map
1. 数据准备
2. 常规的政区图
library(mapdata)
library(maptools)
library(ggplot2)
library(plyr)
library(cowplot)
china_map <- readShapePoly("map/bou2_4p.shp")
x <- china_map@data #读取行政信息
xs <- data.frame(x,id=seq(0:924)-1) #含岛屿共925个形状
china_map1 <- fortify(china_map) #转化为数据框
china_map_data <- join(china_map1, xs, type = "full") #合并两个数据框
p1 <- ggplot(china_map_data, aes(x = long, y = lat))
p2 <- p1 +
theme(
panel.grid = element_blank(),
panel.background = element_rect(fill = "aliceblue"),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
legend.position = "none"
)
p3 <- p2 + geom_polygon(aes(group=group), fill= "white", colour="grey60")
# 读入自己的数据
'''
| | | | | |
|---|---|---|---|---|
|Breed|Bbbr|Long|Lat|Num|
|bohaihei|BHH|117.61|37.77|1|
|chaidamu|CMD|94.75|37.55|2|
|dabieshan|DBS|114.64|30.81|3|
|dengchuan|DC|99.76|25.78|4|
|dianzhong|DZ|101.37|25.2|5|
|diqing|DQ|99.27|28.21|6|
'''
data <- read.csv("sites.csv")
p3 + geom_point(data = data, aes(x = Long, y = Lat, size = Num), alpha = 0.6) + geom_text(data = data, aes(x = Long, y = Lat, label = Bbbr), hjust = 1.2, vjust = 0)
p4 <- p3 + geom_point(data = data, aes(x = Long, y = Lat), alpha = 0.6) + geom_text(data = data, aes(x = Long, y = Lat, label = Bbbr), hjust = 1.2, vjust = 0)
# 画九段线
nine_lines = sf::read_sf('geojson/九段线GS（2019）1719号.geojson')
china = sf::read_sf('geojson/中国省级地图GS（2019）1719号.geojson')
nine_map = ggplot() +
geom_sf(data = china,fill='NA', size=0.5) +
geom_sf(data = nine_lines,color='black',size=0.5)+
coord_sf(ylim = c(-4028017,-1877844),xlim = c(117131.4,2115095),crs="+proj=laea +lat_0=40 +lon_0=104")+ #这是定义九段线的范围，并将投影下的范围改成地理坐标，也可改动里面的范围，选择更合适的九段线范围
theme(
aspect.ratio = 1.25, #调节长宽比
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(fill=NA,color="grey10",linetype=1,size=0.5),
plot.margin=unit(c(0,0,0,0),"mm"))
# 合并大小图
ggdraw() + draw_plot(p4) +draw_plot(nine_map, x = 0.78, y = 0.12, width = 0.12, height = 0.5)
3. 地形图
# 加载R包
pacman::p_load(tidyverse, sf, raster, ggspatial, stars, geoviz, rgeos, sp, rgdal)
# 一定要加载后三个包啊！
# 设置投影
crs_84 <- st_crs("EPSG:4326") ## WGS 84 大地坐标
crs_al <- st_crs("+proj=aea +lat_1=25 +lat_2=47 +lon_0=105") ## Albers Equal Area Conic投影
#地图获取
china_all <- sf::st_read("https://geo.datav.aliyun.com/areas_v3/bound/100000_full.json") %>% st_transform(crs_al)
hainan <-sf::st_read("https://geo.datav.aliyun.com/areas_v3/bound/460000_full.json") %>% st_transform(crs_al)
# 截取地图
tmp_china <- # 去除 海南省，九段线
china_all %>%
filter(!adcode %in% c("460000", "100000_JD")) %>%
st_make_valid() %>%
st_union()
tmp_hainan <- # 海南省去除 三沙市
hainan %>%
filter(!name %in% "三沙市") %>%
st_make_valid() %>%
st_union()
# #### 组合起来中国大陆边框
china_com <- st_union(tmp_china, tmp_hainan)%>% st_as_sf()
#### 海拔获取
dem <- geoviz::mapbox_dem(
lat = 35.8617,
long = 104.1954,
square_km = 2000,
api = "pk.eyJ1IjoiYmVueXNmIiwiYSI6ImNrczBtdWE0ajBwNjcydnBqMjRyZDdsOXkifQ.sUcMdooE7b9uQqzfrnWdSQ"
)
# 备用api: pk.eyJ1IjoibWlzNDE3IiwiYSI6ImNseTlzZDdzNTB2bmMyam9qZnBrNmlma3MifQ.PudTH_3F0M1kDAU-U3twIA
china_dem <- dem %>%
projectRaster(crs = crs_al$wkt) %>% # 修改栅格数据的投影
aggregate(fact = 3) %>% ## 降低分辨率，减少运算量
raster::crop(., raster::extent(china_com)) %>%
raster::mask(china_com) %>%
stars::st_as_stars() %>%
st_as_sf()
# #### 颜色配置
colors <- c(
"#33A02C", "#B2DF8A", "#FDBF6F", "#1F78B4",
"#575c9e", "#837d76", "#7b5c3e", "#E31A1C")
#### 绘制大陆区域
p1 <-
ggplot() +
geom_sf(aes(fill = layer, color = layer), data = china_dem) +
geom_sf(size = .2, fill = "transparent", color = "#060d1b", data = china_all) +
scale_fill_gradientn(colours = colors) +
scale_color_gradientn(colours = colors)
## 截取南海九段线
p2 <-
p1 +
coord_sf(crs = crs_84) + ## 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()
)
#### 读取采样点数据
# 读入自己的数据
data <- read.csv("sites.csv")
'''
| | | | | |
|---|---|---|---|---|
|Breed|Bbbr|Long|Lat|Num|
|bohaihei|BHH|117.61|37.77|1|
|chaidamu|CMD|94.75|37.55|2|
|dabieshan|DBS|114.64|30.81|3|
|dengchuan|DC|99.76|25.78|4|
|dianzhong|DZ|101.37|25.2|5|
|diqing|DQ|99.27|28.21|6|
'''
#### 图形拼接
p1+coord_sf(crs = crs_al, default_crs = crs_84) + geom_point(data=data,aes(x=Long,y=Lat),color="red",size=2) + geom_text(data = data, aes(x = Long, y = Lat, label = Bbbr), size=3, hjust = 1.2, vjust = 0.2)+
annotate(geom="text",x=80,y=18,
label="GS(2019)6379",family="serif",vjust=0,hjust=0) +
##审图号
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)) +
labs(fill = "elevant", color = "elevant") +
theme_bw() +
theme(axis.text = element_text(family ="serif",color="black"),
## 字体改为新罗马
axis.title = element_blank(),
legend.position = c(1,0.8),
legend.justification = c(1,1)) +
annotation_scale(location = "bl") +
# 设置距离刻度尺
annotation_north_arrow(
location = "tl",
style = north_arrow_nautical(
fill = c("grey40", "white"),
line_col = "grey20")
) + annotation_custom(ggplotGrob(p2),
xmin= 122,xmax = 138,ymin=15,ymax = 29)
4. 交互式地图
只有普通牛
import plotly.express as px
import pandas as pd
df =pd.read_csv("breed_info_map.csv")
df.head()
# 假设你有采样地数据
# columns: latitude, longitude, breed, count
# 使用 scatter_mapbox（交互式地图）
fig = px.scatter_mapbox(
df,
lat="Latitude",
lon="Longitude",
color="Breed name", # 按品种区分颜色
size="Sample size", # 数量决定点大小
hover_name="Breed name", # 鼠标悬停显示
hover_data={"Sample size": True, "Latitude": False, "Longitude": False},
zoom=3,
height=800,
size_max=10
)
# 设置地图风格（这里用open-street-map，不需要token）
fig.update_layout(
mapbox_style="open-street-map",
title="采样地点分布",
margin={"r":0,"t":30,"l":0,"b":0}
)
fig.show()
fig.write_html("sampling_map.html", include_plotlyjs="cdn")
区分普通牛和野牛
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
df = pd.read_csv("breeds_info.csv")
domestic_species = ['Bos taurus', 'Bos indicus']
df['SpeciesType'] = df['Species'].apply(
lambda x: 'Domestic' if x in domestic_species else 'Wild/Other'
)
# 颜色映射
groups = df['Group'].unique()
colors = px.colors.qualitative.Plotly
color_map = {g: colors[i % len(colors)] for i, g in enumerate(groups)}
fig = go.Figure()
# 野牛：先画底层大黑圆（作为边框）
wild_df = df[df['SpeciesType'] == 'Wild/Other']
for group in wild_df['Group'].unique():
subset = wild_df[wild_df['Group'] == group]
# 底层：大黑圆
fig.add_trace(go.Scattermapbox(
lat=subset["Latitude (N)"],
lon=subset["Longitude (E)"],
mode='markers',
marker=dict(size=16, color='black'),
showlegend=False,
hoverinfo='skip',
))
# 上层：小彩圆
fig.add_trace(go.Scattermapbox(
lat=subset["Latitude (N)"],
lon=subset["Longitude (E)"],
mode='markers',
marker=dict(size=10, color=color_map[group]),
name=f"{group} (Wild ◉)",
hovertext=subset["Breed"],
customdata=subset[["Species", "Group", "Num"]].values,
hovertemplate="<b>%{hovertext}</b><br>Species: %{customdata[0]}<br>Group: %{customdata[1]}<br>Num: %{customdata[2]}<extra></extra>"
))
# 普通牛：普通圆点
domestic_df = df[df['SpeciesType'] == 'Domestic']
for group in domestic_df['Group'].unique():
subset = domestic_df[domestic_df['Group'] == group]
fig.add_trace(go.Scattermapbox(
lat=subset["Latitude (N)"],
lon=subset["Longitude (E)"],
mode='markers',
marker=dict(size=10, color=color_map[group]),
name=f"{group} (Domestic ●)",
hovertext=subset["Breed"],
customdata=subset[["Species", "Group", "Num"]].values,
hovertemplate="<b>%{hovertext}</b><br>Species: %{customdata[0]}<br>Group: %{customdata[1]}<br>Num: %{customdata[2]}<extra></extra>"
))
fig.update_layout(
mapbox_style="carto-positron",
mapbox=dict(center=dict(lat=30, lon=60), zoom=2),
title="牛品种采样地点分布（◉=野牛，●=普通牛）",
height=1000,
margin={"r":0,"t":30,"l":0,"b":0}
)
fig.show()
fig.write_html("sampling_map.html", include_plotlyjs="cdn")
野牛直接用黑色
import plotly.express as px
import pandas as pd
df = pd.read_csv("breeds_info.csv")
# 创建新的颜色分组列
# 普通牛（Bos taurus, Bos indicus）按 Group 分组，其他物种统一标记为 "Wild/Other"
domestic_species = ['Bos taurus', 'Bos indicus']
df['ColorGroup'] = df.apply(
lambda row: row['Group'] if row['Species'] in domestic_species else 'Wild',
axis=1
)
# 获取所有唯一的 Group 值（不包括 Wild/Other）
groups = df[df['ColorGroup'] != 'Wild']['ColorGroup'].unique().tolist()
# 创建颜色映射：普通牛用 plotly 默认色板，野牛用黑色
colors = px.colors.qualitative.Plotly # 默认色板
color_map = {group: colors[i % len(colors)] for i, group in enumerate(groups)}
color_map['Wild'] = 'black' # 野牛/其他物种用黑色
fig = px.scatter_mapbox(
df,
lat="Latitude (N)",
lon="Longitude (E)",
color="ColorGroup",
color_discrete_map=color_map,
hover_name="Breed",
hover_data={"Species": True, "Group": True, "Num": True},
zoom=3,
height=900,
)
fig.update_layout(
mapbox_style="carto-positron",
title="牛品种采样地点分布（黑色=野牛）",
margin={"r":0,"t":30,"l":0,"b":0}
)
fig.show()
fig.write_html("sampling_map111.html", include_plotlyjs="cdn")
