Loading in raw 2020 election data for FL01
Escambia_County <- read_tsv("~/Desktop/FL-01 Data/ESC_PctResults20201103.txt", col_names = c("COUNTY CODE", "COUNTY NAME", "ELECTION #", "ELECTION DATE", "ELECTION NAME", "PRECINCT IDENTIFIER", "PRECINCT POLLING LOCATION", "TOTAL REG. VOTERS", "TOTAL REG. REP.", "TOTAL REG. DEM.", "TOTAL REG. OTHER PARTY", "CONTEST NAME", "DISTRICT", "CONTEST CODE", "CANDIDATE", "CANDIDATE PARTY", "REG. ID #", "DOE ASSIGNED CANDIDATE #", "VOTE TOTAL"))
## Rows: 7464 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (9): COUNTY CODE, COUNTY NAME, ELECTION DATE, ELECTION NAME, PRECINCT P...
## dbl (10): ELECTION #, PRECINCT IDENTIFIER, TOTAL REG. VOTERS, TOTAL REG. REP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Okaloosa_County <- read_tsv("~/Desktop/FL-01 Data/OKA_PctResults20201103.txt", col_names = c("COUNTY CODE", "COUNTY NAME", "ELECTION #", "ELECTION DATE", "ELECTION NAME", "PRECINCT IDENTIFIER", "PRECINCT POLLING LOCATION", "TOTAL REG. VOTERS", "TOTAL REG. REP.", "TOTAL REG. DEM.", "TOTAL REG. OTHER PARTY", "CONTEST NAME", "DISTRICT", "CONTEST CODE", "CANDIDATE", "CANDIDATE PARTY", "REG. ID #","DOE ASSIGNED CANDIDATE #", "VOTE TOTAL"))
## Rows: 4061 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (9): COUNTY CODE, COUNTY NAME, ELECTION DATE, ELECTION NAME, PRECINCT P...
## dbl (10): ELECTION #, PRECINCT IDENTIFIER, TOTAL REG. VOTERS, TOTAL REG. REP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Santa_Rosa_County <- read_tsv("~/Desktop/FL-01 Data/SAN_PctResults20201103.txt", col_names = c("COUNTY CODE", "COUNTY NAME", "ELECTION #", "ELECTION DATE", "ELECTION NAME", "PRECINCT IDENTIFIER", "PRECINCT POLLING LOCATION", "TOTAL REG. VOTERS", "TOTAL REG. REP.", "TOTAL REG. DEM.", "TOTAL REG. OTHER PARTY", "CONTEST NAME", "DISTRICT", "CONTEST CODE", "CANDIDATE", "CANDIDATE PARTY", "REG. ID #",
"DOE ASSIGNED CANDIDATE #", "VOTE TOTAL"))
## Rows: 3431 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (9): COUNTY CODE, COUNTY NAME, ELECTION DATE, ELECTION NAME, PRECINCT P...
## dbl (10): ELECTION #, PRECINCT IDENTIFIER, TOTAL REG. VOTERS, TOTAL REG. REP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Walton_County <- read_tsv("~/Desktop/FL-01 Data/WAL_PctResults20201103.txt", col_names = c("COUNTY CODE", "COUNTY NAME", "ELECTION #", "ELECTION DATE", "ELECTION NAME", "PRECINCT IDENTIFIER", "PRECINCT POLLING LOCATION", "TOTAL REG. VOTERS", "TOTAL REG. REP.", "TOTAL REG. DEM.", "TOTAL REG. OTHER PARTY", "CONTEST NAME", "DISTRICT", "CONTEST CODE", "CANDIDATE", "CANDIDATE PARTY", "REG. ID #", "DOE ASSIGNED CANDIDATE #", "VOTE TOTAL"))
## Rows: 1531 Columns: 19
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (8): COUNTY CODE, COUNTY NAME, ELECTION DATE, ELECTION NAME, CONTEST NA...
## dbl (11): ELECTION #, PRECINCT IDENTIFIER, PRECINCT POLLING LOCATION, TOTAL ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Removing unnecessary columns
Escambia_County1 <- Escambia_County |>
select(-`COUNTY CODE`, -`ELECTION #`, -`PRECINCT POLLING LOCATION`, -`TOTAL REG. REP.`, -`TOTAL REG. DEM.`, -`TOTAL REG. OTHER PARTY`, -`DISTRICT`, -`CONTEST CODE`, -`REG. ID #`, -`DOE ASSIGNED CANDIDATE #`, -`ELECTION DATE`, `TOTAL REG. VOTERS`, -`CANDIDATE`)
Okaloosa_County1 <- Okaloosa_County |>
select(-`COUNTY CODE`, -`ELECTION #`, -`PRECINCT POLLING LOCATION`, -`TOTAL REG. REP.`, -`TOTAL REG. DEM.`, -`TOTAL REG. OTHER PARTY`, -`DISTRICT`, -`CONTEST CODE`, -`REG. ID #`, -`DOE ASSIGNED CANDIDATE #`, -`ELECTION DATE`, `TOTAL REG. VOTERS`, -`CANDIDATE`)
Santa_Rosa_County1 <- Santa_Rosa_County |>
select(-`COUNTY CODE`, -`ELECTION #`, -`PRECINCT POLLING LOCATION`, -`TOTAL REG. REP.`, -`TOTAL REG. DEM.`, -`TOTAL REG. OTHER PARTY`, -`DISTRICT`, -`CONTEST CODE`, -`REG. ID #`, -`DOE ASSIGNED CANDIDATE #`, -`ELECTION DATE`, `TOTAL REG. VOTERS`, -`CANDIDATE`)
Walton_County1 <- Walton_County |>
select(-`COUNTY CODE`, -`ELECTION #`, -`PRECINCT POLLING LOCATION`, -`TOTAL REG. REP.`, -`TOTAL REG. DEM.`, -`TOTAL REG. OTHER PARTY`, -`DISTRICT`, -`CONTEST CODE`, -`REG. ID #`, -`DOE ASSIGNED CANDIDATE #`, -`ELECTION DATE`, `TOTAL REG. VOTERS`, -`CANDIDATE`)
Filtering out all parties that are not Republican or Democrat
Escambia_County2 <- Escambia_County1 |>
filter(`CANDIDATE PARTY` %in% c("REP", "DEM"))
Okaloosa_County2 <- Okaloosa_County1 |>
filter(`CANDIDATE PARTY` %in% c("REP", "DEM"))
Santa_Rosa_County2 <- Santa_Rosa_County1 |>
filter(`CANDIDATE PARTY` %in% c("REP", "DEM"))
Walton_County2 <- Walton_County1 |>
filter(`CANDIDATE PARTY` %in% c("REP", "DEM"))
Filtering for four key races: President, Congressional
Representative, State Representative, and State Senator; renaming
data
Escambia_County3 <- Escambia_County2 |>
filter(`CONTEST NAME` %in% c("President of the United States", "Representative in Congress", "State Senator", "State Representative"))
Okaloosa_County3 <- Okaloosa_County2 |>
filter(`CONTEST NAME` %in% c("President of the United States", "Representative in Congress", "State Senator", "State Representative"))
Santa_Rosa_County3 <- Santa_Rosa_County2 |>
filter(`CONTEST NAME` %in% c("President of the United States", "Representative in Congress", "State Senator", "State Representative"))
Walton_County3 <- Walton_County2 |>
filter(`CONTEST NAME` %in% c("President of the United States", "Representative in Congress", "State Senator", "State Representative"))
Escambia_County <- Escambia_County3
Okaloosa_County <- Okaloosa_County3
Santa_Rosa_County <- Santa_Rosa_County3
Walton_County <- Walton_County3
Creating dataset organized by precinct and race name for total votes
and share of votes for each party
Escambia_County_processed <- Escambia_County %>%
group_by(`PRECINCT IDENTIFIER`, `CONTEST NAME`) %>%
summarize(
Dem_Votes = sum(`VOTE TOTAL`[`CANDIDATE PARTY` == "DEM"]),
Rep_Votes = sum(`VOTE TOTAL`[`CANDIDATE PARTY` == "REP"]),
Total_Votes = Dem_Votes + Rep_Votes,
Vote_Share_Dem = (Dem_Votes / Total_Votes) * 100,
Vote_Share_Rep = (Rep_Votes / Total_Votes) * 100,
)
## `summarise()` has grouped output by 'PRECINCT IDENTIFIER'. You can override
## using the `.groups` argument.
Okaloosa_County_processed <- Okaloosa_County %>%
group_by(`PRECINCT IDENTIFIER`, `CONTEST NAME`) %>%
summarize(
Dem_Votes = sum(`VOTE TOTAL`[`CANDIDATE PARTY` == "DEM"]),
Rep_Votes = sum(`VOTE TOTAL`[`CANDIDATE PARTY` == "REP"]),
Total_Votes = Dem_Votes + Rep_Votes,
Vote_Share_Dem = (Dem_Votes / Total_Votes) * 100,
Vote_Share_Rep = (Rep_Votes / Total_Votes) * 100,
)
## `summarise()` has grouped output by 'PRECINCT IDENTIFIER'. You can override
## using the `.groups` argument.
Santa_Rosa_County_processed <- Santa_Rosa_County %>%
group_by(`PRECINCT IDENTIFIER`, `CONTEST NAME`) %>%
summarize(
Dem_Votes = sum(`VOTE TOTAL`[`CANDIDATE PARTY` == "DEM"]),
Rep_Votes = sum(`VOTE TOTAL`[`CANDIDATE PARTY` == "REP"]),
Total_Votes = Dem_Votes + Rep_Votes,
Vote_Share_Dem = (Dem_Votes / Total_Votes) * 100,
Vote_Share_Rep = (Rep_Votes / Total_Votes) * 100,
)
## `summarise()` has grouped output by 'PRECINCT IDENTIFIER'. You can override
## using the `.groups` argument.
Walton_County_processed <- Walton_County %>%
group_by(`PRECINCT IDENTIFIER`, `CONTEST NAME`) %>%
summarize(
Dem_Votes = sum(`VOTE TOTAL`[`CANDIDATE PARTY` == "DEM"]),
Rep_Votes = sum(`VOTE TOTAL`[`CANDIDATE PARTY` == "REP"]),
Total_Votes = Dem_Votes + Rep_Votes,
Vote_Share_Dem = (Dem_Votes / Total_Votes) * 100,
Vote_Share_Rep = (Rep_Votes / Total_Votes) * 100,
)
## `summarise()` has grouped output by 'PRECINCT IDENTIFIER'. You can override
## using the `.groups` argument.
Combining data for all four key races and creating a table with the
combined average, standard deviation, and vote total in each precinct;
removing precincts in Walton county that are not in FL01
Escambia_Avg_SD <- Escambia_County_processed %>%
group_by (`PRECINCT IDENTIFIER`) %>%
summarize(
Avg_Vote_Share_Rep = mean(Vote_Share_Rep, na.rm = TRUE),
SD_Vote_Share_Rep = sd(Vote_Share_Rep, na.rm = TRUE),
Total_Vote = sum(Total_Votes, na.rm = TRUE))
Okaloosa_Avg_SD <- Okaloosa_County_processed %>%
group_by (`PRECINCT IDENTIFIER`) %>%
summarize(
Avg_Vote_Share_Rep = mean(Vote_Share_Rep, na.rm = TRUE),
SD_Vote_Share_Rep = sd(Vote_Share_Rep, na.rm = TRUE),
Total_Vote = sum(Total_Votes, na.rm = TRUE))
Santa_Rosa_Avg_SD <- Santa_Rosa_County_processed %>%
group_by (`PRECINCT IDENTIFIER`) %>%
summarize(
Avg_Vote_Share_Rep = mean(Vote_Share_Rep, na.rm = TRUE),
SD_Vote_Share_Rep = sd(Vote_Share_Rep, na.rm = TRUE),
Total_Vote = sum(Total_Votes, na.rm = TRUE))
Walton_Avg_SD <- Walton_County_processed %>%
group_by (`PRECINCT IDENTIFIER`) %>%
summarize(
Avg_Vote_Share_Rep = mean(Vote_Share_Rep, na.rm = TRUE),
SD_Vote_Share_Rep = sd(Vote_Share_Rep, na.rm = TRUE),
Total_Vote = sum(Total_Votes, na.rm = TRUE))
Walton_Avg_SD <- Walton_Avg_SD[-c(13, 17, 11, 12, 7, 9), ]
Finding top 20 GOTV and persuasion targets for each county
GOTV_Target_Escambia <- Escambia_Avg_SD %>%
arrange(desc(Avg_Vote_Share_Rep)) %>%
slice_head (n = 20)
GOTV_Target_Okaloosa <- Okaloosa_Avg_SD %>%
arrange(desc(Avg_Vote_Share_Rep)) %>%
slice_head (n = 20)
GOTV_Target_Santa_Rosa <- Santa_Rosa_Avg_SD %>%
arrange(desc(Avg_Vote_Share_Rep)) %>%
slice_head (n = 20)
GOTV_Target_Walton <- Walton_Avg_SD %>%
arrange(desc(Avg_Vote_Share_Rep)) %>%
slice_head (n = 20)
Persuasion_Target_Escambia <- Escambia_Avg_SD %>%
arrange(desc(SD_Vote_Share_Rep)) %>%
slice_head (n = 20)
Persuasion_Target_Okaloosa <- Okaloosa_Avg_SD %>%
arrange(desc(SD_Vote_Share_Rep)) %>%
slice_head (n = 20)
Persuasion_Target_Santa_Rosa <- Santa_Rosa_Avg_SD %>%
arrange(desc(SD_Vote_Share_Rep)) %>%
slice_head (n = 20)
Persuasion_Target_Walton <- Walton_Avg_SD %>%
arrange(desc(SD_Vote_Share_Rep)) %>%
slice_head (n = 20)
Top 20 GOTV Precinct Targets ordered by average
Below are the top Republican performing precincts in Escambia,
Okaloosa, Santa Rosa, and Walton Counties. These precincts should be
targeted to encourage maximum turnout. Turnout amongst the highest
performing Republican precincts will deliver a decisive victory. Each
county displays 20 precincts except Walton, some of which is another
congressional district.
kable(head(GOTV_Target_Escambia, 20))
| 33 |
91.76810 |
1.3536591 |
3984 |
| 23 |
90.19475 |
1.5138135 |
2621 |
| 18 |
89.85448 |
0.6505612 |
1863 |
| 110 |
87.53774 |
0.4632138 |
5569 |
| 19 |
84.55731 |
1.5971811 |
13626 |
| 11 |
82.62100 |
1.4890985 |
3682 |
| 114 |
74.63832 |
2.6434154 |
9428 |
| 7 |
74.54952 |
2.3132719 |
20237 |
| 74 |
74.30163 |
2.5842987 |
18231 |
| 43 |
73.80893 |
2.6864652 |
18389 |
| 36 |
73.77948 |
1.6694584 |
10390 |
| 5 |
73.50929 |
2.6918974 |
9956 |
| 6 |
72.85871 |
1.8226644 |
5479 |
| 105 |
72.58305 |
1.7279020 |
15702 |
| 94 |
71.26328 |
2.6936326 |
9580 |
| 38 |
71.19797 |
3.1620384 |
12618 |
| 95 |
69.03145 |
1.0436993 |
6910 |
| 67 |
68.48562 |
1.6838444 |
19111 |
| 112 |
67.90490 |
2.8402892 |
20994 |
| 21 |
66.76529 |
1.5096866 |
9665 |
kable(head(GOTV_Target_Okaloosa, 20))
| 2 |
90.59477 |
1.6207346 |
1778 |
| 1 |
88.09928 |
1.0528515 |
6514 |
| 3 |
84.85115 |
1.2547154 |
3458 |
| 4 |
84.59911 |
1.8464249 |
3683 |
| 5 |
83.73587 |
1.0427666 |
2866 |
| 6 |
80.13602 |
0.7639911 |
9762 |
| 7 |
79.30059 |
1.1511412 |
4335 |
| 50 |
77.80069 |
1.9287126 |
7724 |
| 26 |
77.70151 |
2.3503975 |
6912 |
| 49 |
77.68412 |
1.7788390 |
8312 |
| 51 |
76.91882 |
0.9199787 |
4961 |
| 20 |
76.33624 |
2.5741781 |
6342 |
| 33 |
75.63932 |
3.9357481 |
12467 |
| 35 |
74.89617 |
2.1640558 |
8484 |
| 31 |
74.62381 |
4.0017960 |
5544 |
| 8 |
74.30457 |
0.6662187 |
10352 |
| 40 |
74.26835 |
3.4868254 |
7693 |
| 13 |
74.05409 |
1.5838110 |
9248 |
| 36 |
73.54471 |
2.9861517 |
7033 |
| 28 |
73.43934 |
2.9387014 |
900 |
kable(head(GOTV_Target_Santa_Rosa, 20))
| 3 |
92.33125 |
0.9911476 |
2752 |
| 17 |
91.63920 |
0.7496201 |
3050 |
| 19 |
91.33667 |
0.9631709 |
5399 |
| 4 |
90.91390 |
0.8358413 |
5821 |
| 7 |
90.69156 |
1.1202093 |
913 |
| 6 |
90.01009 |
1.5909647 |
410 |
| 5 |
87.17856 |
2.0071812 |
453 |
| 13 |
86.87620 |
1.0855503 |
5144 |
| 11 |
84.53419 |
1.4952229 |
4010 |
| 14 |
84.29655 |
1.3394220 |
2471 |
| 2 |
82.18862 |
1.7636457 |
14280 |
| 30 |
81.07615 |
1.2783814 |
10542 |
| 36 |
78.16950 |
1.5888326 |
7450 |
| 33 |
78.10655 |
0.9455240 |
9410 |
| 9 |
78.05191 |
1.4274258 |
9058 |
| 39 |
76.95295 |
1.3797861 |
6495 |
| 27 |
76.59205 |
1.7383088 |
17993 |
| 20 |
76.55373 |
1.6357532 |
21226 |
| 10 |
75.63078 |
1.1684931 |
5294 |
| 18 |
75.59139 |
1.6520991 |
16377 |
kable(head(GOTV_Target_Walton, 20))
| 210 |
89.72348 |
2.0073090 |
692 |
| 220 |
89.38814 |
1.9397528 |
971 |
| 120 |
88.25445 |
1.2576274 |
1303 |
| 230 |
87.12015 |
0.1240641 |
1615 |
| 110 |
86.74086 |
1.4728802 |
1932 |
| 130 |
85.76438 |
0.6367137 |
2663 |
| 330 |
84.56344 |
0.6654938 |
2339 |
| 420 |
84.00116 |
0.9444516 |
1144 |
| 410 |
78.49413 |
1.4738358 |
1981 |
| 310 |
77.36639 |
1.0994151 |
7601 |
| 510 |
76.99777 |
0.0033239 |
15855 |
| 430 |
75.76938 |
0.7130089 |
11388 |
| 520 |
70.56294 |
0.4218010 |
14581 |
| 540 |
68.73626 |
1.1024281 |
4778 |
| 530 |
64.89445 |
0.5165987 |
7965 |
Top 20 Persuasion Precinct Targets ordered by standard
deviation
Below are the top precincts with the most variability in Escambia,
Okaloosa, Santa Rosa, and Walton Counties. The No Party Affiliates in
these precincts display variability in their voting patterns and should
be targeted.Each county displays 20 precincts except Walton, some of
which is another congressional district.
kable(head(Persuasion_Target_Escambia, 20))
| 25 |
57.86618 |
4.671351 |
4244 |
| 46 |
61.14539 |
4.572128 |
8698 |
| 50 |
49.22722 |
4.518093 |
1985 |
| 58 |
60.69106 |
4.311093 |
3505 |
| 97 |
60.26590 |
4.173555 |
5842 |
| 42 |
62.26347 |
3.848786 |
9578 |
| 40 |
53.95472 |
3.834376 |
5731 |
| 51 |
53.87094 |
3.463194 |
10444 |
| 30 |
61.95124 |
3.425254 |
8375 |
| 111 |
61.18603 |
3.284430 |
10152 |
| 15 |
60.02855 |
3.252475 |
7109 |
| 35 |
54.83921 |
3.251142 |
7241 |
| 38 |
71.19797 |
3.162038 |
12618 |
| 41 |
42.99040 |
3.139596 |
7758 |
| 91 |
57.51551 |
3.139159 |
18002 |
| 102 |
58.73291 |
3.107689 |
8120 |
| 112 |
67.90490 |
2.840289 |
20994 |
| 88 |
51.63387 |
2.748308 |
5832 |
| 94 |
71.26328 |
2.693633 |
9580 |
| 5 |
73.50929 |
2.691897 |
9956 |
kable(head(Persuasion_Target_Okaloosa, 20))
| 45 |
70.40948 |
12.784150 |
10465 |
| 17 |
58.57064 |
5.856780 |
7213 |
| 37 |
73.05527 |
4.325282 |
7131 |
| 32 |
71.80026 |
4.029128 |
6430 |
| 31 |
74.62381 |
4.001796 |
5544 |
| 33 |
75.63932 |
3.935748 |
12467 |
| 48 |
70.02921 |
3.932686 |
263 |
| 29 |
73.12263 |
3.694316 |
5174 |
| 19 |
56.98125 |
3.578202 |
4021 |
| 40 |
74.26835 |
3.486825 |
7693 |
| 27 |
67.64491 |
3.461242 |
612 |
| 21 |
70.16337 |
3.449467 |
6447 |
| 46 |
73.33885 |
3.259262 |
11290 |
| 41 |
71.85998 |
3.240240 |
3446 |
| 22 |
65.16667 |
3.223648 |
5092 |
| 16 |
68.32994 |
3.143951 |
6860 |
| 36 |
73.54471 |
2.986152 |
7033 |
| 18 |
68.00747 |
2.954831 |
6109 |
| 28 |
73.43934 |
2.938701 |
900 |
| 43 |
71.68876 |
2.889194 |
5710 |
kable(head(Persuasion_Target_Santa_Rosa, 20))
| 22 |
66.75810 |
3.100675 |
17089 |
| 32 |
70.47731 |
2.794358 |
12091 |
| 28 |
70.67632 |
2.748004 |
11614 |
| 25 |
70.80462 |
2.226404 |
12627 |
| 37 |
72.39456 |
2.125973 |
8319 |
| 5 |
87.17856 |
2.007181 |
453 |
| 2 |
82.18862 |
1.763646 |
14280 |
| 27 |
76.59205 |
1.738309 |
17993 |
| 18 |
75.59139 |
1.652099 |
16377 |
| 20 |
76.55373 |
1.635753 |
21226 |
| 41 |
69.39468 |
1.597610 |
9851 |
| 6 |
90.01009 |
1.590965 |
410 |
| 36 |
78.16950 |
1.588833 |
7450 |
| 40 |
67.02773 |
1.504309 |
6305 |
| 11 |
84.53419 |
1.495223 |
4010 |
| 24 |
74.91060 |
1.493407 |
24247 |
| 38 |
68.67489 |
1.451267 |
3728 |
| 1 |
63.98663 |
1.430985 |
5946 |
| 9 |
78.05191 |
1.427426 |
9058 |
| 21 |
75.06673 |
1.420525 |
14258 |
kable(head(Persuasion_Target_Walton, 20))
| 210 |
89.72348 |
2.0073090 |
692 |
| 220 |
89.38814 |
1.9397528 |
971 |
| 410 |
78.49413 |
1.4738358 |
1981 |
| 110 |
86.74086 |
1.4728802 |
1932 |
| 120 |
88.25445 |
1.2576274 |
1303 |
| 540 |
68.73626 |
1.1024281 |
4778 |
| 310 |
77.36639 |
1.0994151 |
7601 |
| 420 |
84.00116 |
0.9444516 |
1144 |
| 430 |
75.76938 |
0.7130089 |
11388 |
| 330 |
84.56344 |
0.6654938 |
2339 |
| 130 |
85.76438 |
0.6367137 |
2663 |
| 530 |
64.89445 |
0.5165987 |
7965 |
| 520 |
70.56294 |
0.4218010 |
14581 |
| 230 |
87.12015 |
0.1240641 |
1615 |
| 510 |
76.99777 |
0.0033239 |
15855 |
Breaking down voter file to create file with most useful variables:
voter ID, full name, address, gender, race, party, and precinct.
Escambia_VF <- read_tsv("~/Desktop/FL01 Voter File/ESC_20210112.txt", col_names = c("1"))
## Rows: 242169 Columns: 38
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (36): 1, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, ...
## dbl (2): X2, X21
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Escambia_VF1 <- Escambia_VF |>
select(-`1`, -`X4`, -`X6`, -`X7`, -`X11`, -`X13`, -`X14`, -`X15`, -`X16`, -`X17`, -`X18`, -`X19`, -`X22`, -`X23`, -`X26`, -`X27`, -`X28`, -`X29`, -`X30`, -`X31`, -`X32`, -`X33`, -`X34`, -`X35`, -`X36`, -`X37`, -`X38`)
Escambia_VF1 <- Escambia_VF1 %>%
filter(!str_detect(X3, "\\*"))
Escambia_VF <- Escambia_VF1 %>%
rename(
`Voter ID` = X2,
`Last Name` = X3,
`First Name` = X5,
`Address` = X8,
`Unit #` = X9,
`City` = X10,
`Zip Code` = X12,
`Gender` = X20,
`Race` = X21,
`Party` = X24,
`Precinct` = X25
)
VF_Escambia <- Escambia_VF %>%
unite("Full Name", `First Name`, `Last Name`, sep = " ", na.rm = TRUE) %>%
unite("Full Address", `Address`, `Unit #`, `City`, `Zip Code`, sep = " ", na.rm = TRUE)
VF_Escambia <- VF_Escambia %>%
mutate(Race = case_when(
Race == 1 ~ "American Indian or Alaskan Native",
Race == 2 ~ "Asian or Pacific Islander",
Race == 3 ~ "Black, Not Hispanic",
Race == 4 ~ "Hispanis",
Race == 5 ~ "White, Not Hispanic",
Race == 6 ~ "Other",
Race == 7 ~ "Multiracial",
Race == 9 ~ "Unknown"
))
Okaloosa_VF <- read_tsv("~/Desktop/FL01 Voter File/OKA_20210112.txt", col_names = c("1"))
## Rows: 161380 Columns: 38
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (36): 1, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, ...
## dbl (2): X2, X21
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Okaloosa_VF1 <- Okaloosa_VF |>
select(-`1`, -`X4`, -`X6`, -`X7`, -`X11`, -`X13`, -`X14`, -`X15`, -`X16`, -`X17`, -`X18`, -`X19`, -`X22`, -`X23`, -`X26`, -`X27`, -`X28`, -`X29`, -`X30`, -`X31`, -`X32`, -`X33`, -`X34`, -`X35`, -`X36`, -`X37`, -`X38`)
Okaloosa_VF1 <- Okaloosa_VF1 %>%
filter(!str_detect(X3, "\\*"))
Okaloosa_VF <- Okaloosa_VF1 %>%
rename(
`Voter ID` = X2,
`Last Name` = X3,
`First Name` = X5,
`Address` = X8,
`Unit #` = X9,
`City` = X10,
`Zip Code` = X12,
`Gender` = X20,
`Race` = X21,
`Party` = X24,
`Precinct` = X25
)
VF_Okaloosa <- Okaloosa_VF %>%
unite("Full Name", `First Name`, `Last Name`, sep = " ", na.rm = TRUE) %>%
unite("Full Address", `Address`, `Unit #`, `City`, `Zip Code`, sep = " ", na.rm = TRUE)
VF_Okaloosa <- VF_Okaloosa %>%
mutate(Race = case_when(
Race == 1 ~ "American Indian or Alaskan Native",
Race == 2 ~ "Asian or Pacific Islander",
Race == 3 ~ "Black, Not Hispanic",
Race == 4 ~ "Hispanis",
Race == 5 ~ "White, Not Hispanic",
Race == 6 ~ "Other",
Race == 7 ~ "Multiracial",
Race == 9 ~ "Unknown"
))
Santa_Rosa_VF <- read_tsv("~/Desktop/FL01 Voter File/SAN_20210112.txt", col_names = c("1"))
## Rows: 152980 Columns: 38
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (36): 1, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, ...
## dbl (2): X2, X21
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Santa_Rosa_VF1 <- Santa_Rosa_VF |>
select(-`1`, -`X4`, -`X6`, -`X7`, -`X11`, -`X13`, -`X14`, -`X15`, -`X16`, -`X17`, -`X18`, -`X19`, -`X22`, -`X23`, -`X26`, -`X27`, -`X28`, -`X29`, -`X30`, -`X31`, -`X32`, -`X33`, -`X34`, -`X35`, -`X36`, -`X37`, -`X38`)
Santa_Rosa_VF1 <- Santa_Rosa_VF1 %>%
filter(!str_detect(X3, "\\*"))
Santa_Rosa_VF <- Santa_Rosa_VF1 %>%
rename(
`Voter ID` = X2,
`Last Name` = X3,
`First Name` = X5,
`Address` = X8,
`Unit #` = X9,
`City` = X10,
`Zip Code` = X12,
`Gender` = X20,
`Race` = X21,
`Party` = X24,
`Precinct` = X25
)
VF_Santa_Rosa <- Santa_Rosa_VF %>%
unite("Full Name", `First Name`, `Last Name`, sep = " ", na.rm = TRUE) %>%
unite("Full Address", `Address`, `Unit #`, `City`, `Zip Code`, sep = " ", na.rm = TRUE)
VF_Santa_Rosa <- VF_Santa_Rosa %>%
mutate(Race = case_when(
Race == 1 ~ "American Indian or Alaskan Native",
Race == 2 ~ "Asian or Pacific Islander",
Race == 3 ~ "Black, Not Hispanic",
Race == 4 ~ "Hispanis",
Race == 5 ~ "White, Not Hispanic",
Race == 6 ~ "Other",
Race == 7 ~ "Multiracial",
Race == 9 ~ "Unknown"
))
Walton_VF <- read_tsv("~/Desktop/FL01 Voter File/WAL_20210112.txt", col_names = c("1"))
## Rows: 59602 Columns: 38
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (36): 1, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, ...
## dbl (2): X2, X21
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Walton_VF1 <- Walton_VF |>
select(-`1`, -`X4`, -`X6`, -`X7`, -`X11`, -`X13`, -`X14`, -`X15`, -`X16`, -`X17`, -`X18`, -`X19`, -`X22`, -`X23`, -`X26`, -`X27`, -`X28`, -`X29`, -`X30`, -`X31`, -`X32`, -`X33`, -`X34`, -`X35`, -`X36`, -`X37`, -`X38`)
Walton_VF1 <- Walton_VF1 %>%
filter(!str_detect(X3, "\\*"))
Walton_VF <- Walton_VF1 %>%
rename(
`Voter ID` = X2,
`Last Name` = X3,
`First Name` = X5,
`Address` = X8,
`Unit #` = X9,
`City` = X10,
`Zip Code` = X12,
`Gender` = X20,
`Race` = X21,
`Party` = X24,
`Precinct` = X25
)
VF_Walton <- Walton_VF %>%
unite("Full Name", `First Name`, `Last Name`, sep = " ", na.rm = TRUE) %>%
unite("Full Address", `Address`, `Unit #`, `City`, `Zip Code`, sep = " ", na.rm = TRUE)
VF_Walton <- VF_Walton %>%
mutate(Race = case_when(
Race == 1 ~ "American Indian or Alaskan Native",
Race == 2 ~ "Asian or Pacific Islander",
Race == 3 ~ "Black, Not Hispanic",
Race == 4 ~ "Hispanis",
Race == 5 ~ "White, Not Hispanic",
Race == 6 ~ "Other",
Race == 7 ~ "Multiracial",
Race == 9 ~ "Unknown"
))
Using previously created voter ID file to find top 100 GOTV and top
100 persuasion targets in key precincts across four counties.
VF_1Walton <- VF_Walton %>%
filter(Party == "REP" & Precinct == "210") %>%
slice_head(n = 25)
VFMM_Walton <- VF_1Walton |>
select(-`Voter ID`, -`Gender`, -`Race`, -`Party`, -`Precinct`)
VF_1Escambia <- VF_Escambia %>%
filter(Party == "REP" & Precinct == "33") %>%
slice_head(n = 25)
VFMM_Escambia <- VF_1Escambia |>
select(-`Voter ID`, -`Gender`, -`Race`, -`Party`, -`Precinct`)
VF_1Santa_Rosa <- VF_Santa_Rosa %>%
filter(Party == "REP" & Precinct == "3") %>%
slice_head(n = 25)
VFMM_Santa_Rosa <- VF_1Santa_Rosa |>
select(-`Voter ID`, -`Gender`, -`Race`, -`Party`, -`Precinct`)
VF_1Okaloosa <- VF_Okaloosa %>%
filter(Party == "REP" & Precinct == "02") %>%
slice_head(n = 25)
VFMM_Okaloosa <- VF_1Okaloosa |>
select(-`Voter ID`, -`Gender`, -`Race`, -`Party`, -`Precinct`)
Fl01_GOTV_Mail <- bind_rows(VFMM_Escambia, VFMM_Okaloosa, VFMM_Santa_Rosa, VFMM_Walton)
VF_2Walton <- VF_Walton %>%
filter(Party == "NPA" & Precinct == "210") %>%
slice_head(n = 25)
VFMM1_Walton <- VF_2Walton |>
select(-`Voter ID`, -`Gender`, -`Race`, -`Party`, -`Precinct`)
VF_2Escambia <- VF_Escambia %>%
filter(Party == "NPA" & Precinct == "25") %>%
slice_head(n = 25)
VFMM1_Escambia <- VF_2Escambia |>
select(-`Voter ID`, -`Gender`, -`Race`, -`Party`, -`Precinct`)
VF_2Santa_Rosa <- VF_Santa_Rosa %>%
filter(Party == "NPA" & Precinct == "22") %>%
slice_head(n = 25)
VFMM1_Santa_Rosa <- VF_2Santa_Rosa |>
select(-`Voter ID`, -`Gender`, -`Race`, -`Party`, -`Precinct`)
VF_2Okaloosa <- VF_Okaloosa %>%
filter(Party == "NPA" & Precinct == "45") %>%
slice_head(n = 25)
VFMM1_Okaloosa <- VF_2Okaloosa |>
select(-`Voter ID`, -`Gender`, -`Race`, -`Party`, -`Precinct`)
Fl01_Persuasion_Mail <- bind_rows(VFMM1_Escambia, VFMM1_Okaloosa, VFMM1_Santa_Rosa, VFMM1_Walton)
Top 100 GOTV targets (active registered Republicans) across
Escambia, Okaloosa, Santa Rosa, and Walton Counties
kable(head(VFMM_Escambia, 25))
| MYRLE HANKS |
3540 HANKS RD CENTURY 32535 |
| THOMAS FILLINGIM |
1941 WILMA RD MCDAVID 32568 |
| GWENDOLYN CRANFORD |
3310 OAKSHADE RD CENTURY 32535 |
| GREGORY VAUGHN |
6100 N HIGHWAY 99 CENTURY 32535 |
| SHERRY ROLEY |
7101 GREENLAND RD MCDAVID 325685337 |
| JAMES BOLERJACK |
3560 HANKS RD CENTURY 32535 |
| BYRON TIMS |
5581 HIGHWAY 164 MCDAVID 32568 |
| TERRY EMMONS |
4885 HIGHWAY 168 CENTURY 32535 |
| SHEILA ENFINGER |
11130 HIGHWAY 97 MCDAVID 32568 |
| SAMUEL BERRY |
3620 W HIGHWAY 4 CENTURY 32535 |
| RONALD STABLER |
3501 BREASTWORKS RD MCDAVID 32568 |
| MARVIN BARDIN |
3640 LAMBERT BRIDGE RD MCDAVID 32568 |
| ROBERT BARTLEY |
3410 N PINE BARREN RD MCDAVID 32568 |
| SAMUEL LEPLEY |
4560 SANDY HOLLOW RD CENTURY 325353417 |
| TERRY BRYAN |
3690 OAKSHADE RD CENTURY 32535 |
| RONALD DOVE |
6510 W HIGHWAY 4 CENTURY 32535 |
| MABEL MCELHANEY |
6800 MCELHANEY RD CENTURY 32535 |
| GRACE LONG |
3011 PURDUE RD MCDAVID 32568 |
| VICKI COOK |
6800 MCELHANEY RD CENTURY 32535 |
| CHRISTOPHER DOVE |
10100 HIGHWAY 97 CENTURY 32535 |
| SANDRA FOX-BROWN |
3701 MAYHAW RD MCDAVID 32568 |
| LARRY GODWIN |
5440 N HIGHWAY 99 CENTURY 32535 |
| BETTY PAGE |
3390 W HIGHWAY 4 CENTURY 32535 |
| JERRY WELLS |
3540 N PINE BARREN RD MCDAVID 32568 |
| MARY BROADHEAD |
4190 BRADBERRY RD CENTURY 325352136 |
kable(head(VFMM_Okaloosa, 25))
| Jacqueline Lewis |
6931 Old River RD Baker 325317923 |
| Jaclyn Ware |
1263 Finkel Rd Baker 325317503 |
| Tammey Hart |
1920 Horse Creek Rd Baker 325317214 |
| Stephen Batson |
8152 Highway 189 N Baker 325317246 |
| Eleanor Thurber |
1097 Vernon Jeffers Rd Baker 325317155 |
| Kevin Allen |
2664 Highway 2 Baker 325317408 |
| Sara Henry |
631 Peaden Bridge Rd Baker 325318002 |
| Roddy Steele |
7521 Red Barrow Rd Baker 325317513 |
| Betty LeMarchand |
6947 Lee Cook Rd Baker 325317607 |
| Barbara Laing |
1615 Highway C 180 Baker 325317231 |
| Kirk Simmons |
8021 Mormon Temple Rd Baker 325317123 |
| Robert Welch |
2240 Curtis Madden RD Baker 325317616 |
| Cheryl Barrow |
2515 Nathan Ln Baker 325317413 |
| Timothy Allen |
8299 Jordan Rd Baker 325317275 |
| Carolyn Patrick |
7031 Eadie Cotton RD Baker 325317702 |
| Diane Madden |
1446 Highway C 180 Baker 325317278 |
| Robert Hough |
8306 Thames Rd Baker 325317202 |
| Khader Daoud |
7251 Old River RD Baker 32531 |
| Abigail Kingsbury |
8270 Jordan RD Baker 325317272 |
| Tanner Moll |
2068 L G Russell RD Baker 325317454 |
| Michael Tiley |
2039 L G Russell Rd Baker 325317425 |
| Haley Dorantes |
1356 Dowdy RD Baker 325317266 |
| Chelsi Brooks |
8273 Bowen RD Baker 325317213 |
| David Stewart |
8301 Yellow River Baptist Church Rd Baker 32531 |
| Jennifer Booker |
8145 Beaver Creek RD Baker 325317017 |
kable(head(VFMM_Santa_Rosa, 25))
| Michael Mayne |
3478 Pine Level Church RD Jay 32565 |
| Larry Hornsby |
2858 Harvest RD Jay 32565 |
| Amy Moore |
2947 Bud Diamond RD Jay 32565 |
| Jessie Allred |
3013 Bud Diamond RD Jay 32565 |
| Daniel Haan |
3886 Hazel Godwin RD Jay 32565 |
| Sheila Morris |
2109 Little Rock Rd Jay 32565 |
| Kimberly Nelson |
3354 Farrish RD Jay 32565 |
| Seth Shell |
3516 Ebenezer Church Rd Jay 32565 |
| Tyler Weaver |
3346 Farrish Rd Jay 32565 |
| Peggy Ruth |
12454 Chumuckla HWY Jay 32565 |
| Teresa Bird |
4137 Morristown RD Jay 32565 |
| Glenn Bingham |
1947 Brownsdale Loop Rd Jay 32565 |
| Joseph Pyritz |
12746 Chumuckla HWY Jay 32565 |
| Arron Fillingim |
1886 Brownsdale Loop Rd Jay 32565 |
| Mark Miller |
3039 Harvest RD Jay 32565 |
| Dina Carnley |
4130 Ebenezer Church Rd Jay 32565 |
| Jason McGraw |
12788 Chumuckla HWY Jay 32565 |
| Cody Lewis |
2701 Harvest RD Jay 32565 |
| Danielle Goodson |
4089 N Simmons RD Jay 32565 |
| Shirley Aldridge |
1700 Dykestown Rd Jay 32565 |
| Amanda Hornsby |
2844 Harvest RD Jay 32565 |
| Dawna Horne |
3065 Hubert LN Jay 32565 |
| Jeremy Odom |
3502 Harvest Rd Jay 32565 |
| Nickolas Lawson |
3801 N Simmons RD Jay 32565 |
| Maggie McGee |
1966 Brownsdale Loop RD Jay 32565 |
kable(head(VFMM_Walton, 25))
| CHARLES WILKERSON |
718 YORKEY RD WESTVILLE 32464 |
| ROY WILLIAMS |
30 CO HWY 181 W DEFUNIAK SPGS 32433 |
| RHONDA HARRISON |
915 HEMPHILL RD DEFUNIAK SPGS 32433 |
| SHANNA BARTON |
15229 ST HWY 83 DEFUNIAK SPGS 324331106 |
| O’NEAL POSTON |
86 POSTON RD DEFUNIAK SPGS 32433 |
| PHILLIP CURRID |
2134 COLLINSWORTH RD WESTVILLE 32464 |
| SILVIA HAGERTY |
42 ONE WAY LN DEFUNIAK SPGS 32433 |
| DANIEL HAGERTY |
42 ONE WAY LN DEFUNIAK SPGS 32433 |
| BEN SCHOFIELD |
118 PUNCH BOWL RD DEFUNIAK SPGS 32433 |
| HARRIET SIMMONS |
578 B A KELLY RD DEFUNIAK SPGS 32433 |
| JARRED NELSON |
14602 ST HWY 83 DEFUNIAK SPGS 32433 |
| JAMIE MITCHELL |
707 PUNCH BOWL RD DEFUNIAK SPGS 32433 |
| WILLIAM HEAD |
17725 ST HWY 83 DEFUNIAK SPGS 32433 |
| SAMIE WRIGHT |
216 CO HWY 181 W DEFUNIAK SPGS 32433 |
| LULA LAWRENCE |
15771 ST HWY 83 DEFUNIAK SPGS 32433 |
| CHESTER LEDDON |
106 LEDDON RD DEFUNIAK SPGS 32433 |
| BETTY WEEKS |
362 ST HWY 2 E DEFUNIAK SPGS 32433 |
| JOSHUA COLLINSWORTH |
56 BURGESS TRL DEFUNIAK SPGS 32433 |
| DENNIS WARD |
15229 ST HWY 83 DEFUNIAK SPGS 32433 |
| THOMAS THORN |
15686 ST HWY 83 DEFUNIAK SPGS 32433 |
| JADA ROBERTS |
266 CO HWY 181 E WESTVILLE 32464 |
| DAWN HOWARD |
701 BRAXTON RD WESTVILLE 32464 |
| ARTHUR TRUETT |
13847 ST HWY 83 DEFUNIAK SPGS 32433 |
| STACEY LEDDON |
222 LEDDON RD DEFUNIAK SPGS 32433 |
| KAREN GRAHAM |
1314 ST HWY 2 E DEFUNIAK SPGS 32433 |
Top 100 persuastion targets (active registered No Party Affiliates)
across Escambia, Okaloosa, Santa Rosa, and Walton Counties
kable(head(VFMM1_Escambia, 25))
| JENNIFER WATSON |
2110 LE RUTH DR PENSACOLA 32514 |
| SUSAN VAILLANT |
10100 HILLVIEW DR APT 204A PENSACOLA 32514 |
| DALE HICKS |
9808 BRIDGEWOOD LN PENSACOLA 32514 |
| WILLIAM VARNUM |
9544 MABEL ST PENSACOLA 32514 |
| AKRAM MAYE |
10080 HILLVIEW DR APT 162 PENSACOLA 32514 |
| MARY FISHER |
10228 CREST RIDGE DR PENSACOLA 325142617 |
| LARRY BEALL |
10100 HILLVIEW DR APT 1307 PENSACOLA 325145486 |
| JUANITA SCHOULTZ |
10100 HILLVIEW DR APT 1110 PENSACOLA 32514 |
| JANE WATKINS |
10100 HILLVIEW DR Apt 2311 PENSACOLA 32514 |
| DELORES SMITH |
10040 HILLVIEW DR PENSACOLA 32514 |
| BRETTANIE SMIDT |
10175 CREST RIDGE DR PENSACOLA 32514 |
| CHRISTOPHER BURKART |
9808 SOURWOOD CT PENSACOLA 325145449 |
| KAYLA MULLINS |
2280 WYATT ST PENSACOLA 32514 |
| GARRY CHEN |
9801 BRIDGEWOOD LN PENSACOLA 32514 |
| STEPHAN PACCA |
1938 WOODBRIDGE DR PENSACOLA 32514 |
| JOHN HUDDLESTON |
10093 HILLVIEW DR PENSACOLA 32514 |
| DERRICK CULLIGAN |
1933 WOODBRIDGE DR PENSACOLA 32514 |
| JACKLYN ELLINGTON |
10100 HILLVIEW DR APT 122 PENSACOLA 32514 |
| MADELINE JOHANSEN |
10091 HILLVIEW DR PENSACOLA 325145469 |
| JAMES PEREZ |
1991 WYATT ST PENSACOLA 32514 |
| JOSEPH SHARP |
10080 HILLVIEW DR APT 484A PENSACOLA 32514 |
| MATTHEW DOCKHAM |
2252 WYATT ST PENSACOLA 325147792 |
| BETTY GRAVES |
10095 HILLVIEW DR PENSACOLA 32514 |
| SUE BRILEY GAY |
10100 HILLVIEW DR APT 524 PENSACOLA 32514 |
| LAKERRI MCGEE |
1962 WYATT ST PENSACOLA 325147786 |
kable(head(VFMM1_Okaloosa, 25))
| Michael Tijerina |
432 Hatchee Dr Crestview 325365202 |
| Lisa Glover |
5312 Whitney Ct Crestview 325362208 |
| Ellyn Musante |
515 Wingspan WAY Crestview 325362255 |
| Thomas Wood |
406 Jillian Dr Crestview 325369299 |
| Harold Kapp |
1202 Ego Dr Crestview 32536 |
| Scott Bayless |
512 Wingspan WAY Crestview 325362255 |
| Preston Bass |
368 Riverchase Blvd Crestview 325360000 |
| Brandon McEntire |
120 Crab Apple Ave Crestview 32536 |
| Ashley Harris |
128 Alicia DR Crestview 325365296 |
| Justin May |
103 Alicia DR Crestview 325365297 |
| Yazmeen Deehr |
106 Claire DR Crestview 325369205 |
| Sidney Fowler |
5417 Josh Dr Crestview 325362209 |
| Bruce Powell |
5409 Josh Dr Crestview 325362209 |
| Mark Rosales |
510 Wingspan WAY Crestview 325362255 |
| Robert Byrd |
311 Riverchase Blvd Crestview 325365245 |
| Jason Gall |
353 Riverchase Blvd Crestview 325365260 |
| Jose Polanco |
1242 Northview Dr Crestview 325362211 |
| Dalvin Williams |
504 Krest DR Crestview 32536 |
| Vera Brito De Sousa |
410 Riverchase Blvd Crestview 325364298 |
| Matthew Thomas |
116 Loop DR Crestview 325364259 |
| Jack Cholcher |
106 Campbell Ave Crestview 325369203 |
| Dennis Minnick |
537 Tom Sawyer Ln Crestview 325365264 |
| damien Lett |
743 Majestic DR Crestview 325362254 |
| Sarah Roberts |
105 Trevor Ct Crestview 325365001 |
| Persephonie Vigil |
220 Riverwood DR Crestview 325365010 |
kable(head(VFMM1_Santa_Rosa, 25))
| Edward Welch |
503 Navarre St Gulf Breeze 32561 |
| Melissa Southerland |
415 Shenandoah DR Gulf Breeze 32561 |
| Mary Taylor |
121 Bear Dr Gulf Breeze 32561 |
| Loreli Mendoza |
154 Stearns ST Gulf Breeze 32561 |
| Joseph Gomes |
68 Highpoint DR Gulf Breeze 32561 |
| Cailyn Bodle |
418 CUMBERLAND AVE GULF BREEZE 325614108 |
| Owen Sise |
511 Yesteroaks CIR Gulf Breeze 32561 |
| Brian Williams |
4 San Carlos AVE Gulf Breeze 32561 |
| Patrick Pulley |
433 Montrose BLVD Gulf Breeze 32561 |
| Caroline Rawlings |
5 Breeze ST Gulf Breeze 32561 |
| Lyn Bailey |
589 Bay Cliffs CIR Gulf Breeze 32561 |
| Carl Carlbert |
302 Washington Ave Gulf Breeze 32561 |
| Carrie Musselwhite |
127 Norwich DR Gulf Breeze 32561 |
| Audrey Mc Carthy |
102 Bay Bridge Dr Gulf Breeze 32561 |
| Martic Smith |
512 Eventide Dr Gulf Breeze 32561 |
| Jorge Franco |
203 Dolphin St Gulf Breeze 32561 |
| Seth Friedland |
535 James River Rd Gulf Breeze 32561 |
| Alfred Simmons |
201 Pensacola Beach RD APT A6 Gulf Breeze 32561 |
| Samuel Dalton |
1200 Shoreline Dr UNIT 314 Gulf Breeze 32561 |
| Jo Austin |
326 Deer Point Dr Gulf Breeze 32561 |
| Tjorvi Jonasson |
419 Shenandoah Dr Gulf Breeze 32561 |
| Bradley Blackwell |
101 San Carlos AVE Gulf Breeze 32561 |
| Heather Hansen |
822 Bay Cliffs Rd Gulf Breeze 32561 |
| Andres Candela |
417 Kenilworth Ave Gulf Breeze 32561 |
| Laura Milstead |
34 Bay Bridge Dr Gulf Breeze 32561 |
kable(head(VFMM1_Walton, 25))
| JACALYN LORENZ |
5151 CO HWY 181 W DEFUNIAK SPGS 32433 |
| MIRANDA ADKISON |
3701 CO HWY 181 W DEFUNIAK SPGS 32433 |
| WANDA GODDIN |
13515 ST HWY 83 DEFUNIAK SPGS 32433 |
| RUSSELL CHILDS |
401 MIMS RD DEFUNIAK SPGS 32433 |
| CHEYANNA TOLLISON |
16825 ST HWY 83 DEFUNIAK SPGS 32433 |
| JULIE TURNER |
1515 ST HWY 2 E DEFUNIAK SPGS 32433 |
| DERRELL WOMACK |
449 WOODS RD WESTVILLE 324642153 |
| DYLAN HAWKINS |
1293 HEMPHILL RD DEFUNIAK SPGS 32433 |
| CAITLIN BRAMBLE |
16093 ST HWY 83 DEFUNIAK SPGS 32433 |
| GERALD STUTZ |
1094 CO HWY 181 W DEFUNIAK SPGS 32433 |
| KEVIN SMITH |
3274 CO HWY 181 W DEFUNIAK SPGS 32433 |
| BRITTIN NORRIS |
949 CO HWY 181 E WESTVILLE 32464 |
| ALYSSA JACKSON |
3766 COLLINSWORTH RD WESTVILLE 32464 |
| AMBER WYATT |
13713 ST HWY 83 DEFUNIAK SPGS 32433 |
| JESSICA HAMMAC |
626 PUNCH BOWL RD DEFUNIAK SPGS 32433 |
| JUSTIN YOUNG |
811 PEN WILLIAMS RD DEFUNIAK SPGS 32433 |
| MARY NORRIS |
13847 ST HWY 83 DEFUNIAK SPGS 32433 |
| DAVID WILLIAMS |
193 NATURAL BRIDGE RD DEFUNIAK SPGS 32433 |
| SAVANNAH ARD |
18229 ST HWY 83 DEFUNIAK SPGS 324331115 |
| COREY PEAK |
91 CO HWY 181 E WESTVILLE 32464 |
| ZACHARY WYATT |
13713 ST HWY 83 DEFUNIAK SPGS 32433 |
| JOHN TOLLISON |
16825 ST HWY 83 DEFUNIAK SPGS 32433 |
| JAMES CASEY |
72 CASEY RD DEFUNIAK SPGS 32433 |
| ROXANNA BLEDSOE |
737 HEMPHILL RD DEFUNIAK SPGS 32433 |
| TAYLOR WILLIAMS |
30 CO HWY 181 W DEFUNIAK SPGS 32433 |