BLACK_tract_C <- BLACK_tract[BLACK_tract$year %in% c(2013, 2019),] %>% select(tract, year, estimate) %>% pivot_wider(names_from = year, values_from = estimate)
BLACK_tract_C$Delta <- BLACK_tract_C$`2019` - BLACK_tract_C$`2013`
Change <- rbind(slice_min(BLACK_tract_C, order_by = abs(Delta), n= 1),
slice_max(BLACK_tract_C, order_by = (Delta), n= 1),
slice_min(BLACK_tract_C, order_by = (Delta), n= 1))
names = c("Most Stable","Greatest Increase", "Greatest Decrease")
BLACK_tract <- left_join(BLACK_tract, cbind(Change,names), "tract")San Antonio Promise Zones
df = BLACK_tract
Plot <-
ggplot(data = df, aes(y = estimate, x = year, group = year)) + geom_boxplot(color = "azure4", fill = "linen") + geom_point(alpha = 0.3, color = "darkgrey") + geom_line(aes(group = tract), alpha = 0.09) +
geom_line(
data = subset(
df,
names %in% c("Greatest Increase", "Most Stable", "Greatest Decrease")
),
aes(
group = tract,
color = paste0(names, " (Tract ", tract, ", ", round(Delta), "%", ")")
),
size = 1.25
) +
scale_color_discrete(name = "") +
ggpubr::theme_pubr() +
ylab("Percent of Population Non-Hispanic Black") +
xlab("Year") +
ggtitle(label = "Percent Change in Population Percent Non-Hispanic Black, 2013-2019", subtitle = "Census Tracts San Antonio Promise Zone") +
theme(
plot.title = element_text(
size = 12,
face = "bold",
family = "Sans", hjust = 0.5
),axis.title.x = element_text(face = "bold", size = 9),
axis.title.y = element_text(face = "bold", size =9),
legend.spacing.x = unit(0.2, 'cm'), plot.subtitle = element_text(hjust = 0.5),
legend.background = element_rect(color = "white", fill = "snow2"), legend.text = element_text(size = 7.5), legend.direction = "horizontal", legend.position = "top", plot.caption = element_text(size =8, color = "darkgrey"), plot.background = element_rect(color = "black", fill = NA)
)+labs(caption= "Source: American Community Survey \n Authors: Julia Kay Wolf, Drew Schaefer, Jacob M. Souch")Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.PlotWarning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font databaseWarning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
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family not found in Windows font database
nhblack19 <- get_acs(state = "TX",
county = "Bexar",
geography = "tract",
variables = "DP05_0078PE",
geometry = TRUE,
year = 2019)Getting data from the 2015-2019 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.Using the ACS Data Profile
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county = "Bexar",
geography = "tract",
variables = "DP05_0073PE",
geometry = TRUE,
year = 2013)Getting data from the 2009-2013 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.Using the ACS Data Profile
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|======================================================================| 100%nhblack19$tract <- word(nhblack19$NAME,3, sep= " ")
nhblack19$tract <- str_remove(nhblack19$tract, ",")
nhblack19 <- nhblack19 %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
nhblack13$tract <- word(nhblack13$NAME,3, sep= " ")
nhblack13$tract <- str_remove(nhblack13$tract, ",")
nhblack13 <- nhblack13 %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
nhblack13$black13 <- nhblack13$estimate
nhblack19$black19 <- nhblack19$estimate
st_geometry(nhblack19) <- NULL
nhblackmap <- left_join(nhblack13, nhblack19, by=c("GEOID"="GEOID"))
nhblackmap$`Percent Change` <- (nhblackmap$black13-nhblackmap$black19)
p <- tm_shape(nhblackmap, unit = "mi") +tm_basemap(server = "OpenStreetMap") + tm_polygons(c("Percent Change"), border.alpha = 0.2,
palette="RdYlGn", style = "pretty") + tm_text('tract.y', size = 0.55) + tm_layout(title = "Percent Change Non-Hispanic Black, 2013-2019", title.size = 1,title.fontface = "bold",bg.color = "white" ,legend.bg.color = "snow2",legend.just = "left",inner.margins = 0.09 , legend.frame = FALSE) + tm_compass(position = "left")+ tm_scale_bar(position = "left")
pVariable(s) "Percent Change" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
for (i in 2013:2016){
assign(x = paste0("WHITE",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP05_0072PE", year = i))
}Getting data from the 2009-2013 5-year ACSUsing the ACS Data ProfileGetting data from the 2010-2014 5-year ACSUsing the ACS Data ProfileGetting data from the 2011-2015 5-year ACSUsing the ACS Data ProfileGetting data from the 2012-2016 5-year ACSUsing the ACS Data Profilefor (i in 2017:2019){
assign(x = paste0("WHITE",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP05_0077PE", year = i))
}Getting data from the 2013-2017 5-year ACS
Using the ACS Data ProfileGetting data from the 2014-2018 5-year ACSUsing the ACS Data ProfileGetting data from the 2015-2019 5-year ACSUsing the ACS Data ProfileWHITE2013$year <- 2013
WHITE2014$year <- 2014
WHITE2015$year <- 2015
WHITE2016$year <- 2016
WHITE2017$year <- 2017
WHITE2018$year <- 2018
WHITE2019$year <- 2019
WHITE <- list(WHITE2013, WHITE2014, WHITE2015,WHITE2016, WHITE2017, WHITE2018, WHITE2019)
data_WHITE <- rbind(WHITE2013, WHITE2014, WHITE2015,WHITE2016, WHITE2017, WHITE2018, WHITE2019)
rm(WHITE2013, WHITE2014, WHITE2015,WHITE2016, WHITE2017, WHITE2018, WHITE2019)
data_WHITE$tract <- word(data_WHITE$NAME,3, sep= " ")
data_WHITE$tract <- str_remove(data_WHITE$tract, ",")
WHITE_tract <- data_WHITE %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
WHITE_tract_C <- WHITE_tract[WHITE_tract$year %in% c(2013, 2019),] %>% select(tract, year, estimate) %>% pivot_wider(names_from = year, values_from = estimate)
WHITE_tract_C$Delta <- WHITE_tract_C$`2019` - WHITE_tract_C$`2013`
Change <- rbind(slice_min(WHITE_tract_C, order_by = abs(Delta), n= 1),
slice_max(WHITE_tract_C, order_by = (Delta), n= 1),
slice_min(WHITE_tract_C, order_by = (Delta), n= 1))
names = c("Most Stable","Greatest Increase", "Greatest Decrease")
WHITE_tract <- left_join(WHITE_tract, cbind(Change,names), "tract")
df = WHITE_tract
Plot <-
ggplot(data = df, aes(y = estimate, x = year, group = year)) + geom_boxplot(color = "azure4", fill = "linen") + geom_point(alpha = 0.3, color = "darkgrey") + geom_line(aes(group = tract), alpha = 0.09) +
geom_line(
data = subset(
df,
names %in% c("Greatest Increase", "Most Stable", "Greatest Decrease")
),
aes(
group = tract,
color = paste0(names, " (Tract ", tract, ", ", round(Delta), "%", ")")
),
size = 1.25
) +
scale_color_discrete(name = "") +
ggpubr::theme_pubr() +
ylab("Percent of Population Non-Hispanic White") +
xlab("Year") +
ggtitle(label = "Percent Change in Population Percent Non-Hispanic White, 2013-2019", subtitle = "Census Tracts San Antonio Promise Zone") +
theme(
plot.title = element_text(
size = 12,
face = "bold",
family = "Sans", hjust = 0.5
),axis.title.x = element_text(face = "bold", size = 9),
axis.title.y = element_text(face = "bold", size =9),
legend.spacing.x = unit(0.2, 'cm'), plot.subtitle = element_text(hjust = 0.5),
legend.background = element_rect(color = "white", fill = "snow2"), legend.text = element_text(size = 7.5), legend.direction = "horizontal", legend.position = "top", plot.caption = element_text(size =8, color = "darkgrey"), plot.background = element_rect(color = "black", fill = NA)
)+labs(caption= "Source: American Community Survey \n Authors: Julia Kay Wolf, Drew Schaefer, Jacob M. Souch")
PlotWarning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
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for (i in 2013:2016){
assign(x = paste0("HISPANIC",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP05_0066PE", year = i))
}Getting data from the 2009-2013 5-year ACSUsing the ACS Data ProfileGetting data from the 2010-2014 5-year ACSUsing the ACS Data ProfileGetting data from the 2011-2015 5-year ACSUsing the ACS Data ProfileGetting data from the 2012-2016 5-year ACSUsing the ACS Data Profilefor (i in 2017:2019){
assign(x = paste0("HISPANIC",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP05_0071PE", year = i))
}Getting data from the 2013-2017 5-year ACS
Using the ACS Data ProfileGetting data from the 2014-2018 5-year ACSUsing the ACS Data ProfileGetting data from the 2015-2019 5-year ACSUsing the ACS Data ProfileHISPANIC2013$year <- 2013
HISPANIC2014$year <- 2014
HISPANIC2015$year <- 2015
HISPANIC2016$year <- 2016
HISPANIC2017$year <- 2017
HISPANIC2018$year <- 2018
HISPANIC2019$year <- 2019
HISPANIC <- list(HISPANIC2013, HISPANIC2014, HISPANIC2015,HISPANIC2016, HISPANIC2017, HISPANIC2018, HISPANIC2019)
data_HISPANIC <- rbind(HISPANIC2013, HISPANIC2014, HISPANIC2015,HISPANIC2016, HISPANIC2017, HISPANIC2018, HISPANIC2019)
rm(HISPANIC2013, HISPANIC2014, HISPANIC2015,HISPANIC2016, HISPANIC2017, HISPANIC2018, HISPANIC2019)
data_HISPANIC$tract <- word(data_HISPANIC$NAME,3, sep= " ")
data_HISPANIC$tract <- str_remove(data_HISPANIC$tract, ",")
HISPANIC_tract <- data_HISPANIC %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
HISPANIC_tract_C <- HISPANIC_tract[HISPANIC_tract$year %in% c(2013, 2019),] %>% select(tract, year, estimate) %>% pivot_wider(names_from = year, values_from = estimate)
HISPANIC_tract_C$Delta <- HISPANIC_tract_C$`2019` - HISPANIC_tract_C$`2013`
Change <- rbind(slice_min(HISPANIC_tract_C, order_by = abs(Delta), n= 1),
slice_max(HISPANIC_tract_C, order_by = (Delta), n= 1),
slice_min(HISPANIC_tract_C, order_by = (Delta), n= 1))
names = c("Most Stable","Greatest Increase", "Greatest Decrease")
HISPANIC_tract <- left_join(HISPANIC_tract, cbind(Change,names), "tract")
df = HISPANIC_tract
Plot <-
ggplot(data = df, aes(y = estimate, x = year, group = year)) + geom_boxplot(color = "azure4", fill = "linen") + geom_point(alpha = 0.3, color = "darkgrey") + geom_line(aes(group = tract), alpha = 0.09) +
geom_line(
data = subset(
df,
names %in% c("Greatest Increase", "Most Stable", "Greatest Decrease")
),
aes(
group = tract,
color = paste0(names, " (Tract ", tract, ", ", round(Delta), "%", ")")
),
size = 1.25
) +
scale_color_discrete(name = "") +
ggpubr::theme_pubr() +
ylab("Percent of Population Hispanic") +
xlab("Year") +
ggtitle(label = "Percent Change in Population Percent Hispanic, 2013-2019", subtitle = "Census Tracts San Antonio Promise Zone") +
theme(
plot.title = element_text(
size = 12,
face = "bold",
family = "Sans", hjust = 0.5
),axis.title.x = element_text(face = "bold", size = 9),
axis.title.y = element_text(face = "bold", size =9),
legend.spacing.x = unit(0.2, 'cm'), plot.subtitle = element_text(hjust = 0.5),
legend.background = element_rect(color = "white", fill = "snow2"), legend.text = element_text(size = 7.5), legend.direction = "horizontal", legend.position = "top", plot.caption = element_text(size =8, color = "darkgrey"), plot.background = element_rect(color = "black", fill = NA)
)+labs(caption= "Source: American Community Survey \n Authors: Julia Kay Wolf, Drew Schaefer, Jacob M. Souch")
PlotWarning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database
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family not found in Windows font database
hispanic19 <- get_acs(state = "TX",
county = "Bexar",
geography = "tract",
variables = "DP05_0071PE",
geometry = TRUE,
year = 2019)Getting data from the 2015-2019 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.Using the ACS Data Profilehispanic13 <- get_acs(state = "TX",
county = "Bexar",
geography = "tract",
variables = "DP05_0066PE",
geometry = TRUE,
year = 2013)Getting data from the 2009-2013 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.Using the ACS Data Profilehispanic19$tract <- word(hispanic19$NAME,3, sep= " ")
hispanic19$tract <- str_remove(hispanic19$tract, ",")
hispanic19 <- hispanic19 %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
hispanic13$tract <- word(hispanic13$NAME,3, sep= " ")
hispanic13$tract <- str_remove(hispanic13$tract, ",")
hispanic13 <- hispanic13 %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
hispanic13$hispanic13 <- hispanic13$estimate
hispanic19$hispanic19 <- hispanic19$estimate
st_geometry(hispanic19) <- NULL
hispanicmap <- left_join(hispanic13, hispanic19, by=c("GEOID"="GEOID"))
hispanicmap$`Percent Change` <- (hispanicmap$hispanic13-hispanicmap$hispanic19)
p <- tm_shape(hispanicmap, unit = "mi") +tm_basemap(server = "OpenStreetMap") + tm_polygons(c("Percent Change"), border.alpha = 0.2,
palette="RdYlGn", style = "pretty") + tm_text('tract.y', size = 0.55) + tm_layout(title = "Percent Change Hispanic, 2013-2019", title.size = 1,title.fontface = "bold",bg.color = "white" ,legend.bg.color = "snow2",legend.just = "left",inner.margins = 0.09 , legend.frame = FALSE) + tm_compass(position = "left")+ tm_scale_bar(position = "left")
pVariable(s) "Percent Change" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
for (i in 2013:2018){
assign(x = paste0("HSORMORE",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP02_0066PE", year = i))
}Getting data from the 2009-2013 5-year ACSUsing the ACS Data ProfileGetting data from the 2010-2014 5-year ACSUsing the ACS Data ProfileGetting data from the 2011-2015 5-year ACSUsing the ACS Data ProfileGetting data from the 2012-2016 5-year ACSUsing the ACS Data ProfileGetting data from the 2013-2017 5-year ACSUsing the ACS Data ProfileGetting data from the 2014-2018 5-year ACSUsing the ACS Data Profilefor (i in 2019:2019){
assign(x = paste0("HSORMORE",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP02_0067P", year = i))
}Getting data from the 2015-2019 5-year ACS
Using the ACS Data ProfileHSORMORE2013$year <- 2013
HSORMORE2014$year <- 2014
HSORMORE2015$year <- 2015
HSORMORE2016$year <- 2016
HSORMORE2017$year <- 2017
HSORMORE2018$year <- 2018
HSORMORE2019$year <- 2019
HSORMORE <- list(HSORMORE2013, HSORMORE2014, HSORMORE2015,HSORMORE2016, HSORMORE2017, HSORMORE2018, HSORMORE2019)
data_HSORMORE <- rbind(HSORMORE2013, HSORMORE2014, HSORMORE2015,HSORMORE2016, HSORMORE2017, HSORMORE2018, HSORMORE2019)
rm(HSORMORE2013, HSORMORE2014, HSORMORE2015,HSORMORE2016, HSORMORE2017, HSORMORE2018, HSORMORE2019)
data_HSORMORE$tract <- word(data_HSORMORE$NAME,3, sep= " ")
data_HSORMORE$tract <- str_remove(data_HSORMORE$tract, ",")
HSORMORE_tract <- data_HSORMORE %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
HSORMORE_tract_C <- HSORMORE_tract[HSORMORE_tract$year %in% c(2013, 2019),] %>% select(tract, year, estimate) %>% pivot_wider(names_from = year, values_from = estimate)
HSORMORE_tract_C$Delta <- HSORMORE_tract_C$`2019` - HSORMORE_tract_C$`2013`
Change <- rbind(slice_min(HSORMORE_tract_C, order_by = abs(Delta), n= 1),
slice_max(HSORMORE_tract_C, order_by = (Delta), n= 1),
slice_min(HSORMORE_tract_C, order_by = (Delta), n= 1))
names = c("Most Stable","Greatest Increase", "Greatest Decrease")
HSORMORE_tract <- left_join(HSORMORE_tract, cbind(Change,names), "tract")
df = HSORMORE_tract
Plot <-
ggplot(data = df, aes(y = estimate, x = year, group = year)) + geom_boxplot(color = "azure4", fill = "linen") + geom_point(alpha = 0.3, color = "darkgrey") + geom_line(aes(group = tract), alpha = 0.09) +
geom_line(
data = subset(
df,
names %in% c("Greatest Increase", "Most Stable", "Greatest Decrease")
),
aes(
group = tract,
color = paste0(names, " (Tract ", tract, ", ", round(Delta), "%", ")")
),
size = 1.25
) +
scale_color_discrete(name = "") +
ggpubr::theme_pubr() +
ylab("Percent of Population High School or More") +
xlab("Year") +
ggtitle(label = "Percent Change in Population Percent High School Education or More, 2013-2019", subtitle = "Census Tracts San Antonio Promise Zone") +
theme(
plot.title = element_text(
size = 12,
face = "bold",
family = "Sans", hjust = 0.5
),axis.title.x = element_text(face = "bold", size = 9),
axis.title.y = element_text(face = "bold", size =9),
legend.spacing.x = unit(0.2, 'cm'), plot.subtitle = element_text(hjust = 0.5),
legend.background = element_rect(color = "white", fill = "snow2"), legend.text = element_text(size = 7.5), legend.direction = "horizontal", legend.position = "top", plot.caption = element_text(size =8, color = "darkgrey"), plot.background = element_rect(color = "black", fill = NA)
)+labs(caption= "Source: American Community Survey \n Authors: Julia Kay Wolf, Drew Schaefer, Jacob M. Souch")
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hs19 <- get_acs(state = "TX",
county = "Bexar",
geography = "tract",
variables = "DP02_0067P",
geometry = TRUE,
year = 2019)Getting data from the 2015-2019 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.Using the ACS Data Profilehs13 <- get_acs(state = "TX",
county = "Bexar",
geography = "tract",
variables = "DP02_0066PE",
geometry = TRUE,
year = 2013)Getting data from the 2009-2013 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.Using the ACS Data Profilehs19$tract <- word(hs19$NAME,3, sep= " ")
hs19$tract <- str_remove(hs19$tract, ",")
hs19 <- hs19 %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
hs13$tract <- word(hs13$NAME,3, sep= " ")
hs13$tract <- str_remove(hs13$tract, ",")
hs13 <- hs13 %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
hs13$hs13 <- hs13$estimate
hs19$hs19 <- hs19$estimate
st_geometry(hs19) <- NULL
hsmap <- left_join(hs13, hs19, by=c("GEOID"="GEOID"))
hsmap$`Percent Change` <- (hsmap$hs13-hsmap$hs19)
p <- tm_shape(hsmap, unit = "mi") +tm_basemap(server = "OpenStreetMap") + tm_polygons(c("Percent Change"), border.alpha = 0.2,
palette="RdYlGn", style = "pretty") + tm_text('tract.y', size = 0.55) + tm_layout(title = "Percent Change High School or Higher, 2013-2019", title.size = 1,title.fontface = "bold",bg.color = "white" ,legend.bg.color = "snow2",legend.just = "left",inner.margins = 0.09 , legend.frame = FALSE) + tm_compass(position = "left")+ tm_scale_bar(position = "left")
pVariable(s) "Percent Change" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
for (i in 2013:2018){
assign(x = paste0("COLLORMORE",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP02_0067PE", year = i))
}Getting data from the 2009-2013 5-year ACSUsing the ACS Data ProfileGetting data from the 2010-2014 5-year ACSUsing the ACS Data ProfileGetting data from the 2011-2015 5-year ACSUsing the ACS Data ProfileGetting data from the 2012-2016 5-year ACSUsing the ACS Data ProfileGetting data from the 2013-2017 5-year ACSUsing the ACS Data ProfileGetting data from the 2014-2018 5-year ACSUsing the ACS Data Profilefor (i in 2019:2019){
assign(x = paste0("COLLORMORE",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP02_0068PE", year = i))
}Getting data from the 2015-2019 5-year ACS
Using the ACS Data ProfileCOLLORMORE2013$year <- 2013
COLLORMORE2014$year <- 2014
COLLORMORE2015$year <- 2015
COLLORMORE2016$year <- 2016
COLLORMORE2017$year <- 2017
COLLORMORE2018$year <- 2018
COLLORMORE2019$year <- 2019
COLLORMORE <- list(COLLORMORE2013, COLLORMORE2014, COLLORMORE2015,COLLORMORE2016, COLLORMORE2017, COLLORMORE2018, COLLORMORE2019)
data_COLLORMORE <- rbind(COLLORMORE2013, COLLORMORE2014, COLLORMORE2015,COLLORMORE2016, COLLORMORE2017, COLLORMORE2018, COLLORMORE2019)
rm(COLLORMORE2013, COLLORMORE2014, COLLORMORE2015,COLLORMORE2016, COLLORMORE2017, COLLORMORE2018, COLLORMORE2019)
data_COLLORMORE$tract <- word(data_COLLORMORE$NAME,3, sep= " ")
data_COLLORMORE$tract <- str_remove(data_COLLORMORE$tract, ",")
COLLORMORE_tract <- data_COLLORMORE %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
COLLORMORE_tract_C <- COLLORMORE_tract[COLLORMORE_tract$year %in% c(2013, 2019),] %>% select(tract, year, estimate) %>% pivot_wider(names_from = year, values_from = estimate)
COLLORMORE_tract_C$Delta <- COLLORMORE_tract_C$`2019` - COLLORMORE_tract_C$`2013`
Change <- rbind(slice_min(COLLORMORE_tract_C, order_by = abs(Delta), n= 1),
slice_max(COLLORMORE_tract_C, order_by = (Delta), n= 1),
slice_min(COLLORMORE_tract_C, order_by = (Delta), n= 1))
names = c("Most Stable","Greatest Increase", "Greatest Decrease")
COLLORMORE_tract <- left_join(COLLORMORE_tract, cbind(Change,names), "tract")
df = COLLORMORE_tract
Plot <-
ggplot(data = df, aes(y = estimate, x = year, group = year)) + geom_boxplot(color = "azure4", fill = "linen") + geom_point(alpha = 0.3, color = "darkgrey") + geom_line(aes(group = tract), alpha = 0.09) +
geom_line(
data = subset(
df,
names %in% c("Greatest Increase", "Most Stable", "Greatest Decrease")
),
aes(
group = tract,
color = paste0(names, " (Tract ", tract, ", ", round(Delta), "%", ")")
),
size = 1.25
) +
scale_color_discrete(name = "") +
ggpubr::theme_pubr() +
ylab("Percent of Population College or More") +
xlab("Year") +
ggtitle(label = "Percent Change in Population Percent College Education or More, 2013-2019", subtitle = "Census Tracts San Antonio Promise Zone") +
theme(
plot.title = element_text(
size = 12,
face = "bold",
family = "Sans", hjust = 0.5
),axis.title.x = element_text(face = "bold", size = 9),
axis.title.y = element_text(face = "bold", size =9),
legend.spacing.x = unit(0.2, 'cm'), plot.subtitle = element_text(hjust = 0.5),
legend.background = element_rect(color = "white", fill = "snow2"), legend.text = element_text(size = 7.5), legend.direction = "horizontal", legend.position = "top", plot.caption = element_text(size =8, color = "darkgrey"), plot.background = element_rect(color = "black", fill = NA)
)+labs(caption= "Source: American Community Survey \n Authors: Julia Kay Wolf, Drew Schaefer, Jacob M. Souch")
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for (i in 2013:2019){
assign(x = paste0("POVERTY",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP03_0119P", year = i))
}Getting data from the 2009-2013 5-year ACSUsing the ACS Data ProfileGetting data from the 2010-2014 5-year ACSUsing the ACS Data ProfileGetting data from the 2011-2015 5-year ACSUsing the ACS Data ProfileGetting data from the 2012-2016 5-year ACSUsing the ACS Data ProfileGetting data from the 2013-2017 5-year ACSUsing the ACS Data ProfileGetting data from the 2014-2018 5-year ACSUsing the ACS Data ProfileGetting data from the 2015-2019 5-year ACSUsing the ACS Data ProfilePOVERTY2013$year <- 2013
POVERTY2014$year <- 2014
POVERTY2015$year <- 2015
POVERTY2016$year <- 2016
POVERTY2017$year <- 2017
POVERTY2018$year <- 2018
POVERTY2019$year <- 2019
data_pov <- rbind(POVERTY2013, POVERTY2014, POVERTY2015,POVERTY2016, POVERTY2017, POVERTY2018, POVERTY2019)
Poverty <- list(POVERTY2013, POVERTY2014, POVERTY2015,POVERTY2016, POVERTY2017, POVERTY2018, POVERTY2019)
rm(POVERTY2013, POVERTY2014, POVERTY2015,POVERTY2016, POVERTY2017, POVERTY2018, POVERTY2019)
data_pov$tract <- word(data_pov$NAME,3, sep= " ")
data_pov$tract <- str_remove(data_pov$tract, ",")
POVERTY_tract <- data_pov %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
POVERTY_tract_C <- POVERTY_tract[POVERTY_tract$year %in% c(2013, 2019),] %>% select(tract, year, estimate) %>% pivot_wider(names_from = year, values_from = estimate)
POVERTY_tract_C$Delta <- POVERTY_tract_C$`2019` - POVERTY_tract_C$`2013`
Change <- rbind(slice_min(POVERTY_tract_C, order_by = abs(Delta), n= 1),
slice_max(POVERTY_tract_C, order_by = (Delta), n= 1),
slice_min(POVERTY_tract_C, order_by = (Delta), n= 1))
names = c("Most Stable","Greatest Increase", "Greatest Decrease")
POVERTY_tract <- left_join(POVERTY_tract, cbind(Change,names), "tract")
df = POVERTY_tract
Plot <-
ggplot(data = df, aes(y = estimate, x = year, group = year)) + geom_boxplot(color = "azure4", fill = "linen") + geom_point(alpha = 0.3, color = "darkgrey") + geom_line(aes(group = tract), alpha = 0.09) +
geom_line(
data = subset(
df,
names %in% c("Greatest Increase", "Most Stable", "Greatest Decrease")
),
aes(
group = tract,
color = paste0(names, " (Tract ", tract, ", ", round(Delta), "%", ")")
),
size = 1.25
) +
scale_color_discrete(name = "") +
ggpubr::theme_pubr() +
ylab("Percent of Population in Poverty") +
xlab("Year") +
ggtitle(label = "Percent Change in Population Percent Poverty, 2013-2019", subtitle = "Census Tracts San Antonio Promise Zone") +
theme(
plot.title = element_text(
size = 12,
face = "bold",
family = "Sans", hjust = 0.5
),axis.title.x = element_text(face = "bold", size = 9),
axis.title.y = element_text(face = "bold", size =9),
legend.spacing.x = unit(0.2, 'cm'), plot.subtitle = element_text(hjust = 0.5),
legend.background = element_rect(color = "white", fill = "snow2"), legend.text = element_text(size = 7.5), legend.direction = "horizontal", legend.position = "top", plot.caption = element_text(size =8, color = "darkgrey"), plot.background = element_rect(color = "black", fill = NA)
)+labs(caption= "Source: American Community Survey \n Authors: Julia Kay Wolf, Drew Schaefer, Jacob M. Souch")
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for (i in 2013:2019){
assign(x = paste0("EMPLOY",i),get_acs(state = "TX", county = "Bexar",geography = "tract", variables = "DP03_0005PE", year = i))
}Getting data from the 2009-2013 5-year ACSUsing the ACS Data ProfileGetting data from the 2010-2014 5-year ACSUsing the ACS Data ProfileGetting data from the 2011-2015 5-year ACSUsing the ACS Data ProfileGetting data from the 2012-2016 5-year ACSUsing the ACS Data ProfileGetting data from the 2013-2017 5-year ACSUsing the ACS Data ProfileGetting data from the 2014-2018 5-year ACSUsing the ACS Data ProfileGetting data from the 2015-2019 5-year ACSUsing the ACS Data ProfileEMPLOY2013$year <- 2013
EMPLOY2014$year <- 2014
EMPLOY2015$year <- 2015
EMPLOY2016$year <- 2016
EMPLOY2017$year <- 2017
EMPLOY2018$year <- 2018
EMPLOY2019$year <- 2019
EMPLOY <- list(EMPLOY2013, EMPLOY2014, EMPLOY2015,EMPLOY2016, EMPLOY2017, EMPLOY2018, EMPLOY2019)
data_emp <- rbind(EMPLOY2013, EMPLOY2014, EMPLOY2015,EMPLOY2016, EMPLOY2017, EMPLOY2018, EMPLOY2019)
rm(EMPLOY2013, EMPLOY2014, EMPLOY2015,EMPLOY2016, EMPLOY2017, EMPLOY2018, EMPLOY2019)
data_emp$tract <- word(data_emp$NAME,3, sep= " ")
data_emp$tract <- str_remove(data_emp$tract, ",")
EMPLOYMENT_tract <- data_emp %>% filter( tract %in% c( "1110", "1214.04", "1302", "1303", "1304.01", "1304.02", "1305", "1306", "1307", "1308", "1309", "1310", "1311", "1312", "1313", "1401", "1404", "1405", "1919"))
EMPLOYMENT_tract_C <- EMPLOYMENT_tract[EMPLOYMENT_tract$year %in% c(2013, 2019),] %>% select(tract, year, estimate) %>% pivot_wider(names_from = year, values_from = estimate)
EMPLOYMENT_tract_C$Delta <- EMPLOYMENT_tract_C$`2019` - EMPLOYMENT_tract_C$`2013`
Change <- rbind(slice_min(EMPLOYMENT_tract_C, order_by = abs(Delta), n= 1),
slice_max(EMPLOYMENT_tract_C, order_by = (Delta), n= 1),
slice_min(EMPLOYMENT_tract_C, order_by = (Delta), n= 1))
names = c("Most Stable","Greatest Increase", "Greatest Decrease")
EMPLOYMENT_tract <- left_join(EMPLOYMENT_tract, cbind(Change,names), "tract")
df = EMPLOYMENT_tract
Plot <-
ggplot(data = df, aes(y = estimate, x = year, group = year)) + geom_boxplot(color = "azure4", fill = "linen") + geom_point(alpha = 0.3, color = "darkgrey") + geom_line(aes(group = tract), alpha = 0.09) +
geom_line(
data = subset(
df,
names %in% c("Greatest Increase", "Most Stable", "Greatest Decrease")
),
aes(
group = tract,
color = paste0(names, " (Tract ", tract, ", ", round(Delta), "%", ")")
),
size = 1.25
) +
scale_color_discrete(name = "") +
ggpubr::theme_pubr() +
ylab("Percent of Population 16+ Unemployed") +
xlab("Year") +
ggtitle(label = "Percent Change in Population Percent 16+ Unemployed, 2013-2019", subtitle = "Census Tracts San Antonio Promise Zone") +
theme(
plot.title = element_text(
size = 12,
face = "bold",
family = "Sans", hjust = 0.5
),axis.title.x = element_text(face = "bold", size = 9),
axis.title.y = element_text(face = "bold", size =9),
legend.spacing.x = unit(0.2, 'cm'), plot.subtitle = element_text(hjust = 0.5),
legend.background = element_rect(color = "white", fill = "snow2"), legend.text = element_text(size = 7.5), legend.direction = "horizontal", legend.position = "top", plot.caption = element_text(size =8, color = "darkgrey"), plot.background = element_rect(color = "black", fill = NA)
)+labs(caption= "Source: American Community Survey \n Authors: Julia Kay Wolf, Drew Schaefer, Jacob M. Souch")
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