## code to prepare `wfl_useData_obsStation` dataset goes here
# source pkgs
source("data-raw/set-global.R")
#==== create dir =====
dir_media <- "data-raw/data-tidy/public-site/observe-station/"
dir_fina <- "xlsx/"
gen_dirs_vec(dir_media, dir_fina)
out_dir <- paste0(dir_media, dir_fina)
files_all <- list.files(out_dir)
# file path
files_id <- which(str_detect(files_all,"list-"))
files_sel <- files_all[files_id]
files_path <- paste0(out_dir, files_sel)
# batch read xlsx files
tbl_read <- tibble(url = files_path) %>%
mutate(table = map(url, ~openxlsx::read.xlsx(.x))) %>%
select(-url) %>%
unnest(table)
# just for check
index_dup <- which(duplicated(tbl_read$name))
tbl_check <- tbl_read %>%
.[index_dup,]
#=====match institution to province=====
require("techme")
data("queryTianyan")
dt_match <- queryTianyan %>%
select(name_origin, province) %>%
rename(institution = "name_origin")
data("ProvinceCity")
dt_city <- ProvinceCity %>%
select(city_clean, province_clean)
ptn_city<- paste0(unique(ProvinceCity$city_clean), collapse = "|")
ptn_province <- paste0(unique(ProvinceCity$province_clean), collapse = "|")
list_exclude <- c("province_raw", "province_clean","city_clean")
tbl_out <- tbl_read %>%
left_join(., dt_match, by= "institution" ) %>%
# filter obvious province info
mutate(province_raw = str_extract(institution, ptn_province)) %>%
mutate(province = ifelse(is.na(province), province_raw, province)) %>%
# match city
mutate(city_clean = str_extract(institution, ptn_city)) %>%
left_join(., dt_city, by= "city_clean" ) %>%
mutate(province = ifelse(is.na(province), province_clean, province)) %>%
dplyr::select(-one_of(list_exclude))
# check
check <- sum(is.na(tbl_out$province))
if(check > 0) stop("please check the name of institution!")
# write out
PubObsStation <- tbl_out
usethis::use_data(PubObsStation, overwrite = TRUE)
# write document
require(devtools)
load_all()
use_r("Pub-ObsStation")
document_dt(PubObsStation)
document()
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