library(censusapi)
##
## Attaching package: 'censusapi'
## The following object is masked from 'package:methods':
##
## getFunction
library(readr)
library(tidyverse)
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## ✓ ggplot2 3.3.5 ✓ dplyr 1.0.7
## ✓ tibble 3.1.2 ✓ stringr 1.4.0
## ✓ tidyr 1.1.3 ✓ forcats 0.5.1
## ✓ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(tidycensus)
library(igraph)
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union
## The following objects are masked from 'package:purrr':
##
## compose, simplify
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## crossing
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## as_data_frame
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## decompose, spectrum
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##
## union
library(knitr)
library(corrplot)
## corrplot 0.90 loaded
library(corrgram)
library(rgdal)
## Loading required package: sp
## rgdal: version: 1.5-23, (SVN revision 1121)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
## Path to GDAL shared files: /usr/share/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
## Path to PROJ shared files: /usr/share/proj
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
library(tidyverse)
library(GWmodel)
## Loading required package: maptools
## Checking rgeos availability: TRUE
## Loading required package: robustbase
## Loading required package: Rcpp
## Loading required package: spatialreg
## Loading required package: spData
## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
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## expand, pack, unpack
## Welcome to GWmodel version 2.2-6.
library(spdplyr)
library(stplanr)
library(sf)
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
library(ggplot2)
library(leaflet)
library(SpatialPosition)
## Functions related to Stewart's potential are deprecated.
## Please use the `potential` package instead.
## https://riatelab.github.io/potential/
library(DescTools)
## Registered S3 method overwritten by 'DescTools':
## method from
## reorder.factor gdata
##
## Attaching package: 'DescTools'
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## %c%
library(tmap)
require(gridExtra)
## Loading required package: gridExtra
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## combine
library(grid)
Sys.setenv(CENSUS_KEY= '5dc3854fc03ae75783dcbdbe2a9db1509f250cbf')
census_api_key("5dc3854fc03ae75783dcbdbe2a9db1509f250cbf")
## To install your API key for use in future sessions, run this function with `install = TRUE`.
flows <- getCensus(
name = "acs/flows",
vintage= 2016,
vars= c("MOVEDIN", "MOVEDOUT", "FULL1_NAME", "FULL2_NAME", "GEOID2"),
region = "county:*",
regionin = "state:*"
)
flows$state <- as.numeric(flows$state)
flows$MOVEDIN <- as.numeric(flows$MOVEDIN)
flows$MOVEDOUT <- as.numeric(flows$MOVEDOUT)
#Getting rid of geographies that are not in the USA or not at appropriate scale
flows_USA <- flows %>% drop_na(GEOID2) %>% filter(GEOID2<57000) %>% filter(state<57)
flows_split <- flows_USA %>%
separate(FULL1_NAME, c("County_origin", "State_origin"), sep= ", ")
flows_split <- flows_split %>%
separate(FULL2_NAME, c("County_destination", "State_destination"), sep= ", ")
flows_split <- flows_split %>% filter(State_origin != State_destination)
grouped_flows <- flows_split %>%
group_by(State_origin, State_destination)%>%
summarise(MOVEDIN = sum(MOVEDIN), MOVEDOUT = sum(MOVEDOUT))
## `summarise()` has grouped output by 'State_origin'. You can override using the `.groups` argument.
grouped_flows_df <- as.data.frame(grouped_flows)
ben <- read.csv("Fourth_Wave.csv")
#hi<- ben %>% filter(start == "2020-07-04")
hi<- ben
keeps <- c("Source","Target", "Weight")
df = hi[keeps]
hey <- merge(df, grouped_flows_df, by.x=c("Source", "Target"), by.y=c("State_origin","State_destination"))
yo <- merge(df, grouped_flows_df, by.x=c("Source", "Target"), by.y=c("State_origin","State_destination"),all.y = TRUE)
yo[is.na(yo)] <- 0
keeps <- c("Source","Target", "Weight")
df_1 = hey[keeps]
df_2 = yo[keeps]
row_sub = apply(df_1, 1, function(row) all(row !=0 ))
##Subset as usual
df_1 = df_1[row_sub,]
flow_tidy<- df_1 %>%
rename(
o = Source,
d = Target,
weight = Weight
)
flow_tidy_1<- df_2 %>%
rename(
o = Source,
d = Target,
weight = Weight
)
Income <- get_acs(geography = "state", variables = "B19326_001E" , year = 2016,geometry = TRUE,shift_geo = FALSE)
## Getting data from the 2012-2016 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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Income_shift <- get_acs(geography = "state", variables = "B19326_001E" , year = 2016,geometry = TRUE,shift_geo = TRUE)
## Warning: The `shift_geo` argument is deprecated and will be removed in a future
## release. We recommend using `tigris::shift_geometry()` instead.
## Getting data from the 2012-2016 5-year ACS
## Using feature geometry obtained from the albersusa package
## Please note: Alaska and Hawaii are being shifted and are not to scale.
Income<- rename(Income, c("cmlad11cd" = "NAME"))
Income<- Income %>% filter(GEOID<57)
Income_shift<- rename(Income_shift, c("cmlad11cd" = "NAME"))
Income_shift<- Income_shift %>% filter(GEOID<57)
spdf <- as_Spatial(Income)
spdf_shift <- as_Spatial(Income_shift)
spdf.names<- spdf@data
spdf.names.shift<- spdf_shift@data
net.all <-graph_from_data_frame(flow_tidy, directed = TRUE, vertices = NULL)
net.all
## IGRAPH 6d22a0b DNW- 51 1180 --
## + attr: name (v/c), weight (e/n)
## + edges from 6d22a0b (vertex names):
## [1] Alabama->Alaska Alabama->California
## [3] Alabama->District of Columbia Alabama->Hawaii
## [5] Alabama->Indiana Alabama->Iowa
## [7] Alabama->Maine Alabama->Maryland
## [9] Alabama->Michigan Alabama->Minnesota
## [11] Alabama->Montana Alabama->Nebraska
## [13] Alabama->Nevada Alabama->New Hampshire
## [15] Alabama->New Jersey Alabama->New York
## + ... omitted several edges
net.all.und <- as.undirected(net.all,
mode=c("mutual"),
edge.attr.comb = igraph_opt("edge.attr.comb"))
communities.net.all <- cluster_fast_greedy(net.all.und)
# And convert it to a data.frame
communities.net.all_membership <- membership(communities.net.all) %>%
unclass %>% # we first need to unclass the object
as.data.frame %>% # we convert it to a dataframe
rename(community = ".") %>% # rename the community column
rownames_to_column("cmlad11cd") # we 'move' the rownames to a
# new column in order to do a merge below
wgs84 = '+proj=longlat +datum=WGS84'
states <- spTransform(spdf_shift, CRS(wgs84) )
Category_Map <- merge(x = states , y = communities.net.all_membership, by.x = "cmlad11cd",by.y = "cmlad11cd", duplicateGeoms = T)
Category_Map$community <- as.factor(Category_Map$community)
var <- "community"
Migration<- tm_shape(Category_Map,projection = 2163) +
tm_polygons(var,
palette = c("2" = "#1a1273", "3" = "#d90d0d", "1"="#e87b6d", "4" = "#74c5e3", "5" = "#32CD32"),
border.col = "white",
border.alpha = 1.0) +
tm_legend(legend.position = c("left", "bottom"))
current.mode <- tmap_mode("plot")
## tmap mode set to plotting
tmap_arrange(Migration)
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
q <- quantile(degree(net.all, mode = "out"), .7)
# Then, we create a network with the nodes we DON'T want to plot. In this case the lowest 90%
low_nodes <- V(net.all)[degree(net.all, mode = "out") < q] # 108659 is the 90% percentile. Feel free to play with different numbers
# And this is the network with the 10% of the most central nodes
net.all.central <- delete.vertices(net.all, low_nodes)
net.all.central$layout <- layout_with_fr
plot(net.all.central, # the graph to be plotted.
layout=layout.lgl,
main='US Migration Network', # specifies the title
vertex.label.font=2, # the font of the name labels
vertex.label=V(net.all.central)$id, # no labels for the vertices
vertex.label.font=1, # the font type of the name labels (1 plain, 2 bold, 3, italic, 4 bold italic, 5 symbol)
vertex.label.cex=.5, # specifies the size of the font of the labels. can also be made to vary
edge.arrow.size=0, # specifies the arrow size
vertex.size=degree(net.all.central, mode = "out")*2)
# Network diameter. We do not consider the weights because it affects the measurement.
d.all <- diameter(net.all, directed = TRUE, weights = NA)
d.all
## [1] 3
# Average path length
mean_ditst.all <- mean_distance(net.all)
print(mean_ditst.all)
## [1] 1.538824
# Network density
dens.all <- edge_density(net.all)
print(dens.all)
## [1] 0.4627451
# Clustering Coefficient or Transitivity
trans.all <- transitivity(net.all)
print(trans.all)
## [1] 0.7014403
# Reciprocity
rec.all <- reciprocity(net.all)
print(rec.all)
## [1] 0.5067797
# Assortativity
ass.all <- assortativity_degree(net.all, directed = T)
print(ass.all)
## [1] -0.1084756
# Weighed in-degree centrality
Weighted.In.Degree <- degree(net.all, mode = "in")
# Weighed out-degree centrality
Weighted.Out.Degree <- degree(net.all, mode = "out")
# Weighed degree centrality
Weighted.Degree <- degree(net.all, mode = "all")
# Eigenvector centrality
Eigen.Centrality <- eigen_centrality(net.all)
# page rank centrality
Page.Rank <- page_rank(net.all)
names <- vertex_attr(net.all)[1]
# We are interested in the second elements of the vertex_attr() as the first one includes the local authority codes. Try str(vertex_attr(net.all)) to see why.
# Creates an object with all the centrality measures
centralities <- data.frame(Weighted.In.Degree,Weighted.Out.Degree,Weighted.Degree , Eigen.Centrality$vector, Page.Rank$vector)
centralities <- cbind(State = rownames(centralities), centralities)
rownames(centralities) <- 1:nrow(centralities)
par(mfrow=c(2,1))
p<- ggplot(centralities, aes(x=State, y=Weighted.Out.Degree)) +
geom_bar(stat="identity", fill="steelblue")+
theme_minimal()+
theme(axis.text.x = element_text(angle = 90)) +
scale_fill_brewer(palette = "Set1") +
theme(legend.position="none") +
ylab("US Out Degree Centrality") +
ggtitle("US Out Degree Centrality")
c <- ggplot(centralities, aes(x=State, y=Weighted.In.Degree)) +
geom_bar(stat="identity", fill="steelblue")+
theme_minimal()+
theme(axis.text.x = element_text(angle = 90)) +
scale_fill_brewer(palette = "Set1") +
theme(legend.position="none") +
ylab("US In Degree Centrality") +
ggtitle("US In Degree Centrality")
grid.arrange(p, c, nrow=2 )
wgs84 = '+proj=longlat +datum=WGS84'
states <- spTransform(spdf_shift, CRS(wgs84) )
Category_Map <- merge(x = states , y = centralities, by.x = "cmlad11cd",by.y = "State", duplicateGeoms = T)
Category_Map<- rename(Category_Map, c("In Degree" = "Weighted.In.Degree"))
## Warning in checkNames(value): attempt to set invalid names: this may lead to
## problems later on. See ?make.names
Category_Map$community <- select(Category_Map, "In Degree")
var <- "In Degree"
Migration<- tm_shape(Category_Map,projection = 2163) +
tm_polygons(var,
border.col = "white",
border.alpha = 1.0) +
tm_legend(legend.position = c("left", "bottom"))
current.mode <- tmap_mode("plot")
## tmap mode set to plotting
tmap_arrange(Migration)
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
wgs84 = '+proj=longlat +datum=WGS84'
states <- spTransform(spdf_shift, CRS(wgs84) )
Category_Map <- merge(x = states , y = centralities, by.x = "cmlad11cd",by.y = "State", duplicateGeoms = T)
Category_Map<- rename(Category_Map, c("Out Degree" = "Weighted.Out.Degree"))
## Warning in checkNames(value): attempt to set invalid names: this may lead to
## problems later on. See ?make.names
Category_Map$community <- select(Category_Map, "Out Degree")
var <- "Out Degree"
Migration<- tm_shape(Category_Map,projection = 2163) +
tm_polygons(var,
border.col = "white",
border.alpha = 1.0) +
tm_legend(legend.position = c("left", "bottom"))
current.mode <- tmap_mode("plot")
## tmap mode set to plotting
tmap_arrange(Migration)
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
wgs84 = '+proj=longlat +datum=WGS84'
states <- spTransform(spdf_shift, CRS(wgs84) )
Category_Map <- merge(x = states , y = centralities, by.x = "cmlad11cd",by.y = "State", duplicateGeoms = T)
Category_Map<- rename(Category_Map, c("Eigen" = "Eigen.Centrality.vector"))
Category_Map$community <- select(Category_Map, "Eigen")
var <- "Eigen"
Migration<- tm_shape(Category_Map,projection = 2163) +
tm_polygons(var,
border.col = "white",
border.alpha = 1.0) +
tm_legend(legend.position = c("left", "bottom"))
current.mode <- tmap_mode("plot")
## tmap mode set to plotting
tmap_arrange(Migration)
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
Income <- get_acs(geography = "state", variables = "B19326_001E" , year = 2016,geometry = TRUE,shift_geo = FALSE)
## Getting data from the 2012-2016 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Income_shift <- get_acs(geography = "state", variables = "B19326_001E" , year = 2016,geometry = TRUE,shift_geo = TRUE)
## Warning: The `shift_geo` argument is deprecated and will be removed in a future
## release. We recommend using `tigris::shift_geometry()` instead.
## Getting data from the 2012-2016 5-year ACS
## Using feature geometry obtained from the albersusa package
## Please note: Alaska and Hawaii are being shifted and are not to scale.
Income<- rename(Income, c("cmlad11cd" = "NAME"))
Income<- Income %>% filter(GEOID<57)
Income_shift<- rename(Income_shift, c("cmlad11cd" = "NAME"))
Income_shift<- Income_shift %>% filter(GEOID<57)
spdf <- as_Spatial(Income)
spdf_shift <- as_Spatial(Income_shift)
spdf.names<- spdf@data
spdf.names.shift<- spdf_shift@data
spdf.d <- CreateDistMatrix(spdf, spdf, longlat = T)
rownames(spdf.d) <- spdf$cmlad11cd
colnames(spdf.d) <- spdf$cmlad11cd
spdf.d <- as.data.frame.table(spdf.d, responseName = "value")
spdf.d <- spdf.d %>%
filter(Var1 != Var2)
download_census_variable <- function(variable, title) {
data <- get_acs(geography = "state", variables = variable , year = 2016,geometry = TRUE,shift_geo = FALSE)
data<- data %>% filter(GEOID<57)
data <-subset(data, select = c(NAME,estimate) )
return(data)
}
Employed <- download_census_variable("DP03_0004PE")
## Getting data from the 2012-2016 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
## Using the ACS Data Profile
Employed<- rename(Employed, c("cmlad11cd" = "NAME","Employed"="estimate"))
Over_65 <- download_census_variable("DP02_0014PE")
## Getting data from the 2012-2016 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
## Using the ACS Data Profile
Over_65<- rename(Over_65, c("cmlad11cd" = "NAME","Over_65"="estimate"))
Population <- download_census_variable("B00001_001E")
## Getting data from the 2012-2016 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Population<- rename(Population, c("cmlad11cd" = "NAME","Population"="estimate"))
rent <- download_census_variable("B25031_001E","rent")
## Getting data from the 2012-2016 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
rent<- rename(rent, c("cmlad11cd" = "NAME","rent"="estimate"))
Income_1 <- download_census_variable("B19326_001E")
## Getting data from the 2012-2016 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Income_1 <- rename(Income_1, c("cmlad11cd" = "NAME","Income"="estimate"))
flow_tidy_1
## o d weight
## 1 Alabama Alaska 1
## 2 Alabama Arizona 0
## 3 Alabama Arkansas 0
## 4 Alabama California 1
## 5 Alabama Colorado 0
## 6 Alabama Connecticut 0
## 7 Alabama Delaware 0
## 8 Alabama District of Columbia 1
## 9 Alabama Florida 0
## 10 Alabama Georgia 0
## 11 Alabama Hawaii 1
## 12 Alabama Idaho 0
## 13 Alabama Illinois 0
## 14 Alabama Indiana 1
## 15 Alabama Iowa 1
## 16 Alabama Kansas 0
## 17 Alabama Kentucky 0
## 18 Alabama Louisiana 0
## 19 Alabama Maine 1
## 20 Alabama Maryland 1
## 21 Alabama Massachusetts 0
## 22 Alabama Michigan 1
## 23 Alabama Minnesota 1
## 24 Alabama Mississippi 0
## 25 Alabama Missouri 0
## 26 Alabama Montana 1
## 27 Alabama Nebraska 1
## 28 Alabama Nevada 1
## 29 Alabama New Hampshire 1
## 30 Alabama New Jersey 1
## 31 Alabama New Mexico 0
## 32 Alabama New York 1
## 33 Alabama North Carolina 0
## 34 Alabama North Dakota 1
## 35 Alabama Ohio 0
## 36 Alabama Oklahoma 0
## 37 Alabama Oregon 0
## 38 Alabama Pennsylvania 1
## 39 Alabama Rhode Island 0
## 40 Alabama South Carolina 0
## 41 Alabama South Dakota 0
## 42 Alabama Tennessee 0
## 43 Alabama Texas 0
## 44 Alabama Utah 1
## 45 Alabama Vermont 1
## 46 Alabama Virginia 1
## 47 Alabama Washington 0
## 48 Alabama West Virginia 1
## 49 Alabama Wisconsin 0
## 50 Alabama Wyoming 1
## 51 Alaska Alabama 0
## 52 Alaska Arizona 1
## 53 Alaska Arkansas 1
## 54 Alaska California 1
## 55 Alaska Colorado 0
## 56 Alaska Connecticut 0
## 57 Alaska Delaware 0
## 58 Alaska District of Columbia 1
## 59 Alaska Florida 1
## 60 Alaska Georgia 1
## 61 Alaska Hawaii 1
## 62 Alaska Idaho 0
## 63 Alaska Illinois 0
## 64 Alaska Indiana 0
## 65 Alaska Iowa 0
## 66 Alaska Kansas 1
## 67 Alaska Kentucky 1
## 68 Alaska Louisiana 1
## 69 Alaska Maine 0
## 70 Alaska Maryland 1
## 71 Alaska Massachusetts 1
## 72 Alaska Michigan 0
## 73 Alaska Minnesota 1
## 74 Alaska Mississippi 1
## 75 Alaska Missouri 0
## 76 Alaska Montana 1
## 77 Alaska Nebraska 1
## 78 Alaska Nevada 1
## 79 Alaska New Hampshire 1
## 80 Alaska New Jersey 0
## 81 Alaska New Mexico 0
## 82 Alaska New York 0
## 83 Alaska North Carolina 1
## 84 Alaska North Dakota 0
## 85 Alaska Ohio 0
## 86 Alaska Oklahoma 0
## 87 Alaska Oregon 0
## 88 Alaska Pennsylvania 0
## 89 Alaska Rhode Island 1
## 90 Alaska South Carolina 1
## 91 Alaska South Dakota 1
## 92 Alaska Tennessee 1
## 93 Alaska Texas 1
## 94 Alaska Utah 1
## 95 Alaska Vermont 1
## 96 Alaska Virginia 1
## 97 Alaska Washington 0
## 98 Alaska West Virginia 1
## 99 Alaska Wisconsin 1
## 100 Alaska Wyoming 0
## 101 Arizona Alabama 0
## 102 Arizona Alaska 1
## 103 Arizona Arkansas 1
## 104 Arizona California 0
## 105 Arizona Colorado 0
## 106 Arizona Connecticut 0
## 107 Arizona Delaware 0
## 108 Arizona District of Columbia 1
## 109 Arizona Florida 0
## 110 Arizona Georgia 0
## 111 Arizona Hawaii 1
## 112 Arizona Idaho 0
## 113 Arizona Illinois 0
## 114 Arizona Indiana 0
## 115 Arizona Iowa 1
## 116 Arizona Kansas 1
## 117 Arizona Kentucky 1
## 118 Arizona Louisiana 0
## 119 Arizona Maine 0
## 120 Arizona Maryland 1
## 121 Arizona Massachusetts 0
## 122 Arizona Michigan 1
## 123 Arizona Minnesota 0
## 124 Arizona Mississippi 0
## 125 Arizona Missouri 1
## 126 Arizona Montana 0
## 127 Arizona Nebraska 1
## 128 Arizona Nevada 0
## 129 Arizona New Hampshire 0
## 130 Arizona New Jersey 0
## 131 Arizona New Mexico 0
## 132 Arizona New York 0
## 133 Arizona North Carolina 0
## 134 Arizona North Dakota 1
## 135 Arizona Ohio 0
## 136 Arizona Oklahoma 0
## 137 Arizona Oregon 0
## 138 Arizona Pennsylvania 0
## 139 Arizona Rhode Island 1
## 140 Arizona South Carolina 0
## 141 Arizona South Dakota 0
## 142 Arizona Tennessee 0
## 143 Arizona Texas 1
## 144 Arizona Utah 0
## 145 Arizona Vermont 0
## 146 Arizona Virginia 1
## 147 Arizona Washington 0
## 148 Arizona West Virginia 1
## 149 Arizona Wisconsin 1
## 150 Arizona Wyoming 1
## 151 Arkansas Alabama 1
## 152 Arkansas Alaska 1
## 153 Arkansas Arizona 1
## 154 Arkansas California 1
## 155 Arkansas Colorado 0
## 156 Arkansas Connecticut 0
## 157 Arkansas Delaware 1
## 158 Arkansas District of Columbia 0
## 159 Arkansas Florida 1
## 160 Arkansas Georgia 1
## 161 Arkansas Hawaii 1
## 162 Arkansas Idaho 0
## 163 Arkansas Illinois 0
## 164 Arkansas Indiana 0
## 165 Arkansas Iowa 0
## 166 Arkansas Kansas 0
## 167 Arkansas Kentucky 0
## 168 Arkansas Louisiana 0
## 169 Arkansas Maine 1
## 170 Arkansas Maryland 1
## 171 Arkansas Massachusetts 1
## 172 Arkansas Michigan 0
## 173 Arkansas Minnesota 1
## 174 Arkansas Mississippi 0
## 175 Arkansas Missouri 0
## 176 Arkansas Montana 0
## 177 Arkansas Nebraska 0
## 178 Arkansas Nevada 0
## 179 Arkansas New Hampshire 1
## 180 Arkansas New Jersey 1
## 181 Arkansas New Mexico 0
## 182 Arkansas New York 1
## 183 Arkansas North Carolina 0
## 184 Arkansas North Dakota 1
## 185 Arkansas Ohio 0
## 186 Arkansas Oklahoma 0
## 187 Arkansas Oregon 0
## 188 Arkansas Pennsylvania 1
## 189 Arkansas Rhode Island 1
## 190 Arkansas South Carolina 1
## 191 Arkansas South Dakota 1
## 192 Arkansas Tennessee 1
## 193 Arkansas Texas 0
## 194 Arkansas Utah 0
## 195 Arkansas Vermont 0
## 196 Arkansas Virginia 1
## 197 Arkansas Washington 0
## 198 Arkansas West Virginia 0
## 199 Arkansas Wisconsin 0
## 200 Arkansas Wyoming 1
## 201 California Alabama 0
## 202 California Alaska 0
## 203 California Arizona 1
## 204 California Arkansas 0
## 205 California Colorado 0
## 206 California Connecticut 1
## 207 California Delaware 0
## 208 California District of Columbia 1
## 209 California Florida 0
## 210 California Georgia 0
## 211 California Hawaii 0
## 212 California Idaho 1
## 213 California Illinois 0
## 214 California Indiana 0
## 215 California Iowa 1
## 216 California Kansas 1
## 217 California Kentucky 1
## 218 California Louisiana 0
## 219 California Maine 1
## 220 California Maryland 1
## 221 California Massachusetts 1
## 222 California Michigan 0
## 223 California Minnesota 0
## 224 California Mississippi 1
## 225 California Missouri 1
## 226 California Montana 0
## 227 California Nebraska 1
## 228 California Nevada 1
## 229 California New Hampshire 0
## 230 California New Jersey 0
## 231 California New Mexico 0
## 232 California New York 1
## 233 California North Carolina 1
## 234 California North Dakota 0
## 235 California Ohio 0
## 236 California Oklahoma 0
## 237 California Oregon 0
## 238 California Pennsylvania 1
## 239 California Rhode Island 1
## 240 California South Carolina 1
## 241 California South Dakota 0
## 242 California Tennessee 1
## 243 California Texas 0
## 244 California Utah 0
## 245 California Vermont 1
## 246 California Virginia 1
## 247 California Washington 0
## 248 California West Virginia 1
## 249 California Wisconsin 0
## 250 California Wyoming 0
## 251 Colorado Alabama 0
## 252 Colorado Alaska 0
## 253 Colorado Arizona 0
## 254 Colorado Arkansas 0
## 255 Colorado California 1
## 256 Colorado Connecticut 0
## 257 Colorado Delaware 0
## 258 Colorado District of Columbia 0
## 259 Colorado Florida 0
## 260 Colorado Georgia 1
## 261 Colorado Hawaii 1
## 262 Colorado Idaho 0
## 263 Colorado Illinois 1
## 264 Colorado Indiana 0
## 265 Colorado Iowa 1
## 266 Colorado Kansas 0
## 267 Colorado Kentucky 0
## 268 Colorado Louisiana 1
## 269 Colorado Maine 0
## 270 Colorado Maryland 0
## 271 Colorado Massachusetts 1
## 272 Colorado Michigan 0
## 273 Colorado Minnesota 1
## 274 Colorado Mississippi 1
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## 2258 Vermont Delaware 1
## 2259 Vermont District of Columbia 1
## 2260 Vermont Florida 1
## 2261 Vermont Georgia 0
## 2262 Vermont Hawaii 1
## 2263 Vermont Idaho 1
## 2264 Vermont Illinois 1
## 2265 Vermont Indiana 0
## 2266 Vermont Iowa 1
## 2267 Vermont Kansas 0
## 2268 Vermont Kentucky 1
## 2269 Vermont Louisiana 1
## 2270 Vermont Maine 0
## 2271 Vermont Maryland 1
## 2272 Vermont Massachusetts 1
## 2273 Vermont Michigan 1
## 2274 Vermont Minnesota 1
## 2275 Vermont Mississippi 1
## 2276 Vermont Missouri 1
## 2277 Vermont Montana 1
## 2278 Vermont Nebraska 0
## 2279 Vermont Nevada 1
## 2280 Vermont New Hampshire 0
## 2281 Vermont New Jersey 0
## 2282 Vermont New Mexico 1
## 2283 Vermont New York 0
## 2284 Vermont North Carolina 0
## 2285 Vermont North Dakota 1
## 2286 Vermont Ohio 1
## 2287 Vermont Oklahoma 1
## 2288 Vermont Oregon 1
## 2289 Vermont Pennsylvania 0
## 2290 Vermont Rhode Island 0
## 2291 Vermont South Carolina 1
## 2292 Vermont South Dakota 1
## 2293 Vermont Tennessee 1
## 2294 Vermont Texas 1
## 2295 Vermont Utah 1
## 2296 Vermont Virginia 1
## 2297 Vermont Washington 1
## 2298 Vermont West Virginia 1
## 2299 Vermont Wisconsin 1
## 2300 Vermont Wyoming 1
## 2301 Virginia Alabama 0
## 2302 Virginia Alaska 1
## 2303 Virginia Arizona 1
## 2304 Virginia Arkansas 1
## 2305 Virginia California 1
## 2306 Virginia Colorado 0
## 2307 Virginia Connecticut 1
## 2308 Virginia Delaware 1
## 2309 Virginia District of Columbia 0
## 2310 Virginia Florida 0
## 2311 Virginia Georgia 1
## 2312 Virginia Hawaii 1
## 2313 Virginia Idaho 1
## 2314 Virginia Illinois 0
## 2315 Virginia Indiana 0
## 2316 Virginia Iowa 1
## 2317 Virginia Kansas 1
## 2318 Virginia Kentucky 0
## 2319 Virginia Louisiana 0
## 2320 Virginia Maine 1
## 2321 Virginia Maryland 0
## 2322 Virginia Massachusetts 1
## 2323 Virginia Michigan 0
## 2324 Virginia Minnesota 0
## 2325 Virginia Mississippi 1
## 2326 Virginia Missouri 0
## 2327 Virginia Montana 1
## 2328 Virginia Nebraska 0
## 2329 Virginia Nevada 0
## 2330 Virginia New Hampshire 1
## 2331 Virginia New Jersey 1
## 2332 Virginia New Mexico 1
## 2333 Virginia New York 1
## 2334 Virginia North Carolina 1
## 2335 Virginia North Dakota 1
## 2336 Virginia Ohio 0
## 2337 Virginia Oklahoma 0
## 2338 Virginia Oregon 1
## 2339 Virginia Pennsylvania 1
## 2340 Virginia Rhode Island 1
## 2341 Virginia South Carolina 0
## 2342 Virginia South Dakota 0
## 2343 Virginia Tennessee 1
## 2344 Virginia Texas 1
## 2345 Virginia Utah 1
## 2346 Virginia Vermont 0
## 2347 Virginia Washington 0
## 2348 Virginia West Virginia 0
## 2349 Virginia Wisconsin 1
## 2350 Virginia Wyoming 1
## 2351 Washington Alabama 1
## 2352 Washington Alaska 1
## 2353 Washington Arizona 1
## 2354 Washington Arkansas 0
## 2355 Washington California 1
## 2356 Washington Colorado 0
## 2357 Washington Connecticut 1
## 2358 Washington Delaware 0
## 2359 Washington District of Columbia 1
## 2360 Washington Florida 1
## 2361 Washington Georgia 1
## 2362 Washington Hawaii 1
## 2363 Washington Idaho 1
## 2364 Washington Illinois 1
## 2365 Washington Indiana 1
## 2366 Washington Iowa 0
## 2367 Washington Kansas 0
## 2368 Washington Kentucky 0
## 2369 Washington Louisiana 0
## 2370 Washington Maine 0
## 2371 Washington Maryland 1
## 2372 Washington Massachusetts 0
## 2373 Washington Michigan 1
## 2374 Washington Minnesota 0
## 2375 Washington Mississippi 0
## 2376 Washington Missouri 0
## 2377 Washington Montana 1
## 2378 Washington Nebraska 1
## 2379 Washington Nevada 1
## 2380 Washington New Hampshire 1
## 2381 Washington New Jersey 1
## 2382 Washington New Mexico 1
## 2383 Washington New York 0
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## 2385 Washington North Dakota 1
## 2386 Washington Ohio 1
## 2387 Washington Oklahoma 0
## 2388 Washington Oregon 0
## 2389 Washington Pennsylvania 0
## 2390 Washington Rhode Island 0
## 2391 Washington South Carolina 1
## 2392 Washington South Dakota 1
## 2393 Washington Tennessee 0
## 2394 Washington Texas 1
## 2395 Washington Utah 1
## 2396 Washington Vermont 0
## 2397 Washington Virginia 0
## 2398 Washington West Virginia 1
## 2399 Washington Wisconsin 1
## 2400 Washington Wyoming 1
## 2401 West Virginia Alabama 0
## 2402 West Virginia Alaska 1
## 2403 West Virginia Arizona 1
## 2404 West Virginia Arkansas 0
## 2405 West Virginia California 1
## 2406 West Virginia Colorado 0
## 2407 West Virginia Connecticut 0
## 2408 West Virginia Delaware 1
## 2409 West Virginia District of Columbia 1
## 2410 West Virginia Florida 0
## 2411 West Virginia Georgia 0
## 2412 West Virginia Hawaii 1
## 2413 West Virginia Idaho 1
## 2414 West Virginia Illinois 0
## 2415 West Virginia Indiana 0
## 2416 West Virginia Iowa 0
## 2417 West Virginia Kansas 0
## 2418 West Virginia Kentucky 1
## 2419 West Virginia Louisiana 0
## 2420 West Virginia Maine 0
## 2421 West Virginia Maryland 1
## 2422 West Virginia Massachusetts 1
## 2423 West Virginia Michigan 1
## 2424 West Virginia Minnesota 0
## 2425 West Virginia Mississippi 0
## 2426 West Virginia Missouri 0
## 2427 West Virginia Montana 1
## 2428 West Virginia Nebraska 0
## 2429 West Virginia Nevada 1
## 2430 West Virginia New Hampshire 1
## 2431 West Virginia New Jersey 0
## 2432 West Virginia New Mexico 1
## 2433 West Virginia New York 0
## 2434 West Virginia North Carolina 1
## 2435 West Virginia North Dakota 1
## 2436 West Virginia Ohio 0
## 2437 West Virginia Oklahoma 0
## 2438 West Virginia Oregon 0
## 2439 West Virginia Pennsylvania 0
## 2440 West Virginia Rhode Island 1
## 2441 West Virginia South Carolina 0
## 2442 West Virginia South Dakota 0
## 2443 West Virginia Tennessee 0
## 2444 West Virginia Texas 0
## 2445 West Virginia Utah 1
## 2446 West Virginia Vermont 1
## 2447 West Virginia Virginia 1
## 2448 West Virginia Washington 0
## 2449 West Virginia Wisconsin 1
## 2450 West Virginia Wyoming 1
## 2451 Wisconsin Alabama 0
## 2452 Wisconsin Alaska 0
## 2453 Wisconsin Arizona 1
## 2454 Wisconsin Arkansas 0
## 2455 Wisconsin California 0
## 2456 Wisconsin Colorado 0
## 2457 Wisconsin Connecticut 1
## 2458 Wisconsin Delaware 1
## 2459 Wisconsin District of Columbia 1
## 2460 Wisconsin Florida 1
## 2461 Wisconsin Georgia 0
## 2462 Wisconsin Hawaii 0
## 2463 Wisconsin Idaho 1
## 2464 Wisconsin Illinois 0
## 2465 Wisconsin Indiana 0
## 2466 Wisconsin Iowa 0
## 2467 Wisconsin Kansas 1
## 2468 Wisconsin Kentucky 0
## 2469 Wisconsin Louisiana 0
## 2470 Wisconsin Maine 1
## 2471 Wisconsin Maryland 1
## 2472 Wisconsin Massachusetts 1
## 2473 Wisconsin Michigan 0
## 2474 Wisconsin Minnesota 1
## 2475 Wisconsin Mississippi 0
## 2476 Wisconsin Missouri 0
## 2477 Wisconsin Montana 0
## 2478 Wisconsin Nebraska 0
## 2479 Wisconsin Nevada 0
## 2480 Wisconsin New Hampshire 1
## 2481 Wisconsin New Jersey 1
## 2482 Wisconsin New Mexico 1
## 2483 Wisconsin New York 1
## 2484 Wisconsin North Carolina 0
## 2485 Wisconsin North Dakota 0
## 2486 Wisconsin Ohio 0
## 2487 Wisconsin Oklahoma 0
## 2488 Wisconsin Oregon 1
## 2489 Wisconsin Pennsylvania 1
## 2490 Wisconsin Rhode Island 0
## 2491 Wisconsin South Carolina 1
## 2492 Wisconsin South Dakota 1
## 2493 Wisconsin Tennessee 0
## 2494 Wisconsin Texas 0
## 2495 Wisconsin Utah 0
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## 2497 Wisconsin Virginia 1
## 2498 Wisconsin Washington 1
## 2499 Wisconsin West Virginia 1
## 2500 Wisconsin Wyoming 0
## 2501 Wyoming Alabama 0
## 2502 Wyoming Alaska 0
## 2503 Wyoming Arizona 0
## 2504 Wyoming Arkansas 1
## 2505 Wyoming California 0
## 2506 Wyoming Colorado 0
## 2507 Wyoming Connecticut 0
## 2508 Wyoming Delaware 0
## 2509 Wyoming District of Columbia 1
## 2510 Wyoming Florida 0
## 2511 Wyoming Georgia 1
## 2512 Wyoming Hawaii 0
## 2513 Wyoming Idaho 0
## 2514 Wyoming Illinois 1
## 2515 Wyoming Indiana 0
## 2516 Wyoming Iowa 1
## 2517 Wyoming Kansas 1
## 2518 Wyoming Kentucky 1
## 2519 Wyoming Louisiana 1
## 2520 Wyoming Maine 0
## 2521 Wyoming Maryland 0
## 2522 Wyoming Massachusetts 1
## 2523 Wyoming Michigan 1
## 2524 Wyoming Minnesota 0
## 2525 Wyoming Mississippi 1
## 2526 Wyoming Missouri 1
## 2527 Wyoming Montana 0
## 2528 Wyoming Nebraska 0
## 2529 Wyoming Nevada 0
## 2530 Wyoming New Hampshire 0
## 2531 Wyoming New Jersey 0
## 2532 Wyoming New Mexico 0
## 2533 Wyoming New York 0
## 2534 Wyoming North Carolina 1
## 2535 Wyoming North Dakota 0
## 2536 Wyoming Ohio 1
## 2537 Wyoming Oklahoma 0
## 2538 Wyoming Oregon 0
## 2539 Wyoming Pennsylvania 0
## 2540 Wyoming Rhode Island 1
## 2541 Wyoming South Carolina 1
## 2542 Wyoming South Dakota 0
## 2543 Wyoming Tennessee 0
## 2544 Wyoming Texas 0
## 2545 Wyoming Utah 0
## 2546 Wyoming Vermont 0
## 2547 Wyoming Virginia 1
## 2548 Wyoming Washington 0
## 2549 Wyoming West Virginia 0
## 2550 Wyoming Wisconsin 1
Total_File <- merge(flow_tidy_1, Income_1, by.x = "o",
by.y = "cmlad11cd",
all.x = TRUE)
Total_File <- merge(Total_File, Income_1, by.x = "d",
by.y = "cmlad11cd",
all.x = TRUE)
Total_File <- merge(Total_File, Employed, by.x = "o",
by.y = "cmlad11cd",
all.x = TRUE)
Total_File <- merge(Total_File, Employed, by.x = "d",
by.y = "cmlad11cd",
all.x = TRUE)
## Warning in merge.data.frame(Total_File, Employed, by.x = "d", by.y =
## "cmlad11cd", : column names 'geometry.x', 'geometry.y' are duplicated in the
## result
Total_File <- merge(Total_File, Over_65, by.x = "o",
by.y = "cmlad11cd",
all.x = TRUE)
## Warning in merge.data.frame(Total_File, Over_65, by.x = "o", by.y =
## "cmlad11cd", : column names 'geometry.x', 'geometry.y' are duplicated in the
## result
Total_File <- merge(Total_File, Over_65, by.x = "d",
by.y = "cmlad11cd",
all.x = TRUE)
## Warning in merge.data.frame(Total_File, Over_65, by.x = "d", by.y =
## "cmlad11cd", : column names 'geometry.x', 'geometry.y', 'geometry.x',
## 'geometry.y' are duplicated in the result
Total_File <- merge(Total_File, Population, by.x = "o",
by.y = "cmlad11cd",
all.x = TRUE)
## Warning in merge.data.frame(Total_File, Population, by.x = "o", by.y =
## "cmlad11cd", : column names 'geometry.x', 'geometry.y', 'geometry.x',
## 'geometry.y' are duplicated in the result
Total_File <- merge(Total_File, Population, by.x = "d",
by.y = "cmlad11cd",
all.x = TRUE)
## Warning in merge.data.frame(Total_File, Population, by.x = "d", by.y =
## "cmlad11cd", : column names 'geometry.x', 'geometry.y', 'geometry.x',
## 'geometry.y', 'geometry.x', 'geometry.y' are duplicated in the result
Total_File <- merge(Total_File, spdf.d, by.x=c("o", "d"), by.y=c("Var1", "Var2"))
## Warning in merge.data.frame(Total_File, spdf.d, by.x = c("o", "d"), by.y =
## c("Var1", : column names 'geometry.x', 'geometry.y', 'geometry.x', 'geometry.y',
## 'geometry.x', 'geometry.y' are duplicated in the result
Total_File$weight <- as.factor(Total_File$weight)
Total_File<- na.omit(Total_File)
library(tidyverse)
library("timetk")
library("lubridate")
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:igraph':
##
## %--%, union
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library("broom")
library(knitr)
library("gt")
library("RCurl")
##
## Attaching package: 'RCurl'
## The following object is masked from 'package:tidyr':
##
## complete
library(zoo)
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(dplyr)
library (readr)
urlfile <- "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"
y <- read_csv(url(urlfile))
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## date = col_date(format = ""),
## state = col_character(),
## fips = col_character(),
## cases = col_double(),
## deaths = col_double()
## )
z<- read_csv("GDP.csv")
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Fips = col_double(),
## Area = col_character(),
## `2013` = col_double(),
## `2014` = col_double(),
## `2015` = col_double(),
## `2016` = col_double(),
## `2017` = col_double()
## )
z<- rename(z, GDP = "2017")
get.mav <- function(bp,n=7){
require(zoo)
if(is.na(bp[1])) bp[1] <- mean(bp,na.rm=TRUE)
bp <- na.locf(bp,na.rm=FALSE)
if(length(bp)<n) return(bp)
c(bp[1:(n-1)],rollapply(bp,width=n,mean,align="right"))
}
y <- with(y,y[order(state,date),])
y$seven_day_cases <-
unlist(aggregate(cases~state,y,get.mav,na.action=NULL,n=2)$cases)
y$seven_day_deaths <-
unlist(aggregate(cases~state,y,get.mav,na.action=NULL,n=2)$cases)
y<- y %>% filter(date == "2021-03-21")
Total_File <- merge(Total_File, z, by.x = "o",
by.y = "Area",
all.x = TRUE)
## Warning in merge.data.frame(Total_File, z, by.x = "o", by.y = "Area", all.x
## = TRUE): column names 'geometry.x', 'geometry.y', 'geometry.x', 'geometry.y',
## 'geometry.x', 'geometry.y' are duplicated in the result
Total_File <- merge(Total_File, z, by.x = "d",
by.y = "Area",
all.x = TRUE)
## Warning in merge.data.frame(Total_File, z, by.x = "d", by.y = "Area", all.x
## = TRUE): column names 'geometry.x', 'geometry.y', 'geometry.x', 'geometry.y',
## 'geometry.x', 'geometry.y' are duplicated in the result
Total_File <- merge(Total_File, y, by.x = "o",
by.y = "state",
all.x = TRUE)
## Warning in merge.data.frame(Total_File, y, by.x = "o", by.y = "state", all.x
## = TRUE): column names 'geometry.x', 'geometry.y', 'geometry.x', 'geometry.y',
## 'geometry.x', 'geometry.y' are duplicated in the result
Total_File <- merge(Total_File, y, by.x = "d",
by.y = "state",
all.x = TRUE)
## Warning in merge.data.frame(Total_File, y, by.x = "d", by.y = "state", all.x
## = TRUE): column names 'geometry.x', 'geometry.y', 'geometry.x', 'geometry.y',
## 'geometry.x', 'geometry.y' are duplicated in the result
glm((weight) ~ log(Income.x) + log(Income.y)+ log(Employed.x)+ log(Employed.y) +log(Over_65.x)+log(Over_65.y)+log(Population.x)+log(Population.y)+ log(seven_day_cases.x) + log(seven_day_cases.y) + log(seven_day_deaths.x) + log(seven_day_deaths.y) + log(GDP.x) + log(GDP.y) +log(value), family = binomial,
data = Total_File)
##
## Call: glm(formula = (weight) ~ log(Income.x) + log(Income.y) + log(Employed.x) +
## log(Employed.y) + log(Over_65.x) + log(Over_65.y) + log(Population.x) +
## log(Population.y) + log(seven_day_cases.x) + log(seven_day_cases.y) +
## log(seven_day_deaths.x) + log(seven_day_deaths.y) + log(GDP.x) +
## log(GDP.y) + log(value), family = binomial, data = Total_File)
##
## Coefficients:
## (Intercept) log(Income.x) log(Income.y)
## -16.47791 -0.72163 1.29324
## log(Employed.x) log(Employed.y) log(Over_65.x)
## 0.62212 0.11921 -0.20281
## log(Over_65.y) log(Population.x) log(Population.y)
## 0.32128 0.09908 -0.17996
## log(seven_day_cases.x) log(seven_day_cases.y) log(seven_day_deaths.x)
## -0.10619 0.03647 NA
## log(seven_day_deaths.y) log(GDP.x) log(GDP.y)
## NA 0.50130 -0.17291
## log(value)
## 0.38128
##
## Degrees of Freedom: 2549 Total (i.e. Null); 2536 Residual
## Null Deviance: 3521
## Residual Deviance: 3432 AIC: 3460