149 lines
3.9 KiB
Plaintext
149 lines
3.9 KiB
Plaintext
---
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title: 课后作业8
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author: 姓名
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format: html
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---
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# 数据
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下载airquality.xlsx，并读取数据。
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```{r}
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#| message: false
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#| warning: false
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# 下载至临时文件
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if (FALSE) {
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tmpxlsxpath <- file.path(tempdir(), "airquality.xlsx")
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download.file("https://drwater.rcees.ac.cn/git/course/RWEP/raw/branch/PUB/data/airquality.xlsx",
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destfile = tmpxlsxpath)
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airqualitydf <- readxl::read_xlsx(tmpxlsxpath, sheet = 2)
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metadf <- readxl::read_xlsx(tmpxlsxpath, sheet = 1)
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saveRDS(airqualitydf, "./airqualitydf.RDS")
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saveRDS(metadf, "./metadf.RDS")
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}
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airqualitydf <- readRDS("./airqualitydf.RDS")
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metadf <- readRDS("./metadf.RDS")
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```
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# 描述统计
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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）。
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```{r}
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#| message: false
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#| warning: false
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require(tidyverse)
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airqualitydf |>
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select(datetime, site, AQI) |>
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filter(!is.na(AQI)) |>
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group_by(site) |>
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summarize(AQI.median = median(AQI, na.rm = TRUE)) |>
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left_join(metadf |> select(site, city = Area)) |>
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group_by(city) |>
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filter(n() > 5) |>
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summarize(p = sum(AQI.median < quantile(airqualitydf$AQI, probs = 0.5, na.rm = TRUE)) / n()) |>
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top_n(10, p)
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airqualitydf |>
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select(datetime, site, AQI) |>
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filter(!is.na(AQI)) |>
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group_by(site) |>
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summarize(AQI.median = median(AQI, na.rm = TRUE))
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airqualitydf |>
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select(datetime, site, AQI) |>
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filter(!is.na(AQI)) |>
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left_join(metadf |> select(site, city = Area)) |>
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group_by(city) |>
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filter(length(unique(site)) >= 5) |>
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summarize(p = sum(AQI < quantile(airqualitydf$AQI, probs = 0.2,
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na.rm = TRUE)) / n()) |>
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slice_max(p, n = 10) |>
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knitr::kable()
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```
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# 统计检验
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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。
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```{r}
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#| message: false
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#| warning: false
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if (FALSE) {
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require(infer)
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require(tidyverse)
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testdf <- airqualitydf |>
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select(datetime, site, AQI) |>
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filter(!is.na(AQI)) |>
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left_join(metadf |> select(site, city = Area)) |>
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group_by(city) |>
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filter(length(unique(site)) >= 5) |>
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mutate(dayornight = factor(ifelse(between(hour(datetime), 8, 20), "day", "night"),
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levels = c("day", "night"))
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) |>
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group_by(city) |>
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nest(citydf = -city) |>
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mutate(median_diff = purrr::map_dbl(citydf, ~
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.x |>
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specify(AQI ~ dayornight) |>
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calculate(stat = "diff in medians", order = c("day", "night")) |>
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pull(stat)
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)) |>
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ungroup() |>
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# slice_sample(n = 12) |>
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mutate(null_dist = purrr::map(citydf, ~
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.x |>
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specify(AQI ~ dayornight) |>
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hypothesize(null = "independence") |>
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generate(reps = 1000, type = "permute") |>
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calculate(stat = "diff in medians", order = c("day", "night"))
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)) |>
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mutate(p_value = purrr::map2_dbl(null_dist, median_diff,
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~ get_p_value(.x, obs_stat = .y, direction = "both") |>
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pull(p_value)
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)) |>
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mutate(sigdiff = ifelse(p_value < 0.01, "显著差异", "无显著差异")) |>
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mutate(fig = purrr::pmap(list(null_dist, median_diff, city, sigdiff),
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~ visualize(..1) +
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shade_p_value(obs_stat = ..2, direction = "both") +
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ggtitle(paste0(..3, "：", ..4)) +
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theme_sci(2, 2)
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)) |>
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arrange(p_value)
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saveRDS(testdf, "./testdf.RDS")
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}
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lang <- "cn"
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require(dwfun)
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require(rmdify)
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require(drwateR)
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dwfun::init()
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rmdify::rmd_init()
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testdf <- readRDS("./testdf.RDS")
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require(tidyverse)
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testdf |>
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select(city, median_diff, p_value, sigdiff) |>
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knitr::kable()
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testdf |>
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mutate(grp = (row_number() - 1)%/% 12) |>
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group_by(grp) |>
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nest(grpdf = -grp) |>
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ungroup() |>
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# slice(1) |>
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mutate(gp = purrr::map(grpdf,
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~(.x |>
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pull(fig)) |>
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patchwork::wrap_plots(ncol = 3) +
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dwfun::theme_sci(5, 7))) |>
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pull(gp)
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```
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