気象庁からダウンロードした気温データでヒートマップ作成する

heatmap.R

require(tidyverse)
require(ggthemes)
require(rjson)
require(jsonlite)

# 参考
# https://www.data.jma.go.jp/gmd/risk/obsdl/index.php
# https://www.data.jma.go.jp/gmd/risk/obsdl/top/help3.html#hukajoho
# https://twitter.com/mehori/status/1020644999703089152

# 気象庁からダウンロードしたファイル群を読み込む場合
# https://www.data.jma.go.jp/gmd/risk/obsdl/index.php
#  日平均気温, 日最高気温, 日最低気温 を日別値で選択したことを前提
tempera_kyoto <- list.files(path = "data/kyoto/", pattern="*.csv", full.names=T) %>% 
  map_df(~read_csv(., skip=6, locale=locale(encoding="cp932"),
                   col_names=c("date", 
                               "avg_temp", "avg_temp_quality", "avg_temp_loc",
                               "max_temp", "max_temp_quality", "max_temp_loc",
                               "min_temp", "min_temp_quality", "min_temp_loc"
                               ),
                   col_types = c("cdccdccdcc")
                   )
         ) %>% mutate(date=as.Date(date)) %>%
  mutate(source_id=format(date, "%Y") %>% substr(start=1, stop=3)) %>%
  mutate_if(is.character, factor) %>%
  mutate(location="Kyoto") %>%
  mutate(year=format(date, "%Y") %>%as.integer,
         month_day=paste("2018", format(date, "%m-%d"), sep="-") %>% as.Date)

# https://toyokeizai.net/sp/visual/tko/temperature/ のデータを読み込む場合
tempera_tokyo <- jsonlite::read_json("data/tokyo/temperature.json") %>% lapply(function(x) unlist(x))
tempera_tokyo[[143]] <- c(tempera_tokyo[[143]], rep(NA, length(tempera_tokyo[[142]])-length(tempera_tokyo[[143]])))
tempera_tokyo <- tempera_tokyo %>% as.data.frame
colnames(tempera_tokyo) <- 1876:2018
tempera_tokyo$month_day <- seq(as.Date("2018-06-01"), as.Date("2018-09-30"), by="day")
tempera_tokyo <- tempera_tokyo %>% gather(key = "year", value="avg_temp", -month_day) %>%
  mutate(avg_temp=as.numeric(avg_temp)) %>%
  mutate(year=as.integer(year)) %>%
  mutate(location="Tokyo")


plot_heat <- function(data){
  g <- ggplot(
    data %>% filter(between(month_day, as.Date("2018-06-01"), as.Date("2018-09-30"))),
    aes(x=month_day, y=year, fill=avg_temp, color="grey")
  ) +
    geom_tile(color='grey', width=1, height=1) +
    scale_fill_distiller(name="日毎平均気温 (°C)", palette = "Spectral")  +
    labs(x="月日", y="年",
         caption="数値は気象庁より\n https://www.data.jma.go.jp/gmd/risk/obsdl/index.php") +
    scale_y_reverse() + scale_x_date(date_labels="%m-%d") + scale_color_discrete(guide=F) + coord_equal() +
    theme_tufte() + theme(axis.title.y=element_text(angle=0, vjust=.5))
  return(g)
} 
g <- plot_heat(tempera_kyoto)
print(g + labs(title="京都市の日毎平均気温"))

g <- plot_heat(tempera_tokyo)
print(g + labs(title="東京都の日毎平均気温",
               caption="数値はここから\nhttps://toyokeizai.net/sp/visual/tko/temperature/"))


g <- ggplot(
  tempera_kyoto %>% filter(between(month_day, as.Date("2018-06-01"), as.Date("2018-09-30"))),
  aes(x=date, y=avg_temp, ymin=min_temp,
      ymax=max_temp, color=avg_temp)
) + geom_line() +
  scale_color_distiller(name="", palette = "Spectral") + 
  labs(x="", y="日毎平均気温 (°C)",
       caption="数値は気象庁より\n https://www.data.jma.go.jp/gmd/risk/obsdl/index.php") +
  theme_pander()
print(g + labs(title="京都市の日毎平均気温") + coord_cartesian(ylim=c(10, 38)))


g <- ggplot(
  tempera_tokyo %>% mutate(date=paste(as.character(year), format(month_day, "%m-%d"), sep="-") %>% as.Date()),
  aes(x=date, y=avg_temp, color=avg_temp)
) + geom_line() +
  scale_color_distiller(name="", palette = "Spectral") + 
  labs(x="", y="日毎平均気温 (°C)",
       caption="数値は気象庁より\n https://www.data.jma.go.jp/gmd/risk/obsdl/index.php") +
  theme_pander()
print(g + labs(title="東京都の日毎平均気温",
               caption="数値はここから\nhttps://toyokeizai.net/sp/visual/tko/temperature/") +
        coord_cartesian(ylim=c(10, 38)))