In kongdd/RHtestsHelper: What the Package Does (One Line, Title Case)

# source("scripts/ChinaHW_cluster/main.R")
library(data.table)
# library(tidyfst)
library(Ipaper)
library(purrr)
library(rtrend)
library(gg.layers)
library(tidymet)
library(sf)
library(ggplot2)
library(gg.layers)
library(rcolors)
library(patchwork)

# source("scripts/ChinaHW_cluster/main_vis.R", encoding = "UTF-8")
source('scripts/ChinaHW_cluster/main.R')
source('scripts/ChinaHW_cluster/main_trend.R')

#| vscode: {languageId: r}
get_RegionalMean <- function(df) {
  d_yearly = df %>% get_yearly("site")
  d_region = dt_mean(d_yearly %>% select(-site), .(year))
  # d = d_region %>% melt("year")
  d_region
}

# f_org <- "OUTPUT/ChinaHI/Trend/trend_original.rda"
# f_homo <- "OUTPUT/ChinaHI/Trend/trend_homo.rda"

# 1,626站点，cpt得到修复
f_input <- "data-raw/INPUT/INPUT_met2474_Tmax&RHmax_for_HImax_1951-2022_V2.fst"
df <- import(f_input)
df_raw = df[, .(site, date, value = RH_avg)]
df_Li2020 = import("data-raw/INPUT/Homo_RH_daily_746st_1960_2017_v20230405.fst")

varname = "RH_avg"
fs <- query_fileList(varname)
df_org <- get_DF_INPUTS(df, varname, fs)

l_noRef_day <- import(fs$noRef_day)
l_Ref_day <- import(fs$withRef_day)

df_ref <- l_Ref_day %>% getHomoData(extract.day = TRUE)
df_noref <- l_noRef_day %>% getHomoData(extract.day = FALSE)
df_final <- import(fs$out)

sites = df_Li2020 %>% query_site()

lst = listk(
  Raw = df_raw[site %in% sites, ],
  cpt = df_org[site %in% sites, ],
  Li2020 = df_Li2020,
  noRef = df_noref[site %in% sites, .(site, date, value)], 
  Ref = df_ref[site %in% sites, .(site, date, value)],
  Final = df_final[site %in% sites, .(site, date, value)]
)

# map(lst, query_site) %>% str()
lst_region = map(lst, get_RegionalMean)
d_region = melt_list(lst_region, "type_source")

p = ggplot(d_region[type_source %in% c("Raw", "cpt", "Li2020", "Final")], 
    aes(year, value, color = type_source, shape = type_source)) + 
  geom_point() + 
  geom_line()

write_fig(p, 'Figure2_final_RHtestsV4.pdf', 10, 5, show = FALSE)

sites_ref = l_Ref_day %>% RHtests_rm_empty() %>% names()
sites_noref = l_noRef_day %>% rm_empty() %>% names()

length(sites_ref)
length(sites_noref)

length(l_noRef_day)
length(l_Ref_day)

match2(sites_noref, sites_ref)
match2(names(l_noRef_day), names(l_Ref_day))

df_final <- import_fst(fs$out)
# df_year_org <- df[, .(site, date, value = RH_avg)] %>% dt_day2year() %>% .$year
# merge_final(varname)
# df_final = import_fst(fs$out)

sites = query_site(df_noref)
d_ref = df_ref %>% select(-site) %>% get_yearly()
d_noref = df_noref %>% select(-site) %>% get_yearly()
dat = cbind(d_ref %>% rename(ref = value), noref = d_noref$value)

pdat = melt(dat, c("year"))

## 最后一次
# ggplot(pdat, aes(year, value, color = variable)) + 
#   geom_line()

info = df_final[, .N, .(site, type_homo)][, .(site, type_homo)]
info[, .N, type_homo]

data = list(
  Raw = dt_day2year(df_final[, .(site, date, value = base)])$year, 
  Ref = dt_day2year(df_final[, .(site, date, value)])$year
) %>% melt_list("type_source")

data %<>% merge(info, .)

d_region = df_final %>% select(-type_homo, site) %>% get_yearly()

ggplot(d_region, aes(year, value)) + 
  geom_line(color = "red") + 
  geom_line(aes(y = base), color = "black")

# d_year = select(data, -site) %>% dt_mean(.(type_homo, type_source, year))
p = ggplot(d_year, aes(year, value, color = type_source)) + 
  geom_line() + 
  facet_wrap(~type_homo)
write_fig(p, 'Rplot.pdf', 10, 5, show = F)