朝日新聞は6月27日の朝刊で（デジタル版では26日夜に）、「熱中症、涼しい地域ほど注意 暑さ慣れず、同じ35度でも搬送2倍」との記事を掲載した。東京大の橋爪真弘教授（疫学）に監修してもらいながら、熱中症搬送数と気温との関係を、科学みらい部の市野塊記者と小宮山が分析した。
この文書では、どこからどんなデータを持ってきて、どのように分析したか、Rのコードとともに説明する。
tidyverseとかlubridateといったあたりは定番選手。modelsummary以下の三つは（たしか）回帰分析のためのパッケージ。
library(tidyverse)
library(readxl)
library(lubridate)
library(modelsummary)
library(leaps)
library(MASS)総務省消防庁のウェブサイトにあるデータを読み込んで整形する。データは熱中症で運ばれた人の都道府県別、日別の合計人数のほか、搬送者の年齢、症状の重さ、発生場所の内訳の数字がある。なお、発生場所の記録があるのは2017年以降のみ。
hs08 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h20.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h20.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h20.xlsx",sheet=3))
hs09 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h21.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h21.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h21.xlsx",sheet=3))
hs10 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h22.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h22.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h22.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_h22.xlsx",sheet=4))
hs11 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h23.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h23.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h23.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_h23.xlsx",sheet=4))
hs12 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h24.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h24.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h24.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_h24.xlsx",sheet=4))
hs13 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h25.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h25.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h25.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_h25.xlsx",sheet=4))
hs14 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h26.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h26.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h26.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_h26.xlsx",sheet=4))
hs15 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h27.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h27.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h27.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_h27.xlsx",sheet=4),
read_xlsx("data/hs/heatstroke003_data_h27.xlsx",sheet=5))
hs16 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h28.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h28.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h28.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_h28.xlsx",sheet=4),
read_xlsx("data/hs/heatstroke003_data_h28.xlsx",sheet=5))
# この年以降に発生場所の情報が加わる
hs17 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h29.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h29.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h29.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_h29.xlsx",sheet=4),
read_xlsx("data/hs/heatstroke003_data_h29.xlsx",sheet=5))
hs18 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_h30.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_h30.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_h30.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_h30.xlsx",sheet=4),
read_xlsx("data/hs/heatstroke003_data_h30.xlsx",sheet=5))
hs19 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_r1.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_r1.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_r1.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_r1.xlsx",sheet=4),
read_xlsx("data/hs/heatstroke003_data_r1.xlsx",sheet=5))
hs20 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_r2.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_r2.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_r2.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_r2.xlsx",sheet=4))
hs21 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_r3.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_r3.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_r3.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_r3.xlsx",sheet=4),
read_xlsx("data/hs/heatstroke003_data_r3.xlsx",sheet=5))
hs22 <- bind_rows(read_xlsx("data/hs/heatstroke003_data_r4.xlsx",sheet=1),
read_xlsx("data/hs/heatstroke003_data_r4.xlsx",sheet=2),
read_xlsx("data/hs/heatstroke003_data_r4.xlsx",sheet=3),
read_xlsx("data/hs/heatstroke003_data_r4.xlsx",sheet=4),
read_xlsx("data/hs/heatstroke003_data_r4.xlsx",sheet=5))
hs <- bind_rows(hs08,
hs09,
hs10,
hs11,
hs12,
hs13,
hs14,
hs15,
hs16,
hs17,
hs18,
hs19,
hs20,
hs21,
hs22)
hs[is.na(hs)] <- 0
hs <- hs %>%
rename(date=日付,
prefID=都道府県コード,
cases=`搬送人員（計）`,
age_newborn=`年齢区分：新生児`,
age_infant=`年齢区分：乳幼児`,
age_youth=`年齢区分：少年`,
age_adult=`年齢区分：成人`,
age_elder=`年齢区分：高齢者`,
age_unknown=`年齢区分：不明`,
severity_dead=`傷病程度：死亡`,
severity_serious=`傷病程度：重症`,
severity_moderate=`傷病程度：中等症`,
severity_mild=`傷病程度：軽症`,
severity_other=`傷病程度：その他`,
place_house=`発生場所：住居`,
place_work1=`発生場所：仕事場①`,
place_work2=`発生場所：仕事場②`,
place_school=`発生場所：教育機関`,
place_public_inside=`発生場所：公衆(屋内)`,
place_public_outside=`発生場所：公衆(屋外)`,
place_road=`発生場所：道路`,
place_other=`発生場所：その他`) %>%
mutate(date=as.Date(date)) %>%
left_join(read_csv("data/pref.csv")) %>%
mutate(year=as.double(year(date)),month=as.double(month(date)))hs各都道府県庁所在地における5～9月の日ごと最高気温と平均気温のデータを読み込んで整形する。出典は気象庁。
climate <- bind_rows(read_csv("data/climate/hokkaido.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="北海道",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/aomori.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="青森県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/iwate.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="岩手県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/miyagi.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="宮城県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/akita.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="秋田県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/yamagata.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="山形県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/fukushima.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="福島県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/ibaraki.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="茨城県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/tochigi.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="栃木県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/gunma.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="群馬県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/saitama.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="埼玉県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/chiba.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="千葉県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/tokyo.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="東京都",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/kanagawa.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="神奈川県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/niigata.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="新潟県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/toyama.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="富山県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/ishikawa.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="石川県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/fukui.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="福井県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/yamanashi.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="山梨県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/nagano.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="長野県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/gifu.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="岐阜県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/shizuoka.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="静岡県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/aichi.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="愛知県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/mie.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="三重県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/shiga.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="滋賀県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/kyoto.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="京都府",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/osaka.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="大阪府",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/hyogo.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="兵庫県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/nara.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="奈良県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/wakayama.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="和歌山県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/tottori.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="鳥取県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/shimane.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="島根県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/okayama.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="岡山県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/hiroshima.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="広島県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/yamaguchi.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="山口県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/tokushima.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="徳島県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/kagawa.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="香川県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/ehime.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="愛媛県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/kochi.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="高知県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/fukuoka.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="福岡県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/saga.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="佐賀県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/nagasaki.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="長崎県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/kumamoto.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="熊本県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/oita.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="大分県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/miyazaki.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="宮崎県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/kagoshima.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="鹿児島県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity),
read_csv("data/climate/okinawa.csv",
skip=3,
locale = locale(encoding = "shift-jis"))[,c(1,2,5,8)] %>%
filter(!is.na(年月日)) %>%
mutate(pref="沖縄県",
date=as.Date(年月日),
max_temp=`最高気温(℃)...2`,
avg_temp=`平均気温(℃)...5` ,
humidity=`平均湿度(％)...8`) %>%
dplyr::select(pref,date,max_temp,avg_temp,humidity)) %>%
mutate(year=as.double(year(date)),month=as.double(month(date)))climate