elipousson

library(getACS)
library(tidyverse)

vehicle_access_acs <- get_acs_tables(
  "tract",
  table = "B08201",
  county = "Baltimore city",
  state = "MD"
)

elipousson

# Get zoning metadata from {mapmaryland}
baltimore_zoning_info <- dplyr::filter(
  mapmaryland::md_zoning_info,
  county == stringr::str_remove(map_params$county_name, ", Maryland$")
)


# Create format function with custom colors ----

zoning_colors <- tibble::tribble(

elipousson

# Install packages if needed
# install.packages(c("skimr", "spData", "tidyverse", "sf", "gt"))

# Load packages
library(spData)
library(tidyverse)
library(sf)
library(gt)

# Set a default ggplot2 theme

elipousson

# This is a cleaned up version of your original function
area_square_km <- function(data, ...) {
  data |> 
  dplyr::mutate(
    dplyr::across(
      dplyr::all_of(...),
      ~ .x / 1000000
    )
  )
}

elipousson

library(tidyverse)
library(sf)

c("A", "B", "C")

"ABC"

str_flatten(c("A", "B", "C"))

us_states |> 

elipousson

openapi: 3.0.0
info:
    title: SFTool API
    version: 1.0.0
    description: |
        SFTool API provides machine readable data to develop a service or services to search, 
        display, analyze, retrieve, and view SFTool data about healthy buildings and environmentally responsible purchasing.

        You can find out more about SFTool at [https://sftool.gov](https://sftool.gov).
        Use the api key 'DEMO_KEY' to test the SFTool API.

elipousson

library(sf)

# You can read data from these URLs

# GeoJSON (geojson)
read_sf("https://github.com/ropensci/geojsonio/raw/main/inst/examples/california.geojson")

# Keyhole Markup Language (kml)
read_sf("https://github.com/ropensci/geojsonio/raw/main/inst/examples/poa_annua.kml")

elipousson

library(tidyverse)

select(storms, name)

select(storms, !name)

select(storms, -name)

select(storms, name:long)

elipousson

# pak::pkg_install(c("elipousson/bcpss", "elipousson/mapbaltimore", "elipousson/maplayer", "hrbrthemes"))

library(bcpss)
library(tidyverse)
library(sf)

transportation_survey_resp <- parent_survey_SY1819 |> 
  select(
    starts_with("school_n"),
    c("grade_band", "management_type", "parent_response_count"),

elipousson

library(sf)
library(dplyr)

# Can you convert `storms_sf` to make the feature geometry represent
# storm tracks instead of storm observations?
  
storms_sf <- storms |> 
  st_as_sf(
    coords = c("long", "lat"),
    crs = 4326