uribo

read_ks_pop <- function(path, .tidy = TRUE) {
  d <-
    vroom::vroom(path,
                 locale = vroom::locale(encoding = "cp932"),
                 skip = 1,
                 col_types = paste0(c("cii"),
                                    paste(rep("d", 42), collapse = ""), collapse = ""))
  fix_names <-
    stringi::stri_trans_nfkc(names(d)) %>%
    stringr::str_trim() %>%

uribo

library(fgdr)
library(purrr)
library(sf)
library(h3forr)
library(ggplot2)

sf_hex <-
  geo_to_h3(c(36.0670602, 140.0781036), res = 7) %>% 
  h3_to_geo_boundary() %>% 
  geo_boundary_to_sf()

uribo

library(rlang)
library(purrr, warn.conflicts = FALSE)
library(glue)
library(rAltmetric)
# library(ghql)

reference_style <- "oikos"
path_references_bib <- "references.bib"

qry <- ghql::Query$new()

uribo

library(jmastats)
library(dplyr)
library(sf)
library(assertr)

df_bst_all <-
    read_rsmc_besttrack(path) %>%
    verify(dim(.) == c(65626, 22))

df_bst_all %>%

uribo

library(jmastats)
library(dplyr)
library(sf)
library(assertr)

df_bst_all <-
    read_rsmc_besttrack(path) %>%
    verify(dim(.) == c(65626, 22))

df_bst_all %>%

uribo

######################################
# 農林水産省 過去の主な異常災害等と農林水産被害
# http://www.maff.go.jp/j/saigai/arc/index-1.html
######################################
# 1/3 Setup -------------------------------------------------------------------
library(dplyr)
library(purrr)
library(stringr)
bind_line2df <- function(x, index) {
  x[index] %>%

uribo

library(drake)
library(dplyr)
library(purrr)
library(assertr)
library(tabulizer)

matrix_to_tbl <- function(data) {
  data %>% 
    as.data.frame(stringsAsFactors = FALSE) %>% 
    janitor::clean_names() %>%

uribo

#############################################
# 東京電力街が提供する千葉県内の停電状況
#############################################
library(dplyr)
library(sf)
library(ggplot2)
library(rcartocolor)
library(ggrepel)
library(gridExtra)

uribo

library(tidyverse)
library(tabulizer)
tweak_kameiten_df <- function(data) {
  data %>%
    dplyr::filter(!is.na(`No.`)) %>%
    dplyr::mutate(還元率 = units::set_units(還元率, "%")) %>%
    dplyr::arrange(`No.`) %>%
    tibble::as_tibble()
}
kameiten_na <- function() {

uribo

library(h3forr)
library(jpmesh)
library(sf)
library(dplyr)
library(mapview)
sfc_poly_to_h3 <- function(geometry, res = 7) {
  coords <- sf::st_coordinates(sf::st_centroid(st_geometry(geometry)))
  h3forr::geo_to_h3(c(coords[2], coords[1]), res = res)
}