119 lines
3.2 KiB
Plaintext
119 lines
3.2 KiB
Plaintext
---
|
||
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")
|
||
|
||
}
|
||
|
||
```
|
||
|