descarga_dmc.R

library(tidyverse)


# DMC
agrometR::estaciones_agromet 
agrometR::estaciones_dmc |> 
  filter(str_detect(nombreEstacion, "uinta"))

data <- agrometR::get_agro_data_dmc(
  stations_id = 330020,
  date_start =  "2022-01-01 00:00:00",
  date_end   =  "2023-08-30 23:59:59",
  verbose = TRUE
)

data <- data |> 
  select(station_id, momento, contains("aguaCaida"))

glimpse(data)

data |> 
  # filter(day(momento) %in% c(21:23)) |> 
  pivot_longer(cols = contains("aguaCaida")) |> 
  ggplot(aes(momento, value, group = name)) +
  geom_line() +
  facet_wrap(vars(name), scales = "free", ncol = 1) +
  theme_minimal() 

data |> 
  # View()
  # filter(day(momento) %in% c(21:23), month(momento) %in% 7) |>
  mutate(
    # utc?
    # momento = momento - hours(4),
    momento_dia = floor_date(momento, unit = "day"),
    # desfase
    # fecha_nueva = momento - hours(12),
    # fecha_nueva_dia = ceiling_date(fecha_nueva, unit = "day"),
    hora = case_when(
      hour(momento) <=  6 ~ 1,
      hour(momento) <= 12 ~ 2,
      hour(momento) <= 18 ~ 3,
      hour(momento) <= 24 ~ 4,
      )
    ) |>
  group_by(momento_dia, hora) |> 
  summarise(prec = max(aguaCaida6Horas)) |> 
  ungroup() |> 
  mutate(
    m1 = momento_dia - hours(16),
    # m2 = momento_dia - hours(20),
    # m3 = momento_dia - hours(22),
    # m4 = momento_dia - hours(24),
    # m5 = momento_dia - hours(25)
    momento_dia2 = floor_date(m1, unit = 'day')
  ) |> 
  group_by(momento_dia2) |>
  summarise(prec = sum(prec)) |> 
  ungroup() |>
  group_by(floor_date(momento_dia2, unit = 'month')) |>
  summarise(prec = sum(prec)) |> 
  View()