I want to draw the map of a lake with the bathymetries I have taken with the sonar. I have a .sl2 file (Lowrance sonar) that I have converted to .csv (Namely, Sonar_13_07_1.csv). Finally I get 3 columns with 30665 rows, here is an example of the first rows:
latitude,longitude,waterDepthM
39.8197123940846,-3.11133523036904,0
39.8197193169248,-3.11133523036904,0
39.8197193169248,-3.11134424374202,0
39.8197262397644,-3.11134424374202,0
39.8197331626032,-3.11135325711499,0
39.8197400854413,-3.11135325711499,0
39.8197470082787,-3.11135325711499,0
39.8197539311154,-3.11135325711499,0
39.8197608539514,-3.11135325711499,0
39.8197608539514,-3.11134424374202,0
39.8197677767867,-3.11134424374202,0
39.8197677767867,-3.11135325711499,0
39.8197746996213,-3.11135325711499,0
39.8197746996213,-3.11134424374202,0
39.8197816224553,-3.11134424374202,0
39.8197885452885,-3.11134424374202,0
39.8197885452885,-3.11133523036904,0
39.819795468121,-3.11133523036904,0
39.8198023909528,-3.11132621699607,0
39.8198023909528,-3.11133523036904,0
39.819809313784,-3.11132621699607,0
39.8198162366144,-3.11132621699607,0
39.8198231594442,-3.11132621699607,0
39.8198370051015,-3.11132621699607,0
39.8198300822732,-3.11132621699607,0
39.8198439279292,-3.11132621699607,0
39.8198508507561,-3.11133523036904,0
39.8198508507561,-3.11132621699607,0
39.8198577735824,-3.11133523036904,0
39.8198646964079,-3.11133523036904,0
39.8198646964079,-3.11134424374202,0
39.8198716192328,-3.11135325711499,0
39.8198716192328,-3.11134424374202,0
39.8198716192328,-3.11136227048797,0
39.8198785420569,-3.11135325711499,0
39.8198716192328,-3.11136227048797,-0.691144658182553
39.8198785420569,-3.11135325711499,-0.691144658182553
39.8198716192328,-3.11136227048797,-0.72783260768886
39.8198785420569,-3.11135325711499,-0.72783260768886
39.8198716192328,-3.11136227048797,-0.735494858005278
39.8198785420569,-3.11135325711499,-0.735494858005278
39.8198716192328,-3.11136227048797,-0.754367615888273
39.8198785420569,-3.11135325711499,-0.754367615888273
39.8198716192328,-3.11136227048797,-0.762954301055886
39.8198785420569,-3.11135325711499,-0.762954301055886
39.8198785420569,-3.11136227048797,-0.762954301055886
I manage to plot it like this:
library(ggplot2)
ggplot(Sonar_13_07_1, aes(longitude, latitude, colour = waterDepthM)) + geom_point() + coord_equal() + xlim(x_coords) + ylim(y_coords)
On the other hand, I plotted the base map of the lake:
library(osmdata)
library(sf)
> x_coords <- c(-3.109, -3.117)
> y_coords <- c(39.817, 39.832)
> bounding_box <- matrix(nrow = 2, ncol=2, byrow = T,
+ data = c(x_coords, y_coords),
+ dimnames = list(c("x", "y"),
+ c("min", "max")))
> osm_water_sf <- osmdata::opq(bbox = bounding_box) %>% # Limit query to bounding_box
+ osmdata::add_osm_feature(key = 'natural', value = 'water') %>% # Limit query to waterbodies
+ osmdata::osmdata_sf() # Convert to simple features
ggplot(data=osm_water_sf$osm_polygons) +
geom_sf(color="blue", fill="lightblue") +
xlim(x_coords) + ylim(y_coords)
But finally I don't manage to merge the two plots into one, superimposing the bathymetric data on the base map. Also, my intention would be to obtain contours from the bathymetric data of the lake, but of course, I'm already stuck with the merging of both plots.
Thanks for the suggestion, I have managed to have a spatial data frame like this:
"","waterDepthM","geometry"
"1",0,c(-3.11133523036904, 39.8197123940846)
"2",0,c(-3.11133523036904, 39.8197193169248)
"3",0,c(-3.11134424374202, 39.8197193169248)
"4",0,c(-3.11134424374202, 39.8197262397644)
"5",0,c(-3.11135325711499, 39.8197331626032)
"6",0,c(-3.11135325711499, 39.8197400854413)
"7",0,c(-3.11135325711499, 39.8197470082787)
"8",0,c(-3.11135325711499, 39.8197539311154)
"9",0,c(-3.11135325711499, 39.8197608539514)
"10",0,c(-3.11134424374202, 39.8197608539514)
but when I try to plot both data frames together, I only get a base map of the lake with the depth legend, but no depth values appear.
ggplot() +
+ geom_sf(data=osm_water_sf$osm_polygons, color="blue", fill="NA") +
+ geom_sf(data=Sonar_13_07_1, aes(col=waterDepthM)) +
+ xlim(x_coords) + ylim(y_coords)
plot output
Use the sf package to convert your sonar data frame to a spatial data frame. Something like:
library(sf)
Sonar_13_07_1 = st_as_sf(Sonar_13_07_1, coords=c("longitude","latitude"), crs=4326)
then you have an object you can put in a ggplot construction via an sf geom alongside your OSM vector data. Something like:
ggplot(data=osm_water_sf$osm_polygons) +
geom_sf(color="blue", fill="lightblue") +
geom_sf(data=Sonar_13_07_1, aes(col=waterDepthM)) +
xlim(x_coords) + ylim(y_coords)
I have been through a lot of questions and documentation, and since you need to bill to use ggmaps() (because of google cloud services) I started looking for an alternative. I found maps(), and I'm trying to adapt this solution:
data %>%
rename(x = longitud, y = latitud) %>%
ggplot() +
geom_polygon(aes(x = long, y = lat), data = map_data("world")) +
geom_point(aes(x = x, y = y))
However, I'm getting into a few problems:
If you plot the code above, you will get the right points for the plot (Chile), but the world map is wrongly printed (see the picture above).
I don't need a grey either a colorful map. I just need to plot the country Chile with kind-of-normal formatting (for example, google maps satellite). The coordinates are flows of lakes/mountains and I want to see if I can cluster them by some-visual sector.
I only need a map from Chile, but as I didn't find one I'm using this world map. Is there a way to cut it without losing the connections with the map coordinates?
This is the data:
data <- structure(list(latitud = c(-30.6833000183105, -41.4000015258789,
-43.8189010620117, -34.2731018066406, -47.0666999816895, -40.3166999816895,
-43.4491996765137, -35.7543983459473, -47.1413993835449, -36.6260986328125,
-54.0410995483398, -37.2118988037109, -33.3086013793945, -37.2792015075684,
-35.4524993896484, -36.5856018066406, -18.5832996368408, -18.2325000762939,
-36.4668998718262, -44.75, -44.6591987609863, -44.5936012268066,
-28.4647006988525, -28.6996994018555, -28.5118999481201, -28.6718997955322,
-28.7306003570557, -30.5902996063232, -30.6667003631592, -35.1730995178223,
-48.1591987609863, -48.377498626709, -45.4000015258789, -45.7832984924316,
-29.94580078125, -38.8652992248535, -30.4386005401611, -31.6646995544434,
-51.2000007629395, -51.3328018188477, -51.25, -45.5666999816895,
-45.551700592041, -45.8372001647949, -39.0144004821777, -28.9414005279541,
-28.7502994537354, -38.6081008911133, -34.9844017028809, -32.8403015136719,
-29.9953002929688, -18.3999996185303, -35.9000015258789, -35.6169013977051,
-35.9085998535156, -35.8166999816895, -33.7346992492676, -45.38330078125,
-35.4068984985352, -32.7571983337402, -32.8502998352051, -33.5938987731934,
-36.8386001586914, -33.4961013793945, -20.1119003295898, -27.8043994903564,
-37.7332992553711, -30.9986000061035, -30.8006000518799, -21.9368991851807,
-22.3652992248535, -22.273099899292, -22.0277996063232, -21.9755992889404,
-22.289400100708, -22.2791996002197, -38.4303016662598, -38.6866989135742,
-45.4057998657227, -38.7799987792969, -37.5503005981445, -37.6018981933594,
-37.8997001647949, -38.0368995666504, -37.9897003173828, -37.7047004699707,
-37.7963981628418, -37.7092018127441, -31.5835990905762, -27.3635997772217,
-27.3194007873535, -29.8931007385254, -30.9242000579834, -21.4246997833252,
-36.5703010559082, -38.2008018493652, -38.0661010742188, -38.4333000183105,
-31.7422008514404, -31.6881008148193, -31.8117008209229, -31.7714004516602,
-27.86669921875, -27.5160999298096, -27.9747009277344, -30.7047004699707,
-36.8499984741211, -36.6500015258789, -36.86669921875, -35.3736000061035,
-40.5167007446289, -33.4782981872559, -33.198299407959, -36.0499992370605,
-35.9667015075684, -36.2332992553711, -34.4921989440918, -34.6581001281738,
-32.8166999816895, -47.3499984741211, -47.5, -29.9811000823975,
-32.4413986206055, -22.3922004699707, -22.3430995941162, -21.7124996185303,
-22.4582996368408, -22.4419002532959, -22.4468994140625, -22.5060997009277,
-33.7219009399414, -33.6613998413086, -35.5574989318848), longitud = c(-71.0500030517578,
-73.2166976928711, -72.38330078125, -71.371696472168, -72.8000030517578,
-72.9666976928711, -72.1074981689453, -71.0864028930664, -72.7257995605469,
-72.4891967773438, -68.7975006103516, -72.3242034912109, -70.3572006225586,
-71.9847030639648, -71.7332992553711, -71.5255966186523, -69.0466995239258,
-69.331901550293, -72.6911010742188, -72.7166976928711, -71.8082962036133,
-71.5477981567383, -71.1782989501953, -70.5500030517578, -71.0064010620117,
-70.6464004516602, -70.5066986083984, -71.1714019775391, -71.5333023071289,
-71.0911026000977, -73.0888977050781, -72.9589004516602, -72.5999984741211,
-72.61669921875, -70.5327987670898, -71.7335968017578, -71.002197265625,
-71.2546997070312, -72.9332962036133, -73.1091995239258, -72.5167007446289,
-72.0832977294922, -72.0680999755859, -71.7769012451172, -73.0828018188477,
-70.2481002807617, -70.4828033447266, -72.8478012084961, -72.0100021362305,
-71.0255966186523, -70.5867004394531, -70.3000030517578, -71.5167007446289,
-71.7677993774414, -71.2981033325195, -71.8332977294922, -70.3007965087891,
-72.4666976928711, -72.2082977294922, -70.736701965332, -70.5093994140625,
-70.3792037963867, -73.061897277832, -70.8167037963867, -68.8407974243164,
-70.1268997192383, -72.61669921875, -71.0899963378906, -70.9697036743164,
-68.5330963134766, -68.6418991088867, -68.1438980102539, -68.6207962036133,
-68.6074981689453, -68.3447036743164, -68.2427978515625, -72.0105972290039,
-72.502799987793, -72.6231002807617, -72.9468994140625, -72.5903015136719,
-72.2782974243164, -71.6239013671875, -71.4781036376953, -71.5199966430664,
-71.7683029174805, -71.6988983154297, -71.823600769043, -71.4606018066406,
-70.3392028808594, -70.8380966186523, -71.2514038085938, -70.7731018066406,
-70.053596496582, -71.5547027587891, -71.2988967895508, -71.3497009277344,
-71.2332992553711, -71.1492004394531, -71.2658004760742, -70.9302978515625,
-71.0639038085938, -70.0667037963867, -70.2647018432617, -69.997802734375,
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-69.5167007446289, -68.7363967895508, -68.8886032104492, -68.8775024414062,
-68.988899230957, -71.5550003051758, -71.3371963500977, -71.7067031860352
)), row.names = c(1L, 136L, 262L, 395L, 507L, 605L, 701L, 789L,
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1355030L, 1368899L, 1381979L, 1393175L), class = "data.frame")
The following code uses the simple features (sf) library to show a map of Chile overlaid with the provided datapoints. The bounding box is set in the parameters to st_crop and can be adjusted as needed without distorting the map. The code uses the Admin 0 - Countries shape file, which is in the public domain and free to use.
library(sf)
library(ggplot2)
library(dplyr);
library(magrittr);
# download world shapefile from
# https://www.naturalearthdata.com/downloads/
# 50m-cultural-vectors/50m-admin-0-countries-2/
# and extract zip file
world <- st_read(
# change below line to path of extracted shape file
'c:/path/to/ne_50m_admin_0_countries.shp'
);
world %<>% mutate(active = NAME_EN == 'Chile'); # used to highlight Chile
# convert the dataframe to a sf geometry object
dsf <- data %>%
rowwise %>%
mutate(geometry = list(st_point(c(longitud, latitud)))) %>%
st_as_sf(crs=st_crs(world));
# plot the map
world %>% st_crop(xmin=-90, xmax=-30, ymin=-60, ymax=-10) %>%
ggplot() +
geom_sf(aes(fill=active), show.legend=F) + # world map with Chile highlighted
geom_sf(data=dsf, color='#000000') + # point overlay
scale_fill_manual(values=c('#aaaa66', '#ffffcc')) + # country colors
scale_x_continuous(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
theme_void() + # remove axis labels and gridlines
theme(panel.background=element_rect(fill='lightblue'))
The output is shown below. Note that the map does not distort regions that are cropped.
Additional explanation
The sf package provides support for simple feature (sf) geometries. Simple features provide tools for working with geometries such as polygons and points. There is a cheat sheet here that provides a good overview.
Plotting the base map
As of release 3.0, ggplot2 provides native support for visualizing simple feature geometries. This allows for us to write:
world <- st_read(
# change below line to path of extracted shape file
'c:/path/to/ne_50m_admin_0_countries.shp'
);
ggplot(world) + geom_sf()
Simple feature objects are generally stored in data frames that include a column describing the geometry. This allows us to show a map of Chile like so:
ggplot(world %>% filter(NAME_EN == 'Chile')) + geom_sf()
Or a map with Chile highlighted:
# create new geometry of world map
# cropped to (10°S, 60°S) and (90°W, 30°W)
chileregion <- world %>% st_crop(
xmin=-90,
xmax=-30,
ymin=-60,
ymax=-10)
# show region with Chile highlighted
ggplot(chileregion %>%
mutate(is.chile = factor(NAME_EN == 'Chile'))) +
geom_sf(aes(fill=is.chile), show.legend = F) +
scale_fill_manual(values=c('gray', 'red'))
Plotting the points
We are given a data frame with two columns, latitude and longitude.
To convert to a simple feature, we first use st_point to create a new point with the given coordinate values:
dsf <- data %>% #begin with data
rowwise %>% # dplyr::rowwise applies mutation to each row individually
# create a geometry column that provides the point as a geometry
mutate(geometry = list(st_point(c(longitud, latitud))))
At this point, dsf is simply a data frame with a geometry column; it is not yet a simple feature object. We can use the function st_as_sf to create a sf object from the data frame. In doing so, we will also need to provide a coordinate reference system (CRS) to allow ggplot to project the coordinates provided onto the map rendering. Here we can provide ESPG 4326 as the CRS, which maps the x and y coordinates directly to lat/long:
# call st_as_sf to convert data frame to a simple geometry object.
dsf <- st_as_sf(crs=4326);
Since dsf is now a simple geometry, you can plot as such:
> ggplot(dsf) + geom_sf()
(Note that you could also simply overlay the points on the base map with geom_point(data=data, aes(x=longitud, y=latitud)) without first converting to an sf object. This will work here because the CRS for the base map is also ESPG 4326, which maps x and y directly to longitude and latitude, respectively. Using geom_point, however, will not work in the general case when a coordinate transformation is applied to the geometry.)
Overlaying
With both points now defined as geometries, you can simply overlay:
ggplot() +
geom_sf(data=chileregion) +
geom_sf(data=dsf)
The final plot in the original answer adds some additional visual aesthetics (e.g., blue background) to produce the final map output.
This might help and you can play around with it yourself:
world_map <- map_data("world")
Wmap <- data %>%
rename(x =longitud , y = latitud) %>%
ggplot() +
geom_polygon(aes(x = long, y = lat, group = group), data = world_map, fill = "grey21", color = "grey21") +
geom_point(aes(x = x, y = y, color = 'red')) +
scale_color_identity() +
coord_fixed() +
xlab("") +
ylab("")
Wmap
Chile_map <- map_data("world", region="Chile")
Cmap <- data %>%
rename(x =longitud , y = latitud) %>%
ggplot() +
geom_polygon(aes(x = long, y = lat, group = group), data = Chile_map, fill = "grey21", color = "grey21") +
geom_point(aes(x = x, y = y, color = 'red')) +
scale_color_identity() +
coord_fixed() +
xlab("") +
ylab("")
Cmap
dev.new()
windows.options(width=10, height=6)
vp_inset <- grid::viewport(width = 0.55, height = 0.45, x = -0.1, y = 0.60, just = c("left", "top"))
print(Wmap)
print(Cmap, vp = vp_inset)
NOTE: The group aesthetic determines which cases are connected together into a polygon.
pardon me if it is a basic question, this is my first time to write here, so my thanks in advance.
I have exported a report from Google Analytics with columns Longitude, Latitude and Sessions and I want to add these data points to polygon map I have created in R for administrative regions of Slovakia.
This is what I have for now.
##Load the Raster Library
library(raster)
##Get the Province Shapefile for Slovakia
slovakia_level_1 <- getData('GADM', country='SVK', level=1)
slovakia_level_2 <- getData('GADM', country='SVK', level=2)
##Plot this shapefile
plot(slovakia_level_1)
library(ggmap) ##load the ggmap package so we can access the crime data
## read our dataset with sessions from google analytics ( more on how to read excel files http://www.sthda.com/english/wiki/reading-data-from-excel-files-xls-xlsx-into-r)
library(readxl) ## this is the dataframe from google analytics and i would like to plot these data to the slovakia administrtaive region map
lugera <- read_excel("Analytics 01. [Lugera.sk] - [Reporting View] - [Filtered Data] New Custom Report 20190101-20190627.xlsx")
But i really do not know how to move on. I went based on this article http://data-analytics.net/wp-content/uploads/2014/09/geo2.html but i have stuck when i needed to plot points.
This is a sample how the report from google analytics looks like:
Longitude Latitude Sessions
17.1077 48.1486 25963
0.0000 0.0000 13366
21.2611 48.7164 4732
18.7408 49.2194 3154
21.2393 49.0018 2597
18.0335 48.8849 2462
19.1462 48.7363 2121
17.5833 48.3709 1918
18.0764 48.3061 1278
14.4378 50.0755 1099
20.2954 49.0511 715
18.1571 47.9882 663
18.6245 48.7745 653
17.8272 48.5918 620
18.9190 49.0617 542
19.1371 48.5762 464
-6.2603 53.3498 369
18.1700 48.5589 369
20.5637 48.9453 325
-0.1278 51.5074 284
21.9184 48.7557 258
Can someone help me how to progress from here as I am struggling to figure it out how to plot those points on polygon map.
Is it also possible to create a heat map over particular regions as well, please?
I hope it was clear, but if not, please tell me, i will improve my question, this is my first time to ask.
Thank you very much!
UPDATE
I was trying to reproduce Jay`s answer and the first map with red dots works awesome! Thanks!
But in case of the heat map I am getting errors and cannot reproduce the same map as I am getting several erros.
Belowe is my code how it looks like and I am not sure where is the issue as I tried to name my dataframe as ses the same way as in jay`s answer.
##Load the Raster Library
library(raster) # imports library(sp)
slovakia_level_1 <- getData('GADM', country='SVK', level=1)
##Plot
plot(slovakia_level_1)
points(coordinates(slovakia_level_2), pch=20, col="red")
#ses is my google analytics dataframe where all 3 columns Longitude, Latitude and Sessions are numeric
## it is imported excel file to r and stored as a dataframe
ses
spdf <- SpatialPointsDataFrame(coords=ses[1:2], data=ses[3],
proj4string=CRS(proj4string(slovakia_level_2)))
ppl.sum <- aggregate(x=spdf["Sessions"], by=slovakia_level_2, FUN=sum)
spplot(ppl.sum, "Sessions", main="Sessions in Slovakia")
These are the errors I am getting
spdf <- SpatialPointsDataFrame(coords=ses[1:2], data=ses[3],
+ proj4string=CRS(proj4string(slovakia_level_2)))
Error in proj4string(slovakia_level_2) :
object 'slovakia_level_2' not found
> ppl.sum <- aggregate(x=spdf["Sessions"], by=slovakia_level_2, FUN=sum)
Error in aggregate(x = spdf["Sessions"], by = slovakia_level_2, FUN = sum) :
object 'spdf' not found
> spplot(ppl.sum, "Sessions", main="Sessions in Slovakia")
Error in spplot(ppl.sum, "Sessions", main = "Sessions in Slovakia") :
object 'ppl.sum' not found
Please, take my huge thanks for being so helpful on my first question and I cannot express my respect to all people at StackOverflow.
Thank you
Actually there's a coordinates() function included in the sp package (imported from raster), with which we easily can add the points to the plot.
library(raster) # imports library(sp)
slovakia_level_1 <- getData('GADM', country='SVK', level=1)
slovakia_level_2 <- getData('GADM', country='SVK', level=2)
##Plot
plot(slovakia_level_1)
points(coordinates(slovakia_level_2), pch=20, col="red")
To get a heatmap using your google analytics data (here ses) we can use spplot(), also included in sp. First we need to create a SpatialPointsDataFrame, which - according to this post on gis.stackexchange - we aggregate to match ses$Sessionspoints and polygons from slovakia_level_2.
spdf <- SpatialPointsDataFrame(coords=ses[1:2], data=ses[3],
proj4string=CRS(proj4string(slovakia_level_2)))
ppl.sum <- aggregate(x=spdf["Sessions"], by=slovakia_level_2, FUN=sum)
spplot(ppl.sum, "Sessions", main="Sessions in Slovakia")
Result
Data
# your data from google analytics above
ses <- structure(list(Longitude = c(17.1077, 0, 21.2611, 18.7408, 21.2393,
18.0335, 19.1462, 17.5833, 18.0764, 14.4378, 20.2954, 18.1571,
18.6245, 17.8272, 18.919, 19.1371, -6.2603, 18.17, 20.5637, -0.1278,
21.9184), Latitude = c(48.1486, 0, 48.7164, 49.2194, 49.0018,
48.8849, 48.7363, 48.3709, 48.3061, 50.0755, 49.0511, 47.9882,
48.7745, 48.5918, 49.0617, 48.5762, 53.3498, 48.5589, 48.9453,
51.5074, 48.7557), Sessions = c(25963L, 13366L, 4732L, 3154L,
2597L, 2462L, 2121L, 1918L, 1278L, 1099L, 715L, 663L, 653L, 620L,
542L, 464L, 369L, 369L, 325L, 284L, 258L)), row.names = c(NA,
-21L), class = "data.frame")
The simplest way to do it would be this (slov_df is your dataset):
library(sp)
library(ggplot2)
slov_reg <- fortify(slovakia_level_2)
ggplot() +
geom_polygon(data = slov_reg, aes(x = long, y = lat, group = group), col = "black", fill = NA) +
geom_point(data = slov_df, aes(x = Longitude, y = Latitude))
EDIT:
Nice solution by jay.sf. If you like this let me provide another option:
sp_google <- SpatialPointsDataFrame(coords=slov_df[1:2], data=slov_df[3],
proj4string=CRS(proj4string(slovakia_level_2)))
slovakia_level_2#data$Sessions <- over(slovakia_level_2, sp_google, fn = sum)$Sessions
slovakia_level_2#data$id <- row.names(slovakia_level_2#data)
slov_reg <- fortify(slovakia_level_2, region = "id")
slov_reg <- join(slov_reg, slovakia_level_2#data, by="id")
ggplot() +
geom_polygon(data = slov_reg, aes(x = long, y = lat, group = group, fill = Sessions), col = "black") +
scale_fill_gradient(low = "yellow", high = "red", na.value = "lightgrey") +
theme_bw()
It's a little bit more work, but in the end ggplot offers you a much wider range of customization options. It's a question of your preference.
Good day everyone,
Using the code below I can successfully retrieve a raster from Google using ggmap, plot an annotation_raster using ggplot2, and plot site localities as red dots on top of the raster layer. On the plot the positions don't quite match (they should follow the coastline). I know my sites' positions are correct because they plot where they should be when I upload the data onto Google Earth as a KML file.
Suggestions will be appreciated.
This code will run as is... Note that you need a development version of ggplot2, which is available on github. To install:
# install.packages("devtools")
library(devtools)
install_github("ggplot2")
and for the code:
library(ggplot2)
library(ggmap)
library(grDevices)
theme_set(theme_bw())
# Some coordinates of points to plot:
siteLat = c(-22.94414, -22.67119, -29.25241, -30.31181, -32.80670, -33.01054, -32.75833, - 33.36068, -31.81708, -32.09185, -32.31667, -34.13667, -34.05016, -33.91847, -34.13525, -34.12811, -34.10399, -34.16342, -34.41459, -34.58786, -34.83353, -34.37150, -34.40278, -34.17091, -34.08565, -34.04896, -33.98066, -34.02448, -34.20667, -34.05889, -33.97362, -33.99125, -33.28611, -33.02407, -33.01798, -32.99316, -31.09704, -31.05000, -30.91622, -30.70735, -30.28722, -30.27389, -29.86476, -29.54501, -29.49660, -29.28056, -28.80467, -27.42472)
siteLon = c(14.50175, 14.52134, 16.86710, 17.26951, 17.88522, 17.95063, 18.02778, 18.15731, 18.23065, 18.30262, 18.32222, 18.32674, 18.34971, 18.38217, 18.43592, 18.45077, 18.48364, 18.85908, 19.25493, 19.33971, 20.00439, 21.43518, 21.73972, 22.12749, 23.05532, 23.37925, 23.64567, 23.89933, 24.77944, 25.58889, 25.64724, 25.67788, 27.48889, 27.91626, 27.92182, 27.95036, 30.18395, 30.21666, 30.32982, 30.48474, 30.76026, 30.83556, 31.04479, 31.21662, 31.24665, 31.44403, 32.07567, 32.73333)
siteName = c(seq(1:length(siteLon)))
sites <- as.data.frame(cbind(siteLat, siteLon, siteName))
# specify raster's approximate coordinates:
lats = c(-35, -20)
lons = c(10, 35)
SAMap <- GetMap.bbox(lons, lats, maptype = "satellite")
# extract "real" coords of raster:
lonr <- c(SAMap$BBOX$ll[2], SAMap$BBOX$ur[2])
latr <- c(SAMap$BBOX$ll[1], SAMap$BBOX$ur[1])
# extract raster fill data:
h_raster <- as.raster(SAMap$myTile)
# plot using annotation_raster:
g <- ggplot(sites, aes(siteLon, siteLat))
g + annotation_raster(h_raster, lonr[1], lonr[2], latr[1], latr[2]) +
geom_point(aes(x = siteLon, y = siteLat), colour = "red", data = sites) +
scale_x_continuous(limits = lonr) +
scale_y_continuous(limits = latr)
(Sorry, I cannot post an image as I am new here).
Okay, the problem has been resolved thanks to David Kahle. See this post:
https://groups.google.com/forum/?hl=en&fromgroups#!topic/ggplot2/ABffHL3WTpY
AJ