---
title: 课后作业8
author: 姓名
format: html
---

# 下载airquality.xlsx，并读取数据

```{r}
#| eval: false
#| execute: false
# 下载至临时文件
tmpxlsxpath <- file.path(tempdir(), "airquality.xlsx")
download.file("https://drwater.rcees.ac.cn/git/course/RWEP/raw/branch/PUB/data/airquality.xlsx",
  destfile = tmpxlsxpath)
airqualitydf <- readxl::read_xlsx(tmpxlsxpath, sheet = 2)
metadf <- readxl::read_xlsx(tmpxlsxpath, sheet = 1)
```

# 根据`airqualitydf.xlsx`，按采样点统计白天（8:00-20:00）与夜晚（20:00-8:00）中空气质量指数（AQI）中位数，按城市统计低于所有采样点AQI30%分位值的采样点占比，列出上述占比最高的10个城市（不考虑采样点数低于5个的城市）。

```{r}
#| eval: false
#| execute: false
require(tidyverse)
airqualitydf |>
  select(datetime, site, AQI) |>
  filter(!is.na(AQI)) |>
  group_by(site) |>
  summarize(AQI.median = median(AQI, na.rm = TRUE)) |>
  left_join(metadf |> select(site, city = Area)) |>
  group_by(city) |>
  filter(n() > 5) |>
  summarize(p = sum(AQI.median < quantile(airqualitydf$AQI, probs = 0.5, na.rm = TRUE)) / n()) |>
  top_n(10, p)


airqualitydf |>
  select(datetime, site, AQI) |>
  filter(!is.na(AQI)) |>
  group_by(site) |>
  summarize(AQI.median = median(AQI, na.rm = TRUE))

airqualitydf |>
  select(datetime, site, AQI) |>
  filter(!is.na(AQI)) |>
  left_join(metadf |> select(site, city = Area)) |>
  group_by(city) |>
  filter(length(unique(site)) >= 5) |>
  summarize(p = sum(AQI < quantile(airqualitydf$AQI, probs = 0.2,
    na.rm = TRUE)) / n()) |>
  slice_max(p, n = 10)
```


# 按照不同城市分组，统计白天与夜晚AQI中位数是否具有显著差异。

```{r}
#| eval: false

if (FALSE) {

require(infer)
testdf <- airqualitydf |>
  select(datetime, site, AQI) |>
  filter(!is.na(AQI)) |>
  left_join(metadf |> select(site, city = Area)) |>
  group_by(city) |>
  filter(length(unique(site)) >= 5) |>
  mutate(dayornight = factor(ifelse(between(hour(datetime), 8, 20), "day", "night"),
    levels = c("day", "night"))
  ) |>
  group_by(city) |>
  nest(citydf = -city) |>
  mutate(median_diff = purrr::map_dbl(citydf, ~
    .x |>
      specify(AQI ~ dayornight) |>
      calculate(stat = "diff in medians", order = c("day", "night")) |>
      pull(stat)
)) |>
  ungroup() |>
#  slice_sample(n = 12) |>
  mutate(null_dist = purrr::map(citydf, ~
    .x |>
      specify(AQI ~ dayornight) |>
   hypothesize(null = "independence") |>
   generate(reps = 1000, type = "permute") |>
      calculate(stat = "diff in medians", order = c("day", "night"))
)) |>
  mutate(fig = purrr::pmap(list(null_dist, median_diff, city),
    ~ visualize(..1) +
  shade_p_value(obs_stat = ..2, direction = "both") +
      ggtitle(..3)
  )) |>
  mutate(p_value = purrr::map2_dbl(null_dist, median_diff, 
    ~  get_p_value(.x, obs_stat = .y, direction = "both") |>
      pull(p_value)
  )) |>
  arrange(p_value) |>
  mutate(sigdiff = ifelse(p_value < 0.01, "显著差异", "无显著差异"))

testdf |>
  select(city, sigdiff) |>
knitr::kable()

  lang <- "cn"
(testdf |>
  slice_sample(n = 9) |>
  pull(fig)) |>
patchwork::wrap_plots(ncol = 3) +
dwfun::theme_sci(5, 5)

dwfun::ggsavep("./testdf.pdf")

}

```