287 lines
6.2 KiB
Plaintext
287 lines
6.2 KiB
Plaintext
---
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title: "RWEP L7- Data Transform"
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---
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# Data Source
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```{r}
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dailylanddf <- readr::read_csv("../data/chinawq/daily_land.csv")
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metadatadf <- readxl::read_xls("../data/chinawq/metadata_and_statistics.xls")
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monthlylanddf <- readr::read_csv("../data/chinawq/monthly_ocean.csv")
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weeklylanddf <- readr::read_csv("../data/chinawq/weekly_land.csv")
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```
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```{r}
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require(tidyverse)
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dailylanddf
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dailylanddf |>
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select(.data$MonitoringLocationIdentifier, .data$LongitudeMeasure_WGS84)
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dailylanddf |>
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select("MonitoringLocationIdentifier", "LongitudeMeasure_WGS84")
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dailylanddf |>
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select(1:3)
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dailylanddf |>
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rename(x = 1) |>
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rename(y = 2)
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```
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# 内置function
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```{r}
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dailylanddf |>
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select(1:3) |>
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rename_all(\(x) c("a", "b", "c")) |>
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rename_all(\(x) paste0(x, "_new"))
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dailylanddf |>
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select(1:3) |>
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rename_all(~ c("a", "b", "c")) |>
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rename_all(~ paste0(., "_new"))
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dailylanddf |>
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select(1:3) |>
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rename_all(~ c("x", "y", "z"))
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```
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```{r}
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fulldatadf <- readr::read_csv("../data/chinawq/full_dataset.csv")
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datadf <- fulldatadf |>
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select(lon = 2, lat = 3, date = 4, varname = 5, value = 6) |>
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mutate(value = as.numeric(value)) |>
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tidyr::pivot_wider(names_from = varname, values_from = value)
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saveRDS(datadf, "../data/chinawq/datadf.rds")
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```
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# 统计
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## 统计每月的DO的平均值
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```{r}
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datadf <- readRDS("../data/chinawq/datadf.rds")
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datadf |>
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select(1:7) |>
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mutate(month = month(date)) |>
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select(lon, lat, month, date, everything()) |>
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group_by(month) |>
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summarize(DO_mean = mean(DO, na.rm = TRUE), DO_sd = sd(DO, na.rm = TRUE)) |>
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ggplot(aes(x = month, y = DO_mean)) +
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geom_point() +
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geom_smooth(method = "loess") +
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geom_errorbar(aes(ymin = DO_mean - DO_sd, ymax = DO_mean + DO_sd))
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```
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```{r}
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datadf |>
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select(1:7) |>
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mutate(month = month(date)) |>
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mutate(year = year(date)) |>
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select(lon, lat, year, month, date, everything()) |>
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summarize(
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DO_mean = mean(DO, na.rm = TRUE),
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DO_sd = sd(DO, na.rm = TRUE),
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.by = c(year, month)
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) |>
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ggplot(aes(x = month, y = DO_mean)) +
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geom_point() +
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geom_smooth(method = "loess") +
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geom_errorbar(aes(ymin = DO_mean - DO_sd, ymax = DO_mean + DO_sd)) +
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facet_wrap(~year)
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```
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```{r}
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datadf |>
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select(1:7) |>
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mutate(month = month(date)) |>
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mutate(year = year(date)) |>
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select(-lon, -lat, -date) |>
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group_by(year, month) |>
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summarize_all(\(x) mean(x, na.rm = TRUE))
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```
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## nest
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```{r}
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for (isite in 1:10) {
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m <- datadf |>
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select(1:7) |>
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tidyr::nest(wqdf = -c(lon, lat)) |>
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pull(wqdf) |>
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nth(isite) |>
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lm(formula = DO ~ pH, data = _)
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slope <- coef(m)[2]
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print(slope)
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}
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```
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## purrr::map
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```{r}
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require(tidyverse)
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datadf <- readRDS("../data/chinawq/datadf.rds")
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datadf |>
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select(1:7) |>
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tidyr::nest(wqdf = -c(lon, lat)) |>
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slice(1:10) |>
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mutate(
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m = purrr::map(
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wqdf,
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\(x) {
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lm(formula = DO ~ pH, data = x)
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}
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)
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) |>
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mutate(slope = purrr::map_dbl(m, \(x) coef(x)[2])) |>
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mutate(
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p = purrr::map2(wqdf, slope, \(x, y) {
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x |>
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ggplot(aes(x = pH, y = DO)) +
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geom_smooth(method = "lm") +
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geom_point() +
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ggtitle(paste0("Slope: ", round(y, 2)))
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})
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) |>
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pull(p) |>
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patchwork::wrap_plots() +
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patchwork::plot_layout(ncol = 2)
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```
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```{r}
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longriverdf <- tibble(
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fn = dir(
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"../data/LongRiver/data/",
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pattern = "csv",
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full.names = TRUE,
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recursive = TRUE
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)
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) |>
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mutate(data = purrr::map(fn, readr::read_csv))
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lang <- "cn"
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longriverdf |>
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mutate(year = gsub("^.*([0-9)]{4})-.*$", "\\1", fn)) |>
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mutate(month = gsub("^.*[0-9)]{4}-([0-9]{2}).*$", "\\1", fn)) |>
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mutate(site = gsub("^.*_(.*).csv$", "\\1", fn)) |>
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select(-fn) |>
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slice(1:10) |>
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tidyr::unnest(data) |>
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ggplot(aes(x = site, y = z)) +
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geom_boxplot(colour = "black", size = 0.8) +
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facet_wrap(~month) +
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dwfun::theme_sci()
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```
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```{r}
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longriverdf |>
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mutate(year = gsub("^.*([0-9)]{4})-.*$", "\\1", fn)) |>
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mutate(month = gsub("^.*[0-9)]{4}-([0-9]{2}).*$", "\\1", fn)) |>
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mutate(site = gsub("^.*_(.*).csv$", "\\1", fn)) |>
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select(-fn) |>
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slice(1:12) |>
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mutate(
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p = purrr::map(data, \(x) {
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x |>
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ggplot(aes(tm, z)) +
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geom_point()
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})
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) |>
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pull(p) |>
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patchwork::wrap_plots() |>
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patchwork::plot_layout(ncol = 3)
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```
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```{r}
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# datadf
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# 不同年份，DO 的平均值，
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# x lon
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# y lat
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# colour/ fill Do mean
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# facet: year
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datadf |>
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dplyr::mutate(year = year(date)) |>
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summarize(DO_mean = mean(DO, na.rm = TRUE), .by = c(lon, lat, year)) |>
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dplyr::filter(year > 2006) |>
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ggplot(aes(x = lon, y = lat)) +
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geom_point(aes(fill = DO_mean), shape = 21) +
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scale_fill_viridis_c() +
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facet_wrap(~year) +
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labs(x = "Longitude", y = "Latitude", fill = "DO (mg/L)") +
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theme_bw()
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```
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```{r}
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# x 月份
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# y NH4: pH
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p1 <- datadf |>
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mutate(month = month(date)) |>
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dplyr::filter(NH4N < 100) |>
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ggplot(aes(x = month, y = NH4N)) +
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geom_boxplot(aes(fill = as.factor(month)), colour = "black", size = 0.8) +
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scale_y_log10(
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breaks = scales::trans_breaks("log10", function(x) 10^x),
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labels = scales::trans_format(
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"log10",
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scales::math_format(10^.x, format = "%.1f")
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)
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) +
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scale_x_continuous(breaks = 1:12, labels = month.abb) +
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theme(legend.position = "none")
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p2 <- datadf |>
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mutate(month = month(date)) |>
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ggplot(aes(x = month, y = CODMn)) +
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geom_boxplot(aes(fill = as.factor(month)), colour = "black", size = 0.8) +
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scale_y_log10(
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breaks = scales::trans_breaks("log10", function(x) 10^x),
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labels = scales::trans_format(
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"log10",
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scales::math_format(10^.x, format = "%.1f")
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)
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) +
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scale_x_continuous(breaks = 1:12, labels = month.abb) +
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theme(legend.position = "none")
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p3 <- datadf |>
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mutate(month = month(date)) |>
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dplyr::filter(DO < 20) |>
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ggplot(aes(x = month, y = DO)) +
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geom_boxplot(aes(fill = as.factor(month)), colour = "black", size = 0.8) +
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scale_x_continuous(breaks = 1:12, labels = month.abb) +
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theme(legend.position = "none")
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p4 <- datadf |>
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mutate(month = month(date)) |>
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dplyr::filter(pH > 5) |>
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ggplot(aes(x = month, y = pH)) +
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geom_boxplot(aes(fill = as.factor(month)), colour = "black", size = 0.8) +
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scale_x_continuous(breaks = 1:12, labels = month.abb) +
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theme(legend.position = "none")
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(p1 | p2) / (p3 | p4) + patchwork::plot_annotation(tag_levels = "A")
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```
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