.Rhistory

clean_inc =
income_pln %>%
select(level_1, level_3, value) %>%
rename(breakdown = level_1) %>%
rename(pln_area = level_3)
joined_income =
left_join(clean_inc, crosswalk, by = "pln_area") %>%
mutate(scaled = as.numeric(value * prop_of_pln))
test_income =
joined_income %>%
group_by(breakdown, eld_bounds) %>%
mutate(sum_eld = sum(scaled)) %>%
arrange(breakdown, eld_bounds)
test_income
test_income %>% filter(eld_bounds == "HOLLAND-BUKIT TIMAH", breakdown == "$2,000 - $2,999")
test_income %>%
filter(breakdown == "Total")
total_income =
test_income %>%
filter(breakdown == "Total") %>%
group_by(eld_bounds) %>%
summarise(pop_in_000 = mean(sum_eld)) ## Mean is the same as adding and then dividing!
total_income
## List of Levels
inc_levels <- list('Below $1,000',
'$1,000 - $1,999',
'$2,000 - $2,999',
'$3,000 - $3,999',
'$4,000 - $4,999',
'$5,000 - $5,999',
'$6,000 - $6,999',
'$7,000 - $7,999',
'$8,000 - $8,999',
'$9,000 - $9,999',
'$10,000 - $10,999',
'$11,000 - $11,999',
'$12,000 & Over'
)
#Calculate each level of income as a percentage of total population
income_value_pct <- function(level) {
test_income %>%
filter(breakdown == level) %>%
group_by(eld_bounds) %>%
summarise(pop_in_000 = mean(sum_eld)) %>%
mutate(pct = 100* (pop_in_000 / total_income$pop_in_000)) %>%
mutate(inc_level = level) # We include this so we know what we are looking at
}
eld_income <- lapply(inc_levels, income_value_pct) %>% bind_rows()
eld_income
#Make yuhua histogram
# selectedbounds =
#   tibble(eld_income) %>% filter(eld_bounds == c("PIONEER", "ANG MO KIO"))
ggplot(eld_income, aes(fill=factor(inc_level, levels=rev(inc_levels)), y=pct, x=eld_bounds)) +
geom_bar(position="fill", stat="identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=20),
axis.title.x = element_text(size=30),
axis.title.y = element_text(size = 30),
axis.text.y = element_text(size=20),
legend.box.background = element_rect(),
legend.box.margin = margin(6, 6, 6, 6),
legend.key.size = unit(2,'cm'),
legend.title = element_text(size=18, face='bold'),
legend.text = element_text(size=15)) + xlab("Electoral Boundary Name") +
ylab("Percentage of Population") + labs(fill = "Income Levels") + coord_flip()
edu_pln <- read.csv("data/raw/edu_pln.csv")
edu_pln
# Let's just keep the columns that we want and throw out the rest.
clean_edu =
edu_pln %>%
select(level_1, level_3, value) %>%
rename(breakdown = level_1) %>%
rename(pln_area = level_3)
joined_edu =
left_join(clean_edu, crosswalk, by = "pln_area") %>%
mutate(scaled = as.numeric(value * prop_of_pln))
test_edu =
joined_edu %>%
group_by(breakdown, eld_bounds) %>%
mutate(sum_eld = sum(scaled)) %>%
arrange(breakdown, eld_bounds)
test_edu
total_edu =
test_edu %>%
filter(breakdown == "Total") %>%
group_by(eld_bounds) %>%
summarise(pop_in_000 = mean(sum_eld)) ## Mean is the same as adding and then dividing!
total_edu
## List of Levels
edu_levels <- list('No Qualification',
'Primary',
'Lower Secondary',
'Secondary',
'Post-Secondary (Non-Tertiary)',
'Polytechnic',
'Professional Qualification and Other Diploma',
'University'
)
#Calculate each level of edu as a percentage of total population
edu_value_pct <- function(level) {
test_edu %>%
filter(breakdown == level) %>%
group_by(eld_bounds) %>%
summarise(pop_in_000 = mean(sum_eld)) %>%
mutate(pct = 100* (pop_in_000 / total_edu$pop_in_000)) %>%
mutate(edu_level = level) # We include this so we know what we are looking at
}
eld_edu <- lapply(edu_levels, edu_value_pct) %>% bind_rows()
eld_edu
#Make education barplot
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x=eld_bounds)) +
geom_bar(position="fill", stat="identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=20),
axis.title.x = element_text(size=30),
axis.title.y = element_text(size = 30),
axis.text.y = element_text(size=20),
legend.box.background = element_rect(),
legend.box.margin = margin(6, 6, 6, 6),
legend.key.size = unit(2,'cm'),
legend.title = element_text(size=18, face='bold'),
legend.text = element_text(size=15)) + xlab("Electoral Boundary Name") +
ylab("Percentage of Population") + labs(fill = "Education Levels") + coord_flip()
voting <- read_csv("data/raw/voting.csv")
voted_pap = voting %>% filter(year==2020, party=="PAP") %>% mutate(vote_pap_pct = as.numeric(vote_percentage) * 100) %>% select(constituency, vote_pap_pct) # Data from the 2020 Elections
# We pivot the data to get the income and ed
pivoted_income = eld_income %>% select(-"pop_in_000") %>% pivot_wider(names_from = inc_level, values_from = pct)
pivoted_edu = eld_edu %>% select(-"pop_in_000") %>% pivot_wider(names_from = edu_level, values_from = pct)
eld_2 = eld %>% select(c("Name", "geometry")) # We make a copy of the Electoral Boundary file with the columns we want
join_income_eld = inner_join(eld_2, pivoted_income, by=c("Name"='eld_bounds')) # We join income data to eld data first
join_edu_data = inner_join(join_income_eld, pivoted_edu, by=c("Name"="eld_bounds")) # Join Edu Data with income Data
eld_data =
inner_join(join_edu_data, voted_pap, by=c("Name"="constituency")) %>%
mutate(across(where(is.numeric), round, 2)) #Round values
tibble(eld_data)
write.csv(eld_data, file="eld_data.csv")
write.csv(eld_income, file="eld_income.csv")
write_sf(eld_data, "eld_data.shp")
mypal <- c('#edf8fb', '#b3cde3', '#8c96c6','#8856a7','#810f7c')
tmap_mode("plot")
tm_shape(eld_data)  + tm_polygons("vote_pap_pct", title = "Percentage Voted for PAP", palette = mypal) + tm_borders() +  tm_text("Name", size = 1/3)
tmap_mode("view")
income_map =
tm_shape(eld_data) + tm_text("Name", size = 1/1.3) + tm_borders(lwd = 2) +
tm_polygons("vote_pap_pct", title = "Percentage Voted for PAP",
popup.vars=c(
"Income Below $1,000: " = "Below $1,000",
"Income $1,000 - $1,999: " = "$1,000 - $1,999",
"Income $2,000 - $2,999: " = "$2,000 - $2,999",
"Income $3,000 - $3,999: " = "$3,000 - $3,999",
"Income $4,000 - $4,999: " = "$4,000 - $4,999",
"Income $5,000 - $5,999: " = "$5,000 - $5,999",
"Income $6,000 - $6,999: " = "$6,000 - $6,999",
"Income $7,000 - $7,999: " = "$7,000 - $7,999",
"Income $8,000 - $8,999: " = "$8,000 - $8,999",
"Income $9,000 - $9,999: " = "$9,000 - $9,999",
"Income $10,000 - $10,999: " = "$10,000 - $10,999",
"Income $11,000 - $11,999: " = "$11,000 - $11,999",
"Income above $12,000: " = "$12,000 & Over",
"Percentage who voted for PAP: " = "vote_pap_pct")
) + tm_layout(title="Income (2015) and PAP Vote Share (2020)")
tmap_leaflet(income_map, show = TRUE, add.titles=TRUE)
edu_map =
tm_shape(eld_data) + tm_text("Name", size = 1/1.3) + tm_borders(lwd = 2) +
tm_polygons("vote_pap_pct", title = "Percentage Voted for PAP",
popup.vars=c(
"No Qualification: " = "No Qualification",
"Primary: " = "Primary",
"Lower Secondary: " ="Secondary",
"Secondary: " = "Secondary",
"Post-Secondary (Non-Tertiary): " = "Post-Secondary (Non-Tertiary)",
"Polytechnic: " = "Polytechnic",
"Professional Qualification and Other Diploma: " = "Professional Qualification and Other Diploma",
"University: " = "University",
"Percentage who voted for PAP: " = "vote_pap_pct")
) + tm_layout(title="Highest Edu Qualification (2015) and PAP Vote Share (2020)")
tmap_leaflet(edu_map, show = TRUE, add.titles=TRUE)
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/barchart/barchart.R')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/barchart/barchart.R')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/barchart/barchart.R')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/barchart/barchart.R')
ui <- fluidPage(
titlePanel("Neospec Visualization"),
sidebarLayout(
sidebarPanel(
uiOutput("FaceUnitOut"),
tags$hr(),
uiOutput("FaceTypeOut")
),
mainPanel(
tabsetPanel(
tabPanel("Table", dataTableOutput("table"),6),
h3("Data table view"),
#withSpinner(DT::dataTableOutput("contents"),6),
#dataTableOutput("tt"),
h3("Raw Neospec signatures"),
withSpinner(plotOutput("plts"),6)
)
)
)
)
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
shinyServer(function(input, output) {
output$distPlot <- renderPlot({
x_axis <- input$selectedCol
gg <-
ggplot(mtcars, aes_string(x = x_axis, y = "hp", color = "cyl"))
gg <- gg + geom_point()
gg
})
})
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
eld_data
eld_data$Name
bounds = eld_data$Name
bounds
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
ggplot(eld_income, aes_string(x = x_axis, y = "hp", color = "cyl"))
ggplot(eld_income, aes_string(x = "MARINE PARADE", y = "hp", color = "cyl"))
ggplot(eld_income, aes_string(x = MARINE PARADE, y = "hp", color = "cyl"))
ggplot(eld_income, aes_string(x = 'MARINE PARADE', y = "hp", color = "cyl"))
ggplot(eld_edu, aes_string(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x=eld_bounds))
ggplot(eld_income, aes_string(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x=eld_bounds))
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x=eld_bounds)) +
# ggplot(mtcars, aes_string(x = x_axis, y = "hp", color = "cyl"))
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x=eld_bounds))
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x=eld_bounds))
## List of Levels
edu_levels <- list('No Qualification',
'Primary',
'Lower Secondary',
'Secondary',
'Post-Secondary (Non-Tertiary)',
'Polytechnic',
'Professional Qualification and Other Diploma',
'University'
)
#Make education barplot
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x=eld_bounds)) +
geom_bar(position="fill", stat="identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=20),
axis.title.x = element_text(size=30),
axis.title.y = element_text(size = 30),
axis.text.y = element_text(size=20),
legend.box.background = element_rect(),
legend.box.margin = margin(6, 6, 6, 6),
legend.key.size = unit(2,'cm'),
legend.title = element_text(size=18, face='bold'),
legend.text = element_text(size=15)) + xlab("Electoral Boundary Name") +
ylab("Percentage of Population") + labs(fill = "Education Levels") + coord_flip()
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x=eld_bounds))  +
geom_bar(position="fill", stat="identity")
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG"))  +
geom_bar(position="fill", stat="identity")
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= c("JURONG", "ALJUNIED")))  +
geom_bar(position="fill", stat="identity")
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity")
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") + facet_grid(.~eld_bounds)
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") + facet_grid(."SEMBAWANG")
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") + facet_grid(.~"SEMBAWANG")
test_edu =
eld_edu %>% filter("Name" == c("JURONG", "SEMBAWANG"))
test_edu =
eld_edu %>% filter("Name" = c("JURONG", "SEMBAWANG"))
test_edu =
eld_edu %>% filter("Name" == c("JURONG", "SEMBAWANG"))
test_edu =
eld_edu %>% filter("Name" == "JURONG")
test_edu
test_edu =
eld_edu %>% filter("eld_bounds" == "JURONG")
test_edu
eld_edu
test_edu =
eld_edu %>% filter("eld_bounds" == "HOUGANG")
test_edu
eld_edu
test_edu =
eld_edu %>% filter(eld_bounds == "HOUGANG")
test_edu
test_edu =
eld_edu %>% filter(eld_bounds == c("HOUGANG", "JURONG"))
test_edu
test_edu =
eld_edu %>% filter(eld_bounds %in% c("HOUGANG", "JURONG"))
test_edu
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") + facet_grid(.~eld_bounds)
ggplot(eld_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") #+ facet_grid(.~eld_bounds)
ggplot(test_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") + facet_grid(.~eld_bounds)
ggplot(test_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") + facet_grid(~eld_bounds.)
ggplot(test_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") + facet_grid(.~eld_bounds)
ggplot(test_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") + facet_grid(eld_bounds)
ggplot(test_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "JURONG")) +
geom_bar(position="fill", stat="identity") + facet_grid(~eld_bounds)
ggplot(test_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "test")) +
geom_bar(position="fill", stat="identity") + facet_grid(~eld_bounds)
ggplot(test_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "Boundary Name")) +
geom_bar(position="fill", stat="identity") + facet_grid(~eld_bounds)
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp()
runApp()
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp()
runApp()
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2')
runApp('test')
runApp('test')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test')
bounds
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test/barchart_working.R')
bounds
bounds?
/
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test/barchart_working.R')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test/barchart_working.R')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test/barchart_working.R')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test/barchart_working.R')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2/test/barchart_working.R')
runApp('Downloads/MapDemo')
runApp('Downloads/MapDemo')
runApp('Documents/UChicago/Academics/Spring 2021/GIS 3/MapDemo 2')
runApp('Downloads/MapDemo')
runApp('Downloads/MapDemo')
runApp('Downloads/MapDemo')
runApp('Downloads/MapDemo')
runApp('Downloads/MapDemo')
runApp('Downloads/MapDemo')
runApp('Downloads/MapDemo/shiny_gis3.R')
runApp('Downloads/MapDemo/shiny_gis3.R')
runApp('Downloads/MapDemo/shiny_gis3.R')
runApp('Downloads/MapDemo/shiny_gis3.R')
runApp('Downloads/MapDemo/shiny_gis3.R')
runApp('Downloads/MapDemo/shiny_gis3.R')
shinyAppDir(
system.file("shiny_gis3", package="shiny"),
options = list(width = "100%", height = 700)
)
shinyAppDir(
system.file("/shiny_gis3", package="shiny"),
options = list(width = "100%", height = 700)
)
shinyAppDir(
system.file("shiny_gis3", package="shiny"),
options = list(width = "100%", height = 700)
)
shinyAppDir(
system.file("shiny_gis3.R", package="shiny"),
options = list(width = "100%", height = 700)
)
shinyAppDir(
system.file("shiny_gis3", package="shiny"),
options = list(width = "100%", height = 700)
)
shinyAppDir(
system.file("/Users/joseaevan5/Documents/gis3/shiny_gis3", package="shiny"),
options = list(width = "100%", height = 700)
)
shinyApp(
ui = fluidPage(
# App title ----
titlePanel("Singapore Electoral Boundary Data Explorer"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
sidebarPanel(
h3("Make your choice"),
helpText("Explore Singapore data based on selected variables"),
selectInput(
"selectedCol1",
"Select 1st boundary to compare",
choices = list("ALJUNIED",
"ANG MO KIO",
"BISHAN-TOA PAYOH",
"BUKIT BATOK",
"BUKIT PANJANG",
"CHUA CHU KANG",
"EAST COAST",
"HOLLAND-BUKIT TIMAH",
"HONG KAH NORTH",
"HOUGANG",
"JALAN BESAR",
"JURONG",
"KEBUN BARU",
"MACPHERSON",
"MARINE PARADE",
"MARSILING-YEW TEE",
"MARYMOUNT",
"MOUNTBATTEN",
"NEE SOON",
"PASIR RIS-PUNGGOL",
"PIONEER",
"POTONG PASIR",
"PUNGGOL WEST",
"RADIN MAS",
"SEMBAWANG",
"SENGKANG",
"TAMPINES",
"TANJONG PAGAR",
"WEST COAST",
"YIO CHU KANG",
"YUHUA"),
selected = "Bound 1"
),
selectInput(
"selectedCol2",
"Select 2st boundary to compare",
choices = list("ALJUNIED",
"ANG MO KIO",
"BISHAN-TOA PAYOH",
"BUKIT BATOK",
"BUKIT PANJANG",
"CHUA CHU KANG",
"EAST COAST",
"HOLLAND-BUKIT TIMAH",
"HONG KAH NORTH",
"HOUGANG",
"JALAN BESAR",
"JURONG",
"KEBUN BARU",
"MACPHERSON",
"MARINE PARADE",
"MARSILING-YEW TEE",
"MARYMOUNT",
"MOUNTBATTEN",
"NEE SOON",
"PASIR RIS-PUNGGOL",
"PIONEER",
"POTONG PASIR",
"PUNGGOL WEST",
"RADIN MAS",
"SEMBAWANG",
"SENGKANG",
"TAMPINES",
"TANJONG PAGAR",
"WEST COAST",
"YIO CHU KANG",
"YUHUA"),
selected = "Bound 2"
),
selectInput("Map_Choice",
label = "View electoral boundary level data on an interactive map of Singapore",
choices = list("Income Data",
"Education Data"),
selected = "Income Data"),
h4("About"),
p("This project was completed by Josea Evan, as part of coursework for GIS III at the University of Chicago. Contact: josea [at] uchicago.edu"),
h4("Data Source"),
p("This data is from the Singapore Census. Electoral Data from 2020 Singapore General Election. Income and Education Data crosswalked from 2015 Household Survey.")
),
# Main panel for displaying outputs ----
mainPanel(
# Output: Tabset w/ plot, summary, and table ----
tabsetPanel(type = "tabs",
tabPanel("Education Data", plotOutput("distPlot")),
tabPanel("Income Data", plotOutput("incPlot")),
tabPanel("Map", leafletOutput("working_map"))
)
)
)
),
server <- function(input, output) {
output$distPlot <- renderPlot({
test_edu =
eld_edu %>% filter(eld_bounds %in% c(input$selectedCol1, input$selectedCol2))
ggplot(test_edu, aes(fill=factor(edu_level, levels=rev(edu_levels)), y=pct, x= "Boundary Name")) +
geom_bar(position="fill", stat="identity") + facet_grid(~eld_bounds) +
theme(legend.box.background = element_rect(),
legend.title = element_text(face='bold')) +
labs(fill = "Highest Education Level Attained (2015)") +
ylab("Percentage of Population")
})
#Generate a Violin Plot of Variables
output$incPlot <- renderPlot({
test_inc =
eld_income %>% filter(eld_bounds %in% c(input$selectedCol1, input$selectedCol2))
ggplot(test_inc, aes(fill=factor(inc_level, levels=rev(inc_levels)), y=pct, x= "Boundary Name")) +
geom_bar(position="fill", stat="identity") + facet_grid(~eld_bounds) +
theme(legend.box.background = element_rect(),
legend.title = element_text(face='bold')) +
labs(fill = "Gross Monthly Income from Work (2015)") +
ylab("Percentage of Population")
})
output$working_map <- renderLeaflet({
map <- switch(input$Map_Choice,
"Income Data" = income_map,
"Education Data" = edu_map)
working_map <- tmap_leaflet(map)
})
},
options = list(height = 500)
)
library(shiny); runApp('Documents/gis3/shiny_gis3.R')