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mapping.R
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library(tigris)
library(stringr)
library(dplyr)
library(leaflet)
ipums <- ipums.5.2018
rm(ipums.5.2018)
ipums <- ipums %>% filter(CLASSWKR>0)
us_states <- unique(fips_codes$state)[1:51]
continental_states <- us_states[!us_states %in% c("AK", "HI")]
us_pumas <- rbind_tigris(
lapply(
continental_states, function(x) {
pumas(state = x, cb = TRUE)
}
)
)
plot(us_pumas)
ipums.df <-ipums
ipums.df$PUMA <- str_pad(ipums.df$PUMA, 5, pad = "0")
ipums.df$STATEFIP <- str_pad(ipums.df$STATEFIP, 2, pad = "0")
ipums.df <- ipums.df %>% mutate(GEOID10 = paste(STATEFIP,PUMA, sep=""))
network <- read.csv("network.csv")
total.emp <- ipums.df %>%
group_by(GEOID10) %>%
summarize(
total= sum(PERWT)
)
total.jobs <- ipums.df %>%
filter(CLASSWKR > 0) %>%
group_by(GEOID10,jobs) %>%
summarize(
emp = sum(PERWT)
)
tech.total <- total.emp %>%
left_join(total.jobs, by = "GEOID10") %>%
mutate(share = emp/total) %>%
select(GEOID10,jobs,emp,share) %>%
filter(jobs == "Tech")
tech.wages <- ipums.df %>%
filter(jobs == "Tech") %>%
filter(CLASSWKR == 2) %>%
group_by(GEOID10) %>%
summarise(
pop = sum(PERWT),
mean.wages = sum(INCWAGE * PERWT) / pop
) %>%
select(GEOID10, mean.wages)
tech.total.ws <- ipums.df %>%
filter(INCWAGE > 0 & INCWAGE < 999998 & CLASSWKR == 2) %>%
filter(jobs == "Tech") %>%
group_by(GEOID10) %>%
summarize (
total.ws = sum(PERWT)
)
tech.remote <- ipums.df %>%
filter(INCWAGE > 0 & INCWAGE < 999998 & CLASSWKR == 2) %>%
filter(jobs == "Tech", commute== "Worked at home") %>%
group_by(GEOID10) %>%
summarize (
remote = sum(PERWT),
remote.wages = sum(INCWAGE * PERWT) / remote
) %>%
left_join(tech.total.ws, by="GEOID10") %>%
mutate(remote.share = remote/total.ws) %>%
select(GEOID10, remote, remote.share, remote.wages)
tech.diverse <- ipums.df %>%
filter(jobs == "Tech") %>%
group_by(GEOID10) %>%
summarize (
remote = sum(PERWT),
remote.wages = sum(INCWAGE * PERWT) / remote
) %>%
left_join(tech.total.ws, by="GEOID10") %>%
mutate(remote.share = remote/total.ws) %>%
select(GEOID10, remote, remote.share, remote.wages)
rural <- ipums.df %>%
group_by(GEOID10) %>%
summarize(
rural = mean(rural)
)
rural$rural <- as.factor(rural$rural)
tech.final <- tech.total %>%
left_join(tech.wages, rural, by ="GEOID10") %>%
left_join(rural, by ="GEOID10")%>%
left_join(tech.remote, by ="GEOID10")%>%
select(GEOID10,emp,share, mean.wages, rural,remote, remote.share,remote.wages)
tech.final$remote[is.na(tech.final$remote)] <- 0
tech.final$remote.share[is.na(tech.final$remote.share)] <- 0
tech.final$remote.wages[is.na(tech.final$remote.wages)] <- 0
pumas <- geo_join(us_pumas, tech.final, by = "GEOID10")
pumas$rural[pumas$rural==1] <- NA
share.bins <- c(0, .01, .02, .04, 0.08, 0.16, 1)
emp.bins <- c(0, 250, 500, 1000, 2000, 4000, 1000000)
remote.bins <- c(0,.05, .1, .20, 1)
share.pal <- colorBin("Blues", bins=share.bins)
emp.pal <- colorBin("Greens", bins=emp.bins)
wages.pal <- colorQuantile("Reds", pumas$mean.wages, 5)
remote.pal<- colorBin("Oranges", bins=remote.bins)
rural.pal <- colorFactor(c(rgb(105/255,105/255,105/255), rgb(105/255,105/255,105/255)), na.color = rgb(0, 0, 0, 0), pumas$rural)
puma_popup <- paste0("<br><strong>PUMA: </strong>",
pumas$NAME10,
"<br><strong>Percent Tech Emp: </strong>",
scales::label_percent(accuracy=.1)(pumas$share),
"<br><strong>Total Tech Emp: </strong>",
pumas$emp,
"<br><strong>Average Tech Wages (W&S only): </strong>",
scales::dollar(pumas$mean.wages),
"<br><strong>Percent Remote Tech Workers (W&S only): </strong>",
scales::label_percent(accuracy=.1)(pumas$remote.share),
"<br><strong>Remote Tech Worker Average Wages (W&S only): </strong>",
scales::dollar(pumas$remote.wages)
)
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = pumas,
fillColor = ~share.pal(share),
fillOpacity = 0.8,
color = "#BDBDC3",
weight = 1,
popup = puma_popup,
group = "Tech Share") %>%
addPolygons(data = pumas,
fillColor = ~emp.pal(emp),
fillOpacity = 0.8,
color = "#BDBDC3",
weight = 1,
popup = puma_popup,
group = "Tech Emp") %>%
addPolygons(data = pumas,
fillColor = ~wages.pal(mean.wages),
fillOpacity = 0.8,
color = "#BDBDC3",
weight = 1,
popup = puma_popup,
group = "Tech Wages") %>%
addPolygons(data = pumas,
fillColor = ~remote.pal(remote.share),
fillOpacity = 0.8,
color = "#BDBDC3",
weight = 1,
popup = puma_popup,
group = "Remote Tech") %>%
addPolygons(data = pumas,
fillColor = ~rural.pal(rural),
fillOpacity = 0.30,
color = "#BDBDC3",
weight = 1,
popup = puma_popup,
group = "Metro") %>%
addMarkers(data=network,
~long,
~lat,
popup = ~as.character(city)
) %>%
addLayersControl(
baseGroups = c("Tech Share", "Tech Emp", "Tech Wages", "Remote Tech"),
overlayGroups = c("Metro"),
options = layersControlOptions(collapsed = FALSE)
)
tech.final %>%
filter(rural ==1 & is.na(remote.share))