su2026rwep/coding/L9.qmd

---
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)
```