I know that I can use mapview::mapshot to export a leaflet map from Rstudio as a PNG. I have not been, however, able to precisely specify the zoom level and region of the map to be exported.
Borrowing some code from this answer to a related question let us consider the following MWE.
library(maps)
library(ggmap)
library(maptools)
library(leaflet)
library(mapview)
library(rgdal)
country <- 'italy';
zoomLevel <- 12;
ita.map <- map( country, fill = TRUE, col = 1, plot = F );
ita.map.ids <- sapply( strsplit( ita.map$names, ':' ), function(x) x[1] );
ita.sp <- map2SpatialPolygons( ita.map, IDs=ita.map.ids, proj4string=CRS("+proj=longlat +datum=WGS84"))
bb<-as.numeric(ita.sp#bbox)
m<-leaflet() %>%
setView(12.48,41.89,zoom=zoomLevel) %>%
addTiles() %>%
addPolygons(data=ita.sp)%>%
addExtent(data=ita.sp)
mapshot(m, file = "italy.png")
results in This is the correct zoom level but clearly does not contain all of Italy.
Adding
%>% fitBounds(bb[1],bb[2],bb[3],bb[4])
to the code above yields which is better, but a) does not respect the specified zoom level, and b) has a lot of unwanted horizontal padding.
I presume that adding width and height arguments to the leaflet() call would help but I am unsure how to automatically obtain the correct values. Also the resulting image would be very large necessitating a reduction in resolution.
How can I export the region of the map containing Italy at a specified zoom level without additional padding?
I dont know that what you want is possible (zoom = 12 and all of italy)...the two things seem mutually exclusive to me... unless you have a enormous figure... as you suggested.
I dont know for certain, but maybe the answer to your padding question is in the vwidth option of webshot. I suppose your figure needs to be taller than wide, so vwidth < vheight...
mapshot(m, file = "italy.png", vwidth = 700, vheight = 744)
Related
Is there a way to make popup graphs appear on hover (rather than click) in Mapview? Alternatively, is it possible for the graphs to appear open by default? Rather than produce my own reproducible example, I would defer to the example given with the R Mapview documentation.
I am fairly new to R and Mapview so any guidance is greatly appreciated!
I've just pushed an update to package leafpop which provides the popup functionality used in mapview. This should provide what you want (at least partly - as mapview() will still need to be updated). This allows you to now specify tooltip = TRUE in addPopupImages (in addPopupGraphs via ...). Note that it is encouraged to use addPopup* functions over the classic popup* functions because they also work in non-interactive setting, e.g. when saving a map locally.
library(sf)
library(leaflet)
library(lattice)
library(leafpop)
pt = data.frame(x = 174.764474, y = -36.877245)
pt = st_as_sf(pt, coords = c("x", "y"), crs = 4326)
p2 = levelplot(t(volcano), col.regions = terrain.colors(100))
leaflet() %>%
addTiles() %>%
addCircleMarkers(data = pt, group = "pt") %>%
addPopupGraphs(
list(p2)
, group = "pt"
, width = 300
, height = 400
, tooltip = TRUE
)
Not sure when and how to integrate this into mapview() as this is a bit more complicated than the classic popup* functions (because we need to know something about the map object we create with mapview before we create it...). In any case, I hope this is at least partly useful and helps resolve your issue.
I am creating a map that consists out of 3 map panels. I have set the inner margins to zero in the tm_layout options for all maps, however depending on the dimensions of the output I still have empty white space inside the map frames.
I can reduce it to a minimum if I adjust the setting for width and height with tm_save but I want a clean solution.
I tried to creat a minimum example with the pre-installed tmap package data (though it is here less obvious that there is empty white space):
rm(list=ls(all=TRUE))
library(tmap)
data("NLD_prov")
map1 <- tm_shape(NLD_prov)+tm_fill()+tm_layout(inner.margins = c(0,0,0,0))
map2 <- tm_shape(NLD_prov)+tm_fill()+tm_layout(inner.margins = c(0,0,0,0))
final_map <-tmap_arrange(map1, map2)
final_map
In the original code I have cropped the input shape file to my desired map extent and also used a bounding box but that does not help:
map_extent<-st_bbox(c(xmin = 8, xmax = 9.1,ymin = 53.8, ymax = 55.1),
crs = st_crs(data))
data_cropped<-crop_shape(data, map_extent, polygon = TRUE)
tm_shape(data_cropped, bbox = map_extent)+
...
How can I force the margins to zero?
I want to make a Shiny app where the colouring of a choropleth is based on a numeric value of one of many possible quantitative variables that a user can select from. In simple cases, this is straightforward, but I'm unsure of the best practices when we have 20+ variables, with quite detailed shape files (~2300 polygons).
It might or might not be relevant that the variables might be completely independent to each other such as 'Total Population' or 'Average Temperature' but some of them will have a temporal relationship such as 'Total Population' at 3 or more points in time.
One of the main shapefiles I am using is the ABS Statistical Area 2. Below I give the population density (total population/area) for Australia and a zoomed in view of Sydney to better convey the level of detail I'm interested in.
Australia
Sydney
I have read the shapefile in to R and greatly reduced the complexity/number of points using the ms_simplify() function in the rmapshaper package.
Now as far as Shiny and leaflet go, this is what I have been doing:
Before the server object is defined in server.R, I build a primary map object with all the desired 'layers'. That is, a leaflet with numerous addPolygon() calls to define the colouring of each 'layer' (group).
# Create main map
primary_map <- leaflet() %>%
addProviderTiles(
providers$OpenStreetMap.BlackAndWhite,
options = providerTileOptions(opacity = 0.60)
) %>%
# Layer 0 (blank)
addPolygons(
data = aus_sa2_areas,
group = "blank"
) %>%
# Layer 1
addPolygons(
data = aus_sa2_areas,
fillColor = ~palette_layer_1(aus_sa2_areas$var_1),
smoothFactor = 0.5,
group = "layer_1"
) %>%
...
# Layer N
addPolygons(
data = aus_sa2_areas,
fillColor = ~palette_layer_n(aus_sa2_areas$var_n),
smoothFactor = 0.5,
group = "layer_n"
) %>% ...
All bar the first layer is then hidden using hideGroup() so that the initial rendering of the map doesn't look silly.
hideGroup("layer_1") %>%
hideGroup("layer_2") %>%
...
hideGroup("layer_n")
In the Shiny app, using radio buttons (layer_selection), the user can select the 'layer' they'd like to see. I use observeEvent(input$layer_selection, {}) to watch the status of the radio button options.
To update the plot, I use leafletProxy() and hideGroup() to hide all the groups and then showGroup() to unhide the selected layer.
I apologize for the lack of reproducible example.
Questions
How can I optimise my code? I am eager to make it more performant and/or easy to work with. I've found using hideGroup()'s/showGroup() for each layer selection is far faster than using addPolygon() to a blank map, but this causes the app to take a very significant amount of time to load.
Can I change the variable I am colouring the polygons by, without redrawing or adding those polygons again? To clarify, if I have 2 different variables to plot, both using the same shape data, do I have to do 2 distinct addPolygon() calls?
Is there a more automatic way to sensibly colour the polygons for each layer according to a desired palette (from the viridis package?). Right now I'm finding defining a new palette for each variable, rather cumbersome, eg:
palette_layer_n <- colorNumeric(
palette = "viridis",
domain = aus_sa2_areas$aus_sa2_areas$var_n
)
Side Question
How does this map on the ABS website work? It can be incredibly detailed and yet extremely responsive. Compare the Mesh Block detail to the SA2 (2310 polygons), example below:
Since you haven't gotten any answers yet, I'll post a few things that can maybe help you, based on a simple example.
It would of course be easier if yours was reproducible; and I suppose from looking around you have already seen that there are several related issues / requests (about re-coloring polygons), whereas it doesn't seem that a real solution has made it into any release (of leaflet) yet.
With the below work-around you should be able to avoid multiple addPolygons and can cover an arbitrary number of variables (for now I have just hard-coded a single variable into the modFillCol call though).
library(leaflet)
library(maps)
library(viridis)
mapStates = map("state", fill = TRUE, plot = FALSE)
# regarding Question 3 - the way you set the domain it looks equivalent
# to just not setting it up front, i.e. domain = NULL
myPalette <- colorNumeric(
palette = "viridis",
domain = NULL
)
mp <- leaflet(data = mapStates) %>%
addTiles() %>%
addPolygons(fillColor = topo.colors(10, alpha = NULL), stroke = FALSE)
# utility function to change fill color
modFillCol <- function(x, var_x) {
cls <- lapply(x$x$calls, function(cl) {
if (cl$method == "addPolygons") {
cl$args[[4]]$fillColor <- myPalette(var_x)
}
cl
})
x$x$calls <- cls
x
}
# modify fill color depending on the variable, in this simple example
# I just use the number of characters of the state-names
mp %>%
modFillCol(nchar(mapStates$names))
I made some interactive heatmaps using leaflet (particularly the addHeatmap() command from the leaflet.extras package) and shiny. Having created a desired map, I would like to add a legend to it.
What I am interested in is a "rgb" legend, based on density values deduced by addHeatmap() from pure long/lat coords.
What I need is something like this map - https://www.patrick-wied.at/static/heatmapjs/example-legend-tooltip.html - unfortunately I have no knowledge of JS and can't rewrite this code in terms of R/include the right fragment of JS code for my problem.
What I tried so far is the addLegend() command, which does not give the desired result, as in this case I would need to specify a variable for which the legend would be prepared. I also tried to extract the color range and assigned values from created leaflet object, however with no success.
Here's link to full data to run the reproducible example on:
https://drive.google.com/file/d/1h3jL_PU6DGTtdIWBK02Tt37R7IB2ArH9/view
And here's top 20 records:
structure(list(latitude = c(30.309522, 30.24429616, 30.30038194,
30.27752338, 30.23294081, 30.23038507,
30.34285933, 30.24962237, 30.26594744,
30.20515821, 30.22363485, 30.2759184,
30.28283226, 30.33816909, 30.26611565,
30.18835401, 30.26704789, 30.27456699,
30.19237135, 30.1925213),
longitude = c(-97.73171047, -97.77446858, -97.77885789,
-97.71919076, -97.58937812, -97.76581095,
-97.73598704, -97.72215443, -97.74144275,
-97.8782895, -97.78329845, -97.71321066,
-97.70820152, -97.82413058, -97.7327258,
-97.81606795, -97.68989589, -97.7580592,
-97.7816127, -97.73138523)),
.Names = c("latitude", "longitude"), row.names =
c(NA, 20L), class = "data.frame")
Here's an example code, which I'd like to extend by the mentioned functionality:
library(magrittr)
library(leaflet)
library(leaflet.extras)
data <- read.csv('DATA.csv')
leaflet(data) %>%
addTiles(group="OSM") %>%
addHeatmap(group="heat", lng = ~longitude, lat = ~latitude, max=.5, blur = 60)
And here is the result of that code (on whole dataset):
https://i.stack.imgur.com/6VFNC.jpg
So to sum up what I would like to do: based on such picture I would like to extract the range of the drawn colors along with values assigned to them, and draw legend using that information.
Is there something I am missing? It looks like a pretty simple issue, but I've been struggling to find a solution for past few hours.
Thanks in advance for any help!
EDIT: extended the reproducible example.
Your sample data does not have any values to it to actually map a density overlay.
You can specify the number of bins with colorBin() and then specify those bins with your pal function. You can set the bins differently depending on your needs at the data_values distributions. The help section of colorBin() is helpful in identifying the correct parameters for your needs.
bins <- c(0,1,2,3,4)
pal <- colorBin("Spectral", domain = data_value, bins = bins, na.color = "transparent")
m <-leaflet() %>%
addTiles() %>%
addHeatmap(lng= long_cords, lat = lat_cords, intensity = data_value,
blur = 20, max = 400, radius = 15, cellSize = 3) %>%
addLegend(pal = pal, values = data_value,
title="Heat map legend")
You'll have to play around with the addHeatmap arguments to get an the right transparency and density settings.
I'm trying to follow the example in the link below to create a map with all the markers
Tutorial: How to put dots on a Leaflet map with R
The source file is below
https://www.dropbox.com/s/az1yolknqwoxhb4/test_file.csv?dl=0
And the code that I tried
library(dplyr)
library(leaflet)
test_map <- read.csv("test_file.csv", header = TRUE, stringsAsFactors = FALSE)
m <- leaflet(test_map) %>% addTiles('http://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}.png',
attribution='Map tiles by Stamen Design, CC BY 3.0 — Map data © OpenStreetMap')
m %>% setView()
m %>% addCircles(~long, ~lat,
popup=test_map$index,
weight = 3,
color="#ffa500", stroke = TRUE, fillOpacity = 0.8)
I was able to create the map
However, the map only shows a fraction of points, since the data I have has locations all over Canada. When I tried to sub-select a city say like Toronto then some of the missing points shows up.
I'm not sure if i'm doing anything wrong or if this is a bug.I wonder if there's anyway for me to fix this problem or is there an alternative way to achieve a similar map?
Thank you :)
There are NA values in test_map variable.
add
test_map <- na.omit(test_map)
after reading csv.
By this method i have more markers than your image.