4766355 0.4485981 0.3771930 0.5614035 0.4476190 0.5428571 0.1879699 0.8120301 0.4042553 0.5957447 0.5684211 0.4315789 0.5789474 0.0000000 0.4810127 0.5189873 0.3960396 0.6039604 0.4095238 0.5238095 0.2727273 0.7272727 0.3007519 0.6992481 0.3351351 0.6648649 0.3116883 0.6883117 0.5154639 0.4536082 0.4500000 0.5125000 0.5288462 0.4711538 0.4060150 0.5939850 0.3648649 0.6351351 0.4646465 0.5353535 0.4000000 0.6000000 0.2650602 0.7349398 0.0000000 0.0000000 0.3021978 0.6978022 0.0000000 0.0000000 101 52 44 95 48 47 94 46 44 96 44 52 98 45 52 97 50 44 95 54 41 113 48 56 71 70 0 107 48 51 95 0 55 99 53 46 84 44 37 78 46 28 78 40 31 79 48 31 80 41 36 79 41 38 103 55 48 117 73 44 102 65 37 114 64 43 101 61 40 100 60 40 103 56 47 103 62 41 104 62 42 104 47 57 105 57 47 104 49 55 105 55 43 170 119 51 160 93 67 165 120 45 166 122 44 134 86 47 131 92 36 124 81 39 86 0 86 133 108 25 133 79 54 133 93 40 0 0 0 192 111 81 178 65 112 188 112 76 185 123 62 182 127 55 83 41 25 75 50 23 76 41 27 57 0 56 74 47 27 77 53 24 0 0 0 3 wisconsin
55001002750004 55001 Adams 275 5500100275 Adams C Adams - C 0004 25 41 41 14 3 0004 366 306 350 2 5 2 5 0 1 1 298 1 2 2 2 0 0 1 3339 1177 1987 1661 763 857 0.3525007 0.5950883 0.4593618 0.5159542 -0.1929538 -0.1622051 -0.0941012 -0.0216489 -0.1020447 0.3525007 -0.0392518 -0.0118403 -0.2141659 -0.2273312 -0.1270911 -0.0412000 0.5454545 0.4462810 0.5147059 0.4411765 0.4466019 0.5339806 0.3741497 0.6258503 0.4315789 0.5000000 0.2742475 0.6454849 0.3389831 0.5889831 0.4166667 0.5555556 0.3030303 0.5252525 0.4545455 0.5454545 0.0000000 1.0000000 0.3917526 0.6082474 0.3643411 0.6356589 0.5428571 0.4571429 0.4379562 0.5583942 0.9664430 0.0000000 0.6019108 0.3439490 0.9868421 0.0000000 0.4628099 0.5371901 0.4736842 0.4586466 0.3829787 0.5744681 0.4179894 0.5026455 0.1688312 0.7489177 0.3906250 0.5968750 0.5666667 0.4333333 0.5798319 0.0000000 0.4795918 0.5204082 0.3937008 0.6062992 0.3846154 0.5000000 0.2757353 0.7132353 0.3041475 0.6958525 0.3322034 0.6677966 0.2929293 0.7070707 0.5166667 0.4666667 0.4489796 0.5204082 0.5142857 0.4857143 0.4155251 0.5844749 0.3263158 0.6736842 0.4677419 0.5322581 0.4000000 0.6000000 0.2637363 0.7362637 0.0000000 0.0000000 0.2972973 0.7027027 0.0000000 0.0000000 121 66 54 119 60 59 118 58 56 121 55 66 121 56 65 120 62 56 120 68 52 136 60 70 89 89 0 133 61 63 119 0 69 124 66 58 103 55 46 96 58 35 92 49 38 97 59 38 98 51 44 98 51 47 116 60 56 147 92 55 129 82 47 141 81 54 127 77 50 125 75 50 190 95 82 168 101 66 168 97 71 175 80 95 189 95 79 175 85 90 182 91 70 299 193 82 274 153 120 272 194 75 273 201 72 236 139 80 220 145 56 222 130 62 149 0 144 231 173 39 219 128 91 217 151 66 0 0 0 324 180 135 314 108 189 320 191 125 295 197 98 296 208 88 99 52 30 95 67 26 89 52 32 76 0 75 95 64 31 99 70 29 0 0 0 3 wisconsin
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55005143250002 55005 Barron 14325 5500514325 Chetek C Chetek - C 0002 25 75 75 25 7 0002 566 443 545 4 8 0 8 0 1 0 434 2 5 0 2 0 0 0 2819 1305 1486 2855 1549 1221 0.4629301 0.5271373 0.5425569 0.4276708 -0.1099342 -0.1129454 -0.1180755 -0.0288732 -0.1077770 -0.1214320 -0.1620699 -0.0675047 -0.1370699 -0.1267113 -0.1076914 -0.0687155 0.5728643 0.4120603 0.5758755 0.3774319 0.5810056 0.3854749 0.4918033 0.5000000 0.5430464 0.4569536 0.4338843 0.5661157 0.4619883 0.5263158 0.4512195 0.5487805 0.4153846 0.5000000 0.5707071 0.4141414 0.5843621 0.4156379 0.6250000 0.3636364 0.5304348 0.4695652 0.5197368 0.4539474 0.3891213 0.6108787 0.3674699 0.6265060 0.0000000 0.9870130 0.3846154 0.6153846 0.5276382 0.4723618 0.5622490 0.3895582 0.4875000 0.4791667 0.5324675 0.4480519 0.3313253 0.6506024 0.4149378 0.5850622 0.6000000 0.4000000 0.5896414 0.4103586 0.5706215 0.4293785 0.5316456 0.4683544 0.5222930 0.4777070 0.0000000 1.0000000 0.4107143 0.5892857 0.3950617 0.6049383 0.3816794 0.6183206 0.5404040 0.4393939 0.5600000 0.3942857 0.6153846 0.3846154 0.5266272 0.4733728 0.4320000 0.5680000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.4242424 0.5757576 0.0000000 0.0000000 0.4462810 0.5537190 199 114 82 196 105 91 195 104 88 198 113 82 199 105 94 198 107 87 200 120 80 257 97 148 243 101 142 249 97 140 251 103 148 0 0 0 179 69 104 173 77 87 173 66 90 176 64 110 175 69 98 177 76 101 178 67 110 244 122 120 230 108 122 240 115 117 237 111 126 0 0 0 151 69 82 152 74 78 154 73 81 152 69 79 154 69 82 156 60 96 157 75 82 242 137 105 239 146 93 170 170 0 0 0 0 171 90 79 160 95 62 158 87 67 166 104 61 166 108 55 169 80 89 168 99 69 165 95 70 246 135 111 154 152 0 241 141 100 243 147 96 0 0 0 130 65 54 125 70 54 119 62 56 130 80 50 125 71 54 131 81 50 121 67 54 7 wisconsin
55005143250003 55005 Barron 14325 5500514325 Chetek C Chetek - C 0003 25 75 75 25 7 0003 542 407 514 6 12 4 6 0 0 0 391 3 6 2 5 0 0 0 3367 1565 1775 2724 1480 1156 0.4648055 0.5271755 0.5433186 0.4243759 -0.1058752 -0.1147864 -0.1199899 -0.0203009 -0.1058752 -0.1206646 -0.1639371 -0.0670127 -0.1351945 -0.1244062 -0.0999004 -0.0682342 0.5706806 0.4136126 0.5795918 0.3755102 0.5847953 0.3859649 0.4851064 0.4978723 0.5428571 0.4571429 0.4464286 0.5535714 0.4666667 0.5333333 0.4625850 0.5374150 0.4088398 0.4972376 0.5706806 0.4083770 0.5854701 0.4145299 0.6287425 0.3652695 0.5318182 0.4681818 0.5168539 0.4662921 0.3882784 0.6117216 0.3597884 0.6402116 0.0000000 0.9944444 0.3828571 0.6171429 0.5235602 0.4764398 0.5625000 0.3875000 0.4848485 0.4761905 0.5277778 0.4611111 0.3174603 0.6772487 0.4151625 0.5848375 0.6000000 0.4000000 0.5892116 0.4107884 0.5647059 0.4352941 0.5330396 0.4669604 0.5191257 0.4808743 0.0000000 1.0000000 0.4175258 0.5824742 0.3916084 0.6083916 0.3806818 0.6193182 0.5368421 0.4421053 0.5595238 0.3928571 0.6235955 0.3764045 0.5333333 0.4666667 0.4470588 0.5529412 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.4263158 0.5736842 0.0000000 0.0000000 0.4550898 0.5449102 191 109 79 190 101 89 188 100 84 191 109 78 191 100 91 190 102 84 190 114 76 245 92 142 234 97 137 240 93 135 241 99 142 0 0 0 171 66 100 168 74 84 166 63 88 167 61 105 168 66 94 170 74 96 150 48 99 235 117 114 220 103 117 231 110 112 227 106 121 0 0 0 175 80 95 177 87 90 177 85 92 178 83 92 180 83 95 178 67 111 183 88 95 280 155 125 273 167 106 193 193 0 0 0 0 195 104 91 185 109 73 184 103 79 189 121 68 189 128 60 195 91 104 194 113 81 190 109 81 294 158 136 180 179 0 277 162 115 286 174 112 0 0 0 181 90 74 164 93 71 166 86 78 175 108 67 170 94 76 176 109 67 167 91 76 7 wisconsin
55005143250004 55005 Barron 14325 5500514325 Chetek C Chetek - C 0004 25 75 75 25 7 0004 535 431 526 0 6 2 0 1 0 0 427 0 2 1 0 1 0 0 3142 1460 1649 2743 1477 1163 0.4646722 0.5248250 0.5384615 0.4239883 -0.1007728 -0.1021294 -0.1133625 -0.0183787 -0.1037489 -0.1190188 -0.1603278 -0.0647396 -0.1353278 -0.1211019 -0.1033752 -0.0686612 0.5654450 0.4136126 0.5668016 0.3724696 0.5780347 0.3872832 0.4830508 0.4957627 0.5454545 0.4545455 0.4453125 0.5546875 0.4689266 0.5310734 0.4604317 0.5395683 0.4096386 0.4759036 0.5684211 0.4105263 0.5836910 0.4163090 0.6250000 0.3630952 0.5294118 0.4660633 0.5235294 0.4588235 0.3886640 0.6113360 0.3491124 0.6508876 0.0000000 0.9941176 0.3885350 0.6114650 0.5235602 0.4712042 0.5583333 0.3875000 0.4826087 0.4739130 0.5294118 0.4588235 0.3121387 0.6820809 0.4072727 0.5927273 0.6000000 0.4000000 0.5857741 0.4142259 0.5680473 0.4319527 0.5333333 0.4666667 0.5232558 0.4767442 0.0000000 1.0000000 0.4219653 0.5780347 0.3933824 0.6066176 0.3875000 0.6125000 0.5368421 0.4368421 0.5595238 0.3869048 0.6149425 0.3850575 0.5310734 0.4689266 0.4480519 0.5519481 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.4269006 0.5730994 0.0000000 0.0000000 0.4600000 0.5400000 191 108 79 187 100 87 187 100 83 190 108 78 191 100 90 190 102 83 190 114 76 247 92 140 233 97 136 240 93 134 239 99 140 0 0 0 173 67 100 169 74 84 168 64 86 168 61 105 168 65 94 169 73 96 166 60 104 236 117 114 221 103 117 230 109 111 225 105 120 0 0 0 165 75 90 171 82 89 170 81 89 170 78 89 170 78 90 174 67 107 172 82 90 256 142 114 247 151 96 176 176 0 0 0 0 177 94 83 167 98 67 166 93 70 169 110 59 173 118 54 177 83 94 173 100 73 171 98 73 278 150 128 170 169 0 275 163 112 272 165 107 0 0 0 166 79 68 152 84 65 151 77 71 157 96 61 154 85 69 160 98 62 150 81 69 7 wisconsin
55005143500001 55005 Barron 14350 5500514350 Chetek T Chetek - T 0001 43 75 75 25 7 0001 992 863 980 5 4 0 3 0 0 0 857 1 2 0 3 0 0 0 8192 4109 3888 7740 4473 3107 0.5015869 0.4746094 0.5779070 0.4014212 -0.0883411 -0.1134366 -0.1119100 -0.0260592 -0.1082134 -0.1082492 -0.1096397 -0.0409840 -0.1287273 -0.1469754 -0.0854152 -0.0181499 0.5899281 0.3866906 0.6150235 0.3552426 0.6134969 0.3783231 0.5276461 0.4549763 0.5404858 0.4291498 0.4807396 0.4961479 0.4935622 0.4678112 0.5186782 0.4784483 0.4878706 0.4231806 0.6098004 0.3793103 0.6098361 0.3901639 0.6112266 0.3721414 0.5425710 0.4557596 0.5010183 0.4378819 0.4290221 0.5694006 0.3912134 0.5543933 0.0000000 0.9190476 0.4027027 0.5972973 0.5587703 0.4412297 0.5987261 0.3694268 0.4927769 0.4735152 0.5271630 0.4346076 0.3411017 0.5953390 0.4676471 0.5308824 0.6303142 0.3696858 0.6485623 0.3514377 0.5870021 0.4129979 0.5197368 0.4786184 0.5443299 0.4556701 0.0000000 0.9773756 0.4454148 0.5545852 0.4564254 0.5435746 0.3597884 0.6402116 0.5956284 0.3861566 0.5995763 0.3707627 0.6337449 0.3662551 0.5859031 0.4140969 0.4578652 0.5421348 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.4654788 0.5345212 0.0000000 0.0000000 0.4786325 0.5213675 556 328 215 550 324 226 547 317 220 551 336 209 553 309 244 549 327 212 541 341 200 639 227 393 610 238 372 628 232 376 626 220 406 0 0 0 489 185 300 480 198 272 478 183 273 481 179 294 472 175 283 477 197 280 543 207 330 633 288 334 599 273 325 623 295 307 608 291 316 0 0 0 494 212 267 477 225 252 480 214 266 491 215 246 497 216 262 486 178 308 485 221 264 649 322 312 634 361 272 442 432 0 0 0 0 466 218 230 456 240 188 458 202 227 478 265 187 472 281 161 454 188 266 458 254 204 449 240 209 696 333 361 420 386 0 680 361 318 677 368 309 0 0 0 371 157 181 350 187 161 350 161 186 370 221 149 356 193 163 378 242 136 351 183 168 7 wisconsin

Aggregate to district

partisanship_agg <- partisanship %>% 
  group_by(state, district_code) %>% 
  # for example, sum cumulative patisanship vars
  summarise(across(contains("_CUM_") | PERSONS|BLACK|WHITE|HISPANIC| ASIAN|AMINDIAN, sum))

partisanship_agg %>% kablebox()
state district_code TOT_CUM_10 REP_CUM_10 DEM_CUM_10 TOT_CUM_18 REP_CUM_18 DEM_CUM_18 PERSONS BLACK WHITE HISPANIC ASIAN AMINDIAN
wisconsin 1 4437292 2280126 2071947 4537574 2442686 1990024 710876 41925 586219 63234 13020 4069
wisconsin 2 4927711 1623956 3146138 5270304 1596654 3545424 710879 36961 597772 41423 27450 4226
wisconsin 3 4412612 1900706 2387209 4382247 2063236 2192455 710852 9293 662599 14983 15690 6671
wisconsin 4 3993979 1145711 2769431 3970435 967776 2916847 710864 248291 314963 110488 26002 5758
wisconsin 5 5171517 3115365 1956694 5180957 3209380 1861704 710882 14595 636455 35607 18369 3964
wisconsin 6 4764948 2550134 2110076 4646052 2607838 1917625 710866 13186 647625 27087 16314 5058
wisconsin 7 4612601 2190869 2307569 4523226 2472956 1930310 710896 5932 660631 12537 11797 18472
wisconsin 8 4534299 2312703 2120470 4582730 2534497 1926476 710871 10477 632147 30697 15289 20375

Merge in aggregate partisanship

d3 <- d2 %>% left_join(partisanship_agg) %>% arrange(desc(state))

d4 <- d3 %>%
  select(bioname, state, district_code, icpsr,
         # speech
         speeches_n, file,
         # community
         facebook_sci, flow_count,
         # race 
         PERSONS, BLACK, WHITE, HISPANIC, ASIAN, AMINDIAN,
         # partisanship
         starts_with("REP"), starts_with("DEM"), starts_with("OTH")) %>%
  distinct() %>% 
  ungroup()

d4 %>% 
  kablebox()
bioname state district_code icpsr speeches_n file facebook_sci flow_count PERSONS BLACK WHITE HISPANIC ASIAN AMINDIAN REP_CUM_10 REP_CUM_18 DEM_CUM_10 DEM_CUM_18
MOORE, Gwendolynne S. (Gwen) wisconsin 4 20537 1 CREC-2020-07-09-pt1-PgH3078-6-000108-20537.txt 1088428 4389604 710864 248291 314963 110488 26002 5758 1145711 967776 2769431 2916847
HENSARLING, Jeb texas 5 20352 4 CREC-2018-11-14-pt1-PgH9525-000064-20352.txt NA 4465371 NA NA NA NA NA NA NA NA NA NA
HENSARLING, Jeb texas 5 20352 4 CREC-2018-11-14-pt1-PgH9525-000065-20352.txt NA 4465371 NA NA NA NA NA NA NA NA NA NA
HENSARLING, Jeb texas 5 20352 4 CREC-2018-11-14-pt1-PgH9525-000066-20352.txt NA 4465371 NA NA NA NA NA NA NA NA NA NA
HENSARLING, Jeb texas 5 20352 4 CREC-2018-11-14-pt1-PgH9525-000067-20352.txt NA 4465371 NA NA NA NA NA NA NA NA NA NA
POE, Ted texas 2 20528 2 CREC-2018-11-02-pt1-PgE1491-7-000005-20528.txt NA 3906385 NA NA NA NA NA NA NA NA NA NA
POE, Ted texas 2 20528 2 CREC-2018-11-02-pt1-PgE1491-7-000006-20528.txt NA 3906385 NA NA NA NA NA NA NA NA NA NA
OLSON, Pete texas 22 20948 2 CREC-2020-04-22-pt1-PgE380-3-000031-20948.txt NA 4477506 NA NA NA NA NA NA NA NA NA NA
OLSON, Pete texas 22 20948 2 CREC-2020-04-22-pt1-PgE380-3-000032-20948.txt NA 4477506 NA NA NA NA NA NA NA NA NA NA
CASTRO, Joaquin texas 20 21362 7 CREC-2020-04-22-pt1-PgH1904-5-000033-21362.txt NA 5115287 NA NA NA NA NA NA NA NA NA NA
CASTRO, Joaquin texas 20 21362 7 CREC-2020-04-22-pt1-PgH1904-5-000034-21362.txt NA 5115287 NA NA NA NA NA NA NA NA NA NA
CASTRO, Joaquin texas 20 21362 7 CREC-2020-04-22-pt1-PgH1904-5-000035-21362.txt NA 5115287 NA NA NA NA NA NA NA NA NA NA
CASTRO, Joaquin texas 20 21362 7 CREC-2020-04-22-pt1-PgH1904-5-000036-21362.txt NA 5115287 NA NA NA NA NA NA NA NA NA NA
CASTRO, Joaquin texas 20 21362 7 CREC-2020-04-22-pt1-PgH1904-5-000037-21362.txt NA 5115287 NA NA NA NA NA NA NA NA NA NA
CASTRO, Joaquin texas 20 21362 7 CREC-2020-04-22-pt1-PgH1904-5-000038-21362.txt NA 5115287 NA NA NA NA NA NA NA NA NA NA
CASTRO, Joaquin texas 20 21362 7 CREC-2020-04-22-pt1-PgH1904-5-000039-21362.txt NA 5115287 NA NA NA NA NA NA NA NA NA NA
BABIN, Brian texas 36 21551 2 CREC-2020-02-06-pt1-PgE140-4-000014-21551.txt NA 4982044 NA NA NA NA NA NA NA NA NA NA
BABIN, Brian texas 36 21551 2 CREC-2020-02-06-pt1-PgE140-4-000015-21551.txt NA 4982044 NA NA NA NA NA NA NA NA NA NA
ARRINGTON, Jodey Cook texas 19 21700 1 CREC-2020-06-11-pt1-PgH2389-7-000158-21700.txt NA 7055603 NA NA NA NA NA NA NA NA NA NA
GOODEN, Lance texas 5 21925 1 CREC-2020-04-03-pt1-PgH1876-3-000004-21925.txt NA 4465371 NA NA NA NA NA NA NA NA NA NA

Docs

Improve documentation by editing this google sheet

docs <- d2 %>% slice(2) %>%
  #FIXME when we figure out long/wide format 
  left_join(partisanship) %>% ungroup() %>% arrange(desc(state)) 

docs <- tibble(
  variable = names(docs),
  class = lapply(docs, class) %>%
    unlist(., use.names=FALSE),
  example = docs %>% slice(1) %>% 
    mutate_all(as.character()) %>%
    as.matrix() %>% c()
       ) 

# google doc
library(googlesheets4)
gs4_auth(email = "devin.jl@gmail.com")

docs_gs <- read_sheet("https://docs.google.com/spreadsheets/d/1VLX92ZPPMFy3ypocym549vM82SyN27t51-cwcLfIacA")

docs %<>% left_join(docs_gs %>% select(-class, -example))

sheet_write(docs,  "https://docs.google.com/spreadsheets/d/1VLX92ZPPMFy3ypocym549vM82SyN27t51-cwcLfIacA", "docs")


docs %>% 
  kable() %>% 
  kable_styling() %>% 
  scroll_box(height = "600px")
variable class example type source description SQL table
file character CREC-2020-07-09-pt1-PgH3078-6-000108-20537.txt member-behavior congressional record parsed congressional record speech file name in data/txt folder speeches
speech_id character 000108 member-behavior parser id assigned by parser. See https://judgelord.github.io/cr/speakers#Save_text_parsed_by_member_name speeches
icpsr integer 20537 member-key voteview legislator id, unique to a member in a party (party-switchers get a new ICPSR id) speeches, members
date Date 2020-07-09 member-behavior congressional record YYYY-MM-DD speeches
year integer 2020 member-behavior congressional record YYYY speeches
congress numeric 116 member congressional record NA speeches, members
chamber character House member congressional record NA speeches, members
bioname character MOORE, Gwendolynne S. (Gwen) member voteview voteview canonical name, unique to each legislator speeches, members
id character MH11620537 member voteview NA members
party_code integer 100 member-party voteview 100 = Democrat, 200 = Republican members
cqlabel character (WI-04) geographic identification voteview (state_abbrev-district_code) members
state character wisconsin geographic identification voteview NA members
state_abbrev character WI geographic identification voteview NA members
bioImgURL character 020537.jpg member voteview https://github.com/voteview/Rvoteview/blob/master/vignettes/README.md members
seo_name character gwendolynne-s-gwen-moore member voteview https://github.com/voteview/Rvoteview/blob/master/vignettes/README.md members
district_code numeric 4 geographic identification voteview Integer 0-99. Identifier for the district that the member represents within their state (e.g. 3 for the Alabama 3rd Congressional District). Senate members are given district_code 0. Members who represent historical “at-large” districts are assigned 99, 98, or 1 in various https://github.com/voteview/Rvoteview/blob/master/vignettes/README.md members, connectivity, demographics, partisanship
party_name character Democratic Party member-party voteview Integer 1-9999. Identifying code for the member’s party. Please see documentation for Party Data for more information about which party_code identifiers refer to which parties. members
nominate.dim2 numeric -0.249 member-behavior voteview https://github.com/voteview/Rvoteview/blob/master/vignettes/README.md members
nominate.dim1 numeric -0.528 member-behavior voteview https://github.com/voteview/Rvoteview/blob/master/vignettes/README.md members
nominate.geo_mean_probability numeric 0.9612078 member-behavior voteview https://github.com/voteview/Rvoteview/blob/master/vignettes/README.md members
party_size integer 236 member-party voteview https://github.com/voteview/Rvoteview/blob/master/vignettes/README.md members
speeches_n integer 1 member-behavior congressional record total number of speeches per icpsr YYYY-YYYY members
facebook_sci integer 1088428 connectivity https://data.humdata.org within-district facebook connections. The SCI uses an anonymized snapshot of active Facebook users and their friendship networks to measure the intensity of social connectedness between locations. Users are assigned to locations based on their information and activity on Facebook, including the stated city on their Facebook profile, and device and connection information. Formally, the Social Connectedness Index between two locations i and j is defined as: SCIi,j = Connections(i,j) Usersi ∗ Usersj Where Usersi and Usersj are the number of Facebook users in locations i and j, and Connections(i,j) is the total number of Facebook friendship connections between individuals in the two locations. connectivity
flow_count numeric 4389604 connectivity https://www.safegraph.com/ within-district travel connectivity
flow_count_border numeric 667951 connectivity https://www.safegraph.com/ within-district travel in border census blocks NA
facebook_sci_out integer 180506 NA NA NA NA
flow_count_out numeric 2103750 NA NA NA NA
flow_count_border_out numeric 620362 NA NA NA NA
facebook_sci_ratio numeric 6.029872 NA NA NA NA
flow_ratio numeric 2.086562 NA NA NA NA
flow_ratio_border numeric 1.076712 NA NA NA NA
GEO_ID character 5001500US5504 geographic identification US Census us congress district unique id demographics
district_name character Congressional District 4 (115th Congress), Wisconsin district-demographics US Census NA demographics
name character SF1DP1002 district-demographics US Census NA demographics
value numeric 100 district-demographics US Census party code? demographics
format character Percent district-demographics US Census “Number” or “Percent” (row 2 of the CD_DEMO_DATA") demographics
type character SEX AND AGE district-demographics US Census Census variable catagory (row 3 of CD_DEMO_DATA) demographics
measure character Total population district-demographics US Census Row 4 of CD_DEMO_DATA demographics
group character NA district-demographics US Census Row 5 of CD_DEMO_DATA demographics
GEOID character 55079054500001 geographic identification WI LTSB/US Census ward unique id demographics
CNTY_FIPS numeric 55079 geographic identification WI LTSB/US Census county unique id demographics
CNTY_NAME character Milwaukee geographic identification WI LTSB/US Census county name demographics
COUSUBFP numeric 5450 geographic identification WI LTSB/US Census county sub id demographics
MCD_FIPS numeric 5507905450 geographic identification WI LTSB/US Census mcd unique id demographics
MCD_NAME character Bayside geographic identification WI LTSB/US Census mcd name demographics
CTV character V geographic identification WI LTSB/US Census type value demographics
LABEL character Bayside - V 0001 geographic identification WI LTSB/US Census precinct label demographics
LSAD numeric 47 geographic identification WI LTSB/US Census designation demographics
DISTRICT numeric 23 geographic identification WI LTSB/US Census assembly district demographics
ASM numeric 23 geographic identification WI LTSB/US Census assembly district demographics
SEN numeric 8 geographic identification WI LTSB/US Census senate district demographics
CON numeric 4 geographic identification WI LTSB/US Census congressional district demographics
STR_WARDS character 0001 geographic identification WI LTSB/US Census ward simple demographics
PERSONS numeric 938 ward demographics WI LTSB/US Census total persons demographics
PERSONS18 numeric 785 ward demographics WI LTSB/US Census total persons over 18 demographics
WHITE numeric 775 ward demographics WI LTSB/US Census white persons demographics
BLACK numeric 61 ward demographics WI LTSB/US Census black persons demographics
HISPANIC numeric 24 ward demographics WI LTSB/US Census hispanic persons demographics
ASIAN numeric 67 ward demographics WI LTSB/US Census asian persons demographics
AMINDIAN numeric 3 ward demographics WI LTSB/US Census native american persons demographics
PISLAND numeric 0 ward demographics WI LTSB/US Census pacific islander persons demographics
OTHER numeric 5 ward demographics WI LTSB/US Census other persons demographics
OTHERMLT numeric 3 ward demographics WI LTSB/US Census other persons demographics
WHITE18 numeric 664 ward demographics WI LTSB/US Census white persons over 18 demographics
BLACK18 numeric 45 ward demographics WI LTSB/US Census black persons over 18 demographics
HISPANIC18 numeric 19 ward demographics WI LTSB/US Census hispanic persons over 18 demographics
ASIAN18 numeric 50 ward demographics WI LTSB/US Census asian persons over 18 demographics
AMINDIAN18 numeric 2 ward demographics WI LTSB/US Census native american persons over 18 demographics
PISLAND18 numeric 0 ward demographics WI LTSB/US Census pacific islander persons over 18 demographics
OTHER18 numeric 2 ward demographics WI LTSB/US Census other persons over 18 demographics
OTHERMLT18 numeric 3 ward demographics WI LTSB/US Census other persons over 18 demographics
TOT_CUM_10 numeric 9044 cumulative value Calculated cumulative raw vote of statewide elections from 2002-2010 under the 2002 district maps partisanship
REP_CUM_10 numeric 3357 cumulative value Calculated cumulative raw vote of statewide elections for rep candidates from 2002-2010 under the 2002 district maps partisanship
DEM_CUM_10 numeric 5576 cumulative value Calculated cumulative raw vote of statewide elections for dem candidates from 2002-2010 under the 2002 district maps partisanship
TOT_CUM_18 numeric 7500 cumulative value Calculated cumulative raw vote of statewide elections from 2012-2020 under the 2012 district maps partisanship
REP_CUM_18 numeric 2900 cumulative value Calculated cumulative raw vote of statewide elections for rep candidates from 2012-2020 under the 2012 district maps partisanship
DEM_CUM_18 numeric 4454 cumulative value Calculated cumulative raw vote of statewide elections for dem candidates from 2012-2020 under the 2012 district maps partisanship
REP_PCT_PROJECTION numeric 0.3711853 cumulative value Calculated unweighted percentage projection of rep vote share based on previous ten years - 2002-2010 included partisanship
DEM_PCT_PROJECTION numeric 0.6165414 cumulative value Calculated unweighted percentage projection of dem vote share based on previous ten years - 2002-2010 included partisanship
REP_PCT_PROJ_18 numeric 0.3866667 cumulative value Calculated unweighted percentage projection of rep vote share based on previous ten years - same but from 2012-2018 partisanship
DEM_PCT_PROJ_18 numeric 0.5938667 cumulative value Calculated unweighted percentage projection of dem vote share based on previous ten years - same but from 2012-2018 partisanship
REP_PCT_DIF_GOV18 numeric 0.01599406 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-GOVREP18_P partisanship
REP_PCT_DIF_PRE16 numeric 0.05138836 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-PREREP16_P partisanship
REP_PCT_DIF_GOV14 numeric -0.07963436 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-GOVREP14_P partisanship
REP_PCT_DIF_PRE12 numeric -0.04898275 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-PREREP12_P partisanship
REP_PCT_DIF_USH18 numeric 0.04706009 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-USHREP18_P partisanship
REP_PCT_DIF_USH16 numeric 0.3711853 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-USHREP16_P partisanship
REP_PCT_DIF_USH14 numeric -0.06974295 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-USHREP14_P partisanship
REP_PCT_DIF_USH12 numeric -0.0740837 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-USHREP12_P partisanship
REP_PCT_DIF_WSA18 numeric 0.006716452 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-WSAREP18_P partisanship
REP_PCT_DIF_WSA16 numeric -0.584955 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-WSAREP16_P partisanship
REP_PCT_DIF_WSA14 numeric -0.110768 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-WSAREP14_P partisanship
REP_PCT_DIF_WSA12 numeric -0.1111895 cumulative value Calculated difference in projection and specific race - REP_PCT_PROJECTION-WSAREP12_P partisanship
GOVREP18_P numeric 0.3551913 election result WI LTSB governor rep % vote share 2018 partisanship
GOVDEM18_P numeric 0.6284153 election result WI LTSB governor dem % vote share 2018 partisanship
PREREP16_P numeric 0.319797 election result WI LTSB president rep % vote share 2016 partisanship
PREDEM16_P numeric 0.6175973 election result WI LTSB president dem % vote share 2016 partisanship
GOVREP14_P numeric 0.4508197 election result WI LTSB governor 2014 p partisanship
GOVDEM14_P numeric 0.5409836 election result WI LTSB governor 2014 p partisanship
PREREP12_P numeric 0.4201681 election result WI LTSB president 2012 p partisanship
PREDEM12_P numeric 0.5663866 election result WI LTSB president 2012 p partisanship
GOVREP10_P numeric 0.4046346 election result WI LTSB governor 2010 p partisanship
GOVDEM10_P numeric 0.5918004 election result WI LTSB governor 2010 p partisanship
PREREP08_P numeric 0.366306 election result WI LTSB president 2008 p partisanship
PREDEM08_P numeric 0.6228748 election result WI LTSB president 2008 p partisanship
GOVREP06_P numeric 0.3412229 election result WI LTSB governor 2006 p partisanship
GOVDEM06_P numeric 0.6508876 election result WI LTSB governor 2006 p partisanship
PREREP04_P numeric 0.4119318 election result WI LTSB president 2004 p partisanship
PREDEM04_P numeric 0.5795455 election result WI LTSB president 2004 p partisanship
GOVREP02_P numeric 0.375 election result WI LTSB governor 2002 p partisanship
GOVDEM02_P numeric 0.5791667 election result WI LTSB governor 2002 p partisanship
USHREP18_P numeric 0.3241252 election result WI LTSB us congress rep % vote share 2018 partisanship
USHDEM18_P numeric 0.6556169 election result WI LTSB us congress dem % vote share 2018 partisanship
USHREP16_P numeric 0 election result WI LTSB ush 2016 p partisanship
USHDEM16_P numeric 0.6613546 election result WI LTSB ush 2016 p partisanship
USHREP14_P numeric 0.4409283 election result WI LTSB ush 2014 p partisanship
USHDEM14_P numeric 0.535865 election result WI LTSB ush 2014 p partisanship
USHREP12_P numeric 0.445269 election result WI LTSB ush 2012 p partisanship
USHDEM12_P numeric 0.5269017 election result WI LTSB ush 2012 p partisanship
USHREP10_P numeric 0.4670433 election result WI LTSB ush 2010 p partisanship
USHDEM10_P numeric 0.5028249 election result WI LTSB ush 2010 p partisanship
USHREP08_P numeric 0.6598639 election result WI LTSB ush 2008 p partisanship
USHDEM08_P numeric 0 election result WI LTSB ush 2008 p partisanship
USHREP06_P numeric 0.3798768 election result WI LTSB ush 2006 p partisanship
USHDEM06_P numeric 0.6119097 election result WI LTSB ush 2006 p partisanship
USHREP04_P numeric 0.4641288 election result WI LTSB ush 2004 p partisanship
USHDEM04_P numeric 0.5241581 election result WI LTSB ush 2004 p partisanship
USHREP02_P numeric 0.7802198 election result WI LTSB ush 2002 p partisanship
USHDEM02_P numeric 0 election result WI LTSB ush 2002 p partisanship
USSREP18_P numeric 0.3169399 election result WI LTSB us senate rep % vote share 2018 partisanship
USSDEM18_P numeric 0.6812386 election result WI LTSB us senate dem % vote share 2018 partisanship
USSREP16_P numeric 0.3924915 election result WI LTSB uss 2016 p partisanship
USSDEM16_P numeric 0.5955631 election result WI LTSB uss 2016 p partisanship
USSREP12_P numeric 0.4421769 election result WI LTSB uss 2012 p partisanship
USSDEM12_P numeric 0.5459184 election result WI LTSB uss 2012 p partisanship
USSREP10_P numeric 0.3838028 election result WI LTSB uss 2010 p partisanship
USSDEM10_P numeric 0.6161972 election result WI LTSB uss 2010 p partisanship
USSREP06_P numeric 0.212 election result WI LTSB uss 2006 p partisanship
USSDEM06_P numeric 0.772 election result WI LTSB uss 2006 p partisanship
USSREP04_P numeric 0.3171429 election result WI LTSB uss 2004 p partisanship
USSDEM04_P numeric 0.6814286 election result WI LTSB uss 2004 p partisanship
WSAREP18_P numeric 0.3644689 election result WI LTSB wisconsin state assembly rep % vote share 2018 partisanship
WSADEM18_P numeric 0.6336996 election result WI LTSB wisconsin state assembly dem % vote share 2018 partisanship
WSAREP16_P numeric 0.9561404 election result WI LTSB wsa 2016 p partisanship
WSADEM16_P numeric 0 election result WI LTSB wsa 2016 p partisanship
WSAREP14_P numeric 0.4819533 election result WI LTSB wsa 2014 p partisanship
WSADEM14_P numeric 0.5159236 election result WI LTSB wsa 2014 p partisanship
WSAREP12_P numeric 0.4823748 election result WI LTSB wsa 2012 p partisanship
WSADEM12_P numeric 0.5157699 election result WI LTSB wsa 2012 p partisanship
WSAREP10_P numeric 0.9868852 election result WI LTSB wsa 2010 p partisanship
WSADEM10_P numeric 0 election result WI LTSB wsa 2010 p partisanship
WSAREP08_P numeric 0.500821 election result WI LTSB wsa 2008 p partisanship
WSADEM08_P numeric 0.4975369 election result WI LTSB wsa 2008 p partisanship
WSAREP06_P numeric 0.378 election result WI LTSB wsa 2006 p partisanship
WSADEM06_P numeric 0.622 election result WI LTSB wsa 2006 p partisanship
WSAREP04_P numeric 0.9595687 election result WI LTSB wsa 2004 p partisanship
WSADEM04_P numeric 0 election result WI LTSB wsa 2004 p partisanship
WSAREP02_P numeric 0.458613 election result WI LTSB wsa 2002 p partisanship
WSADEM02_P numeric 0.541387 election result WI LTSB wsa 2002 p partisanship
WAGREP18_P numeric 0.3449541 election result WI LTSB wisconsin attorney general rep % vote share 2018 partisanship
WAGDEM18_P numeric 0.6385321 election result WI LTSB wisconsin attorney general dem % vote share 2018 partisanship
WAGREP14_P numeric 0.434238 election result WI LTSB wag 2014 p partisanship
WAGDEM14_P numeric 0.5427975 election result WI LTSB wag 2014 p partisanship
WAGREP10_P numeric 0.4757463 election result WI LTSB wag 2010 p partisanship
WAGDEM10_P numeric 0.5242537 election result WI LTSB wag 2010 p partisanship
WAGREP06_P numeric 0.392 election result WI LTSB wag 2006 p partisanship
WAGDEM06_P numeric 0.606 election result WI LTSB wag 2006 p partisanship
WAGREP02_P numeric 0.4313305 election result WI LTSB wag 2002 p partisanship
WAGDEM02_P numeric 0.5686695 election result WI LTSB wag 2002 p partisanship
WSSREP16_P numeric 0.9404762 election result WI LTSB wisconsin state senate rep % vote share 2016 partisanship
WSSDEM16_P numeric 0 election result WI LTSB wisconsin state senate dem % vote share 2016 partisanship
WSSREP12_P numeric 0.888563 election result WI LTSB wss 2012 p partisanship
WSSDEM12_P numeric 0.01759531 election result WI LTSB wss 2012 p partisanship
WSSREP08_P numeric 0.3637795 election result WI LTSB wss 2008 p partisanship
WSSDEM08_P numeric 0.6346457 election result WI LTSB wss 2008 p partisanship
WSSREP06_P numeric 0 election result WI LTSB wss 2006 p partisanship
WSSDEM06_P numeric 0 election result WI LTSB wss 2006 p partisanship
WSSREP04_P numeric 0.489019 election result WI LTSB wss 2004 p partisanship
WSSDEM04_P numeric 0.510981 election result WI LTSB wss 2004 p partisanship
WSSREP02_P numeric 0 election result WI LTSB wss 2002 p partisanship
WSSDEM02_P numeric 0 election result WI LTSB wss 2002 p partisanship
GOVTOT18 numeric 549 election result WI LTSB governor total raw votes 2018 partisanship
GOVREP18 numeric 195 election result WI LTSB governor rep raw votes 2018 partisanship
GOVDEM18 numeric 345 election result WI LTSB governor dem raw votes 2018 partisanship
SOSTOT18 numeric 544 election result WI LTSB secretary of state total raw votes 2018 partisanship
SOSREP18 numeric 179 election result WI LTSB secretary of state rep raw votes 2018 partisanship
SOSDEM18 numeric 363 election result WI LTSB secretary of state dem raw votes 2018 partisanship
TRSTOT18 numeric 534 election result WI LTSB treasurer total raw votes 2018 partisanship
TRSREP18 numeric 174 election result WI LTSB treasurer rep raw votes 2018 partisanship
TRSDEM18 numeric 352 election result WI LTSB treasurer dem raw votes 2018 partisanship
USHTOT18 numeric 543 election result WI LTSB us house total raw votes 2018 partisanship
USHREP18 numeric 176 election result WI LTSB us house rep raw votes 2018 partisanship
USHDEM18 numeric 356 election result WI LTSB us house dem raw votes 2018 partisanship
USSTOT18 numeric 549 election result WI LTSB us senate total raw votes 2018 partisanship
USSREP18 numeric 174 election result WI LTSB us senate rep raw votes 2018 partisanship
USSDEM18 numeric 374 election result WI LTSB us senate dem raw votes 2018 partisanship
WAGTOT18 numeric 545 election result WI LTSB attorney general total raw votes 2018 partisanship
WAGREP18 numeric 188 election result WI LTSB attorney general rep raw votes 2018 partisanship
WAGDEM18 numeric 348 election result WI LTSB attorney general dem raw votes 2018 partisanship
WSATOT18 numeric 546 election result WI LTSB state assembly total raw votes 2018 partisanship
WSAREP18 numeric 199 election result WI LTSB state assembly rep raw votes 2018 partisanship
WSADEM18 numeric 346 election result WI LTSB state assembly dem raw votes 2018 partisanship
PRETOT16 numeric 591 election result WI LTSB president total raw votes 2016 partisanship
PREDEM16 numeric 365 election result WI LTSB president dem raw votes 2016 partisanship
PREREP16 numeric 189 election result WI LTSB president rep raw votes 2016 partisanship
USHTOT16 numeric 502 election result WI LTSB us house total raw votes 2016 partisanship
USHDEM16 numeric 332 election result WI LTSB us house dem raw votes 2016 partisanship
USHREP16 numeric 0 election result WI LTSB us house rep raw votes 2016 partisanship
USSTOT16 numeric 586 election result WI LTSB us senate total raw votes 2016 partisanship
USSDEM16 numeric 349 election result WI LTSB us senate dem raw votes 2016 partisanship
USSREP16 numeric 230 election result WI LTSB us senate rep raw votes 2016 partisanship
WSATOT16 numeric 342 election result WI LTSB state assembly total raw votes 2016 partisanship
WSADEM16 numeric 0 election result WI LTSB state assembly dem raw votes 2016 partisanship
WSAREP16 numeric 327 election result WI LTSB state assembly rep raw votes 2016 partisanship
WSSTOT16 numeric 336 election result WI LTSB state senate total raw votes 2016 partisanship
WSSDEM16 numeric 0 election result WI LTSB state senate dem raw votes 2016 partisanship
WSSREP16 numeric 316 election result WI LTSB state senate rep raw votes 2016 partisanship
GOVTOT14 numeric 488 election result WI LTSB governor total raw votes 2014 partisanship
GOVDEM14 numeric 264 election result WI LTSB governor dem raw votes 2014 partisanship
GOVREP14 numeric 220 election result WI LTSB governor rep raw votes 2014 partisanship
SOSTOT14 numeric 475 election result WI LTSB secretary of state total raw votes 2014 partisanship
SOSDEM14 numeric 266 election result WI LTSB secretary of state dem raw votes 2014 partisanship
SOSREP14 numeric 190 election result WI LTSB secretary of state rep raw votes 2014 partisanship
TRSTOT14 numeric 465 election result WI LTSB treasurer total raw votes 2014 partisanship
TRSDEM14 numeric 244 election result WI LTSB treasurer dem raw votes 2014 partisanship
TRSREP14 numeric 199 election result WI LTSB treasurer rep raw votes 2014 partisanship
USHTOT14 numeric 474 election result WI LTSB us house total raw votes 2014 partisanship
USHDEM14 numeric 254 election result WI LTSB us house dem raw votes 2014 partisanship
USHREP14 numeric 209 election result WI LTSB us house rep raw votes 2014 partisanship
WAGTOT14 numeric 479 election result WI LTSB attorney general total raw votes 2014 partisanship
WAGDEM14 numeric 260 election result WI LTSB attorney general dem raw votes 2014 partisanship
WAGREP14 numeric 208 election result WI LTSB attorney general rep raw votes 2014 partisanship
WSATOT14 numeric 471 election result WI LTSB state assembly total raw votes 2014 partisanship
WSADEM14 numeric 243 election result WI LTSB state assembly dem raw votes 2014 partisanship
WSAREP14 numeric 227 election result WI LTSB state assembly rep raw votes 2014 partisanship
GOVTOT12 numeric 512 election result WI LTSB governor total raw votes 2012 partisanship
GOVDEM12 numeric 266 election result WI LTSB governor dem raw votes 2012 partisanship
GOVREP12 numeric 244 election result WI LTSB governor rep raw votes 2012 partisanship
PRETOT12 numeric 595 election result WI LTSB president total raw votes 2012 partisanship
PREDEM12 numeric 337 election result WI LTSB president dem raw votes 2012 partisanship
PREREP12 numeric 250 election result WI LTSB president rep raw votes 2012 partisanship
USHTOT12 numeric 539 election result WI LTSB us house total raw votes 2012 partisanship
USHDEM12 numeric 284 election result WI LTSB us house dem raw votes 2012 partisanship
USHREP12 numeric 240 election result WI LTSB us house rep raw votes 2012 partisanship
USSTOT12 numeric 588 election result WI LTSB us senate total raw votes 2012 partisanship
USSDEM12 numeric 321 election result WI LTSB us senate dem raw votes 2012 partisanship
USSREP12 numeric 260 election result WI LTSB us senate rep raw votes 2012 partisanship
WSATOT12 numeric 539 election result WI LTSB state assembly total raw votes 2012 partisanship
WSADEM12 numeric 278 election result WI LTSB state assembly dem raw votes 2012 partisanship
WSAREP12 numeric 260 election result WI LTSB state assembly rep raw votes 2012 partisanship
WSSTOT12 numeric 341 election result WI LTSB state senate total raw votes 2012 partisanship
WSSDEM12 numeric 6 election result WI LTSB state senate dem raw votes 2012 partisanship
WSSREP12 numeric 303 election result WI LTSB state senate rep raw votes 2012 partisanship
GOVTOT10 numeric 561 election result WI LTSB governor total raw votes 2010 partisanship
GOVDEM10 numeric 332 election result WI LTSB governor dem raw votes 2010 partisanship
GOVREP10 numeric 227 election result WI LTSB governor rep raw votes 2010 partisanship
SOSTOT10 numeric 515 election result WI LTSB secretary of state total raw votes 2010 partisanship
SOSDEM10 numeric 334 election result WI LTSB secretary of state dem raw votes 2010 partisanship
SOSREP10 numeric 180 election result WI LTSB secretary of state rep raw votes 2010 partisanship
TRSTOT10 numeric 502 election result WI LTSB treasurer total raw votes 2010 partisanship
TRSDEM10 numeric 290 election result WI LTSB treasurer dem raw votes 2010 partisanship
TRSREP10 numeric 210 election result WI LTSB treasurer rep raw votes 2010 partisanship
USHTOT10 numeric 531 election result WI LTSB us house total raw votes 2010 partisanship
USHDEM10 numeric 267 election result WI LTSB us house dem raw votes 2010 partisanship
USHREP10 numeric 248 election result WI LTSB us house rep raw votes 2010 partisanship
USSTOT10 numeric 568 election result WI LTSB us senate total raw votes 2010 partisanship
USSDEM10 numeric 350 election result WI LTSB us senate dem raw votes 2010 partisanship
USSREP10 numeric 218 election result WI LTSB us senate rep raw votes 2010 partisanship
WAGTOT10 numeric 536 election result WI LTSB attorney general total raw votes 2010 partisanship
WAGDEM10 numeric 281 election result WI LTSB attorney general dem raw votes 2010 partisanship
WAGREP10 numeric 255 election result WI LTSB attorney general rep raw votes 2010 partisanship
WSATOT10 numeric 305 election result WI LTSB state assembly total raw votes 2010 partisanship
WSADEM10 numeric 0 election result WI LTSB state assembly dem raw votes 2010 partisanship
WSAREP10 numeric 301 election result WI LTSB state assembly rep raw votes 2010 partisanship
PRETOT08 numeric 647 election result WI LTSB president total raw votes 2008 partisanship
PREDEM08 numeric 403 election result WI LTSB president dem raw votes 2008 partisanship
PREREP08 numeric 237 election result WI LTSB president rep raw votes 2008 partisanship
USHTOT08 numeric 441 election result WI LTSB us house total raw votes 2008 partisanship
USHDEM08 numeric 0 election result WI LTSB us house dem raw votes 2008 partisanship
USHREP08 numeric 291 election result WI LTSB us house rep raw votes 2008 partisanship
WSATOT08 numeric 609 election result WI LTSB state assembly total raw votes 2008 partisanship
WSADEM08 numeric 303 election result WI LTSB state assembly dem raw votes 2008 partisanship
WSAREP08 numeric 305 election result WI LTSB state assembly rep raw votes 2008 partisanship
WSSTOT08 numeric 635 election result WI LTSB state senate total raw votes 2008 partisanship
WSSDEM08 numeric 403 election result WI LTSB state senate dem raw votes 2008 partisanship
WSSREP08 numeric 231 election result WI LTSB state senate rep raw votes 2008 partisanship
GOVTOT06 numeric 507 election result WI LTSB governor total raw votes 2006 partisanship
GOVDEM06 numeric 330 election result WI LTSB governor dem raw votes 2006 partisanship
GOVREP06 numeric 173 election result WI LTSB governor rep raw votes 2006 partisanship
SOSTOT06 numeric 484 election result WI LTSB secretary of state total raw votes 2006 partisanship
SOSDEM06 numeric 332 election result WI LTSB secretary of state dem raw votes 2006 partisanship
SOSREP06 numeric 136 election result WI LTSB secretary of state rep raw votes 2006 partisanship
TRSTOT06 numeric 478 election result WI LTSB treasurer total raw votes 2006 partisanship
TRSDEM06 numeric 285 election result WI LTSB treasurer dem raw votes 2006 partisanship
TRSREP06 numeric 177 election result WI LTSB treasurer rep raw votes 2006 partisanship
USHTOT06 numeric 487 election result WI LTSB us house total raw votes 2006 partisanship
USHDEM06 numeric 298 election result WI LTSB us house dem raw votes 2006 partisanship
USHREP06 numeric 185 election result WI LTSB us house rep raw votes 2006 partisanship
USSTOT06 numeric 500 election result WI LTSB us senate total raw votes 2006 partisanship
USSDEM06 numeric 386 election result WI LTSB us senate dem raw votes 2006 partisanship
USSREP06 numeric 106 election result WI LTSB us senate rep raw votes 2006 partisanship
WAGTOT06 numeric 500 election result WI LTSB attorney general total raw votes 2006 partisanship
WAGDEM06 numeric 303 election result WI LTSB attorney general dem raw votes 2006 partisanship
WAGREP06 numeric 196 election result WI LTSB attorney general rep raw votes 2006 partisanship
WSATOT06 numeric 500 election result WI LTSB state assembly total raw votes 2006 partisanship
WSADEM06 numeric 311 election result WI LTSB state assembly dem raw votes 2006 partisanship
WSAREP06 numeric 189 election result WI LTSB state assembly rep raw votes 2006 partisanship
WSSTOT06 numeric 0 election result WI LTSB state senate total raw votes 2006 partisanship
WSSDEM06 numeric 0 election result WI LTSB state senate dem raw votes 2006 partisanship
WSSREP06 numeric 0 election result WI LTSB state senate rep raw votes 2006 partisanship
PRETOT04 numeric 704 election result WI LTSB president total raw votes 2004 partisanship
PREDEM04 numeric 408 election result WI LTSB president dem raw votes 2004 partisanship
PREREP04 numeric 290 election result WI LTSB president rep raw votes 2004 partisanship
USHTOT04 numeric 683 election result WI LTSB us house total raw votes 2004 partisanship
USHDEM04 numeric 358 election result WI LTSB us house dem raw votes 2004 partisanship
USHREP04 numeric 317 election result WI LTSB us house rep raw votes 2004 partisanship
USSTOT04 numeric 700 election result WI LTSB us senate total raw votes 2004 partisanship
USSDEM04 numeric 477 election result WI LTSB us senate dem raw votes 2004 partisanship
USSREP04 numeric 222 election result WI LTSB us senate rep raw votes 2004 partisanship
WSATOT04 numeric 371 election result WI LTSB state assembly total raw votes 2004 partisanship
WSADEM04 numeric 0 election result WI LTSB state assembly dem raw votes 2004 partisanship
WSAREP04 numeric 356 election result WI LTSB state assembly rep raw votes 2004 partisanship
WSSTOT04 numeric 683 election result WI LTSB state senate total raw votes 2004 partisanship
WSSDEM04 numeric 349 election result WI LTSB state senate dem raw votes 2004 partisanship
WSSREP04 numeric 334 election result WI LTSB state senate rep raw votes 2004 partisanship
GOVTOT02 numeric 480 election result WI LTSB governor total raw votes 2002 partisanship
GOVDEM02 numeric 278 election result WI LTSB governor dem raw votes 2002 partisanship
GOVREP02 numeric 180 election result WI LTSB governor rep raw votes 2002 partisanship
SOSTOT02 numeric 451 election result WI LTSB secretary of state total raw votes 2002 partisanship
SOSDEM02 numeric 298 election result WI LTSB secretary of state dem raw votes 2002 partisanship
SOSREP02 numeric 150 election result WI LTSB secretary of state rep raw votes 2002 partisanship
TRSTOT02 numeric 445 election result WI LTSB treasurer total raw votes 2002 partisanship
TRSDEM02 numeric 224 election result WI LTSB treasurer dem raw votes 2002 partisanship
TRSREP02 numeric 199 election result WI LTSB treasurer rep raw votes 2002 partisanship
USHTOT02 numeric 364 election result WI LTSB us house total raw votes 2002 partisanship
USHDEM02 numeric 0 election result WI LTSB us house dem raw votes 2002 partisanship
USHREP02 numeric 284 election result WI LTSB us house rep raw votes 2002 partisanship
WAGTOT02 numeric 466 election result WI LTSB attorney general total raw votes 2002 partisanship
WAGDEM02 numeric 265 election result WI LTSB attorney general dem raw votes 2002 partisanship
WAGREP02 numeric 201 election result WI LTSB attorney general rep raw votes 2002 partisanship
WSATOT02 numeric 447 election result WI LTSB state assembly total raw votes 2002 partisanship
WSADEM02 numeric 242 election result WI LTSB state assembly dem raw votes 2002 partisanship
WSAREP02 numeric 205 election result WI LTSB state assembly rep raw votes 2002 partisanship
WSSTOT02 numeric 0 election result WI LTSB state senate total raw votes 2002 partisanship
WSSDEM02 numeric 0 election result WI LTSB state senate dem raw votes 2002 partisanship
WSSREP02 numeric 0 election result WI LTSB state senate rep raw votes 2002 partisanship

Toy Data

member_district <- d4

# RDATA 
save(member_district, file = here::here("data", "member_district_example.Rdata"))

# CSV
write_csv(member_district, path = here::here("data", "member_district_example.csv"))

# SQLite database
library(DBI)
library(RSQLite)
con <- dbConnect(RSQLite::SQLite(), here::here("data", "member_district_example.sqlite"))

dbWriteTable(con, "members", members, overwrite = T)

dbWriteTable(con, "speeches", speeches, overwrite = T)

dbWriteTable(con, "connectivity", c, overwrite = T)

dbWriteTable(con, "demographics", dem, overwrite = T)

dbWriteTable(con, "partisanship", partisanship, overwrite = T)

# Example SQL fetch results:
speeches2020 <- dbGetQuery(con, "SELECT * FROM speeches WHERE year = '2020'") 
dbListTables(con)
## [1] "connectivity" "demographics" "members"      "partisanship" "speeches"
dbDisconnect(con)