## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = identical(Sys.getenv("NOT_CRAN"), "true")
)

## ----setup--------------------------------------------------------------------
# library(healthatlas)

## -----------------------------------------------------------------------------
# ha_set("chicagohealthatlas.org")

## -----------------------------------------------------------------------------
# ha_get()

## -----------------------------------------------------------------------------
# topics <- ha_topics(progress = FALSE)
# topics

## ----include = FALSE----------------------------------------------------------
# library(dplyr)

## -----------------------------------------------------------------------------
# library(dplyr)
# library(purrr)
# 
# # filter by dataset
# topics %>%
#   filter(map_lgl(topic_datasets, ~ "healthy-chicago-survey" %in% .x$key))
# 
# # filter by subcategory
# topics %>%
#   filter(map_lgl(topic_subcategories, ~ "diet-exercise" %in% .x$key))
# 
# # filter by keyword
# topics %>%
#   filter(map_lgl(topic_keywords, ~ "activity" %in% .x))

## -----------------------------------------------------------------------------
# subcategories <- ha_subcategories()
# subcategories

## -----------------------------------------------------------------------------
# ha_topics("diet-exercise")

## -----------------------------------------------------------------------------
# coverage <- ha_coverage("HCSFVAP", progress = FALSE)
# coverage

## -----------------------------------------------------------------------------
# ease_of_access <- ha_data(
#   topic_key = "HCSFVAP",
#   population_key = "",
#   period_key = "2022-2023",
#   layer_key = "neighborhood"
# )
# ease_of_access

## -----------------------------------------------------------------------------
# combinations_of_data <- ha_data(
#   topic_key = c("POP", "UMP"),
#   population_key = c("", "H"),
#   period_key = c("2017-2021", "2018-2022", "invalid"),
#   layer_key = "neighborhood"
# )
# combinations_of_data

## -----------------------------------------------------------------------------
# library(tibble)
# library(purrr)
# 
# # creating a table of data I want
# metadata <- tribble(
#   ~topic_key , ~population_key , ~period_key , ~layer_key     ,
#   "POP"      , ""              , "2017-2021" , "neighborhood" ,
#   "HCSFVAP"  , ""              , "2020-2021" , "neighborhood" ,
#   "UMP"      , "H"             , "2017-2021" , "neighborhood" ,
# )
# 
# metadata %>%
#   pmap(ha_data)

## -----------------------------------------------------------------------------
# layers <- ha_layers()
# layers

## -----------------------------------------------------------------------------
# community_areas <- ha_layer("neighborhood")
# community_areas

## -----------------------------------------------------------------------------
# ease_of_access <- ha_data(
#   topic_key = "HCSFVAP",
#   population_key = "",
#   period_key = "2022-2023",
#   layer_key = "neighborhood",
#   geometry = TRUE
# )
# ease_of_access

## -----------------------------------------------------------------------------
# library(ggplot2)
# 
# plot <- ggplot(ease_of_access) +
#   geom_sf(aes(fill = value), alpha = 0.7) +
#   scale_fill_distiller(palette = "GnBu", direction = 1) +
#   labs(
#     title = "Easy Access to Fruits and Vegetables within Chicago",
#     fill = "Percent of adults who reported\nthat it is very easy for them to\nget fresh fruits and vegetables."
#   ) +
#   theme_minimal()
# plot

## -----------------------------------------------------------------------------
# point_layers <- ha_point_layers()
# point_layers

## -----------------------------------------------------------------------------
# grocery_stores <- ha_point_layer("7d9caf3c-75e6-4382-8c97-069696a3efbf")

## -----------------------------------------------------------------------------
# plot +
#   geom_sf(data = grocery_stores, size = 0.5)