都道府県別のコロナ新規感染者数を7日間平均で図示するコマンド（R）

コロナの新規感染者数を7日間平均で図示する場合、Rのsliderパッケージが有用なので、コマンド例として次に公開しておく。
色々と検討してみてほしい。

#必要なパッケージ
library(tidyverse)
library(lubridate)
library(slider)

#新規感染者数のデータセットの読み込み、インターネットから直接読み込み
covid_19_pref <- read_csv("https://covid19.mhlw.go.jp/public/opendata/newly_confirmed_cases_daily.csv")
covid_19_pref$Date <- as.Date(covid_19_pref$Date)
covid_19_pref <- covid_19_pref %>% gather(key = "Prefecture", value = "Newly confirmed cases", "ALL":"Okinawa")
covid_19_pref <- covid_19_pref %>% group_by(Prefecture) %>% 
  mutate(new_case_n_m7 = slide_vec(.x=`Newly confirmed cases`	, .f=mean, .before=6)) 

#累積死亡者数のデータセットの読み込み、インターネットから直接読み込み
death_num <- read_csv("https://covid19.mhlw.go.jp/public/opendata/deaths_cumulative_daily.csv")
death_num$Date <- as.Date(death_num$Date)
death_num <- death_num %>% gather(key = "Prefecture", value ="Deaths(Cumulative)", "ALL":"Okinawa")
covid_19_pref2 <- covid_19_pref %>% select("Date"=Date,"Prefecture"=Prefecture,"Value"=new_case_n_m7)
covid_19_pref2 <- covid_19_pref2 %>% mutate(Depart = "新規感染者（7日平均）")
death_num2 <- death_num %>% select("Date"=Date,"Prefecture"=Prefecture,"Value"=`Deaths(Cumulative)`)
death_num2 <- death_num2 %>% mutate(Depart = "累積死者数")

#人口数データは直接テーブルを組む
Prefecture <- c("ALL", "Hokkaido", "Aomori", "Iwate", "Miyagi", "Akita", "Yamagata", "Fukushima", "Ibaraki", "Tochigi", "Gunma", "Saitama", "Chiba", "Tokyo", "Kanagawa", "Niigata", "Toyama", "Ishikawa", "Fukui", "Yamanashi", "Nagano", "Gifu", "Shizuoka", "Aichi", "Mie", "Shiga", "Kyoto", "Osaka", "Hyogo", "Nara", "Wakayama", "Tottori", "Shimane", "Okayama", "Hiroshima", "Yamaguchi", "Tokushima", "Kagawa", "Ehime", "Kochi", "Fukuoka", "Saga", "Nagasaki", "Kumamoto", "Oita", "Miyazaki", "Kagoshima", "Okinawa")
Population <- c(127094745, 5381733, 1308265, 1279594, 2333899, 1023119, 1123891, 1914039, 2916976, 1974255, 1973115, 7266534, 6222666, 13515271, 9126214, 2304264, 1066328, 1154008, 786740, 834930, 2098804, 2031903, 3700305, 7483128, 1815865, 1412916, 2610353, 8839469, 5534800, 1364316, 963579, 573441, 694352, 1921525, 2843990, 1404729, 755733, 976263, 1385262, 728276, 5101556, 832832, 1377187, 1786170, 1166338, 1104069, 1648177, 1433566)
pop <- data.frame(Prefecture, Population)
pop <-pop %>% mutate(Pop_index = Population/100000)

#新規感染者数と死者数を組み合わせ。人口数をふって、データベースに整形
deaath_and_new <- bind_rows(covid_19_pref2,death_num2)
deaath_and_new <- left_join(x=deaath_and_new,y=pop,by="Prefecture")

#都道府県を順番に並べ漢字に直す
deaath_and_new$Prefecture <- deaath_and_new$Prefecture %>% 
  str_replace_all(c(
    "ALL"="00_全国",
    "Hokkaido"="01_北海道",
    "Aomori"="02_青森県",
    "Iwate"="03_岩手県",
    "Miyagi"="04_宮城県",
    "Akita"="05_秋田県",
    "Yamagata"="06_山形県",
    "Fukushima"="07_福島県",
    "Ibaraki"="08_茨城県",
    "Tochigi"="09_栃木県",
    "Gunma"="10_群馬県",
    "Saitama"="11_埼玉県",
    "Chiba"="12_千葉県",
    "Tokyo"="13_東京都",
    "Kanagawa"="14_神奈川県",
    "Niigata"="15_新潟県",
    "Toyama"="16_富山県",
    "Ishikawa"="17_石川県",
    "Fukui"="18_福井県",
    "Yamanashi"="19_山梨県",
    "Nagano"="20_長野県",
    "Gifu"="21_岐阜県",
    "Shizuoka"="22_静岡県",
    "Aichi"="23_愛知県",
    "Mie"="24_三重県",
    "Shiga"="25_滋賀県",
    "Kyoto"="26_京都府",
    "Osaka"="27_大阪府",
    "Hyogo"="28_兵庫県",
    "Nara"="29_奈良県",
    "Wakayama"="30_和歌山県",
    "Tottori"="31_鳥取県",
    "Shimane"="32_島根県",
    "Okayama"="33_岡山県",
    "Hiroshima"="34_広島県",
    "Yamaguchi"="35_山口県",
    "Tokushima"="36_徳島県",
    "Kagawa"="37_香川県",
    "Ehime"="38_愛媛県",
    "Kochi"="39_高知県",
    "Fukuoka"="40_福岡県",
    "Saga"="41_佐賀県",
    "Nagasaki"="42_長崎県",
    "Kumamoto"="43_熊本県",
    "Oita"="44_大分県",
    "Miyazaki"="45_宮崎県",
    "Kagoshima"="46_鹿児島県",
    "Okinawa"="47_沖縄県"))

#描画コマンド、ファセットで都道府県別に分けたものとフィルターで個別自治体を取り出すコマンド、フィルターの日付を調整すれば期間を変更できる
deaath_and_new %>% 
  filter(Date >= "2021-04-01" & Date <= "2022-01-22") %>% 
  filter(Depart == "新規感染者（7日平均）") %>%
  ggplot(aes(x=Date,y=Value/Pop_index))+
  geom_line()+
  labs(
    title = "7日間平均新規感染者数推移(Source:https://covid19.mhlw.go.jp/)",
    x="2021年4月1日〜2022年1月22日",
    y="人口10万人あたり数",
    colour="")+
  theme_bw(base_family = "HiraKakuPro-W3")+
  facet_wrap(~Prefecture,nrow=8, scale = "free")

deaath_and_new %>% 
  filter(Prefecture =="47_沖縄県") %>%
  filter(Date >= "2021-04-01" & Date <= "2022-01-22") %>% 
  filter(Depart == "新規感染者（7日平均）") %>%
  ggplot(aes(x=Date,y=Value/Pop_index))+
  geom_line()+
  labs(
    title = "沖縄県7日間平均新規感染者数推移",
    x="2021年4月1日〜2022年1月22日",
    y="人口10万人あたり数",
    colour="")+
  theme_bw(base_family = "HiraKakuPro-W3")
#end