Pre-data
setwd("C:/Work/Kaggle/kdd/code/")
require(ellipse)
## Loading required package: ellipse
require(ggplot2)
## Loading required package: ggplot2
library(maps)
library(zipcode)
## Error: there is no package called 'zipcode'
df <- read.csv("../data/train.csv")
head(df)
## projectid teacher_acctid
## 1 00001ccc0e81598c4bd86bacb94d7acb 96963218e74e10c3764a5cfb153e6fea
## 2 00002bff514104264a6b798356fdd893 3414541eb63108700b188648f866f483
## 3 00002d691c05c51a5fdfbb2baef0ba25 7ad6abc974dd8b62773f79f6cbed48d5
## 4 0000b38bbc7252972f7984848cf58098 e1aa1ae5301d0cda860c4d9c89c24919
## 5 0000ee613c92ddc5298bf63142996a5c e0c0a0214d3c2cfdc0ab6639bc3c5342
## 6 0000fa3aa8f6649abab23615b546016d 2a578595fe351e7fce057e048c409b18
## schoolid school_ncesid school_latitude
## 1 9f3f9f2c2da7edda5648ccd10554ed8c 1.710e+11 41.81
## 2 cbaae3265eda78d330cb8ab1a9217071 6.033e+10 35.20
## 3 56502bae9e97bab5eb54f9001878f469 6.030e+10 34.14
## 4 30fcfca739b17be54ce3f1ee46980340 2.311e+11 44.44
## 5 38bb0d62aa613c2f933de56c9df855b7 5.101e+11 38.85
## 6 3432ed3d4466fac2f2ead83ab354e333 6.410e+10 34.30
## school_longitude school_city school_state school_zip school_metro
## 1 -87.67 Chicago IL 60609 urban
## 2 -118.84 Arvin CA 93203 <NA>
## 3 -118.06 Arcadia CA 91007 urban
## 4 -70.20 Livermore ME 4253 rural
## 5 -77.15 Falls Church VA 22041 suburban
## 6 -119.30 Ventura CA 93001 urban
## school_district school_county school_charter school_magnet
## 1 Pershing Elem Network Cook f f
## 2 Arvin Union School District Kern f f
## 3 Arcadia Unified School Dist Los Angeles f f
## 4 Rsu 73 Androscoggin f f
## 5 Fairfax Co Public School Dist Fairfax f t
## 6 Ventura Unif School District Ventura f f
## school_year_round school_nlns school_kipp school_charter_ready_promise
## 1 f f f f
## 2 f f f f
## 3 f f f f
## 4 f f f f
## 5 f f f f
## 6 t f f f
## teacher_prefix teacher_teach_for_america teacher_ny_teaching_fellow
## 1 Mrs. f f
## 2 Mrs. f f
## 3 Mr. f f
## 4 Mrs. f f
## 5 Ms. f f
## 6 Mrs. f f
## primary_focus_subject primary_focus_area secondary_focus_subject
## 1 Mathematics Math & Science Visual Arts
## 2 Literacy Literacy & Language Literature & Writing
## 3 Literature & Writing Literacy & Language Literacy
## 4 Literature & Writing Literacy & Language Mathematics
## 5 ESL Literacy & Language Special Needs
## 6 Civics & Government History & Civics Literature & Writing
## secondary_focus_area resource_type poverty_level grade_level
## 1 Music & The Arts Supplies highest poverty Grades PreK-2
## 2 Literacy & Language Supplies highest poverty Grades PreK-2
## 3 Literacy & Language Books moderate poverty Grades 3-5
## 4 Math & Science Technology highest poverty Grades PreK-2
## 5 Special Needs Technology high poverty Grades PreK-2
## 6 Literacy & Language Books highest poverty Grades 3-5
## fulfillment_labor_materials total_price_excluding_optional_support
## 1 30 1273.8
## 2 35 477.3
## 3 35 892.3
## 4 30 547.9
## 5 30 384.9
## 6 35 240.1
## total_price_including_optional_support students_reached
## 1 1498.6 31
## 2 561.5 20
## 3 1049.8 250
## 4 644.5 36
## 5 452.8 19
## 6 282.5 28
## eligible_double_your_impact_match eligible_almost_home_match date_posted
## 1 f f 2013-04-14
## 2 t f 2010-09-08
## 3 f f 2010-12-10
## 4 t f 2013-09-27
## 5 f f 2013-12-11
## 6 t f 2012-04-07
## spent is_exciting at_least_1_teacher_referred_donor fully_funded
## 1 31.42 f f t
## 2 66.62 f f t
## 3 77.48 f f f
## 4 444.51 f f t
## 5 168.90 f t t
## 6 30.89 f f t
## at_least_1_green_donation great_chat
## 1 t f
## 2 t f
## 3 f t
## 4 t f
## 5 t f
## 6 t f
## three_or_more_non_teacher_referred_donors
## 1 t
## 2 f
## 3 t
## 4 f
## 5 t
## 6 t
## one_non_teacher_referred_donor_giving_100_plus
## 1 t
## 2 t
## 3 f
## 4 t
## 5 t
## 6 t
## donation_from_thoughtful_donor great_messages_proportion
## 1 f 33
## 2 f 50
## 3 f 100
## 4 f 50
## 5 f 0
## 6 f 40
## teacher_referred_count non_teacher_referred_count
## 1 0 3
## 2 0 2
## 3 0 5
## 4 0 2
## 5 2 4
## 6 0 6
work.df <- df[, !(names(df) %in% c("projectid", "teacher_acctid", "schoolid",
"school_ncesid"))]
rm(df)
work.df[work.df == ""] <- NA
sapply(work.df, class)
## school_latitude
## "numeric"
## school_longitude
## "numeric"
## school_city
## "factor"
## school_state
## "factor"
## school_zip
## "integer"
## school_metro
## "factor"
## school_district
## "factor"
## school_county
## "factor"
## school_charter
## "factor"
## school_magnet
## "factor"
## school_year_round
## "factor"
## school_nlns
## "factor"
## school_kipp
## "factor"
## school_charter_ready_promise
## "factor"
## teacher_prefix
## "factor"
## teacher_teach_for_america
## "factor"
## teacher_ny_teaching_fellow
## "factor"
## primary_focus_subject
## "factor"
## primary_focus_area
## "factor"
## secondary_focus_subject
## "factor"
## secondary_focus_area
## "factor"
## resource_type
## "factor"
## poverty_level
## "factor"
## grade_level
## "factor"
## fulfillment_labor_materials
## "integer"
## total_price_excluding_optional_support
## "numeric"
## total_price_including_optional_support
## "numeric"
## students_reached
## "integer"
## eligible_double_your_impact_match
## "factor"
## eligible_almost_home_match
## "factor"
## date_posted
## "factor"
## spent
## "numeric"
## is_exciting
## "factor"
## at_least_1_teacher_referred_donor
## "factor"
## fully_funded
## "factor"
## at_least_1_green_donation
## "factor"
## great_chat
## "factor"
## three_or_more_non_teacher_referred_donors
## "factor"
## one_non_teacher_referred_donor_giving_100_plus
## "factor"
## donation_from_thoughtful_donor
## "factor"
## great_messages_proportion
## "integer"
## teacher_referred_count
## "integer"
## non_teacher_referred_count
## "integer"
Table size
dim(work.df)
## [1] 664098 43
% of NAs in each column
print(apply(work.df, MARGIN = 2, FUN = function(x) {
sum(is.na(x)) * 100/length(x)
}))
## school_latitude
## 0.000e+00
## school_longitude
## 0.000e+00
## school_city
## 0.000e+00
## school_state
## 0.000e+00
## school_zip
## 6.023e-04
## school_metro
## 1.233e+01
## school_district
## 1.428e-01
## school_county
## 2.560e-03
## school_charter
## 0.000e+00
## school_magnet
## 0.000e+00
## school_year_round
## 0.000e+00
## school_nlns
## 0.000e+00
## school_kipp
## 0.000e+00
## school_charter_ready_promise
## 0.000e+00
## teacher_prefix
## 6.023e-04
## teacher_teach_for_america
## 0.000e+00
## teacher_ny_teaching_fellow
## 0.000e+00
## primary_focus_subject
## 5.873e-03
## primary_focus_area
## 5.873e-03
## secondary_focus_subject
## 3.130e+01
## secondary_focus_area
## 3.130e+01
## resource_type
## 6.776e-03
## poverty_level
## 0.000e+00
## grade_level
## 1.355e-03
## fulfillment_labor_materials
## 5.283e+00
## total_price_excluding_optional_support
## 0.000e+00
## total_price_including_optional_support
## 0.000e+00
## students_reached
## 2.198e-02
## eligible_double_your_impact_match
## 0.000e+00
## eligible_almost_home_match
## 0.000e+00
## date_posted
## 0.000e+00
## spent
## 1.386e+00
## is_exciting
## 6.742e+00
## at_least_1_teacher_referred_donor
## 2.096e+01
## fully_funded
## 6.742e+00
## at_least_1_green_donation
## 2.096e+01
## great_chat
## 6.742e+00
## three_or_more_non_teacher_referred_donors
## 2.096e+01
## one_non_teacher_referred_donor_giving_100_plus
## 2.096e+01
## donation_from_thoughtful_donor
## 2.096e+01
## great_messages_proportion
## 3.382e+01
## teacher_referred_count
## 2.096e+01
## non_teacher_referred_count
## 2.096e+01
summary(work.df, na.action = na.pass)
## school_latitude school_longitude school_city school_state
## Min. :18.2 Min. :-176.6 Chicago : 28435 CA :126242
## 1st Qu.:34.0 1st Qu.:-112.0 Los Angeles: 22097 NY : 73182
## Median :37.7 Median : -87.7 Brooklyn : 20364 NC : 43478
## Mean :37.2 Mean : -93.3 Bronx : 18247 IL : 40167
## 3rd Qu.:40.8 3rd Qu.: -79.2 New York : 12679 TX : 39661
## Max. :67.3 Max. : -66.6 Houston : 9677 FL : 30605
## (Other) :552599 (Other):310763
## school_zip school_metro
## Min. : 410 rural : 80253
## 1st Qu.: 27589 suburban:152234
## Median : 50702 urban :349703
## Mean : 52926 NA's : 81908
## 3rd Qu.: 85043
## Max. : 99950
## NA's :4
## school_district school_county
## New York City Dept Of Ed : 55710 Los Angeles : 50223
## Los Angeles Unif Sch Dist : 39917 Cook : 31458
## Charlotte-mecklenburg Sch Dist: 8844 Kings (Brooklyn) : 19551
## Clark Co School District : 7962 Bronx : 18121
## Hillsborough Co Pub Sch Dist : 7564 New York (Manhattan): 13954
## (Other) :543153 (Other) :530774
## NA's : 948 NA's : 17
## school_charter school_magnet school_year_round school_nlns school_kipp
## f:606501 f:601828 f:631067 f:654157 f:659704
## t: 57597 t: 62270 t: 33031 t: 9941 t: 4394
##
##
##
##
##
## school_charter_ready_promise teacher_prefix
## f:660844 Dr. : 12
## t: 3254 Mr. : 84968
## Mr. & Mrs.: 2
## Mrs. :316590
## Ms. :262522
## NA's : 4
##
## teacher_teach_for_america teacher_ny_teaching_fellow
## f:626349 f:652660
## t: 37749 t: 11438
##
##
##
##
##
## primary_focus_subject primary_focus_area
## Literacy :201378 Literacy & Language:298758
## Literature & Writing: 82189 Math & Science :164339
## Mathematics : 80929 Music & The Arts : 59893
## Special Needs : 41959 Applied Learning : 49534
## Applied Sciences : 31615 Special Needs : 41959
## (Other) :225989 (Other) : 49576
## NA's : 39 NA's : 39
## secondary_focus_subject secondary_focus_area
## Literature & Writing: 69603 Literacy & Language:162525
## Literacy : 66142 Math & Science :110416
## Mathematics : 55947 Applied Learning : 67252
## Special Needs : 28577 History & Civics : 36122
## ESL : 24596 Music & The Arts : 35507
## (Other) :211340 (Other) : 44383
## NA's :207893 NA's :207893
## resource_type poverty_level grade_level
## Books :142394 high poverty :173561 Grades 3-5 :204443
## Other : 69791 highest poverty :383489 Grades 6-8 :114539
## Supplies :229213 low poverty : 16711 Grades 9-12 : 98989
## Technology:216145 moderate poverty: 90337 Grades PreK-2:246118
## Trips : 5499 NA's : 9
## Visitors : 1011
## NA's : 45
## fulfillment_labor_materials total_price_excluding_optional_support
## Min. : 9 Min. : 0
## 1st Qu.:17 1st Qu.: 267
## Median :30 Median : 411
## Mean :27 Mean : 542
## 3rd Qu.:35 3rd Qu.: 586
## Max. :35 Max. :10250017
## NA's :35082
## total_price_including_optional_support students_reached
## Min. : 0 Min. : 0
## 1st Qu.: 318 1st Qu.: 22
## Median : 488 Median : 30
## Mean : 645 Mean : 97
## 3rd Qu.: 696 3rd Qu.: 100
## Max. :12500021 Max. : 999999
## NA's :146
## eligible_double_your_impact_match eligible_almost_home_match
## f:485746 f:626060
## t:178352 t: 38038
##
##
##
##
##
## date_posted spent is_exciting
## 2012-09-30: 1490 Min. : 0 f :582616
## 2011-12-08: 1424 1st Qu.: 46 t : 36710
## 2013-09-01: 1367 Median : 123 NA's: 44772
## 2013-09-03: 1324 Mean : 259
## 2012-01-30: 1245 3rd Qu.: 300
## 2014-03-08: 1207 Max. :10000000
## (Other) :656041 NA's :9205
## at_least_1_teacher_referred_donor fully_funded at_least_1_green_donation
## f :400268 f :188643 f :146235
## t :124660 t :430683 t :378693
## NA's:139170 NA's: 44772 NA's:139170
##
##
##
##
## great_chat three_or_more_non_teacher_referred_donors
## f :432156 f :252550
## t :187170 t :272378
## NA's: 44772 NA's:139170
##
##
##
##
## one_non_teacher_referred_donor_giving_100_plus
## f :147119
## t :377809
## NA's:139170
##
##
##
##
## donation_from_thoughtful_donor great_messages_proportion
## f :517992 Min. : 0
## t : 6936 1st Qu.: 25
## NA's:139170 Median : 50
## Mean : 53
## 3rd Qu.: 83
## Max. :100
## NA's :224611
## teacher_referred_count non_teacher_referred_count
## Min. : 0 Min. : 0
## 1st Qu.: 0 1st Qu.: 1
## Median : 0 Median : 3
## Mean : 1 Mean : 4
## 3rd Qu.: 0 3rd Qu.: 5
## Max. :151 Max. :304
## NA's :139170 NA's :139170
geography.inputs <- c("school_latitude", "school_latitude", "school_longitude",
"school_city", "school_state", "school_zip", "school_metro", "school_district",
"school_county")
state.tab <- table(work.df$is_exciting, work.df$school_state)
state.tab
##
## AK AL AR AZ CA CO CT DC DE FL
## f 1142 4905 5085 8186 111483 5882 6688 6047 1344 26254
## t 87 231 160 756 7023 593 458 600 119 1779
##
## GA HI IA ID IL IN KS KY La LA
## f 12928 2162 2851 1599 35537 15262 2520 3853 3 11017
## t 808 178 127 107 2385 781 148 222 0 641
##
## MA MD ME MI MN MO MS MT NC ND
## f 7684 8070 2911 10173 3755 10549 6325 601 39372 411
## t 839 898 252 846 381 726 297 44 1824 24
##
## NE NH NJ NM NV NY OH OK OR PA
## f 1351 1200 8259 2179 8007 66547 6449 13549 5740 12127
## t 85 98 546 235 323 3276 569 552 469 1166
##
## RI SC SD TN TX UT VA VT WA WI
## f 1814 16945 808 12503 35373 8427 9093 471 8851 5948
## t 170 642 79 760 1827 276 770 30 863 483
##
## WV WY
## f 2003 373
## t 144 13
data(zipcode)
## Warning: data set 'zipcode' not found
mp <- list()
mp["AL"] <- "alabama"
mp["AK"] <- "alaska"
mp["AZ"] <- "arizona"
mp["AR"] <- "arkansas"
mp["CA"] <- "california"
mp["CO"] <- "colorado"
mp["CT"] <- "connecticut"
mp["DE"] <- "delaware"
mp["FL"] <- "florida"
mp["GA"] <- "georgia"
mp["HI"] <- "hawaii"
mp["ID"] <- "idaho"
mp["IL"] <- "illinois"
mp["IN"] <- "indiana"
mp["IA"] <- "iowa"
mp["KS"] <- "kansas"
mp["KY"] <- "kentucky"
mp["LA"] <- "louisiana"
mp["ME"] <- "maine"
mp["MD"] <- "maryland"
mp["MA"] <- "massachusetts"
mp["MI"] <- "michigan"
mp["MN"] <- "minnesota"
mp["MS"] <- "mississippi"
mp["MO"] <- "missouri"
mp["MT"] <- "montana"
mp["NE"] <- "nebraska"
mp["NV"] <- "nevada"
mp["NH"] <- "new hampshire"
mp["NJ"] <- "new jersey"
mp["NM"] <- "new mexico"
mp["NY"] <- "new york"
mp["NC"] <- "north carolina"
mp["ND"] <- "north dakota"
mp["OH"] <- "ohio"
mp["OK"] <- "oklahoma"
mp["OR"] <- "oregon"
mp["PA"] <- "pennsylvania"
mp["RI"] <- "rhode island"
mp["SC"] <- "south carolina"
mp["SD"] <- "south dakota"
mp["TN"] <- "tennessee"
mp["TX"] <- "texas"
mp["UT"] <- "utah"
mp["VT"] <- "vermont"
mp["VA"] <- "virginia"
mp["WA"] <- "washington"
mp["WV"] <- "west virginia"
mp["WI"] <- "wisconsin"
mp["WY"] <- "wyoming"
mp["DC"] <- "district of columbia"
all_states <- map_data("state")
# plot all states with ggplot
all_states$pozitive <- 0
all_states$negative <- 0
for (r in levels(factor(all_states$region))) {
inds <- which(as.character(all_states$region) == r)
s.ind <- which(colnames(state.tab) == names(which(r == mp)))
if (0 == length(s.ind))
browser()
all_states$pozitive[inds] <- state.tab["t", s.ind]
all_states$negative[inds] <- state.tab["f", s.ind]
}
p <- ggplot()
p <- p + geom_polygon(data = all_states, aes(x = long, y = lat, group = group,
fill = pozitive), colour = "Black") + scale_fill_gradient(name = "Positive ",
low = "#FEE6CE", high = "#E6550D")
p <- p + xlab("longitude") + ylab("latitude")
p
p <- ggplot()
p <- p + geom_polygon(data = all_states, aes(x = long, y = lat, group = group,
fill = negative), colour = "Black") + scale_fill_gradient(name = "Negative ",
low = "#EFEDF5", high = "#756BB1")
p <- p + xlab("longitude") + ylab("latitude")
p
city.tab <- table(work.df$is_exciting, work.df$school_city)
city.tab
##
## Abbeville Abbotsford Abbott Abercrombie Aberdeen Abilene Abingdon
## f 166 1 3 2 70 92 21
## t 9 0 0 0 3 4 0
##
## Abington Abita Springs Abrams Absecon Academy Acampo Accident Accokeek
## f 8 39 1 4 4 4 17 6
## t 1 0 0 0 0 0 0 0
##
## Accomac Accord Ackerman Acton Acushnet Acworth Ada Adair Adairsville
## f 21 3 1 14 1 29 50 4 1
## t 1 0 0 3 0 1 5 1 0
##
## Adak Adams Adams Center Adamstown Adamsville Addison Adel Adelanto
## f 1 9 4 1 8 46 17 37
## t 0 3 2 0 0 4 0 2
##
## Adelphi Adena Admire Adrian Advance Afton Agawam Agoura Hills Agra
## f 33 4 1 10 4 17 2 7 2
## t 7 0 0 0 0 1 0 0 0
##
## Agua Dulce Aguila Ahoskie Ahwahnee Aiea Aiken Airway Hgts Aitkin
## f 0 3 15 2 58 235 2 3
## t 0 0 0 0 6 9 1 0
##
## Akiachak Akiak Akron Alabaster Alachua Alakanuk Alameda Alamo
## f 1 1 105 9 64 8 653 104
## t 0 0 9 4 17 2 64 1
##
## Alamogordo Alamosa Alba Albany Albemarle Albert Lea Alberta Alberton
## f 7 5 1 1352 21 5 22 5
## t 0 0 0 33 0 0 0 0
##
## Albertson Albertville Albia Albion Albuquerque Alburnett Alcoa Aldan
## f 23 665 3 4 906 2 8 2
## t 0 15 0 0 87 0 2 0
##
## Alden Aldie Aledo Aleknagik Alex Bay Alex City Alexander Alexandria
## f 5 6 8 2 1 7 4 704
## t 0 0 0 0 0 0 0 66
##
## Alfred Algoma Algona Algonac Algonquin Alhambra Aliceville Aliquippa
## f 3 0 4 7 18 23 1 8
## t 0 0 0 0 0 3 0 0
##
## Aliso Viejo Allamuchy Allegan Alleman Allen Allen Park Allendale
## f 43 1 2 25 91 2 7
## t 2 0 0 0 5 1 0
##
## Allenstown Allenton Allentown Alliance Allison Park Allons Allston
## f 4 12 128 179 7 3 91
## t 0 0 3 16 0 0 6
##
## Alma Almena Almond Almont Aloha Alpena Alpha Alpharetta Alpine Alsea
## f 27 3 6 4 58 31 4 68 25 0
## t 1 0 0 0 12 0 0 6 2 0
##
## Alsip Alta Alta Loma Alta Vista Altadena Altamont Altamonte Spg
## f 6 1 4 1 57 5 39
## t 0 0 0 0 7 0 2
##
## Altavista Altenburg Altha Altheimer Alton Altoona Alturas Altus
## f 1 1 8 2 39 82 5 92
## t 0 0 0 0 3 1 0 1
##
## Alum Creek Alva Alvarado Alvaton Alverton Alvin Alviso Alvord Amana
## f 2 7 3 1 2 82 4 5 5
## t 0 1 0 1 0 7 2 0 0
##
## Amanda Amanda Park Amarillo Amber Amboy Ambridge Amelia Amelia Ct Hse
## f 1 6 43 2 5 2 5 16
## t 0 0 4 0 0 0 0 1
##
## Amenia American Cyn American Fls American Fork Americus Amery Ames
## f 2 155 1 60 11 2 29
## t 0 4 0 5 4 0 2
##
## Amesbury Amherst Amite Amity Amityville Ammon Amo Amoret Amory
## f 19 26 137 8 62 11 2 2 7
## t 0 3 2 0 5 1 0 0 1
##
## Amsterdam Anacoco Anacortes Anadarko Anaheim Anahola Anahuac Anamosa
## f 34 8 1 3 1040 18 3 1
## t 1 0 1 0 41 1 0 0
##
## Anchorage Andale Andalusia Anderson Anderson Is Andersonville Andes
## f 656 5 40 802 1 4 1
## t 40 0 2 21 0 0 0
##
## Andover Andrew Andrews Angie Angier Angleton Angola Angoon Ankeny
## f 45 3 314 2 32 18 5 1 6
## t 4 0 3 0 1 1 0 0 0
##
## Ann Arbor Anna Annandale Annapolis Annawan Anniston Anoka Ansley
## f 79 24 84 129 2 150 5 2
## t 3 0 10 24 0 3 1 2
##
## Anson Ansonia Antelope Anthem Anthony Antigo Antioch Antlers Antonito
## f 65 32 48 38 4 3 1001 10 3
## t 0 4 2 0 0 1 42 1 0
##
## Antwerp Apache Apache Jct Apalachicola Apalachin Apex Apison Apollo
## f 1 3 45 1 9 184 4 3
## t 0 0 7 0 0 11 0 1
##
## Apollo Beach Apopka Appalachia Apple Creek Apple Valley Appleton
## f 72 114 5 2 54 110
## t 5 10 0 0 5 3
##
## Appleton City Appomattox Aptos Aquebogue Aquilla Arab Arabi
## f 49 4 38 6 2 6 5
## t 0 0 1 0 0 0 0
##
## Aransas Pass Arapahoe Ararat Arboga Arbor Vitae Arbuckle Arcadia
## f 1 11 3 8 1 12 323
## t 0 0 0 0 0 1 27
##
## Arcanum Arcata Archdale Archer Archer City Archie Arco Arcola Arden
## f 0 14 14 10 1 1 3 5 379
## t 1 0 1 1 0 0 0 0 18
##
## Arden Hills Ardmore Arena Argos Argyle Ariel Arimo Ariton Arizona City
## f 2 141 1 4 12 3 3 2 2
## t 0 1 0 0 0 0 0 0 0
##
## Arkadelphia Arkansas City Arkansaw Arkport Arlee Arleta Arlington
## f 29 4 1 1 35 270 770
## t 0 0 0 0 1 15 68
##
## Arlington Hts Arma Armada Armagh Armona Armorel Armour Armstrong
## f 21 27 2 27 8 3 3 2
## t 1 1 0 1 0 0 0 0
##
## Armuchee Arnaudville Arnett Arnold Arnoldsburg Aromas Arp Arrington
## f 1 1 2 40 1 1 13 2
## t 0 0 0 5 0 0 2 0
##
## Arroyo Grande Arroyo Seco Artesia Arthur Arthurdale Arvada Arverne
## f 9 1 8 7 0 83 28
## t 3 0 1 2 0 6 0
##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
## Cayce Cayuga Cazenovia Cecil Cecilia Cedar City Cedar Creek
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##
## Cedar Falls Cedar Grove Cedar Hill Cedar Hills Cedar Lake Cedar Park
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##
## Cedar Rapids Cedar Springs Cedar Vale Cedar Valley Cedarburg Cedaredge
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##
## Cedarhurst Cedartown Cedarville Celebration Celina Cement Centennial
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##
## Center Center Conway Center Point Centerburg Centereach Centerport
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##
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##
## Central Islip Central Lake Central Point Central Sq Centralia Centre
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##
## Centreville Ceres Ceresco Cerritos Cerro Gordo Chadbourn Chadron
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##
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##
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##
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##
## Chaplin Chapman Chapmansboro Chaptico Chardon Charleroi Charles City
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##
## Charles Town Charleston Charlestown Charlotte CHARLOTTE Charlotte C H
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##
## Charlottesville Charlottesvle Charlton Charm Charmco Chaska Chassell
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##
## Chataignier Chateaugay Chatfield Chatham Chatsworth Chattanooga
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##
## Chaumont Chauvin Chebanse Cheboygan Check Checotah Cheektowaga
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## Chehalis Chelan Chelmsford Chelsea Cheltenham Chenega Bay Cheney
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##
## Chenoa Chepachet Cheraw Cherokee Cherry Hill Cherry Plain
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##
## Cherry Valley Cherryvale Cherryville Chesaning Chesapeak Bch
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##
## Chesapeake Chesapeake Cy Cheshire Chesnee Chester Chester Sprgs
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##
## Chesterfield Chesterton Chestertown Chestnut Hill Chestnut Rdg
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##
## Cheswick Chetek Chetopa Chevak Cheverly Chevy Chase Chewelah Cheyenne
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##
## Chicago Chicago Hts Chicago Ridge Chickamauga Chickasaw Chickasha
## f 25596 142 38 4 3 13
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##
## Chico Chicopee Chiefland Childersburg Childress Chilhowee Chilhowie
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##
## Chillicothe Chiloquin Chilton Chimacum China China Grove China Spring
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##
## Chino Chino Hills Chino Valley Chinquapin Chippewa Fls Chireno
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##
## Chisago City Chocowinity Choctaw Choteau Choudrant Chouteau Chowchilla
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##
## Chrisman Christiana Christiansbrg Christopher Chualar Chuckey Chugiak
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##
## Chula Vista Chuluota Church Hill Church Point Church Road Church Rock
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##
## Churchville Churdan Cibolo Cicero Cincinnati Cinnaminson Circle Pines
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##
## Circleville Cisco Cissna Park Citra Citronelle Citrus Hts Citrus Spgs
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##
## Citrus Spring City Of Indus Clackamas Clairton Clanton Clara Clare
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##
## Claremont Claremore Clarence Clarendon Clarendon Hls Clarinda Clarion
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##
## Clarissa Clark Clarkdale Clarkesville Clarks Summit Clarksburg
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##
## Clarksdale Clarkson Clarkston Clarksville Clarkton Clatskanie Clawson
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##
## Claxton Clay Clay Center Claymont Claysville Clayton Clear Lake
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##
## Clear Spring Clearbrook Clearfield Clearlake Clearlake Oks Clearville
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##
## Clearwater Cleburne Cleghorn Clemmons Clemson Clendenin Cleo Springs
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
## Ennice Ennis Enola Enterprise Entiat Enumclaw Ephraim Ephrata Epping
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##
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##
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##
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##
## Estill Spgs Ethan Ethridge Etiwanda Etna Etoile Etowah Etters Ettrick
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##
## Eubank Euclid Eudora Eufaula Eugene Euless Eunice Eupora Eureka
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##
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##
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##
## Ewing Excel Exclsor Sprgs Exeter Export Exton Eyota Ezel Fabens
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##
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##
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##
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##
## Fairmont Fairmount Fairplay Fairport Fairview Fairview Hts
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##
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##
## Fall City Fall Creek Fall River Fallbrook Falling Wtrs Fallon
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##
## Falls Church Falls City Falls Village Fallsburg Fallston Falmouth
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##
## Fancy Farm Fancy Gap Far Rockaway Fargo Faribault Farina Farmer City
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##
## Farmers Brnch Farmersburg Farmersville Farmerville Farmingdale
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##
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##
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##
## Federal Hgts Federal Way Feeding Hills Felch Felicity Fellows
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
## Flowery Br Flowood Floyd Floyds Knobs Flushing Folcroft Foley
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##
## Follansbee Folsom Fond Du Lac Fonda Fontana Fontanelle Foothill Ranch
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##
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##
## Forest Grove Forest Hill Forest Hills Forest Lake Forest Park
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
## Grulla Grundy Grundy Center Gruver Gt Barrington Guadalupe Gueydan
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
## Hardwick Hardy Harker Hts Harlan Harlem Harleton Harlingen Harlowton
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
## Hayfield Haymarket Haynesville Hayneville Hays Haysville Hayti
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##
## Hayward Hazard Hazel Crest Hazel Green Hazel Park Hazelton Hazelwood
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##
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##
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##
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##
## Heiskell Helena Helendale Helix Helotes Helper Hemet Hemingway Hemlock
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##
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##
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##
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##
## Herkimer Herlong Herman Hermann Herminie Hermiston Hermitage Hermon
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##
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##
## Herscher Hershey Hertford Hesperia Hesperus Hesston Hettinger
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##
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##
## Hiawatha Hibbing Hickman Hickory Hickory Crnrs Hickory Flat
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##
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##
## Higginsville High Island High Point High Ridge High Springs Highfalls
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##
## Highgate Ctr Highland Highland City Highland Hgts Highland Hls
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##
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##
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##
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##
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##
## Hinesville Hinsdale Hinton Hiram Hiseville Hitchcock Hixson Ho Ho Kus
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##
## Hoagland Hobart Hobbs Hobe Sound Hoboken Hockessin Hockley Hodgdon
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##
## Hodges Hodgkins Hoffman Est Hogansville Hohenwald Hokes Bluff Holbrook
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##
## Holcomb Holcombe Holden Holdenville Holgate Holiday Holladay Holland
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##
## Holland Patnt Hollandale Holley Holliday Hollidaysburg Hollis
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##
## Hollis Center Hollister Holliston Holloman Afb Holly Holly Hill
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##
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##
## Holton Holtsville Holtville Holtwood Holualoa Holyoke Holyrood
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##
## Homedale Homer Homer City Homer Glen Homestead Hometown Homewood
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##
## Hominy Honaker Hondo Honea Path Honeoye Honeoye Falls Honesdale
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##
## Honey Brook Honey Grove Honeydew Honokaa Honolulu Honor Hood River
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##
## Hooker Hooks Hooksett Hookstown Hoolehua Hoopa Hooper Hooper Bay
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##
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##
## Hope Valley Hopedale Hopewell Hopkins Hopkinsville Hoquiam Horatio
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##
## Horicon Horn Lake Hornbeak Hornell Horse Cave Horseheads Horseshoe Bnd
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##
## Horton Hortonville Hosford Hoskinston Hospers Hot Springs Hotchkiss
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##
## Houghton Houghton Lake Houlton Houma House Springs Houston Houtzdale
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##
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##
## Howey In Hls Howland Hoxie Hoyt Hubbard Huber Heights Hubert Hubertus
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##
## Huddleston Hudson Hudson Falls Hudsonville Hueytown Huffman Huger
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##
## Hughes Hughes Spgs Hughesville Hughson Hugo Hugoton Hulbert Hull
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##
## Humansville Humble Humboldt Hume Humeston Hunter Huntersville
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##
## Huntingburg Huntingdon Huntingdon Vy Huntingtn Bch Huntingtn Sta
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##
## Huntington Huntington Pk Huntington Wd Huntingtown Huntland Huntsville
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##
## Hurdle Mills Hurley Hurlock Huron Hurricane Hurst Hustisford
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##
## Hustonville Hutchinson Hutto Huxley Hvre De Grace Hyannis Hyattsville
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##
## Hyde Park Hyden Hydro Hymera Hyrum Iberia Ida Ida Grove Idabel
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##
## Idaho City Idaho Falls Ignacio Igo Ijamsville Iliff Ilion
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##
## Illinois City Illiopolis Ilwaco Imboden Imlay City Immokalee Imperial
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##
## Imperial Bch Incline Vlg Independence Indialantic Indian Head
## f 12 0 525 8 8
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##
## Indian Lake Indian Land Indian Orch Indian Spgs Indian Trail Indiana
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##
## Indianapolis Indianola Indiantown Indio Indn Hbr Bch Industry Inez
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##
## Ingleside Inglewood Inglis Ingram Inkster Inman Inola Interlachen
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##
## Interlochen Intl Falls Inver Grove Inverness Inwood Iola Iona Ione
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##
## Ionia Iota Iowa Iowa City Iowa Falls Iowa Park Ipswich Ira
## f 5 2 1 30 15 9 2 2
## t 0 0 0 3 0 1 0 0
##
## Irasburg Irmo Iron Mountain Iron River Iron Station Irondale Ironton
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##
## Ironwood Iroquois Irrigon Irvine Irving Irvington Irwin Irwinton
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##
## Isanti Iselin Ishpeming Islamorada Island Lake Island Park Island Pond
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##
## Isle Isleton Islip Islip Terrace Issaquah Italy Itasca Ithaca
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##
## Itta Bena Iuka Iva Ivanhoe Ivins Ixonia Jacksboro Jackson
## f 36 8 24 4 14 0 9 544
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##
## Jackson Ctr Jackson Hts Jacksonville Jaffrey Jal Jamaica
## f 1 292 1225 1 1 599
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##
## Jamaica Plain Jamesburg Jamesport Jamestown Jamesville Jamison Jamul
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##
## Jane Jane Lew Janesville Jarrell Jarrettsville Jarvisburg Jasonville
## f 0 0 131 1 1 1 8
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##
## Jasper Jaxville Bch Jay Jber Jeanerette Jeannette Jeff City
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##
## Jefferson Jefferson Cty Jefferson Hls Jeffersonvlle Jemez Pueblo
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##
## Jemison Jena Jenison Jenkins Jenkintown Jenks Jennings Jensen Beach
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##
## Jericho Jermyn Jerome Jersey City Jersey Shore Jerseyville Jessup
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##
## Jesup Jewell Jim Falls Jim Thorpe Joanna Joaquin Joelton Johannesburg
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##
## Johns Creek Johns Island Johnsburg Johnson Johnson City Johnson Creek
## f 57 69 1 2 97 1
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##
## Johnsonburg Johnsonville Johnston Johnston City Johnstown Joliet Jones
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##
## Jones Creek Jonesboro Jonesborough Jonesburg Jonesport Jonestown
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##
## Jonesville Joplin Joppa Jordan Joseph Joseph City Joshua Joshua Tree
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##
## Jourdanton Joyce Juda Judsonia Julesburg Julian Jumping Br Junction
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##
## Junction City Juneau Jupiter Jurupa Valley Justice Justin Kahoka
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##
## Kahuku Kahului Kailua Kailua Kona Kake Kalaheo Kalama Kalamazoo
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##
## Kalida Kalispell Kalkaska Kamas Kamiah Kamuela Kanab Kaneohe Kankakee
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##
## Kannapolis Kansas Kansas City Kapaa Kapaau Kaplan Kapolei Karnack
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##
## Karnes City Karns City Kasilof Kasson Katonah Katy Kaufman Kaukauna
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##
## Kaunakakai Kayenta Kaysville Keaau Kealakekua Keansburg Kearney Kearns
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##
## Kearny Keene Keenesburg Keiser Keithville Keizer Kekaha Kelford Kell
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##
## Keller Kellogg Kellyville Kelseyville Kelso Kemah Kemmerer Kemp
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##
## Kempton Kenai Kenansville Kendall Kendall Park Kendallville Kendrick
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
## Maumee Maumelle Maunaloa Maupin Maurice Mauston Max Meadows Maxton
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##
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##
## Maylene Maynard Maynardville Mayo Mayodan Maypearl Mays Landing
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##
## Mc Bee Mc Calla Mc Clave Mc Coll Mc Connellsbg Mc Cool Mc Cordsville
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##
## Mc Cormick Mc Crory Mc Dade Mc Dermott Mc Donald Mc Dowell Mc Farland
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##
## Mc Graw Mc Gregor Mc Guire Afb Mc Intosh Mc Kean Mc Kee Mc Kees Rocks
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##
## Mc Kenney Mc Kenzie Mc Kinney Mc Lain Mc Laughlin Mc Lean Mc Leansboro
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##
## Mc Leansville Mc Louth Mc Minnville Mc Neal Mc Rae Mc Veytown Mcadoo
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##
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##
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##
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##
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##
## Mcpherson Mead Meade Meadow Vista Meadows Dan Meadowview Meadville
## f 19 1 1 1 1 6 57
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##
## Mebane Mecca Mechanic Fls Mechanicsburg Mechanicsville Mechanicsvlle
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##
## Mechanicville Medfield Medford Media Mediapolis Medical Lake
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##
## Medicine Ldg Medina Medinah Medora Medway Meeker Meeteetse Melbourne
## f 1 61 1 6 4 6 0 97
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##
## Melbourne Bch Melcher Melissa Melrose Melrose Park Melvern Melvindale
## f 1 1 15 27 17 1 6
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##
## Memphis Mena Menahga Menasha Mendenhall Mendham Mendocino Mendon
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##
## Mendota Mendota Hts Menifee Menlo Park Menoken Menominee Menomonee Fls
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##
## Menomonie Mentone Mentor Mequon Meraux Merced Mercedes Mercer
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##
## Mercersburg Meredosia Meriden Meridian Meridianville Merigold Merkel
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##
## Merriam Merrick Merrill Merrillville Merrimac Merrimack Merrionett Pk
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##
## Merritt Is Merryville Mesa Mescalero Mesick Mesilla Mesilla Park
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##
## Mesopotamia Mesquite Metairie Metamora Methuen Metlakatla Metropolis
## f 0 271 230 5 21 1 9
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##
## Metter Metuchen Metz Mexia Mexico Meyersdale Meyersville Miami
## f 1 6 1 13 2 3 1 2271
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##
## Miami Beach Miami Gardens Miami Lakes Miami Springs Miamisburg
## f 271 80 27 22 14
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##
## Miamitown Micanopy Michie Michigan City Michigan Ctr Michigantown
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##
## Micro Middle Island Middle Village Middleboro Middlebourne Middleburg
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##
## Middleburgh Middlebury Middlefield Middleport Middlesex Middleton
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##
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##
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##
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##
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##
## Mill Spring Mill Valley Millbrae Millbrook Millburn Millbury
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##
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##
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##
## Millville Milo Milpitas Milton Milton Frwtr Miltona Miltonvale
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##
## Milwaukee Milwaukie Mims Minatare Minco Minden Mindoro Mineola
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##
## Mineral Mineral Point Mineral Spgs Mineral Wells Minerva Minford
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##
## Minier Minneapolis Minneola Minneota Minnetonka Minoa Minocqua Minong
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##
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##
## Miramar Miranda Misenheimer Mishawaka Mishicot Mission Mission Hills
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##
## Mission Viejo Missoula Missouri City Missouri Vly Mitchell
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##
## Mitchellville Moab Moapa Moatsville Moberly Mobile Mobridge
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##
## Mocksville Modesto Moffat Moffett Mogadore Mohave Valley Mohawk
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##
## Mohegan Lake Mojave Mokena Molalla Moline Molino Momence Monahans
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
## N Plainfield N Providence N Richlnd Hls N Ridgeville N Riverside
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##
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##
## N Tonawanda N Versailles N Wilkesboro N Yarmouth Naalehu Naches Naco
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##
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##
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##
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##
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##
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##
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##
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## Neptune Beach Nerstrand Nesconset Nettie Nettleton Nevada Nevada City
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##
## New Albany New Albin New Alexandria New Augusta New Baden
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##
## New Baltimore New Bedford New Berlin New Bern New Bethlehem
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##
## New Bloomfld New Boston New Braunfels New Bremen New Brighton
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##
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##
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##
## New Concord New Cumberlnd New Deal New Egypt New Ellenton New England
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##
## New Fairfield New Franklin New Freeport New Glarus New Gloucester
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##
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##
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##
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##
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##
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##
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##
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##
## New Tazewell New Ulm New Underwood New Vienna New Washingtn
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##
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##
## New York Mls New Zion Newark Newark Valley Newaygo Newberg Newbern
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##
## Newberry Newberry Spgs Newburg Newburgh Newbury Newbury Park
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##
## Newburyport Newcastle Newcomb Newcomerstown Newell Newfane Newfolden
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##
## Newhall Newington Newkirk Newland Newman Newmarket Newnan Newport
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##
## Newport Beach Newport Coast Newport Ctr Newport News Newsoms Newton
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##
## Newton Centre Newton Falls Newton Grove Newtonville Newtown Newtown Sq
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##
## Newville Niagara Niagara Falls Niangua Nibley Nicasio Niceville
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##
## Nicholasville Nickelsville Nicollet Niederwald Nightmute Nikiski Niles
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##
## Nine Mile Fls Ninety Six Ninnekah Niobrara Nipomo Nisswa Nitro Niwot
## f 1 6 4 2 5 1 7 3
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##
## Nixa No Oaks Noatak Noble Nobleboro Noblesville Nocona Noel Nogales
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##
## Nokesville Nokomis Nolanville Nolensville Nora Nora Springs Norborne
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##
## Norco Norcross Norfolk Norfork Norlina Normal Normalville Norman
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##
## Normandy Park Normangee Norridge Norridgewock Norris Norris City
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##
## Norristown North North Adams North Andover North Anson North Augusta
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##
## North Aurora North Babylon North Bend North Bergen North Berwick
## f 2 17 11 15 7
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##
## North Branch North Caldwel North Canton North Chicago North Conway
## f 20 1 4 6 15
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##
## North Dighton North East North Easton North English North Fork
## f 1 10 4 26 1
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##
## North Hampton North Haven North Hills North Jackson North Judson
## f 1 1 744 0 5
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##
## North Lake North Liberty North Logan North Mankato North Merrick
## f 1 11 7 13 1
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##
## North Miami North Ogden North Olmsted North Plainfi North Plains
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##
## North Platte North Pole North Port North Potomac North Reading
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##
## North Rose North Tustin North Vernon North Webster North Windham
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##
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##
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##
## Nortonville Norwalk Norway Norwell Norwich Norwood Notasulga
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##
## Nottingham Novato Novelty Novi Novice Nowata Nuevo Nunam Iqua Nunda
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##
## Nutley Nutter Fort NY Nya Nyack Nyssa O Fallon O Neals Oak Creek
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##
## Oak Forest Oak Grove Oak Hall Oak Harbor Oak Hill Oak Lawn Oak Park
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##
## Oak Point Oak Ridge Oak View Oakboro Oakdale Oakes Oakfield Oakhurst
## f 15 19 3 3 36 1 2 3
## t 1 2 4 0 1 0 0 0
##
## Oakland Oakland City Oakland Gdns Oakland Park Oakley Oaklyn Oakman
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##
## Oakmont Oakridge Oakton Oakville Oakwood Oark Oberlin Ocala
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##
## Occidental Ocean Ocean City Ocean Park Ocean Shores Ocean Springs
## f 0 0 0 6 1 199
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##
## Oceana Oceanport Oceanside Ochelata Ocilla Ocoee Oconomowoc Oconto
## f 7 2 159 10 1 22 18 11
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##
## Oconto Falls Ocracoke Odell Odem Oden Odenton Odenville Odessa Odin
## f 2 1 1 9 1 10 8 279 3
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##
## Oelwein Offerle Ogallala Ogden Ogdensburg Ogema Oglala Oglesby
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##
## Oglethorpe Ohatchee Oil City Ojo Caliente Okanogan Okarche Okatie
## f 2 2 3 1 4 2 9
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##
## Okawville Okay Okeechobee Okeene Okemah Okemos Oklahoma City Oklee
## f 4 8 26 1 7 0 2082 3
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##
## Okmulgee Okolona Ola Olalla Olanta Olathe Old Bethpage Old Bridge
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##
## Old Fort Old Greenwich Old Hickory Old Orchd Bch Old Saybrook Old Town
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##
## Old Westbury Oldsmar Olean Oley Olin Olive Branch Olive Hill
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##
## Olivehurst Oliver Spgs Olivet Olivia Olla Olmstead Olmsted Falls
## f 55 12 2 1 5 1 3
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##
## Olmstedville Olney Olustee Olympia Olympia Flds Omaha Omak Omar
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##
## Omega Omro Ona Onaga Onalaska Onamia Onawa Onaway Oneida Oneonta
## f 1 4 3 1 16 2 2 7 26 13
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##
## Onley Ontario Ontario Ctr Oologah Ooltewah Oostburg Opa Locka Opelika
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##
## Opelousas Opp Oran Orange Orange Beach Orange City Orange Cove
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##
## Orange Park Orangeburg Orangefield Orangevale Orangeville Orchard
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##
## Orchard Park Ordway Ore City Oregon Oregon City Orem Orfordville
## f 3 0 26 15 20 81 2
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##
## Orinda Orion Oriskany Orland Orland Park Orlando Orleans Ormond Beach
## f 44 0 1 39 5 1915 4 32
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##
## Oro Grande Oro Valley Orofino Orono Oroville Orrick Orrington Orrum
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##
## Orrville Orting Ortonville Orwell Orwigsburg Osage Beach Osawatomie
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##
## Osceola Osceola Mills Oscoda Oshkosh Oskaloosa Osprey Osseo Ossian
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##
## Ossineke Ossining Osteen Oswego Osyka Othello Otis Otsego Ottawa
## f 13 8 1 43 1 4 1 39 29
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##
## Otter Lake Ottumwa Otwell Outlook Ovando Overbrook Overland Park
## f 0 54 3 2 1 0 28
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##
## Overton Ovid Oviedo Owasso Owatonna Owego Owensboro Owensville
## f 30 1 23 64 23 3 16 15
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##
## Owenton Owings Owings Mills Owingsville Owosso Oxford Oxford Jct
## f 7 4 9 16 12 207 0
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##
## Oxnard Oxon Hill Oyster Bay Ozark Ozone Park OZONE PARK P C Beach
## f 138 17 1 161 435 1 182
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##
## Paauilo Pace Pacific Pacific Beach Pacific Grove Pacific Plsds
## f 0 58 30 2 8 35
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##
## Pacifica Packwood Pacoima Pacolet Paducah Page Pageland Pagosa Spgs
## f 347 1 408 1 64 10 20 8
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##
## Pahala Pahoa Pahokee Pahrump Paia Paicines Painesville Paint Rock
## f 4 31 1 5 4 1 24 1
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##
## Painted Post Paintsville Paisley Palacios Palatine Palatka Palermo
## f 12 1 3 1 31 10 8
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##
## Palestine Palisade Palm Bay Palm Bch Gdns Palm City Palm Coast
## f 190 2 157 172 10 103
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##
## Palm Desert Palm Harbor Palm Springs Palmdale Palmer Palmerton
## f 14 14 22 104 36 3
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##
## Palmetto Palmetto Bay Palmyra Palo Alto Palo Cedro Palo Verde
## f 70 9 48 49 2 2
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##
## Palos Heights Palos Hills Palos Park Pampa Pamplico Pana Panama
## f 3 12 1 1 13 2 1
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##
## Panama City Pandora Pangburn Panola Panorama City Paola Paoli Papaikou
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##
## Papillion Parachute Paradis Paradise Paradise Vly Paragould Paramount
## f 42 6 1 27 1 25 29
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##
## Paramus Parchment Paris Parishville Park City Park Falls Park Forest
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##
## Park Hall Park Hill Park Hills Park Rapids Park Ridge Parkdale Parker
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##
## Parker City Parkers Pr Parkersburg Parkland Parksley Parkton Parlier
## f 1 1 76 13 0 12 1
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##
## Parlin Parma Parma Heights Parmelee Parrish Parrottsville Parsippany
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##
## Parsons Partridge Pasadena Pascagoula Pasco Pascoag Paso Robles
## f 4 8 723 302 37 24 20
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##
## Pass Christin Passaic Patagonia Pataskala Patchogue Pateros Paterson
## f 12 63 1 30 2 1 783
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##
## Patrick Spgs Patricksburg Patterson Patton Paul Paulding Paulina
## f 7 1 65 10 1 7 15
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##
## Pauline Paullina Pauls Valley Paulsboro Pauma Valley Pavilion Paw Paw
## f 23 1 66 1 1 3 5
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##
## Pawcatuck Pawhuska Pawleys Isl Pawnee Pawtucket Paxton Payette Payne
## f 1 12 440 41 26 23 78 1
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##
## Paynesville Payson Pea Ridge Peabody Peach Springs Peachland
## f 5 37 12 69 1 3
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##
## Peachtree Corners Peachtree Cty Peaks Island Pearblossom Pearcy
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##
## Pearisburg Pearl Pearl City Pearl River Pearland Pearsall Pearson
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##
## Pecatonica Pecos Peculiar Peekskill Pegram Pekin Pel Rapids
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##
## Pelahatchie Pelham Pelican Pelion Pell City Pella Pellston Pelzer
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##
## Pemaquid Pemberton Pemberville Pembine Pembroke Pembroke Park
## f 2 2 2 3 38 6
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##
## Pembroke Pines Penacook Penasco Pendleton Penelope Penfield Penhook
## f 55 1 5 40 4 1 4
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##
## Peninsula Penn Laird Penn Valley Penngrove Pennsauken Pennsboro
## f 2 18 0 8 19 2
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##
## Pennsburg Penobscot Pensacola Peoria Peoria Hts Peotone Pepin
## f 23 4 144 266 1 1 1
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##
## Pepper Pike Pepperell Pequot Lakes Peralta Perham Perkasie Perkins
## f 1 2 1 1 1 3 11
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##
## Perkinston Perrin Perris Perry Perryopolis Perrysburg Perryton
## f 6 1 105 62 2 10 0
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##
## Perryville Perth Amboy Peru Petal Petaluma Petersburg Peterstown
## f 10 48 57 91 46 118 14
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##
## Petoskey Petrolia Pewaukee Peyton Pfafftown Pflugerville Pharr Phelan
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##
## Phelps Phenix Phenix City Phil Campbell Philadelphia Philippi
## f 1 3 243 36 6018 1
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##
## Philipsburg Phillips Phillipsburg Philo Philomath Phippsburg Phoenicia
## f 15 1 40 4 11 5 1
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##
## Phoenix Phoenixville Piasa Picacho Picayune Pickens Pickerington
## f 2425 17 2 59 32 19 24
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##
## Pickford Pico Rivera Piedmont Pierce Pierce City Pierceton Pierre
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##
## Pierson Pigeon Forge Piggott Pikeville Pilot Mtn Pilot Point
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##
## Pilot Station Pima Pinckard Pinckney Pinconning Pine Pine Beach
## f 0 0 2 2 2 2 1
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##
## Pine Bluff Pine Bush Pine City Pine Grove Pine Hall Pine Hill
## f 139 7 1 3 4 3
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##
## Pine Island Pine Level Pine Mountain Pine Prairie Pine Valley
## f 10 17 6 1 1
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##
## Pinecrest Pinedale Pinehurst Pineland Pinellas Park Pinetops Pinetown
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##
## Pinetta Pineville Pinewood Piney Flats Pink Hill Pinnacle Pinola
## f 1 215 4 3 9 9 5
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##
## Pinole Pinon Hills Pinson Pioneer Pipe Creek Piper City Pipersville
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##
## Piqua Pirtleville Piru Piscataway Pisgah Pitcairn Pitkin Pitman
## f 17 2 7 55 77 2 1 22
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##
## Pittsboro Pittsburg Pittsburgh Pittsfield Pittsford Pittsgrove
## f 173 432 523 23 2 1
## t 12 23 41 1 0 0
##
## Pittston Pittsville Pixley Placentia Placerville Plain City
## f 5 5 1 96 16 11
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##
## Plain Dealing Plainfield Plainsboro Plainview Plainville Plainwell
## f 10 126 2 18 7 5
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##
## Plaistow Plano Plant City Plantation Plantersville Plantsville
## f 1 92 480 45 3 2
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##
## Plaquemine Platte Platte City Plattekill Platteville Plattsburg
## f 5 5 7 2 7 0
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##
## Plattsburgh Plattsmouth Playa Del Rey Pleasant Grv Pleasant Hill
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##
## Pleasant Hope Pleasant Plns Pleasant Pr Pleasant View Pleasanton
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##
## Pleasantville Plover Pls Vrds Est Plumas Lake Plummer Plymouth
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##
## Plymouth Mtng Plympton Pnte Vdra Bch Poca Pocahontas Pocasset
## f 6 26 15 2 6 2
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##
## Pocatello Pocola Pocomoke City Pocono Pines Poestenkill Poinciana
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##
## Point Arena Point Marion Poland Polk City Polkton Pollock
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##
## Pollocksville Pollok Polo Polson Pomaria Pomeroy Pomfret Pomona
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##
## Pompano Beach Pompton Lakes Ponca City Ponce De Leon Ponchatoula
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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##
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## t 3 6 0 0 1 0
##
## Winthrop Hbr Winton Wirtz Wisc Dells Wisc Rapids Wiscasset Wise
## f 1 5 10 7 14 4 7
## t 0 0 1 0 3 2 0
##
## Wittenberg Wittmann Wixom Woburn Woden Wolcott Wolf Lake Wolf Point
## f 0 11 4 4 7 7 1 51
## t 0 0 0 0 0 0 0 2
##
## Wolfe City Wolfforth Wolsey Wolverine Wonder Lake Wood Dale Wood Ridge
## f 1 16 4 1 5 0 27
## t 0 0 0 1 0 0 2
##
## Woodbridge Woodburn Woodbury Woodhaven Woodhull Woodinville Woodlake
## f 308 15 140 35 3 9 4
## t 21 2 2 1 0 0 0
##
## Woodland Woodland Hls Woodland Park Woodlawn Woodleaf Woodmere
## f 48 511 1 8 17 1
## t 13 48 0 0 0 0
##
## Woodridge Woodruff Woods Cross Woodsboro Woodside Woodson Woodstock
## f 15 69 55 0 177 1 80
## t 0 2 0 0 9 0 7
##
## Woodstown Woodville Woodward Woodworth Woolwich Woolwich Twp
## f 5 44 7 1 1 3
## t 1 0 0 0 0 0
##
## Woonsocket Wooster Wooten Worcester Worden Worland Worth Worthing
## f 71 2 0 445 1 1 1 1
## t 9 0 1 60 0 0 0 0
##
## Worthington Worton Wray Wrens Wrenshall Wrentham Wright Wright City
## f 11 0 1 3 15 3 1 48
## t 0 0 0 0 5 0 0 1
##
## Wrightstown Wrightsville Wrightwood Writsvlle Bch Wshngtn Ct Hs
## f 7 4 3 9 4
## t 0 0 0 0 0
##
## Wurtland Wyandanch Wyandotte Wyckoff Wyeville Wylie Wyncote Wynne
## f 1 155 27 12 1 20 3 45
## t 0 13 7 0 0 4 1 0
##
## Wynnewood Wynona Wyoming Wyomissing Wytheville Xenia Yadkinville
## f 4 3 58 6 11 14 41
## t 0 0 3 0 0 1 4
##
## Yakima Yale Yamhill Yampa Yanceyville Yankeetown Yankton Yantis
## f 107 11 1 0 4 0 6 1
## t 4 0 0 0 1 0 0 0
##
## Yaphank Yardley Yarmouth Yatahey Yates Center Yazoo Yazoo City Yeadon
## f 0 8 5 5 1 2 21 11
## t 1 0 0 2 0 1 5 0
##
## Yellow Spgs Yellville Yelm Yemassee Yerington Yermo Yoakum Yoncalla
## f 4 12 13 0 0 3 1 2
## t 0 1 1 1 1 0 0 0
##
## Yonkers Yorba Linda York York Haven York Springs Yorkshire Yorktown
## f 768 87 349 2 7 1 30
## t 25 5 30 0 0 0 2
##
## Yorktown Hts Yorkville Youngstown Youngsville Youngtown Yountville
## f 14 9 127 105 1 2
## t 2 1 19 2 0 0
##
## Ypsilanti Yreka Yuba City Yucaipa Yucca Valley Yukon Yulee Yuma
## f 83 5 52 29 77 164 10 96
## t 16 1 2 3 3 3 0 14
##
## Zachary Zanesville Zapata Zearing Zebulon Zeeland Zellwood Zephyr
## f 18 6 0 2 57 5 4 31
## t 2 0 1 0 0 0 0 0
##
## Zephyr Cove Zephyrhills Zillah Zimmerman Zion Zionsville Zionville
## f 4 54 4 2 11 183 18
## t 0 6 1 0 2 10 1
##
## Zoarville Zolfo Springs Zuni Zwolle
## f 1 3 11 12
## t 0 0 2 0
Tables and Fisher Test for Categorical variables vs. output
output <- "is_exciting"
categorical.inputs <- names(sapply(work.df, class)[sapply(work.df, class) ==
"factor"])
categorical.inputs <- setdiff(categorical.inputs, output)
categorical.inputs <- setdiff(categorical.inputs, geography.inputs)
categorical.inputs
## [1] "school_charter"
## [2] "school_magnet"
## [3] "school_year_round"
## [4] "school_nlns"
## [5] "school_kipp"
## [6] "school_charter_ready_promise"
## [7] "teacher_prefix"
## [8] "teacher_teach_for_america"
## [9] "teacher_ny_teaching_fellow"
## [10] "primary_focus_subject"
## [11] "primary_focus_area"
## [12] "secondary_focus_subject"
## [13] "secondary_focus_area"
## [14] "resource_type"
## [15] "poverty_level"
## [16] "grade_level"
## [17] "eligible_double_your_impact_match"
## [18] "eligible_almost_home_match"
## [19] "date_posted"
## [20] "at_least_1_teacher_referred_donor"
## [21] "fully_funded"
## [22] "at_least_1_green_donation"
## [23] "great_chat"
## [24] "three_or_more_non_teacher_referred_donors"
## [25] "one_non_teacher_referred_donor_giving_100_plus"
## [26] "donation_from_thoughtful_donor"
for (input in categorical.inputs) {
work.df[, input] <- factor(work.df[, input])
tab <- table(work.df[, output], work.df[, input])
print(tab)
print(fisher.test(tab))
}
##
## f t
## f 534306 48310
## t 31636 5074
##
## Fisher's Exact Test for Count Data
##
## data: tab
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.719 1.830
## sample estimates:
## odds ratio
## 1.774
##
##
## f t
## f 527472 55144
## t 33339 3371
##
## Fisher's Exact Test for Count Data
##
## data: tab
## p-value = 0.07433
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.9322 1.0032
## sample estimates:
## odds ratio
## 0.9672
##
##
## f t
## f 553182 29434
## t 35096 1614
##
## Fisher's Exact Test for Count Data
##
## data: tab
## p-value = 1.399e-08
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.8206 0.9098
## sample estimates:
## odds ratio
## 0.8643
##
##
## f t
## f 573755 8861
## t 35983 727
##
## Fisher's Exact Test for Count Data
##
## data: tab
## p-value = 2.809e-11
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.210 1.412
## sample estimates:
## odds ratio
## 1.308
##
##
## f t
## f 578911 3705
## t 36221 489
##
## Fisher's Exact Test for Count Data
##
## data: tab
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.915 2.320
## sample estimates:
## odds ratio
## 2.11
##
##
## f t
## f 579726 2890
## t 36444 266
##
## Fisher's Exact Test for Count Data
##
## data: tab
## p-value = 1.633e-08
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.286 1.661
## sample estimates:
## odds ratio
## 1.464
##
##
## Dr. Mr. Mr. & Mrs. Mrs. Ms.
## f 10 74196 2 280322 228086
## t 0 4781 0 14944 16985
## Error: FEXACT error 40. Out of workspace.
numericas.inputs <- names(sapply(work.df, class)[sapply(work.df, class) != "factor"])
numericas.inputs <- setdiff(numericas.inputs, geography.inputs)
numericas.inputs
## [1] "fulfillment_labor_materials"
## [2] "total_price_excluding_optional_support"
## [3] "total_price_including_optional_support"
## [4] "students_reached"
## [5] "spent"
## [6] "great_messages_proportion"
## [7] "teacher_referred_count"
## [8] "non_teacher_referred_count"
num.inputs.qu.list <- c("spent", "total_price_excluding_optional_support", "total_price_including_optional_support",
"students_reached", "teacher_referred_count", "non_teacher_referred_count")
for (nm in numericas.inputs) {
if (nm %in% num.inputs.qu.list) {
qa.cond <- work.df[, nm] < quantile(work.df[, nm], 0.9, na.rm = T)
hist(work.df[qa.cond, nm], main = nm, col = "blue", xlab = "", freq = F)
} else {
hist(work.df[, nm], main = nm, col = "blue", xlab = "", freq = F)
}
}
cor.matr <- cor(work.df[, numericas.inputs], use = "na.or.complete")
print(cor.matr)
## fulfillment_labor_materials
## fulfillment_labor_materials 1.000000
## total_price_excluding_optional_support 0.021214
## total_price_including_optional_support 0.011529
## students_reached -0.002823
## spent -0.004416
## great_messages_proportion -0.072254
## teacher_referred_count 0.161983
## non_teacher_referred_count 0.062437
## total_price_excluding_optional_support
## fulfillment_labor_materials 0.021214
## total_price_excluding_optional_support 1.000000
## total_price_including_optional_support 0.999836
## students_reached 0.006114
## spent 0.729494
## great_messages_proportion -0.016848
## teacher_referred_count 0.097383
## non_teacher_referred_count 0.072223
## total_price_including_optional_support
## fulfillment_labor_materials 0.011529
## total_price_excluding_optional_support 0.999836
## total_price_including_optional_support 1.000000
## students_reached 0.006163
## spent 0.728930
## great_messages_proportion -0.016045
## teacher_referred_count 0.094951
## non_teacher_referred_count 0.072224
## students_reached spent
## fulfillment_labor_materials -2.823e-03 -0.004416
## total_price_excluding_optional_support 6.114e-03 0.729494
## total_price_including_optional_support 6.163e-03 0.728930
## students_reached 1.000e+00 0.006247
## spent 6.247e-03 1.000000
## great_messages_proportion -1.786e-03 -0.009342
## teacher_referred_count -1.709e-03 0.109674
## non_teacher_referred_count 1.269e-05 0.057132
## great_messages_proportion
## fulfillment_labor_materials -0.072254
## total_price_excluding_optional_support -0.016848
## total_price_including_optional_support -0.016045
## students_reached -0.001786
## spent -0.009342
## great_messages_proportion 1.000000
## teacher_referred_count 0.006955
## non_teacher_referred_count 0.063725
## teacher_referred_count
## fulfillment_labor_materials 0.161983
## total_price_excluding_optional_support 0.097383
## total_price_including_optional_support 0.094951
## students_reached -0.001709
## spent 0.109674
## great_messages_proportion 0.006955
## teacher_referred_count 1.000000
## non_teacher_referred_count 0.103249
## non_teacher_referred_count
## fulfillment_labor_materials 6.244e-02
## total_price_excluding_optional_support 7.222e-02
## total_price_including_optional_support 7.222e-02
## students_reached 1.269e-05
## spent 5.713e-02
## great_messages_proportion 6.373e-02
## teacher_referred_count 1.032e-01
## non_teacher_referred_count 1.000e+00
plotcorr(cor.matr)
plot.df <- work.df[, c(numericas.inputs, output)]
no.plot.names <- output
for (nm in names(plot.df)) {
no.plot.names <- c(no.plot.names, nm)
names.diff <- setdiff(names(plot.df), no.plot.names)
if (length(names.diff) < 1)
break
if (nm %in% num.inputs.qu.list) {
qa1.cond <- (plot.df[, nm] < quantile(plot.df[, nm], 0.9, na.rm = T))
} else {
qa1.cond <- rep(T, nrow(plot.df))
}
for (nm2 in names.diff) {
if (nm2 %in% num.inputs.qu.list) {
qa2.cond <- (plot.df[, nm2] < quantile(plot.df[, nm2], 0.9, na.rm = T))
} else {
qa2.cond <- rep(T, nrow(plot.df))
}
qa.cond <- qa1.cond & qa2.cond
g <- ggplot(data = plot.df[qa.cond, ], aes(x = plot.df[qa.cond, nm],
y = plot.df[qa.cond, nm2], color = plot.df[qa.cond, output])) +
geom_point(size = 3, alpha = 0.75)
g <- g + xlab(nm) + ylab(nm2) + scale_colour_discrete(name = output)
plot(g)
}
}
## Warning: Removed 28759 rows containing missing values (geom_point).
## Warning: Removed 28490 rows containing missing values (geom_point).
## Warning: Removed 31778 rows containing missing values (geom_point).
## Warning: Removed 35887 rows containing missing values (geom_point).
## Warning: Removed 224612 rows containing missing values (geom_point).
## Warning: Removed 164798 rows containing missing values (geom_point).
## Warning: Removed 164393 rows containing missing values (geom_point).
## Warning: Removed 134 rows containing missing values (geom_point).
## Warning: Removed 5804 rows containing missing values (geom_point).
## Warning: Removed 193341 rows containing missing values (geom_point).
## Warning: Removed 116751 rows containing missing values (geom_point).
## Warning: Removed 116751 rows containing missing values (geom_point).
## Warning: Removed 136 rows containing missing values (geom_point).
## Warning: Removed 5745 rows containing missing values (geom_point).
## Warning: Removed 192865 rows containing missing values (geom_point).
## Warning: Removed 116720 rows containing missing values (geom_point).
## Warning: Removed 116720 rows containing missing values (geom_point).
## Warning: Removed 8591 rows containing missing values (geom_point).
## Warning: Removed 201074 rows containing missing values (geom_point).
## Warning: Removed 123571 rows containing missing values (geom_point).
## Warning: Removed 123577 rows containing missing values (geom_point).
## Warning: Removed 200069 rows containing missing values (geom_point).
## Warning: Removed 125268 rows containing missing values (geom_point).
## Warning: Removed 125007 rows containing missing values (geom_point).
## Warning: Removed 224621 rows containing missing values (geom_point).
## Warning: Removed 223139 rows containing missing values (geom_point).
## Warning: Removed 139170 rows containing missing values (geom_point).
