A code sample from the in-class live coding practice session for week 3 of the GES 668 Building Spatial Datasets (Fall 2023) course: https://bldgspatialdata.github.io/website/weeks/week_03.html

2023-09-13_bldgspatialdata_in-class_code-sample.R

library(tidyverse)

select(storms, name)

select(storms, !name)

select(storms, -name)

select(storms, name:long)

select(storms, c(name, starts_with("l"), ends_with("diameter")))

select(storms, latitude, longitude)

select(storms, lat, long)

select(
  storms,
  all_of(c("lat", "latitude", "y", "lon", "long", "longitude", "x"))
  )

filter(storms, status == "hurricane")

filter(storms, !is.na(category))

filter(storms, wind %in% c(20, 30, 40, 50, 60, 70))

filter(storms, wind < 15)

filter(storms, wind %in% c(1:14))

storms |> 
  filter(!is.na(category), category > 3) |> 
  ggplot() +
  geom_point(
    aes(long, lat, color = as.factor(category))
    ) +
  scale_color_brewer(type = "seq")

storms |> 
  filter(
  between(hurricane_force_diameter, 110, 125),
  between(tropicalstorm_force_diameter, 230, 250)
) |> 
  View()

storms |> 
  mutate(
    ratio = pressure / wind,
    .before = any_of(c("year", "yr"))
  )

storms |> 
  group_by(year, name) |> 
  mutate(
    prior_wind = lag(wind),
    wind_change = wind - prior_wind,
    .before = everything()
  ) |> 
  relocate(
    wind,
    .before = everything()
  ) |> 
  View()

mutate(
  storms,
  beaufort_desc = case_when(
    wind < 1 ~ "Calm",
    wind < 4 ~ "Light Air",
    wind < 8 ~ "Light Breeze",
    wind < 13 ~ "Gentle Breeze",
    wind < 19 ~ "Moderate Breeze",
    wind < 25 ~ "Fresh Breze",
    wind < 32 ~ "Strong Breeze",
    wind < 39 ~ "Near Gale",
    wind < 47 ~ "Gale",
    wind < 55 ~ "Strong Gale",
    wind < 64 ~ "Whole Gale",
    wind < 75 ~ "Storm Force",
    .default = "Hurricane Force"
  ),
  .before = everything()
) |> 
  View()


storms |>
  filter(!is.na(category)) |> 
  group_by(year) |> 
  summarise(
    avg_month = mean(month),
    count = n_distinct(name),
    duration = max(month) - min(month),
    # min_month = min(month),
    # max_month = max(month),
    min_wind = min(wind),
    max_wind = max(wind)
  ) |> 
  ggplot(aes(year, count)) +
  geom_col()

storms |> 
  slice_max(order_by = month)