L10

2026-06-04 15:55:13 +08:00
parent 7aeb071fba
commit 028ef1c471
6 changed files with 1889 additions and 1 deletions
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+---
+title: "L9 - Data Virtualization"
+format: html
+editor: visual
+---
+
+# Load Data
+
+```{r}
+# getwd()
+# setwd("coding")
+require(tidyverse)
+datadf <- readRDS("../data/chinawq/datadf.rds")
+```
+
+# Data view
+
+```{r}
+skimr::skim(datadf)
+
+head(datadf)
+
+tail(datadf)
+
+names(datadf)
+
+summary(datadf)
+
+str(datadf)
+```
+
+# Plot
+
+```{r}
+#| warning: false
+#| message: false
+# 每月NH4N与pH的相关性散点图
+p <- datadf |>
+  dplyr::filter(NH4N < 100) |>
+  dplyr::filter(between(year(date), 2016, 2019)) |>
+  mutate(month = month(date)) |>
+  ggplot(aes(CODMn, NH4N)) +
+  geom_point(shape = 21, size = 0.8, fill = "orange") +
+  geom_smooth(method = "gam", color = "red") +
+  scale_x_log10() +
+  scale_y_log10() +
+  labs(
+    x = "COD<sub>Mn</sub> (mg L<sup>-1</sup>)",
+    y = "Ammonia (mg L<sup>-1</sup>)"
+  ) +
+  facet_wrap(~month, scale = "free", ncol = 4) +
+  theme(
+    axis.title.x = ggtext::element_markdown(),
+    axis.title.y = ggtext::element_markdown()
+  )
+print(p)
+
+plotly::ggplotly(p)
+```
+
+```{r}
+#| warning: false
+#| message: false
+# 每月NH4N与pH的相关性散点图
+p <- datadf |>
+  dplyr::filter(NH4N < 100) |>
+  dplyr::filter(between(year(date), 2016, 2019)) |>
+  mutate(month = month(date)) |>
+  ggplot(aes(factor(month), NH4N)) +
+  geom_jitter(size = 0.1, colour = "gray95") +
+  geom_violin(fill = "orange", alpha = 0.3) +
+  scale_y_log10() +
+  labs(x = "Month", y = "Ammonia (mg L<sup>-1</sup>)") +
+  theme_classic() +
+  theme(axis.title.y = ggtext::element_markdown())
+print(p)
+
+plotly::ggplotly(p)
+```
+
+# Plotly
+
+```{r}
+
+p <- datadf |>
+  dplyr::filter(year(date) == 2018) |>
+  ggplot(aes(date, NH4N)) +
+  geom_point()
+
+ggsave("L9-1.pdf", width = 4, height = 3)
+
+# install.packages("plotly")
+plotly::ggplotly(p)
+```
+
+
+# Map - sf
+
+```{r}
+# install.packages("sf")
+# install.packages("sfext")
+
+require(sf)
+require(sfext)
+
+chinawqsf <- sf::read_sf(
+  "../data/chinawq/Monitoring_sites/Monitoring_sites.shp"
+)
+
+chinawqsf |>
+  ggplot() +
+  geom_sf(shape = 21, size = 1, fill = "orange")
+
+chinamapsf <- sf::read_sf("../data/中国省级地图GS（2019）1719号.geojson")
+ninelinesf <- sf::read_sf("../data/九段线GS（2019）1719号.geojson")
+
+chinamapsf <- sf::read_sf(
+  "https://git.drwater.net/course/su2026rwep/raw/branch/PUB/data/中国省级地图GS（2019）1719号.geojson"
+)
+ninelinesf <- sf::read_sf(
+  "https://git.drwater.net/course/su2026rwep/raw/branch/PUB/data/九段线GS（2019）1719号.geojson"
+)
+
+chinacrs <- "+proj=laea +lat_0=40 +lon_0=104"
+
+mapeR::get_chinacrs()
+
+chinawqsf |>
+  ggplot() +
+  geom_sf(data = chinamapsf) +
+  geom_sf(data = ninelinesf) +
+  geom_sf(shape = 21, size = 1, fill = "orange") +
+  coord_sf(datum = chinacrs)
+```
+
+
+```{r}
+require(mapeR)
+map_chinese()
+```
+
+```{r}
+chinamapsf <- sf::read_sf("../data/中国省级地图GS（2019）1719号.geojson")
+ninelinesf <- sf::read_sf("../data/九段线GS（2019）1719号.geojson")
+
+chinamapsf <- sf::read_sf(
+  "https://git.drwater.net/course/su2026rwep/raw/branch/PUB/data/中国省级地图GS（2019）1719号.geojson"
+)
+ninelinesf <- sf::read_sf(
+  "https://git.drwater.net/course/su2026rwep/raw/branch/PUB/data/九段线GS（2019）1719号.geojson"
+)
+chinacrs <- "+proj=laea +lat_0=40 +lon_0=104"
+
+# 安装
+install.packages("ggspatial")
+
+datadf |>
+  sf::st_as_sf(coords = c("lon", "lat"), crs = 4326) |>
+  sf::st_transform(crs = chinacrs) |>
+  dplyr::filter(!is.na(CODMn)) |>
+  ggplot() +
+  geom_sf(data = chinamapsf, aes(fill = CNAME), alpha = 0.2) +
+  geom_sf(data = ninelinesf) +
+  geom_sf(aes(colour = log1p(CODMn))) +
+  labs(x = NULL, y = NULL, fill = NULL, colour = "CODMn") +
+  scale_colour_viridis_c() +
+  ggspatial::annotation_north_arrow(location = "tl") +
+  ggspatial::annotation_scale(location = "bl") +
+  theme(legend.position = "none")
+
+
+mapeR::map_chinese
+```
+
+# 具体某个湖泊
+
+```{r}
+sf::read_sf("../data/taihu.shp") |>
+  ggplot() +
+  geom_sf()
+
+
+sitesf <- sf::read_sf("~/Desktop/qingcaosha.kml")
+
+st_layers("~/Desktop/qingcaosha.kml")
+
+sitesf <- sf::read_sf("~/Desktop/qingcaosha.kml")
+boundsf <- sf::read_sf("~/Desktop/qingcaosha.kml", layer = "qingcaosha")
+
+ggplot() +
+  geom_sf(data = boundsf) +
+  geom_sf(data = sitesf) +
+  geom_sf_text(data = sitesf, aes(label = Name), vjust = 1, size = 3)
+```
+
+
+
+
+
+
+
+