library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorscensus <- read_csv("census.csv")## Rows: 3142 Columns: 35
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): state, county
## dbl (33): census_id, total_pop, men, women, hispanic, white, black, native, ...
##
## 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.str(census)## spc_tbl_ [3,142 × 35] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ census_id : num [1:3142] 1001 1003 1005 1007 1009 ...
## $ state : chr [1:3142] "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ county : chr [1:3142] "Autauga" "Baldwin" "Barbour" "Bibb" ...
## $ total_pop : num [1:3142] 55221 195121 26932 22604 57710 ...
## $ men : num [1:3142] 26745 95314 14497 12073 28512 ...
## $ women : num [1:3142] 28476 99807 12435 10531 29198 ...
## $ hispanic : num [1:3142] 2.6 4.5 4.6 2.2 8.6 4.4 1.2 3.5 0.4 1.5 ...
## $ white : num [1:3142] 75.8 83.1 46.2 74.5 87.9 22.2 53.3 73 57.3 91.7 ...
## $ black : num [1:3142] 18.5 9.5 46.7 21.4 1.5 70.7 43.8 20.3 40.3 4.8 ...
## $ native : num [1:3142] 0.4 0.6 0.2 0.4 0.3 1.2 0.1 0.2 0.2 0.6 ...
## $ asian : num [1:3142] 1 0.7 0.4 0.1 0.1 0.2 0.4 0.9 0.8 0.3 ...
## $ pacific : num [1:3142] 0 0 0 0 0 0 0 0 0 0 ...
## $ citizen : num [1:3142] 40725 147695 20714 17495 42345 ...
## $ income : num [1:3142] 51281 50254 32964 38678 45813 ...
## $ income_per_cap: num [1:3142] 24974 27317 16824 18431 20532 ...
## $ poverty : num [1:3142] 12.9 13.4 26.7 16.8 16.7 24.6 25.4 20.5 21.6 19.2 ...
## $ child_poverty : num [1:3142] 18.6 19.2 45.3 27.9 27.2 38.4 39.2 31.6 37.2 30.1 ...
## $ professional : num [1:3142] 33.2 33.1 26.8 21.5 28.5 18.8 27.5 27.3 23.3 29.3 ...
## $ service : num [1:3142] 17 17.7 16.1 17.9 14.1 15 16.6 17.7 14.5 16 ...
## $ office : num [1:3142] 24.2 27.1 23.1 17.8 23.9 19.7 21.9 24.2 26.3 19.5 ...
## $ construction : num [1:3142] 8.6 10.8 10.8 19 13.5 20.1 10.3 10.5 11.5 13.7 ...
## $ production : num [1:3142] 17.1 11.2 23.1 23.7 19.9 26.4 23.7 20.4 24.4 21.5 ...
## $ drive : num [1:3142] 87.5 84.7 83.8 83.2 84.9 74.9 84.5 85.3 85.1 83.9 ...
## $ carpool : num [1:3142] 8.8 8.8 10.9 13.5 11.2 14.9 12.4 9.4 11.9 12.1 ...
## $ transit : num [1:3142] 0.1 0.1 0.4 0.5 0.4 0.7 0 0.2 0.2 0.2 ...
## $ walk : num [1:3142] 0.5 1 1.8 0.6 0.9 5 0.8 1.2 0.3 0.6 ...
## $ other_transp : num [1:3142] 1.3 1.4 1.5 1.5 0.4 1.7 0.6 1.2 0.4 0.7 ...
## $ work_at_home : num [1:3142] 1.8 3.9 1.6 0.7 2.3 2.8 1.7 2.7 2.1 2.5 ...
## $ mean_commute : num [1:3142] 26.5 26.4 24.1 28.8 34.9 27.5 24.6 24.1 25.1 27.4 ...
## $ employed : num [1:3142] 23986 85953 8597 8294 22189 ...
## $ private_work : num [1:3142] 73.6 81.5 71.8 76.8 82 79.5 77.4 74.1 85.1 73.1 ...
## $ public_work : num [1:3142] 20.9 12.3 20.8 16.1 13.5 15.1 16.2 20.8 12.1 18.5 ...
## $ self_employed : num [1:3142] 5.5 5.8 7.3 6.7 4.2 5.4 6.2 5 2.8 7.9 ...
## $ family_work : num [1:3142] 0 0.4 0.1 0.4 0.4 0 0.2 0.1 0 0.5 ...
## $ unemployment : num [1:3142] 7.6 7.5 17.6 8.3 7.7 18 10.9 12.3 8.9 7.9 ...
## - attr(*, "spec")=
## .. cols(
## .. census_id = col_double(),
## .. state = col_character(),
## .. county = col_character(),
## .. total_pop = col_double(),
## .. men = col_double(),
## .. women = col_double(),
## .. hispanic = col_double(),
## .. white = col_double(),
## .. black = col_double(),
## .. native = col_double(),
## .. asian = col_double(),
## .. pacific = col_double(),
## .. citizen = col_double(),
## .. income = col_double(),
## .. income_per_cap = col_double(),
## .. poverty = col_double(),
## .. child_poverty = col_double(),
## .. professional = col_double(),
## .. service = col_double(),
## .. office = col_double(),
## .. construction = col_double(),
## .. production = col_double(),
## .. drive = col_double(),
## .. carpool = col_double(),
## .. transit = col_double(),
## .. walk = col_double(),
## .. other_transp = col_double(),
## .. work_at_home = col_double(),
## .. mean_commute = col_double(),
## .. employed = col_double(),
## .. private_work = col_double(),
## .. public_work = col_double(),
## .. self_employed = col_double(),
## .. family_work = col_double(),
## .. unemployment = col_double()
## .. )
## - attr(*, "problems")=<externalptr>str(census)## spc_tbl_ [3,142 × 35] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ census_id : num [1:3142] 1001 1003 1005 1007 1009 ...
## $ state : chr [1:3142] "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ county : chr [1:3142] "Autauga" "Baldwin" "Barbour" "Bibb" ...
## $ total_pop : num [1:3142] 55221 195121 26932 22604 57710 ...
## $ men : num [1:3142] 26745 95314 14497 12073 28512 ...
## $ women : num [1:3142] 28476 99807 12435 10531 29198 ...
## $ hispanic : num [1:3142] 2.6 4.5 4.6 2.2 8.6 4.4 1.2 3.5 0.4 1.5 ...
## $ white : num [1:3142] 75.8 83.1 46.2 74.5 87.9 22.2 53.3 73 57.3 91.7 ...
## $ black : num [1:3142] 18.5 9.5 46.7 21.4 1.5 70.7 43.8 20.3 40.3 4.8 ...
## $ native : num [1:3142] 0.4 0.6 0.2 0.4 0.3 1.2 0.1 0.2 0.2 0.6 ...
## $ asian : num [1:3142] 1 0.7 0.4 0.1 0.1 0.2 0.4 0.9 0.8 0.3 ...
## $ pacific : num [1:3142] 0 0 0 0 0 0 0 0 0 0 ...
## $ citizen : num [1:3142] 40725 147695 20714 17495 42345 ...
## $ income : num [1:3142] 51281 50254 32964 38678 45813 ...
## $ income_per_cap: num [1:3142] 24974 27317 16824 18431 20532 ...
## $ poverty : num [1:3142] 12.9 13.4 26.7 16.8 16.7 24.6 25.4 20.5 21.6 19.2 ...
## $ child_poverty : num [1:3142] 18.6 19.2 45.3 27.9 27.2 38.4 39.2 31.6 37.2 30.1 ...
## $ professional : num [1:3142] 33.2 33.1 26.8 21.5 28.5 18.8 27.5 27.3 23.3 29.3 ...
## $ service : num [1:3142] 17 17.7 16.1 17.9 14.1 15 16.6 17.7 14.5 16 ...
## $ office : num [1:3142] 24.2 27.1 23.1 17.8 23.9 19.7 21.9 24.2 26.3 19.5 ...
## $ construction : num [1:3142] 8.6 10.8 10.8 19 13.5 20.1 10.3 10.5 11.5 13.7 ...
## $ production : num [1:3142] 17.1 11.2 23.1 23.7 19.9 26.4 23.7 20.4 24.4 21.5 ...
## $ drive : num [1:3142] 87.5 84.7 83.8 83.2 84.9 74.9 84.5 85.3 85.1 83.9 ...
## $ carpool : num [1:3142] 8.8 8.8 10.9 13.5 11.2 14.9 12.4 9.4 11.9 12.1 ...
## $ transit : num [1:3142] 0.1 0.1 0.4 0.5 0.4 0.7 0 0.2 0.2 0.2 ...
## $ walk : num [1:3142] 0.5 1 1.8 0.6 0.9 5 0.8 1.2 0.3 0.6 ...
## $ other_transp : num [1:3142] 1.3 1.4 1.5 1.5 0.4 1.7 0.6 1.2 0.4 0.7 ...
## $ work_at_home : num [1:3142] 1.8 3.9 1.6 0.7 2.3 2.8 1.7 2.7 2.1 2.5 ...
## $ mean_commute : num [1:3142] 26.5 26.4 24.1 28.8 34.9 27.5 24.6 24.1 25.1 27.4 ...
## $ employed : num [1:3142] 23986 85953 8597 8294 22189 ...
## $ private_work : num [1:3142] 73.6 81.5 71.8 76.8 82 79.5 77.4 74.1 85.1 73.1 ...
## $ public_work : num [1:3142] 20.9 12.3 20.8 16.1 13.5 15.1 16.2 20.8 12.1 18.5 ...
## $ self_employed : num [1:3142] 5.5 5.8 7.3 6.7 4.2 5.4 6.2 5 2.8 7.9 ...
## $ family_work : num [1:3142] 0 0.4 0.1 0.4 0.4 0 0.2 0.1 0 0.5 ...
## $ unemployment : num [1:3142] 7.6 7.5 17.6 8.3 7.7 18 10.9 12.3 8.9 7.9 ...
## - attr(*, "spec")=
## .. cols(
## .. census_id = col_double(),
## .. state = col_character(),
## .. county = col_character(),
## .. total_pop = col_double(),
## .. men = col_double(),
## .. women = col_double(),
## .. hispanic = col_double(),
## .. white = col_double(),
## .. black = col_double(),
## .. native = col_double(),
## .. asian = col_double(),
## .. pacific = col_double(),
## .. citizen = col_double(),
## .. income = col_double(),
## .. income_per_cap = col_double(),
## .. poverty = col_double(),
## .. child_poverty = col_double(),
## .. professional = col_double(),
## .. service = col_double(),
## .. office = col_double(),
## .. construction = col_double(),
## .. production = col_double(),
## .. drive = col_double(),
## .. carpool = col_double(),
## .. transit = col_double(),
## .. walk = col_double(),
## .. other_transp = col_double(),
## .. work_at_home = col_double(),
## .. mean_commute = col_double(),
## .. employed = col_double(),
## .. private_work = col_double(),
## .. public_work = col_double(),
## .. self_employed = col_double(),
## .. family_work = col_double(),
## .. unemployment = col_double()
## .. )
## - attr(*, "problems")=<externalptr>as.factor(census$census_id)## [1] 1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 1021 1023
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## [1633] 30069 30071 30073 30075 30077 30079 30081 30083 30085 30087 30089 30091
## [1645] 30093 30095 30097 30099 30101 30103 30105 30107 30109 30111 31001 31003
## [1657] 31005 31007 31009 31011 31013 31015 31017 31019 31021 31023 31025 31027
## [1669] 31029 31031 31033 31035 31037 31039 31041 31043 31045 31047 31049 31051
## [1681] 31053 31055 31057 31059 31061 31063 31065 31067 31069 31071 31073 31075
## [1693] 31077 31079 31081 31083 31085 31087 31089 31091 31093 31095 31097 31099
## [1705] 31101 31103 31105 31107 31109 31111 31113 31115 31117 31119 31121 31123
## [1717] 31125 31127 31129 31131 31133 31135 31137 31139 31141 31143 31145 31147
## [1729] 31149 31151 31153 31155 31157 31159 31161 31163 31165 31167 31169 31171
## [1741] 31173 31175 31177 31179 31181 31183 31185 32001 32003 32005 32007 32009
## [1753] 32011 32013 32015 32017 32019 32021 32023 32027 32029 32031 32033 32510
## [1765] 33001 33003 33005 33007 33009 33011 33013 33015 33017 33019 34001 34003
## [1777] 34005 34007 34009 34011 34013 34015 34017 34019 34021 34023 34025 34027
## [1789] 34029 34031 34033 34035 34037 34039 34041 35001 35003 35005 35006 35007
## [1801] 35009 35011 35013 35015 35017 35019 35021 35023 35025 35027 35028 35029
## [1813] 35031 35033 35035 35037 35039 35041 35043 35045 35047 35049 35051 35053
## [1825] 35055 35057 35059 35061 36001 36003 36005 36007 36009 36011 36013 36015
## [1837] 36017 36019 36021 36023 36025 36027 36029 36031 36033 36035 36037 36039
## [1849] 36041 36043 36045 36047 36049 36051 36053 36055 36057 36059 36061 36063
## [1861] 36065 36067 36069 36071 36073 36075 36077 36079 36081 36083 36085 36087
## [1873] 36089 36091 36093 36095 36097 36099 36101 36103 36105 36107 36109 36111
## [1885] 36113 36115 36117 36119 36121 36123 37001 37003 37005 37007 37009 37011
## [1897] 37013 37015 37017 37019 37021 37023 37025 37027 37029 37031 37033 37035
## [1909] 37037 37039 37041 37043 37045 37047 37049 37051 37053 37055 37057 37059
## [1921] 37061 37063 37065 37067 37069 37071 37073 37075 37077 37079 37081 37083
## [1933] 37085 37087 37089 37091 37093 37095 37097 37099 37101 37103 37105 37107
## [1945] 37109 37111 37113 37115 37117 37119 37121 37123 37125 37127 37129 37131
## [1957] 37133 37135 37137 37139 37141 37143 37145 37147 37149 37151 37153 37155
## [1969] 37157 37159 37161 37163 37165 37167 37169 37171 37173 37175 37177 37179
## [1981] 37181 37183 37185 37187 37189 37191 37193 37195 37197 37199 38001 38003
## [1993] 38005 38007 38009 38011 38013 38015 38017 38019 38021 38023 38025 38027
## [2005] 38029 38031 38033 38035 38037 38039 38041 38043 38045 38047 38049 38051
## [2017] 38053 38055 38057 38059 38061 38063 38065 38067 38069 38071 38073 38075
## [2029] 38077 38079 38081 38083 38085 38087 38089 38091 38093 38095 38097 38099
## [2041] 38101 38103 38105 39001 39003 39005 39007 39009 39011 39013 39015 39017
## [2053] 39019 39021 39023 39025 39027 39029 39031 39033 39035 39037 39039 39041
## [2065] 39043 39045 39047 39049 39051 39053 39055 39057 39059 39061 39063 39065
## [2077] 39067 39069 39071 39073 39075 39077 39079 39081 39083 39085 39087 39089
## [2089] 39091 39093 39095 39097 39099 39101 39103 39105 39107 39109 39111 39113
## [2101] 39115 39117 39119 39121 39123 39125 39127 39129 39131 39133 39135 39137
## [2113] 39139 39141 39143 39145 39147 39149 39151 39153 39155 39157 39159 39161
## [2125] 39163 39165 39167 39169 39171 39173 39175 40001 40003 40005 40007 40009
## [2137] 40011 40013 40015 40017 40019 40021 40023 40025 40027 40029 40031 40033
## [2149] 40035 40037 40039 40041 40043 40045 40047 40049 40051 40053 40055 40057
## [2161] 40059 40061 40063 40065 40067 40069 40071 40073 40075 40077 40079 40081
## [2173] 40083 40085 40087 40089 40091 40093 40095 40097 40099 40101 40103 40105
## [2185] 40107 40109 40111 40113 40115 40117 40119 40121 40123 40125 40127 40129
## [2197] 40131 40133 40135 40137 40139 40141 40143 40145 40147 40149 40151 40153
## [2209] 41001 41003 41005 41007 41009 41011 41013 41015 41017 41019 41021 41023
## [2221] 41025 41027 41029 41031 41033 41035 41037 41039 41041 41043 41045 41047
## [2233] 41049 41051 41053 41055 41057 41059 41061 41063 41065 41067 41069 41071
## [2245] 42001 42003 42005 42007 42009 42011 42013 42015 42017 42019 42021 42023
## [2257] 42025 42027 42029 42031 42033 42035 42037 42039 42041 42043 42045 42047
## [2269] 42049 42051 42053 42055 42057 42059 42061 42063 42065 42067 42069 42071
## [2281] 42073 42075 42077 42079 42081 42083 42085 42087 42089 42091 42093 42095
## [2293] 42097 42099 42101 42103 42105 42107 42109 42111 42113 42115 42117 42119
## [2305] 42121 42123 42125 42127 42129 42131 42133 44001 44003 44005 44007 44009
## [2317] 45001 45003 45005 45007 45009 45011 45013 45015 45017 45019 45021 45023
## [2329] 45025 45027 45029 45031 45033 45035 45037 45039 45041 45043 45045 45047
## [2341] 45049 45051 45053 45055 45057 45059 45061 45063 45065 45067 45069 45071
## [2353] 45073 45075 45077 45079 45081 45083 45085 45087 45089 45091 46003 46005
## [2365] 46007 46009 46011 46013 46015 46017 46019 46021 46023 46025 46027 46029
## [2377] 46031 46033 46035 46037 46039 46041 46043 46045 46047 46049 46051 46053
## [2389] 46055 46057 46059 46061 46063 46065 46067 46069 46071 46073 46075 46077
## [2401] 46079 46081 46083 46085 46087 46089 46091 46093 46095 46097 46099 46101
## [2413] 46102 46103 46105 46107 46109 46111 46115 46117 46119 46121 46123 46125
## [2425] 46127 46129 46135 46137 47001 47003 47005 47007 47009 47011 47013 47015
## [2437] 47017 47019 47021 47023 47025 47027 47029 47031 47033 47035 47037 47039
## [2449] 47041 47043 47045 47047 47049 47051 47053 47055 47057 47059 47061 47063
## [2461] 47065 47067 47069 47071 47073 47075 47077 47079 47081 47083 47085 47087
## [2473] 47089 47091 47093 47095 47097 47099 47101 47103 47105 47107 47109 47111
## [2485] 47113 47115 47117 47119 47121 47123 47125 47127 47129 47131 47133 47135
## [2497] 47137 47139 47141 47143 47145 47147 47149 47151 47153 47155 47157 47159
## [2509] 47161 47163 47165 47167 47169 47171 47173 47175 47177 47179 47181 47183
## [2521] 47185 47187 47189 48001 48003 48005 48007 48009 48011 48013 48015 48017
## [2533] 48019 48021 48023 48025 48027 48029 48031 48033 48035 48037 48039 48041
## [2545] 48043 48045 48047 48049 48051 48053 48055 48057 48059 48061 48063 48065
## [2557] 48067 48069 48071 48073 48075 48077 48079 48081 48083 48085 48087 48089
## [2569] 48091 48093 48095 48097 48099 48101 48103 48105 48107 48109 48111 48113
## [2581] 48115 48117 48119 48121 48123 48125 48127 48129 48131 48133 48135 48137
## [2593] 48139 48141 48143 48145 48147 48149 48151 48153 48155 48157 48159 48161
## [2605] 48163 48165 48167 48169 48171 48173 48175 48177 48179 48181 48183 48185
## [2617] 48187 48189 48191 48193 48195 48197 48199 48201 48203 48205 48207 48209
## [2629] 48211 48213 48215 48217 48219 48221 48223 48225 48227 48229 48231 48233
## [2641] 48235 48237 48239 48241 48243 48245 48247 48249 48251 48253 48255 48257
## [2653] 48259 48261 48263 48265 48267 48269 48271 48273 48275 48277 48279 48281
## [2665] 48283 48285 48287 48289 48291 48293 48295 48297 48299 48301 48303 48305
## [2677] 48307 48309 48311 48313 48315 48317 48319 48321 48323 48325 48327 48329
## [2689] 48331 48333 48335 48337 48339 48341 48343 48345 48347 48349 48351 48353
## [2701] 48355 48357 48359 48361 48363 48365 48367 48369 48371 48373 48375 48377
## [2713] 48379 48381 48383 48385 48387 48389 48391 48393 48395 48397 48399 48401
## [2725] 48403 48405 48407 48409 48411 48413 48415 48417 48419 48421 48423 48425
## [2737] 48427 48429 48431 48433 48435 48437 48439 48441 48443 48445 48447 48449
## [2749] 48451 48453 48455 48457 48459 48461 48463 48465 48467 48469 48471 48473
## [2761] 48475 48477 48479 48481 48483 48485 48487 48489 48491 48493 48495 48497
## [2773] 48499 48501 48503 48505 48507 49001 49003 49005 49007 49009 49011 49013
## [2785] 49015 49017 49019 49021 49023 49025 49027 49029 49031 49033 49035 49037
## [2797] 49039 49041 49043 49045 49047 49049 49051 49053 49055 49057 50001 50003
## [2809] 50005 50007 50009 50011 50013 50015 50017 50019 50021 50023 50025 50027
## [2821] 51001 51003 51005 51007 51009 51011 51013 51015 51017 51019 51021 51023
## [2833] 51025 51027 51029 51031 51033 51035 51036 51037 51041 51043 51045 51047
## [2845] 51049 51051 51053 51057 51059 51061 51063 51065 51067 51069 51071 51073
## [2857] 51075 51077 51079 51081 51083 51085 51087 51089 51091 51093 51095 51097
## [2869] 51099 51101 51103 51105 51107 51109 51111 51113 51115 51117 51119 51121
## [2881] 51125 51127 51131 51133 51135 51137 51139 51141 51143 51145 51147 51149
## [2893] 51153 51155 51157 51159 51161 51163 51165 51167 51169 51171 51173 51175
## [2905] 51177 51179 51181 51183 51185 51187 51191 51193 51195 51197 51199 51510
## [2917] 51520 51530 51540 51550 51570 51580 51590 51595 51600 51610 51620 51630
## [2929] 51640 51650 51660 51670 51678 51680 51683 51685 51690 51700 51710 51720
## [2941] 51730 51735 51740 51750 51760 51770 51775 51790 51800 51810 51820 51830
## [2953] 51840 53001 53003 53005 53007 53009 53011 53013 53015 53017 53019 53021
## [2965] 53023 53025 53027 53029 53031 53033 53035 53037 53039 53041 53043 53045
## [2977] 53047 53049 53051 53053 53055 53057 53059 53061 53063 53065 53067 53069
## [2989] 53071 53073 53075 53077 54001 54003 54005 54007 54009 54011 54013 54015
## [3001] 54017 54019 54021 54023 54025 54027 54029 54031 54033 54035 54037 54039
## [3013] 54041 54043 54045 54047 54049 54051 54053 54055 54057 54059 54061 54063
## [3025] 54065 54067 54069 54071 54073 54075 54077 54079 54081 54083 54085 54087
## [3037] 54089 54091 54093 54095 54097 54099 54101 54103 54105 54107 54109 55001
## [3049] 55003 55005 55007 55009 55011 55013 55015 55017 55019 55021 55023 55025
## [3061] 55027 55029 55031 55033 55035 55037 55039 55041 55043 55045 55047 55049
## [3073] 55051 55053 55055 55057 55059 55061 55063 55065 55067 55069 55071 55073
## [3085] 55075 55077 55078 55079 55081 55083 55085 55087 55089 55091 55093 55095
## [3097] 55097 55099 55101 55103 55105 55107 55109 55111 55113 55115 55117 55119
## [3109] 55121 55123 55125 55127 55129 55131 55133 55135 55137 55139 55141 56001
## [3121] 56003 56005 56007 56009 56011 56013 56015 56017 56019 56021 56023 56025
## [3133] 56027 56029 56031 56033 56035 56037 56039 56041 56043 56045
## 3142 Levels: 1001 1003 1005 1007 1009 1011 1013 1015 1017 1019 1021 1023 ... 56045colSums(is.na(census))## census_id state county total_pop men
## 0 0 0 0 0
## women hispanic white black native
## 0 0 0 0 0
## asian pacific citizen income income_per_cap
## 0 0 0 1 0
## poverty child_poverty professional service office
## 0 1 0 0 0
## construction production drive carpool transit
## 0 0 0 0 0
## walk other_transp work_at_home mean_commute employed
## 0 0 0 0 0
## private_work public_work self_employed family_work unemployment
## 0 0 0 0 0census[is.na(census)] <- 0
#glimpse(census)
## chacolSums(is.na(census))## census_id state county total_pop men
## 0 0 0 0 0
## women hispanic white black native
## 0 0 0 0 0
## asian pacific citizen income income_per_cap
## 0 0 0 0 0
## poverty child_poverty professional service office
## 0 0 0 0 0
## construction production drive carpool transit
## 0 0 0 0 0
## walk other_transp work_at_home mean_commute employed
## 0 0 0 0 0
## private_work public_work self_employed family_work unemployment
## 0 0 0 0 0census[is.na(census)] <- 0
## chasummary(census)## census_id state county total_pop
## Min. : 1001 Length:3142 Length:3142 Min. : 85
## 1st Qu.:18178 Class :character Class :character 1st Qu.: 11028
## Median :29176 Mode :character Mode :character Median : 25768
## Mean :30384 Mean : 100737
## 3rd Qu.:45081 3rd Qu.: 67552
## Max. :56045 Max. :10038388
## men women hispanic white
## Min. : 42 Min. : 43 Min. : 0.000 Min. : 0.90
## 1st Qu.: 5546 1st Qu.: 5466 1st Qu.: 1.900 1st Qu.:65.60
## Median : 12826 Median : 12907 Median : 3.700 Median :84.60
## Mean : 49565 Mean : 51172 Mean : 8.826 Mean :77.28
## 3rd Qu.: 33319 3rd Qu.: 34122 3rd Qu.: 9.000 3rd Qu.:93.30
## Max. :4945351 Max. :5093037 Max. :98.700 Max. :99.80
## black native asian pacific
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.00000
## 1st Qu.: 0.600 1st Qu.: 0.100 1st Qu.: 0.200 1st Qu.: 0.00000
## Median : 2.100 Median : 0.300 Median : 0.500 Median : 0.00000
## Mean : 8.879 Mean : 1.766 Mean : 1.258 Mean : 0.08475
## 3rd Qu.:10.175 3rd Qu.: 0.600 3rd Qu.: 1.200 3rd Qu.: 0.00000
## Max. :85.900 Max. :92.100 Max. :41.600 Max. :35.30000
## citizen income income_per_cap poverty
## Min. : 80 Min. : 0 Min. : 8292 Min. : 1.4
## 1st Qu.: 8254 1st Qu.: 38825 1st Qu.:20471 1st Qu.:12.0
## Median : 19434 Median : 45095 Median :23577 Median :16.0
## Mean : 70804 Mean : 46815 Mean :24338 Mean :16.7
## 3rd Qu.: 50728 3rd Qu.: 52249 3rd Qu.:27138 3rd Qu.:20.3
## Max. :6046749 Max. :123453 Max. :65600 Max. :53.3
## child_poverty professional service office
## Min. : 0.00 Min. :13.50 Min. : 5.00 Min. : 4.10
## 1st Qu.:16.10 1st Qu.:26.70 1st Qu.:15.90 1st Qu.:20.20
## Median :22.50 Median :30.00 Median :18.00 Median :22.40
## Mean :23.28 Mean :31.04 Mean :18.26 Mean :22.13
## 3rd Qu.:29.48 3rd Qu.:34.40 3rd Qu.:20.20 3rd Qu.:24.30
## Max. :72.30 Max. :74.00 Max. :36.60 Max. :35.40
## construction production drive carpool
## Min. : 1.70 Min. : 0.00 Min. : 5.20 Min. : 0.00
## 1st Qu.: 9.80 1st Qu.:11.53 1st Qu.:76.60 1st Qu.: 8.50
## Median :12.20 Median :15.40 Median :80.60 Median : 9.90
## Mean :12.74 Mean :15.82 Mean :79.08 Mean :10.33
## 3rd Qu.:15.00 3rd Qu.:19.40 3rd Qu.:83.60 3rd Qu.:11.88
## Max. :40.30 Max. :55.60 Max. :94.60 Max. :29.90
## transit walk other_transp work_at_home
## Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.1000 1st Qu.: 1.400 1st Qu.: 0.900 1st Qu.: 2.800
## Median : 0.4000 Median : 2.400 Median : 1.300 Median : 4.000
## Mean : 0.9675 Mean : 3.307 Mean : 1.614 Mean : 4.697
## 3rd Qu.: 0.8000 3rd Qu.: 4.000 3rd Qu.: 1.900 3rd Qu.: 5.700
## Max. :61.7000 Max. :71.200 Max. :39.100 Max. :37.200
## mean_commute employed private_work public_work
## Min. : 4.90 Min. : 62 Min. :25.00 Min. : 5.80
## 1st Qu.:19.30 1st Qu.: 4524 1st Qu.:70.90 1st Qu.:13.10
## Median :22.90 Median : 10644 Median :75.80 Median :16.10
## Mean :23.15 Mean : 46387 Mean :74.44 Mean :17.35
## 3rd Qu.:26.60 3rd Qu.: 29254 3rd Qu.:79.80 3rd Qu.:20.10
## Max. :44.00 Max. :4635465 Max. :88.30 Max. :66.20
## self_employed family_work unemployment
## Min. : 0.000 Min. :0.0000 Min. : 0.000
## 1st Qu.: 5.400 1st Qu.:0.1000 1st Qu.: 5.500
## Median : 6.900 Median :0.2000 Median : 7.500
## Mean : 7.921 Mean :0.2915 Mean : 7.815
## 3rd Qu.: 9.400 3rd Qu.:0.3000 3rd Qu.: 9.700
## Max. :36.600 Max. :9.8000 Max. :29.400str(census)## spc_tbl_ [3,142 × 35] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ census_id : num [1:3142] 1001 1003 1005 1007 1009 ...
## $ state : chr [1:3142] "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ county : chr [1:3142] "Autauga" "Baldwin" "Barbour" "Bibb" ...
## $ total_pop : num [1:3142] 55221 195121 26932 22604 57710 ...
## $ men : num [1:3142] 26745 95314 14497 12073 28512 ...
## $ women : num [1:3142] 28476 99807 12435 10531 29198 ...
## $ hispanic : num [1:3142] 2.6 4.5 4.6 2.2 8.6 4.4 1.2 3.5 0.4 1.5 ...
## $ white : num [1:3142] 75.8 83.1 46.2 74.5 87.9 22.2 53.3 73 57.3 91.7 ...
## $ black : num [1:3142] 18.5 9.5 46.7 21.4 1.5 70.7 43.8 20.3 40.3 4.8 ...
## $ native : num [1:3142] 0.4 0.6 0.2 0.4 0.3 1.2 0.1 0.2 0.2 0.6 ...
## $ asian : num [1:3142] 1 0.7 0.4 0.1 0.1 0.2 0.4 0.9 0.8 0.3 ...
## $ pacific : num [1:3142] 0 0 0 0 0 0 0 0 0 0 ...
## $ citizen : num [1:3142] 40725 147695 20714 17495 42345 ...
## $ income : num [1:3142] 51281 50254 32964 38678 45813 ...
## $ income_per_cap: num [1:3142] 24974 27317 16824 18431 20532 ...
## $ poverty : num [1:3142] 12.9 13.4 26.7 16.8 16.7 24.6 25.4 20.5 21.6 19.2 ...
## $ child_poverty : num [1:3142] 18.6 19.2 45.3 27.9 27.2 38.4 39.2 31.6 37.2 30.1 ...
## $ professional : num [1:3142] 33.2 33.1 26.8 21.5 28.5 18.8 27.5 27.3 23.3 29.3 ...
## $ service : num [1:3142] 17 17.7 16.1 17.9 14.1 15 16.6 17.7 14.5 16 ...
## $ office : num [1:3142] 24.2 27.1 23.1 17.8 23.9 19.7 21.9 24.2 26.3 19.5 ...
## $ construction : num [1:3142] 8.6 10.8 10.8 19 13.5 20.1 10.3 10.5 11.5 13.7 ...
## $ production : num [1:3142] 17.1 11.2 23.1 23.7 19.9 26.4 23.7 20.4 24.4 21.5 ...
## $ drive : num [1:3142] 87.5 84.7 83.8 83.2 84.9 74.9 84.5 85.3 85.1 83.9 ...
## $ carpool : num [1:3142] 8.8 8.8 10.9 13.5 11.2 14.9 12.4 9.4 11.9 12.1 ...
## $ transit : num [1:3142] 0.1 0.1 0.4 0.5 0.4 0.7 0 0.2 0.2 0.2 ...
## $ walk : num [1:3142] 0.5 1 1.8 0.6 0.9 5 0.8 1.2 0.3 0.6 ...
## $ other_transp : num [1:3142] 1.3 1.4 1.5 1.5 0.4 1.7 0.6 1.2 0.4 0.7 ...
## $ work_at_home : num [1:3142] 1.8 3.9 1.6 0.7 2.3 2.8 1.7 2.7 2.1 2.5 ...
## $ mean_commute : num [1:3142] 26.5 26.4 24.1 28.8 34.9 27.5 24.6 24.1 25.1 27.4 ...
## $ employed : num [1:3142] 23986 85953 8597 8294 22189 ...
## $ private_work : num [1:3142] 73.6 81.5 71.8 76.8 82 79.5 77.4 74.1 85.1 73.1 ...
## $ public_work : num [1:3142] 20.9 12.3 20.8 16.1 13.5 15.1 16.2 20.8 12.1 18.5 ...
## $ self_employed : num [1:3142] 5.5 5.8 7.3 6.7 4.2 5.4 6.2 5 2.8 7.9 ...
## $ family_work : num [1:3142] 0 0.4 0.1 0.4 0.4 0 0.2 0.1 0 0.5 ...
## $ unemployment : num [1:3142] 7.6 7.5 17.6 8.3 7.7 18 10.9 12.3 8.9 7.9 ...
## - attr(*, "spec")=
## .. cols(
## .. census_id = col_double(),
## .. state = col_character(),
## .. county = col_character(),
## .. total_pop = col_double(),
## .. men = col_double(),
## .. women = col_double(),
## .. hispanic = col_double(),
## .. white = col_double(),
## .. black = col_double(),
## .. native = col_double(),
## .. asian = col_double(),
## .. pacific = col_double(),
## .. citizen = col_double(),
## .. income = col_double(),
## .. income_per_cap = col_double(),
## .. poverty = col_double(),
## .. child_poverty = col_double(),
## .. professional = col_double(),
## .. service = col_double(),
## .. office = col_double(),
## .. construction = col_double(),
## .. production = col_double(),
## .. drive = col_double(),
## .. carpool = col_double(),
## .. transit = col_double(),
## .. walk = col_double(),
## .. other_transp = col_double(),
## .. work_at_home = col_double(),
## .. mean_commute = col_double(),
## .. employed = col_double(),
## .. private_work = col_double(),
## .. public_work = col_double(),
## .. self_employed = col_double(),
## .. family_work = col_double(),
## .. unemployment = col_double()
## .. )
## - attr(*, "problems")=<externalptr>unique(census$Employed)## Warning: Unknown or uninitialised column: `Employed`.## NULLcensus$Employed / 100 ## Warning: Unknown or uninitialised column: `Employed`.## numeric(0)census$is_higher <- census$men > census$women
census$is_womenhigh <- census$women > census$men
census$is_equal <- census$women == census$men
print(census$is_higher)## [1] FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [73] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [97] FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE
## [109] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
## [145] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE
## [193] FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [205] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [217] FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE
## [229] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [241] TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
## [253] TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE
## [265] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
## [277] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
## [289] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE
## [301] TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
## [325] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE
## [337] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [349] TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
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## [3133] FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUEwomen_true <- sum(census$is_womenhigh, na.rm = TRUE)sum(census$unemployment < 0.1)## [1] 4census %>%
arrange(desc(mean_commute)) %>%
head(10) %>%
select(census_id, county, state, mean_commute)## # A tibble: 10 × 4
## census_id county state mean_commute
## <dbl> <chr> <chr> <dbl>
## 1 42103 Pike Pennsylvania 44
## 2 36005 Bronx New York 43
## 3 24017 Charles Maryland 42.8
## 4 51187 Warren Virginia 42.7
## 5 36081 Queens New York 42.6
## 6 36085 Richmond New York 42.6
## 7 51193 Westmoreland Virginia 42.5
## 8 8093 Park Colorado 42.4
## 9 36047 Kings New York 41.7
## 10 54015 Clay West Virginia 41.4Arranged the top 10 data in descending order by percent of women in total population using the head function and then selected the county variable to display.
percent_women <- census$women / census$total_pop
census %>%
arrange(desc(percent_women)) %>%
head(10) %>%
select(county) ## # A tibble: 10 × 1
## county
## <chr>
## 1 Norton city
## 2 Pulaski
## 3 Sumter
## 4 Sharkey
## 5 Franklin city
## 6 Highland
## 7 Edwards
## 8 Staunton city
## 9 De Baca
## 10 Livingstonrace_all <- census$hispanic + census$white + census$black + census$native + census$asian + census$pacific
census %>%
arrange(desc(race_all)) %>%
head(10) %>%
select(county)## # A tibble: 10 × 1
## county
## <chr>
## 1 Gosper
## 2 Hooker
## 3 Bailey
## 4 Edwards
## 5 Nance
## 6 Claiborne
## 7 Duval
## 8 Kenedy
## 9 Kent
## 10 Presidio#9b.
str(census)
avg_state <- mean(group_by(race_all)
9c.
No there aren’t any counties that to over 100. I created the over variable to find the count but it was equal to zero. I still checked using the sum and na.rm variables.
Over <- census$race_all > 100## Warning: Unknown or uninitialised column: `race_all`.over_all <- sum(census$Over, na.rm = TRUE)## Warning: Unknown or uninitialised column: `Over`.9d. There are none that add up to 100%. I created the Exact variable to find the count but it was equal to zero. I still checked using the sum and na.rm variables.
No there aren’t any counties that to over 100
Exact <- census$race_all == 100## Warning: Unknown or uninitialised column: `race_all`.Exact_all <- sum(census$Exact, na.rm = TRUE)## Warning: Unknown or uninitialised column: `Exact`.