I am trying to create an interactive map of Germany with plotly and ggplot. At last, I'd like to overlay geom_points with tooltips, but so far I am struggeling with the aspect ratio.
I create the plot like this:
library(tidyverse)
library(ggplot2)
library(plotly)
p <- map_data("world") %>%
filter(region=="Germany") %>%
ggplot(aes(x=long, y = lat, group = group)) +
geom_polygon(fill="grey") +
coord_map()
When simply plotting it with ggplot, the aspect ratio of Germany is how I know it from school.
p
When converting it to a plotly plot with ggplotly the plot is distorted.
ggplotly(p)
Any ideas how to fix it? I don't know whether this is relevant, but I used to run this code in an R Markdown document.
Thanks a lot.
I'll guess you can't get a decent level of detail from the world map you used. With maps there are basically two types: generic, and, GIS (real mapping data).
Below is a non-distorted map of Germany. Here are the steps and data sources.
Download the GIS data - map of Germany. Source, https://gadm.org/download_country_v3.html
Choose the level of detail. 0 - the outline of the country, 1 - the country w/states. These files will have, sp.rds , extensions.
Cut and Paste the downloaded geographical reference data file into your working directory - this is easier than creating a path from the working directory to some other folder location. Since this data is actual geospatial data used in creating this plot of Germany, this data/plot can be used as a base map or overlay map with google maps, Overstreet maps, etc..
Use the following libraries and two lines of code to create the plot of Germany.
library(maptools)
library(raster)
library(rgdal)
gadm <- readRDS("gadm36_DEU_0_sp.rds") # get the gis file from w/d.
plot(gadm)
I'm rather new to creating maps and stuff in R, so please bear with me.
I'm trying to plot a building outline in R using data from OpenStreetMap, instead of having to define a polygon myself for plotting the outline of the building. But I cannot get my head around it.
The example below is what I have done so far to get the location:
library(leaflet)
leaflet() %>%
setView(lng=10.153523, lat=57.207181, zoom = 16) %>%
addTiles() %>%
addMarkers(lng=10.153523, lat=57.207181, popup="My building example")
And the example building data is available on https://www.openstreetmap.org/way/481541280
How do I integrate this building outline data into my R script? Is there a neat way of doing this other than extracting the points one by one and making a polygon using leaflet in R?
I'm plotting ACS data (counts of low-income children) for each county across the entire U.S. I don't have any markers, just a county shapefile and 6 values for each county that are in separate layers.
Unsurprisingly this is too large for browsers to handle. I've seen some similar questions here that deal with clustering, but since I have nothing to cluster I'm starting a new question.
I've tried breaking it up into 6 regional maps, but even those are too much.
Is there some way to lower the footprint, but still have all the county specific data?
I've looked at tilemill and other options for only loading the data the user is currently viewing and based on the zoom level, but I can't find any information on how to go from an R generated leaflet map to something like that. Alternatives using this route are useful as well.
You can try to simplify the polygons using the rmapshaper package and then try visualising it with mapview. The former will help get rid of 'unnecessary' polygon vertices, while the latter has dedicated functions to enable leaflet rendering of large data (ballpark around 100k features - depending on complexity).
You might also consider the tigris package, which gets you direct access to low-resolution county shapefiles within R:
library(tigris)
library(leaflet)
cty <- counties(cb = TRUE, resolution = "20m")
leaflet(cty) %>% addTiles() %>% addPolygons()
This should allow your Leaflet map to perform well in-browser as the polygons are pre-simplified, if you choose to go this route.
I have a very small SpatialLinesDataFrame which I need to plot with Leaflet in R.
Unfortunately, for some reason I'm not able to do so.
This is the data (it's 12KB).
I try to do:
library(leaflet)
load("mylines.Rdata")
leaflet() %>% addTiles() %>% addPolylines(data=mylines)
However the resulting map does not make sense, I can only see a line in the top of the screen and this is not what should be plotted.
This is the result:
Instead, If I do:
library(mapview)
mapview(mylines)
Result:
It works perfectly, despite mapview using leaflet underneath.
What am I doing wrong in the Leaflet syntax?
I am used to working with leaflet providing rasters, so I usually use the addRasterImage function, which needs data projected over leaflet's display projection (EPSG:3857). However, for polygons and lines, as #TimSalabim correctly pointed out, this is not the case.
So the solution in this case was to just not reproject the data to the leaflet projection beforehand, and provide it in lat-lon coordinates (EPSG:4326).
mapview worked since it does this automagically.
There are clearly a number of packages in R for all sorts of spatial analysis. That can by seen in the CRAN Task View: Analysis of Spatial Data. These packages are numerous and diverse, but all I want to do is some simple thematic maps. I have data with county and state FIPS codes and I have ESRI shape files of county and state boundaries and the accompanying FIPS codes which allows joining with the data. The shape files could be easily converted to other formats, if needed.
So what's the most straight forward way to create thematic maps with R?
This map looks like it was created with an ESRI Arc product, but this is the type of thing I would like to do with R:
alt text http://www.infousagov.com/images/choro.jpg Map copied from here.
The following code has served me well. Customize it a little and you are done.
(source: eduardoleoni.com)
library(maptools)
substitute your shapefiles here
state.map <- readShapeSpatial("BRASIL.shp")
counties.map <- readShapeSpatial("55mu2500gsd.shp")
## this is the variable we will be plotting
counties.map#data$noise <- rnorm(nrow(counties.map#data))
heatmap function
plot.heat <- function(counties.map,state.map,z,title=NULL,breaks=NULL,reverse=FALSE,cex.legend=1,bw=.2,col.vec=NULL,plot.legend=TRUE) {
##Break down the value variable
if (is.null(breaks)) {
breaks=
seq(
floor(min(counties.map#data[,z],na.rm=TRUE)*10)/10
,
ceiling(max(counties.map#data[,z],na.rm=TRUE)*10)/10
,.1)
}
counties.map#data$zCat <- cut(counties.map#data[,z],breaks,include.lowest=TRUE)
cutpoints <- levels(counties.map#data$zCat)
if (is.null(col.vec)) col.vec <- heat.colors(length(levels(counties.map#data$zCat)))
if (reverse) {
cutpointsColors <- rev(col.vec)
} else {
cutpointsColors <- col.vec
}
levels(counties.map#data$zCat) <- cutpointsColors
plot(counties.map,border=gray(.8), lwd=bw,axes = FALSE, las = 1,col=as.character(counties.map#data$zCat))
if (!is.null(state.map)) {
plot(state.map,add=TRUE,lwd=1)
}
##with(counties.map.c,text(x,y,name,cex=0.75))
if (plot.legend) legend("bottomleft", cutpoints, fill = cutpointsColors,bty="n",title=title,cex=cex.legend)
##title("Cartogram")
}
plot it
plot.heat(counties.map,state.map,z="noise",breaks=c(-Inf,-2,-1,0,1,2,Inf))
Thought I would add some new information here since there has been some activity around this topic since the posting. Here are two great links to "Choropleth Map R Challenge" on the Revolutions blog:
Choropleth Map R Challenge
Choropleth Challenge Results
Hopefully these are useful for people viewing this question.
All the best,
Jay
Check out the packages
library(sp)
library(rgdal)
which are nice for geodata, and
library(RColorBrewer)
is useful for colouring. This map is made with the above packages and this code:
VegMap <- readOGR(".", "VegMapFile")
Veg9<-brewer.pal(9,'Set2')
spplot(VegMap, "Veg", col.regions=Veg9,
+at=c(0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5),
+main='Vegetation map')
"VegMapFile" is a shapefile and "Veg" is the variable displayed. Can probably be done better with a little work. I don`t seem to be allowed to upload image, here is an link to the image:
Take a look at the PBSmapping package (see borh the vignette/manual and the demo) and
this O'Reilly Data Mashups in R article (unfortunately it is not free of charge but it worth 4.99$ to download, according Revolutions blog ).
It is just three lines!
library(maps);
colors = floor(runif(63)*657);
map("state", col = colors, fill = T, resolution = 0)
Done!!
Just change the second line to any vector of 63 elements (each element between 0 and 657, which are members of colors())
Now if you want to get fancy you can write:
library(maps);
library(mapproj);
colors = floor(runif(63)*657);
map("state", col = colors, fill = T, projection = "polyconic", resolution = 0);
The 63 elements represent the 63 regions whose names you can get by running:
map("state")$names;
The R Graphics Gallery has a very similar map which should make for a good starting point. The code is here: www.ai.rug.nl/~hedderik/R/US2004 . You'd need to add a legend with the legend() function.
If you stumble upon this question in the 2020ies, use the magnificent tmap package. It's very simple and straightforward and revolutionized making maps in R. Do not bother to investigate this complicated code.
Check the vignette here.