什么是“最简单”的方式按一定比例在ggmap

Question

添加到地图真的，我到处搜寻，并在地图编码一个规模这么难...... Adding scale bar to ggplot map

Is there a way to add a scale bar (for linear distances) to ggmap?

是否可以制作一条简单的线条，其比例与我们在该功能中选择的缩放预设不同？

我有这个简单的地图：

library(ggmap) 
pngMAP_df2 = get_map(location = c(-90.5, -0.5), source = "google", zoom = 8,color = "bw") 
s = ggmap(pngMAP_df2) 
s 

我想补充，以及在该图GPS坐标：

myGPS = data.frame(lat=c(-0.6850556,-0.6854722, -0.6857778 ),lon=c(-90.22275,-90.22261, -90.22272)) 

是否容易实现？

我只是想添加一些真正简单的东西。就像一条总是有一个圆形数字的行，表示地图中的缩放比例。

此外，是否可以使用此代码使地图看起来更简单。像看到白色的水和黑色的土地的轮廓？

感谢，

Answer 1

喜欢的东西：

library(rgdal) 
library(rgeos) 
library(ggplot2) 
library(ggthemes) 
library(ggsn) 

URL <- "https://osm2.cartodb.com/api/v2/sql?filename=public.galapagos_islands&q=select+*+from+public.galapagos_islands&format=geojson&bounds=&api_key=" 
fil <- "gal.json" 
if (!file.exists(fil)) download.file(URL, fil) 

gal <- readOGR(fil, "OGRGeoJSON") 

# sample some points BEFORE we convert gal 
rand_pts <- SpatialPointsDataFrame(spsample(gal, 100, type="random"), data=data.frame(id=1:100)) 

gal <- gSimplify(gUnaryUnion(spTransform(gal, CRS("+init=epsg:31983")), id=NULL), tol=0.001) 

gal_map <- fortify(gal) 

# now convert our points to the new CRS 
rand_pts <- spTransform(rand_pts, CRS("+init=epsg:31983")) 

# and make it something ggplot can use 
rand_pts_df <- as.data.frame(rand_pts) 

gg <- ggplot() 
gg <- gg + geom_map(map=gal_map, data=gal_map, 
        aes(x=long, y=lat, map_id=id), 
        color="black", fill="#7f7f7f", size=0.25) 
gg <- gg + geom_point(data=rand_pts_df, aes(x=x, y=y), color="steelblue") 
gg <- gg + coord_equal() 
gg <- gg + scalebar(gal_map, dist=100, location="topright", st.size=2) 
gg <- gg + theme_map() 
gg 

Answer 2

这将是与地图上的特定点的完整的答案。

library(rgdal) 
library(rgeos) 
library(ggplot2) 
library(ggthemes) 
library(ggsn) 
myGPS = data.frame(lat=c(-0.6850556,-0.6854722, -0.6857778 ),lon=c(-90.22275,-90.22261, -90.22272)) 
coord.deg = myGPS 

class(coord.deg) 
## "data.frame" 
coordinates(coord.deg)<-~lon+lat 
class(coord.deg) 
## "SpatialPointsDataFrame" 
## attr(,"package") 
## "sp" 

# does it have a projection/coordinate system assigned? 
proj4string(coord.deg) # nope 
## NA 

# Manually tell R what the coordinate system is 
proj4string(coord.deg)<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84") 

# now we can use the spTransform function to project. We will project 
# the mapdata and for coordinate reference system (CRS) we will 
# assign the projection from counties 

coord.deg<-spTransform(coord.deg, CRS(proj4string(gal))) 
# double check that they match 
identical(proj4string(coord.deg),proj4string(gal)) 
## [1] TRUE 
my_pts <- SpatialPointsDataFrame(coords = coord.deg, data=data.frame(id=1:length(coord.deg))) 

URL <- "https://osm2.cartodb.com/api/v2/sql?filename=public.galapagos_islands&q=select+*+from+public.galapagos_islands&format=geojson&bounds=&api_key=" 
fil <- "gal.json" 
if (!file.exists(fil)) download.file(URL, fil) 
gal <- readOGR(fil, "OGRGeoJSON") 
gal <- gSimplify(gUnaryUnion(spTransform(gal, CRS("+init=epsg:31983")), id=NULL), tol=0.001) 
gal_map <- fortify(gal) 
rand_pts <- spTransform(my_pts, CRS("+init=epsg:31983")) 

# ggplot can't deal with a SpatialPointsDataFrame so we can convert back to a data.frame 
my_pts <- data.frame(my_pts) 
my_pts.final = my_pts[,2:3] 
# we're not dealing with lat/long but with x/y 
# this is not necessary but for clarity change variable names 
names(my_pts.final)[names(my_pts.final)=="lat"]<-"y" 
names(my_pts.final)[names(my_pts.final)=="lon"]<-"x" 

gg <- ggplot() 
gg <- gg + geom_map(map=gal_map, data=gal_map, 
        aes(x=long, y=lat, map_id=id), 
        color="black", fill="#FFFFFF", size=.5) 
gg <- gg + coord_equal() 
gg <- gg + ggsn:::scalebar(gal_map, dist=50, location="bottomleft", st.size=5) 
gg <- gg + theme_map() 
gg <- gg + geom_point(data=my_pts.final, aes(x=x, y=y), color="red") 
gg