R - Labels geom_point() - r

All,
Please see below code to a R script. I am simply trying to populate a map of UK with list of stores (storeLocations) and customers (CustomerLocations).
STRTRADECODE is the column name within StoreLocations table which contains name of a particular store.
I am unable to output the labels. Please help.
Thanks in advance.
library(RgoogleMaps)
library(maps)
library(ggmap)
library(ggplot)
UKMap <- qmap("United Kingdom", zoom = 6.0)
storeOverlay <- geom_point(aes(x = longitude, y = latitude),
data = StoreLocations, colour = "red")
storeOverlay <- storeOverlay + geom_text(data= StoreLocations, aes(label=STRTRADECODE))
CustomerOverlay <- geom_point(aes(x = longitude, y = latitude),
data = CustomerLocations, colour = "green")
UKMap + CustomerOverlay + storeOverlay

As I said in my commentary before, you have to add the lon and lat to geom_text, so ggplot knows, where to put the text.
Here is a working example (I included nudge_x, so the Text/Label is not directly on the point)
library(RgoogleMaps)
library(maps)
library(ggmap)
library(ggplot)
STRTRADECODE <- c("London","Sheffield","Glasgow")
StoreLocations <- as.data.frame(STRTRADECODE,stringsAsFactors=F)
StoreLocations %>%
mutate_geocode(STRTRADECODE) %>%
rename(longitude = lon,latitude=lat) -> StoreLocations
CustomerLocations <- StoreLocations
CustomerLocations$longitude <- CustomerLocations$longitude - 1
UKMap <- qmap("United Kingdom", zoom = 6.0)
UKMap +
geom_point(mapping=aes(x = longitude,
y = latitude),
data = StoreLocations,
colour = "red"
) +
geom_text(
mapping=aes(x = longitude,
y = latitude,
label = STRTRADECODE
),
data= StoreLocations,
nudge_x = 0.8
) +
geom_point(aes(x = longitude,
y = latitude
),
data = CustomerLocations,
colour = "green"
)

Related

Using gBuffer from rgeos with correct projection

I want to show 15 mile radius circles around points in a map using gBuffer. As far as I can tell I have the points and the map in the same projection, but when I produce the circles on the map, they are too large. Here is my code. The tigerline files for the state and counties can be found at https://www.census.gov/cgi-bin/geo/shapefiles/index.php.
library(tidyverse)
library(rgdal)
library(rgeos)
library(ggplot2)
state <- readOGR('C:\\Users\\Mesonet\\Desktop\\map_folder\\tl_2020_us_state\\tl_2020_us_state.shp')
state <- state[which(state$STATEFP == '46'),]
state <- spTransform(state, CRS("+init=epsg:3857"))
counties <- readOGR('C:\\Users\\Mesonet\\Desktop\\map_folder\\tl_2020_us_county\\tl_2020_us_county.shp')
counties <- counties[which(counties$STATEFP == '46'),]
counties <- spTransform(counties, CRS("+init=epsg:3857"))
sites <- data.frame(Lon = c(-98.1096,-98.27935), Lat = c(43.9029, 43.717258))
coordinates(sites) <- ~Lon + Lat
proj4string(sites) <- CRS("+proj=longlat")
sites <- spTransform(sites, CRS = CRS("+init=epsg:3857"))
# Miles to meters conversion
mile2meter <- function(x){x * 1609.344}
# Buffer creation
site_buffer <- gBuffer(sites, width = mile2meter(15))
png('C:\\Users\\Mesonet\\Desktop\\map_folder\\new_test.png', height = 3000, width = 42*100, res = 100)
ggplot() + geom_path(counties, mapping = aes(x = long, y = lat, group = group), size = 1.75,
alpha = 0.45, col = 'darkgreen') + geom_path(state, mapping = aes(x = long, y = lat, group =
group), size = 0.8) + theme(axis.text = element_blank()) + geom_polygon(site_buffer, mapping
= aes(x = long, y = lat, group = group), fill = '#0000FF', alpha = 1, size = 2)
dev.off()
These two locations are 15.35 miles apart, but the plot shows two circles that overlap each other by a couple miles. I can't figure out why, since from what I can see everything is in the same projection, but I might be wrong. Thank you.

Create shaded polygons around points with ggplot2

I saw yesterday this beautiful map of McDonalds restaurants in USA. I wanted to replicate it for France (I found some data that can be downloaded here).
I have no problem plotting the dots:
library(readxl)
library(ggplot2)
library(raster)
#open data
mac_do_FR <- read_excel("./mcdo_france.xlsx")
mac_do_FR_df <- as.data.frame(mac_do_FR)
#get a map of France
mapaFR <- getData("GADM", country="France", level=0)
#plot dots on the map
ggplot() +
geom_polygon(data = mapaFR, aes(x = long, y = lat, group = group),
fill = "transparent", size = 0.1, color="black") +
geom_point(data = mac_do_FR_df, aes(x = lon, y = lat),
colour = "orange", size = 1)
I tried several methods (Thiessen polygons, heat maps, buffers), but the results I get are very poor. I can't figure out how the shaded polygons were plotted on the American map. Any pointers?
Here's my result, but it did take some manual data wrangling.
Step 1: Get geospatial data.
library(sp)
# generate a map of France, along with a fortified dataframe version for ease of
# referencing lat / long ranges
mapaFR <- raster::getData("GADM", country="France", level=0)
map.FR <- fortify(mapaFR)
# generate a spatial point version of the same map, defining your own grid size
# (a smaller size yields a higher resolution heatmap in the final product, but will
# take longer to calculate)
grid.size = 0.01
points.FR <- expand.grid(
x = seq(min(map.FR$long), max(map.FR$long), by = grid.size),
y = seq(min(map.FR$lat), max(map.FR$lat), by = grid.size)
)
points.FR <- SpatialPoints(coords = points.FR, proj4string = mapaFR#proj4string)
Step 2: Generate a voronoi diagram based on store locations, & obtain the corresponding polygons as a SpatialPolygonsDataFrame object.
library(deldir)
library(dplyr)
voronoi.tiles <- deldir(mac_do_FR_df$lon, mac_do_FR_df$lat,
rw = c(min(map.FR$long), max(map.FR$long),
min(map.FR$lat), max(map.FR$lat)))
voronoi.tiles <- tile.list(voronoi.tiles)
voronoi.center <- lapply(voronoi.tiles,
function(l) data.frame(x.center = l$pt[1],
y.center = l$pt[2],
ptNum = l$ptNum)) %>%
data.table::rbindlist()
voronoi.polygons <- lapply(voronoi.tiles,
function(l) Polygon(coords = matrix(c(l$x, l$y),
ncol = 2),
hole = FALSE) %>%
list() %>%
Polygons(ID = l$ptNum)) %>%
SpatialPolygons(proj4string = mapaFR#proj4string) %>%
SpatialPolygonsDataFrame(data = voronoi.center,
match.ID = "ptNum")
rm(voronoi.tiles, voronoi.center)
Step 3. Check which voronoi polygon each point on the map overlaps with, & calculate its distance to the corresponding nearest store.
which.voronoi <- over(points.FR, voronoi.polygons)
points.FR <- cbind(as.data.frame(points.FR), which.voronoi)
rm(which.voronoi)
points.FR <- points.FR %>%
rowwise() %>%
mutate(dist = geosphere::distm(x = c(x, y), y = c(x.center, y.center))) %>%
ungroup() %>%
mutate(dist = ifelse(is.na(dist), max(dist, na.rm = TRUE), dist)) %>%
mutate(dist = dist / 1000) # convert from m to km for easier reading
Step 4. Plot, adjusting the fill gradient parameters as needed. I felt the result of a square root transformation looks quite good for emphasizing distances close to a store, while a log transformation is rather too exaggerated, but your mileage may vary.
ggplot() +
geom_raster(data = points.FR %>%
mutate(dist = pmin(dist, 100)),
aes(x = x, y = y, fill = dist)) +
# optional. shows outline of France for reference
geom_polygon(data = map.FR,
aes(x = long, y = lat, group = group),
fill = NA, colour = "white") +
# define colour range, mid point, & transformation (if desired) for fill
scale_fill_gradient2(low = "yellow", mid = "red", high = "black",
midpoint = 4, trans = "sqrt") +
labs(x = "longitude",
y = "latitude",
fill = "Distance in km") +
coord_quickmap()

ggmap and plot shows different zone on map

With help of ggmap and plot I want to show the centers of states on the map. The result should be something like this
I tried this block of code but is doesnt show above map
data(state)
cen_df <- as.data.frame(state.center)
library(ggmap)
library(ggplot2)
d <- data.frame(lat = cen_df[2],
lon = cen_df[1])
US <- get_map("united states", zoom = 12)
p <- ggmap(US)
p + geom_point(data = d, aes(x = lon, y = lat), color = "red", size = 30, alpha = 0.5)
ggplot_build(p)
But it shows something lie this:
Any help?
I modified your code as follows. The zoom should be 4. It is also better to use base_layer argument to put your ggplot2 object.
data(state)
library(ggmap)
library(ggplot2)
d <- data.frame(lat = state.center$y,
lon = state.center$x)
US <- get_map("united states", zoom = 4)
p <- ggmap(US, base_layer = ggplot(data = d)) +
geom_point(aes(x = lon, y = lat), color = "red", size = 2, alpha = 0.5)
p

Boundary polygon of lat lon collection

I have a table containing all the latitudes and longitudes of some locations in a city called queryResult and I do the following:
1 - Get the Raster map of the city[Blackpool for instance]
cityMapRaster = get_map(location = 'Blackpool', zoom = 12, source = 'google', maptype = 'roadmap')
dataToShow <- ggmap(cityMapRaster) + geom_point(aes(x = Longitude, y = Latitude), data = queryResult, alpha = .5, color = "darkred", size = 1)
print(dataToShow)
and this will return the following points on the map
Now I want to draw the outer boundary [city border line] of all these latitude and longitudes similar to the following expected result
Update 1 : Providing input data and applying suggested ahull solution:
ggmap(cityMapRaster) + geom_point(aes(x = Longitude, y = Latitude), data = queryResult, alpha = .5, color = "darkred") + ahull.gg
I applied the ahull solution suggested by #spacedman and #cuttlefish44 and got the following result which is far different than the expected polygon:
You can download the .csv file containing all latitudes and longitudes from the following link : Blackpool Lat,Lon
Googles suggested area boundary looks like the following :
If you don't want a simple convex hull (and the polygon you've drawn is far from convex) then look at alpha-shapes in the alphahull package.
I wrote an example of how to get a polygon from an alpha-shape using that package and some points that make up a complex Norway boundary:
http://rpubs.com/geospacedman/alphasimple
You should be able to follow that to get a polygon for your data, and it might even be simpler now since that was a few years ago.
Here's a reproducible example of how to use chull to calculate a convex hull solution to this. I just generate some random points for queryResult, as you did not provide data.
If you prefer a concave hull boundary, then see the answer from #Spacedman
library(ggmap)
cityMapRaster = get_map(location = 'Blackpool', zoom = 12, source = 'google', maptype = 'roadmap')
extent = attr(cityMapRaster, "bb")
queryResult = data.frame(Longitude = rnorm(200, as.numeric(extent[2] + extent[4])/2, 0.01),
Latitude = rnorm(200, as.numeric(extent[1] + extent[3])/2, 0.02))
boundary = chull(as.matrix(queryResult))
ggmap(cityMapRaster) +
geom_point(aes(x = Longitude, y = Latitude),
data = queryResult, alpha = .5, color = "darkred", size = 2) +
geom_path(aes(x = Longitude, y = Latitude), data = queryResult[c(boundary, boundary[1]),])
I suppose queryResult is x and y datasets. As far as I see, your boundary isn't convex hull, so I used alphahull package.
## example `queryResult`
set.seed(1)
df <- data.frame(Longitude = runif(200, -3.05, -2.97), Latitude = rnorm(200, 53.82, 0.02))
library(alphahull)
ahull.obj <- ahull(df, alpha = 0.03)
plot(ahull.obj) # to check
# ahull_track() returns the output as a list of geom_path objs
ahull.gg <- ahull_track(df, alpha=0.03, nps = 1000)
## change graphic param
for(i in 1:length(ahull.gg)) ahull.gg[[i]]$aes_params$colour <- "green3"
ggmap(cityMapRaster) +
geom_point(aes(x = Longitude, y = Latitude), data = df, alpha = .5, color = "darkred") +
ahull.gg
## if you like not curve but linear
ashape.obj <- ashape(df, alpha = 0.015)
plot(ashape.obj) # to check
ashape.df <- as.data.frame(ashape.obj$edge[,c("x1", "x2", "y1", "y2")])
ggmap(cityMapRaster) +
geom_point(aes(x = Longitude, y = Latitude), data = df, alpha = .5, color = "darkred") +
geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), data = ashape.df, colour="green3", alpha=0.8)

How to plot data on a map without using Google map (image)?

I have a csv file with variables name "Latitude","Longitude","PM10 concentration". You can download data here. I want to plot PM10 data on a map of South Korea according to their latitude and Longitude. Also I want to show them as bubble with different size and color.
Following this example I have already plotted PM10 data on Google Map. But now I want do this without using Google map rather by creating spatial object or in any other way.
I tried to write some code but I have download the spatial data for administration area (GADM) of South Korea. But I am not sure that approach is right or wrong.
library(rgdal)
library(ggplot2)
library(maptools)
map<-readOGR('D:/BACKUP/R/GSTAT/R File/shape file korea map',layer ='KOR_adm2')
summary(kmap)
EPSG<-make_EPSG()
EPSG[grepl("WGS 84$", EPSG$note), ]
kmap84<-spTransform(kmap, CRS("+init=epsg:4326"))
kmaps<-fortify(kmap84)
I don't understand what should I do next.
Here's an example:
library(raster)
library(ggplot2)
download.file("https://docs.google.com/uc?id=0ByY3OAw62EShakxJZkplOXZ0RGM&export=download", tf <- tempfile(fileext = ".csv"))
df <- read.csv(tf, row.names = 1)
skorea <- getData("GADM", country = "South Korea", level = 2)
skorea <- fortify(skorea)
ggplot() +
geom_map(data = skorea, map = skorea, aes(x = long, y = lat, map_id = id, group = group),
fill = NA, colour = "black") +
geom_point(data = df, aes(x = LON, y = LAT, size = PM10), colour = "red", alpha = .5) +
scale_size(range = c(1, 5))

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