chirps

gistfile1.txt

library(tidyverse)
library(googlesheets4)
link <- "https://docs.google.com/spreadsheets/d/1OXHDK2S2rc5pp-aGKaTwvP8bSdo5faa6YN8QGqGDl7U/edit?usp=sharing"
gs4_deauth()
meteo_eea <- gs4_get(link)
bce22 <- read_sheet(meteo_eea, sheet="2022") %>% 
  mutate(date = ymd(date))
bce22
# remotes::install_github("ropensci/chirps", build_vignettes = TRUE)
pacman::p_load(chirps, terra, tidyverse)
dates <- c("2022-01-01","2022-12-31")
lonlat_eea <- data.frame(lon = c(-58.305014), lat = c(-37.769968))
lluvias22 <- get_chirps(lonlat_eea, dates, server = "ClimateSERV")
lluvias22 

bce22 %>% 
  left_join(lluvias22) -> bce_full_22
bce_full_22

bce_full_22 %>% 
  ggplot() + 
  aes(x=date, y=rain-chirps) + 
  geom_col() 

bce_full_22 %>% 
  filter(date < ymd("2022-10-01")) %>% 
  rename(eea=rain) %>% 
  group_by(date=if_else(day(date) >= 30,
                        floor_date(date, "20 days"),
                        floor_date(date, "10 days"))) %>%
  summarize_at(vars(eea, chirps), sum) %>% 
  mutate(month = month(date)) %>%
  ungroup %>%
  group_by(month) %>%
  mutate(decada = row_number()) %>%
  ungroup %>% 
  unite(md, month, decada, sep="_") %>%
  pivot_longer(c(eea, chirps)) %>% 
  ggplot() + 
  aes(x=md, y=value, fill=name) + 
  geom_col(alpha=.5, position = "identity") + 
  coord_flip() + 
  labs(y="mm por decada")