Instructions: To complete this assignment, work the following problems and publish your completed work on RPubs.
Select a leaflet provider and a color palette to construct a choropleth of median income by census tract in a US county of your choice.
library(tidyverse)## -- Attaching packages ----------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.0 v purrr 0.2.4
## v tibble 1.4.2 v dplyr 0.7.8
## v tidyr 0.8.0 v stringr 1.3.1
## v readr 1.1.1 v forcats 0.3.0## -- Conflicts -------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(tidycensus)
library(leaflet)
library(stringr)
library(sf)## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3census_api_key("3aed5d3d39de1e3a2e1679e3fe3090230530c296", install = TRUE, overwrite = TRUE)## Your original .Renviron will be backed up and stored in your R HOME directory if needed.## Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY").
## To use now, restart R or run `readRenviron("~/.Renviron")`## [1] "3aed5d3d39de1e3a2e1679e3fe3090230530c296"readRenviron("~/.Renviron")slc_value <- get_acs(geography = "tract",
variables = "B06011_001",
state = "CA",
county = "Kern",
geometry = TRUE)## 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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|=================================================================| 100%pal <- colorNumeric(palette = "viridis",
domain = slc_value$estimate)
slc_value %>%
st_transform(crs = "+init=epsg:4326") %>%
leaflet(width = "100%") %>%
addProviderTiles(provider = "CartoDB.Positron") %>%
addPolygons(popup = ~ str_extract(NAME, "^([^,]*)"),
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.7,
color = ~ pal(estimate)) %>%
addLegend("bottomright",
pal = pal,
values = ~ estimate,
title = "Median Income",
labFormat = labelFormat(prefix = "$"),
opacity = 1)Reproduce your choropleth with the same color palette but a different provider.
slc_value <- get_acs(geography = "tract",
variables = "B06011_001",
state = "CA",
county = "Kern",
geometry = TRUE)## 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)`.pal <- colorNumeric(palette = "viridis",
domain = slc_value$estimate)
slc_value %>%
st_transform(crs = "+init=epsg:4326") %>%
leaflet(width = "100%") %>%
addProviderTiles(provider = "Esri.WorldImagery") %>%
addPolygons(popup = ~ str_extract(NAME, "^([^,]*)"),
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.7,
color = ~ pal(estimate)) %>%
addLegend("bottomright",
pal = pal,
values = ~ estimate,
title = "Median Income",
labFormat = labelFormat(prefix = "$"),
opacity = 1)Again: Reproduce your choropleth with the same color palette but a different provider.
slc_value <- get_acs(geography = "tract",
variables = "B06011_001",
state = "CA",
county = "Kern",
geometry = TRUE)## 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)`.pal <- colorNumeric(palette = "viridis",
domain = slc_value$estimate)
slc_value %>%
st_transform(crs = "+init=epsg:4326") %>%
leaflet(width = "100%") %>%
addProviderTiles(provider = "HikeBike.HikeBike") %>%
addPolygons(popup = ~ str_extract(NAME, "^([^,]*)"),
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.7,
color = ~ pal(estimate)) %>%
addLegend("bottomright",
pal = pal,
values = ~ estimate,
title = "Median Income",
labFormat = labelFormat(prefix = "$"),
opacity = 7)Select one provider from the three that you’ve tried. Try a different color palette from a different source.
slc_value <- get_acs(geography = "tract",
variables = "B06011_001",
state = "CA",
county = "Kern",
geometry = TRUE)## 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)`.pal <- colorNumeric(palette = "magma",
domain = slc_value$estimate)
slc_value %>%
st_transform(crs = "+init=epsg:4326") %>%
leaflet(width = "100%") %>%
addProviderTiles(provider = "HikeBike.HikeBike") %>%
addPolygons(popup = ~ str_extract(NAME, "^([^,]*)"),
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.7,
color = ~ pal(estimate)) %>%
addLegend("bottomright",
pal = pal,
values = ~ estimate,
title = "Median Income",
labFormat = labelFormat(prefix = "$"),
opacity = 7)Again, stick with the current provider, but try a color palette from a third source.
library(ggthemes)
slc_value <- get_acs(geography = "tract",
variables = "B06011_001",
state = "CA",
county = "Kern",
geometry = TRUE)## 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)`.pal <- colorNumeric(palette = "Blues",
domain = slc_value$estimate)
slc_value %>%
st_transform(crs = "+init=epsg:4326") %>%
leaflet(width = "100%") %>%
addProviderTiles(provider = "HikeBike.HikeBike") %>%
addPolygons(popup = ~ str_extract(NAME, "^([^,]*)"),
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.7,
color = ~ pal(estimate)) %>%
addLegend("bottomright",
pal = pal,
values = ~ estimate,
title = "Median Income",
labFormat = labelFormat(prefix = "$"),
opacity = 7)From the providers and color palettes, which you have tried, which one do you prefer?
Providers:
CartoDB.Positron, Esri.WorldImagery, HikeBike.HikeBike,
Palettes: viridis, magma, Blues,
I preferred the HikeBike provider because I like the detail that is used more than the others. It shows cities, interstates/highways and large area names, etc
I like the Blues color because I am partial to the color blue. Althought the viridis palette (which seems kind of the standard from observation) is easier to read and differentiate.