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R语言绘制研究区概况图
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之前用R语言绘图一直用的是tmap包，今天试了试ggplot2包，效果还可以但没用达到自己的预期，记录在知乎方便自己复现（读者朋友有好的绘图方法也可以分享给我）~ 效果如下：  一、绘制左边的标准中国地图（研究区用橙色填充）# 加载包
library(sf)
library(stars)
library(terra)
library(ggspatial)
library(tidyverse)读取中国省级行政区划数据和九段线数据并设置投影为阿尔伯斯等积投影# 绘制中国地图常用的Albers Equal Area Conic投影
albers = "+proj=aea +lat_1=25 +lat_2=47 +lon_0=105"
# 读取中国省级行政区划数据和九段线数据并设置投影为阿尔伯斯等积投影
province = read_sf('./data/province.geojson') |>
st_transform(st_crs(albers))
nineline = read_sf('./data/nineline.geojson') |>
st_transform(st_crs(albers))展示一下上一步读取的省级行政区划和九段线数据 plot(province[1]) plot(nineline[1]) 合并省级行政区划和九段线province = st_union(province,nineline)绘制大图fig1 = ggplot() +
geom_sf(size = .2, fill = "transparent", color = "#060d1b", data=province)
fig1 绘制南海小地图：fig2 = fig1+
coord_sf(crs = st_crs('epsg:4326')) + ## 将投影坐标转换为大地坐标
scale_x_continuous(expand = c(0, 0), limits = c(107, 122), breaks = seq(70, 140, 10)) +
scale_y_continuous(expand = c(0, 0), limits = c(2, 24), breaks = seq(10, 60, 10)) +
guides(fill = "none", color = "none") +
theme_bw() +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank()
)
fig2 读取渭河流域边界矢量数据并转换投影至阿尔伯斯投影：weihe = read_sf('./data/渭河流域边界/') |>
st_transform(st_crs(albers))
plot(weihe[1]) 拼接大图和南海小地图并对渭河流域地区填充颜色：fig1 +
geom_sf(data=weihe,size = 3.5,
fill = "#FDBF6F", color = "#E31A1C")+
coord_sf(crs = st_crs(albers), default_crs = st_crs('epsg:4326')) +
scale_x_continuous(expand = c(0, 0),
limits=c(72,142),
breaks=seq(70, 140, 10)) +
scale_y_continuous(expand = c(0, 0),
limits = c(17,55.5),
breaks = seq(10, 60, 10)) +
theme_bw() +
theme(
axis.text = element_text(family ="serif",color="black"),
axis.title = element_blank()) +
annotation_scale(location = "bl") +
annotation_north_arrow(location = "tl", style = north_arrow_nautical(
fill = c("grey40", "white"), line_col = "grey20")) +
annotation_custom(ggplotGrob(fig2),
xmin= 122,xmax = 138,
ymin=15,ymax = 29) -> total.china
total.china 二、绘制右边以NDVI作底图的渭河流域研究区图读取相应的地理矢量数据：weihe_boundry = read_sf('./data/渭河流域边界/')
river1 = read_sf('./data/weihebasin_rivers/river3.shp') |> vect() |> st_as_sf()
river2 = read_sf('./data/weihebasin_rivers/river4.shp') |> vect() |> st_as_sf()
river3 = read_sf('./data/weihebasin_rivers/river5.shp') |> vect() |> st_as_sf()
citypoint = read_sf('./data/省市县-湖泊/地级市渭河流域_point.shp')|>
vect() |> st_as_sf()作为底图的NDVI数据的裁切和格式转换：ndvi = terra::rast('./data/image/ndvi.tif') |>
terra::crop(terra::vect(weihe_boundry),mask=T) |>
st_as_stars() |>
st_as_sf()定义绘制NDVI底图时的色带：colors = c('#d73027',
'#f46d43',
'#fdae61',
'#fee08b',
'#d9ef8b',
'#a6d96a',
'#66bd63',
'#1a9850')绘制渭河流域区域图： ggplot() + geom_sf(aes(fill=NDVI_mean,color=NDVI_mean),data=ndvi) + geom_sf(data = river1,lwd = 1, fill = "transparent", color = "#045a8d")+ geom_sf(data = river2,lwd = 0.6, fill = "transparent", color = "#2b8cbe")+ geom_sf(data = river3,lwd = 0.3, fill = "transparent", color = "#74a9cf")+ scale_fill_gradientn(colours = colors)+ scale_color_gradientn(colours = colors)+ labs(fill = "NDVI", color = "NDVI") + geom_sf_text(data = citypoint, mapping=aes(label = NAME))+ annotation_scale(location = "bl") + annotation_north_arrow(location = "tr", style = north_arrow_nautical( fill = c("grey40", "white"), line_col = "grey20"))+ theme_bw() + theme( panel.grid=element_blank(), legend.position = c(0.15,0.9), legend.justification = c(1,1), axis.text= element_blank(), axis.ticks = element_blank(), axis.title = element_blank() ) -> study.weihe study.weihe 三、将左右图连接到一张图上并导出# 拼接两图
library(patchwork)
fig = total.china+study.weihe
fig 导出绘图结果： ggsave("./figure/study_area.png", fig, width = 15, height = 6, dpi=600)文中用的所有代码如下： # 加载包 library(sf) library(stars) library(terra) library(ggspatial) library(tidyverse) # 绘制中国地图常用的Albers Equal Area Conic投影 albers = "+proj=aea +lat_1=25 +lat_2=47 +lon_0=105" # 读取中国省级行政区划数据和九段线数据并设置投影为阿尔伯斯等积投影 province = read_sf('./data/province.geojson') |> st_transform(st_crs(albers)) nineline = read_sf('./data/nineline.geojson') |> st_transform(st_crs(albers)) # 看一眼 plot(province[1]) plot(nineline[1]) province = st_union(province,nineline) fig1 = ggplot() + geom_sf(size = .2, fill = "transparent", color = "#060d1b", data=province) fig1 fig2 = fig1+ coord_sf(crs = st_crs('epsg:4326')) + ## 将投影坐标转换为大地坐标 scale_x_continuous(expand = c(0, 0), limits = c(107, 122), breaks = seq(70, 140, 10)) + scale_y_continuous(expand = c(0, 0), limits = c(2, 24), breaks = seq(10, 60, 10)) + guides(fill = "none", color = "none") + theme_bw() + theme( axis.text = element_blank(), axis.ticks = element_blank(), axis.title = element_blank() ) fig2 weihe = read_sf('./data/渭河流域边界/') |> st_transform(st_crs(albers)) plot(weihe[1]) fig1 + geom_sf(data=weihe,size = 3.5, fill = "#FDBF6F", color = "#E31A1C")+ coord_sf(crs = st_crs(albers), default_crs = st_crs('epsg:4326')) + scale_x_continuous(expand = c(0, 0), limits=c(72,142), breaks=seq(70, 140, 10)) + scale_y_continuous(expand = c(0, 0), limits = c(17,55.5), breaks = seq(10, 60, 10)) + theme_bw() + theme( axis.text = element_text(family ="serif",color="black"), axis.title = element_blank()) + annotation_scale(location = "bl") + annotation_north_arrow(location = "tl", style = north_arrow_nautical( fill = c("grey40", "white"), line_col = "grey20")) + annotation_custom(ggplotGrob(fig2), xmin= 122,xmax = 138, ymin=15,ymax = 29) -> total.china total.china # 绘制渭河流域研究区域图 weihe_boundry = read_sf('./data/渭河流域边界/') river1 = read_sf('./data/weihebasin_rivers/river3.shp') |> vect() |> st_as_sf() river2 = read_sf('./data/weihebasin_rivers/river4.shp') |> vect() |> st_as_sf() river3 = read_sf('./data/weihebasin_rivers/river5.shp') |> vect() |> st_as_sf() citypoint = read_sf('./data/省市县-湖泊/地级市渭河流域_point.shp')|> vect() |> st_as_sf() coords = st_coordinates(citypoint) citypoint$long = coords[,1] citypoint$lat = coords[,2] ndvi = terra::rast('./data/image/ndvi.tif') |> terra::crop(terra::vect(weihe_boundry),mask=T) |> st_as_stars() |> st_as_sf() colors = c('#d73027', '#f46d43', '#fdae61', '#fee08b', '#d9ef8b', '#a6d96a', '#66bd63', '#1a9850') ggplot() + geom_sf(aes(fill=NDVI_mean,color=NDVI_mean),data=ndvi) + geom_sf(data = river1,lwd = 1, fill = "transparent", color = "#045a8d")+ geom_sf(data = river2,lwd = 0.6, fill = "transparent", color = "#2b8cbe")+ geom_sf(data = river3,lwd = 0.3, fill = "transparent", color = "#74a9cf")+ scale_fill_gradientn(colours = colors)+ scale_color_gradientn(colours = colors)+ labs(fill = "NDVI", color = "NDVI") + geom_sf_text(data = citypoint, mapping=aes(x = long, y = lat, label = NAME))+ annotation_scale(location = "bl") + annotation_north_arrow(location = "tr", style = north_arrow_nautical( fill = c("grey40", "white"), line_col = "grey20"))+ theme_bw() + theme( panel.grid=element_blank(), legend.position = c(0.15,0.9), legend.justification = c(1,1), axis.text= element_blank(), axis.ticks = element_blank(), axis.title = element_blank() ) -> study.weihe study.weihe # 拼接两图 library(patchwork) fig = total.china+study.weihe fig ggsave("./figure/study_area.png", fig, width = 15, height = 6, dpi=600)# 加载包 library(sf) library(stars) library(terra) library(ggspatial) library(tidyverse) # 绘制中国地图常用的Albers Equal Area Conic投影 albers = "+proj=aea +lat_1=25 +lat_2=47 +lon_0=105" # 读取中国省级行政区划数据和九段线数据并设置投影为阿尔伯斯等积投影 province = read_sf('./data/province.geojson') |> st_transform(st_crs(albers)) nineline = read_sf('./data/nineline.geojson') |> st_transform(st_crs(albers)) # 看一眼 plot(province[1]) plot(nineline[1]) province = st_union(province,nineline) fig1 = ggplot() + geom_sf(size = .2, fill = "transparent", color = "#060d1b", data=province) fig1 fig2 = fig1+ coord_sf(crs = st_crs('epsg:4326')) + ## 将投影坐标转换为大地坐标 scale_x_continuous(expand = c(0, 0), limits = c(107, 122), breaks = seq(70, 140, 10)) + scale_y_continuous(expand = c(0, 0), limits = c(2, 24), breaks = seq(10, 60, 10)) + guides(fill = "none", color = "none") + theme_bw() + theme( axis.text = element_blank(), axis.ticks = element_blank(), axis.title = element_blank() ) fig2 weihe = read_sf('./data/渭河流域边界/') |> st_transform(st_crs(albers)) plot(weihe[1]) fig1 + geom_sf(data=weihe,size = 3.5, fill = "#FDBF6F", color = "#E31A1C")+ coord_sf(crs = st_crs(albers), default_crs = st_crs('epsg:4326')) + scale_x_continuous(expand = c(0, 0), limits=c(72,142), breaks=seq(70, 140, 10)) + scale_y_continuous(expand = c(0, 0), limits = c(17,55.5), breaks = seq(10, 60, 10)) + theme_bw() + theme( axis.text = element_text(family ="serif",color="black"), axis.title = element_blank()) + annotation_scale(location = "bl") + annotation_north_arrow(location = "tl", style = north_arrow_nautical( fill = c("grey40", "white"), line_col = "grey20")) + annotation_custom(ggplotGrob(fig2), xmin= 122,xmax = 138, ymin=15,ymax = 29) -> total.china total.china # 绘制渭河流域研究区域图 river1 = read_sf('./data/weihebasin_rivers/river3.shp') |> vect() |> st_as_sf() river2 = read_sf('./data/weihebasin_rivers/river4.shp') |> vect() |> st_as_sf() river3 = read_sf('./data/weihebasin_rivers/river5.shp') |> vect() |> st_as_sf() citypoint = read_sf('./data/省市县-湖泊/地级市渭河流域_point.shp')|> vect() |> st_as_sf() ndvi = terra::rast('./data/image/ndvi.tif') |> terra::crop(terra::vect(weihe_boundry),mask=T) |> st_as_stars() |> st_as_sf() colors = c('#d73027', '#f46d43', '#fdae61', '#fee08b', '#d9ef8b', '#a6d96a', '#66bd63', '#1a9850') ggplot() + geom_sf(aes(fill=NDVI_mean,color=NDVI_mean),data=ndvi) + geom_sf(data = river1,lwd = 1, fill = "transparent", color = "#045a8d")+ geom_sf(data = river2,lwd = 0.6, fill = "transparent", color = "#2b8cbe")+ geom_sf(data = river3,lwd = 0.3, fill = "transparent", color = "#74a9cf")+ scale_fill_gradientn(colours = colors)+ scale_color_gradientn(colours = colors)+ labs(fill = "NDVI", color = "NDVI") + geom_sf_text(data = citypoint, mapping=aes(label = NAME))+ annotation_scale(location = "bl") + annotation_north_arrow(location = "tr", style = north_arrow_nautical( fill = c("grey40", "white"), line_col = "grey20"))+ theme_bw() + theme( panel.grid=element_blank(), legend.position = c(0.15,0.9), legend.justification = c(1,1), axis.text= element_blank(), axis.ticks = element_blank(), axis.title = element_blank() ) -> study.weihe study.weihe # 拼接两图 library(patchwork) fig = total.china+study.weihe fig ggsave("./figure/study_area.png", fig, width = 15, height = 6, dpi=600)完结收工！ 文中使用的NDVI数据从GEE平台获取；渭河流域边界、地级市、河流矢量数据来自渭河流域基础地理数据集；待笔者项目完成后可分享相应的数据~ |
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