2023-10-11_bldgspatialdata_class-code-sample.R

library(tidyverse)
library(sf)

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

"ABC"

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

us_states |> 
  group_by(DIVISION) |> 
  summarise(
    names = str_flatten_comma(NAME,
                              collapse = ", ",
                              last = " and ")
  )

# Selecting states
us_states[us_states$NAME == "Maryland", ]

us_states[us_states$NAME == "Virginia", ]

us_states[us_states$NAME == "California", ]

# Deselecting states
us_states[us_states$NAME %in% c("Virginia", "California"), ]

us_states[us_states$NAME %in% c("California"), ]

us_states |> 
  filter(NAME == "Maryland")

us_states_out <- st_write(us_states, "us_states.geojson")

avg_scalek_round <- function(x) {
  round(mean(x) / 1000, digits = 2)
}

scalek_round <- function(x) {
  round(x / 1000, digits = 2)
}

us_states |> 
  mutate(
    ALAND_K = scalek_round(ALAND),
    AWATER_K = scalek_round(AWATER)
  )

storms |> 
  group_by(category) |> 
  summarise(
    avg_wind = avg_scalek_round(wind)
  )


avg_scalek_round <- function(x) {
  round(mean(x) / 1000, digits = 2)
}

scale_round <- function(x, y) {
  round(x / y, digits = 2)
}

avg_scale_round <- function(x, scale = 1000, na.rm = TRUE) {
  round(mean(x, na.rm = na.rm) / scale, digits = 2)
}

us_states |> 
  mutate(
    ALAND_K = round(ALAND / 1000, digits = 2),
    AWATER_K = round(AWATER / 1000, digits = 2)
  )

storms |> 
  group_by(category) |> 
  summarise(
    avg_wind = avg_scale_round(wind)
  )