I'm trying to publish a map on Shiny, but keep running into this issue.
Let's call the data.frame region. Here are the columns:
library(mapedit)
library(mapview)
library(shiny)
library(leaflet)
library(leaflet.extras)
region$address
region$city
region$state
region$zip
region$county
region$xcol (these are the longitude coordinates)
region$ycol (these are the latitude coordinates)
But when I run the mapview(region)#map it produces the following error:
Error:
oops! Arguments xcol and/or ycol are missing!
You probably expected turf_clean to be a spatial object.
However it is of class data.frame.
Either convert turf_clean to a spatial object or provide xcol and ycol.
I provided the x and y cols, but it's still not producing what I need.
The library sf and its st_as_sf() is a way to convert your data.frame to a spatial object. crs refers to the datum and projection, which I just guessed assuming your lat/long are relative to the WGS84 datum. I believe the projection is what maps.google.com uses - web mercator auxiliary sphere.
library(sf)
turf_clean <- st_as_sf(region, coords = c("xcol", "ycol"), crs = 4326)
Related
I have hundreds of shapefiles without a coordinate reference system. My goal is the overlay the spatial polygons over the WorldClim raster layer. I used this approach before without any problems. However, this time the coordinates from my shapefiles are strange for me. Each coordinate for bounding box and coords within polygons is composed of 8 digit numbers without comma or dot to separate de decimals.
This is the bounding box from one of the shapes:
SHP bbox: xmin:-17367529, xmax:17367529, ymin:-5997367 and ymax:7052489
which are clearly different from the bounding box of the WorldClim raster layer.
WorldClim bbox: xmin=-180,xmax=180,ymin=-60 and ymax=90
When I tried to overlay the shapefile over the raster layer using plot command nothing happens.
plot(shapefile, add=T)
I understood that this is a projection problem. Then I tried to assign the same coordinate system of the WorldClim raster layer in the shapefile using the CRS function. However, the result remains the same (i.e. the shapefiles do not over the raster). In the sequence, I tried to use the spTransform function from the rgdal package to reproject the shapefile coordinates. However, because shapefile does not have any reference system the function does not work and I do not know how to reproject the shapefile in order to match with the raster layer. I've been researching for a few days about how to deal with this problem and I believe that the absence of a reference system is a key point to the problem. However, I'm failing to overcome this problem and I would like to know if someone could help how to deal with this situation.
You need to define the projection of shape files first using proj4string(meuse) or crs(shapefile)<-crs string then you can use spTransform:
library(rgdal)
data(meuse)
coordinates(meuse) <- c("x", "y")
Here you have the spatial data with x and y but you do not have the crs yet! So if you use spTransform it will fail.
summary(meuse) #proj4string : [NA] so below line fails!
meuse.utm <- spTransform(meuse, CRS("+proj=utm +zone=32 +datum=WGS84"))
# Error in spTransform(xSP, CRSobj, ...) :
# No transformation possible from NA reference system
To get around this, as mentioned above, you first need to define the projection as below:
proj4string(meuse) <- CRS(paste("+init=epsg:28992",
"+towgs84=565.237,50.0087,465.658,-0.406857,0.350733,-1.87035,4.0812"))
summary(meuse) #proj4string : epsg:28992... and then you may use spTransform
and then:
meuse.utm <- spTransform(meuse, CRS("+proj=utm +zone=32 +datum=WGS84"))
1. The problem
I'm trying to extract the intersection of two polygons shapes in R. The first is the watershed polygon "ws_polygon_2", and the second is the Voronoi polygons of 5 rain gauges which was constructed from the Excel sheet "DATA.xlsx", both available here: link.
The code is the following:
#[1] Montagem da tabela de coordenadas dos postos pluviométricos
library(sp)
library(readxl)
dados_precipitacao_1985 <- read_excel(path="C:/Users/.../DATA.xlsx")
coordinates(dados_precipitacao_1985) <- ~ x + y
proj4string(dados_precipitacao_1985) <- CRS("+proj=longlat +datum=WGS84")
d_prec <- spTransform(dados_precipitacao_1985, CRSobj = "+init=epsg:3857")
#[2] Coleta dos dados espaciais da bacia hidrográfica
library(rgdal)
bacia_Caio_Prado <- readOGR(dsn="C:/Users/...", layer="ws_polygon_2")
bacia_WGS <- spTransform(bacia_Caio_Prado, CRSobj = "+proj=longlat +datum=WGS84")
bacia_UTM <- spTransform(bacia_Caio_Prado, CRSobj = "+init=epsg:3857")
#[3] Poligonos de Thiessen - 1 INTERPOLAÇÃO
library(dismo)
library(rgeos)
library(raster)
library(mapview)
limits_voronoi_WGS <- c(-40.00,-38.90,-5.00,-4.50)
v_WGS <- voronoi(dados_precipitacao_1985, ext=limits_voronoi_WGS)
bc <- aggregate(bacia_WGS)
u_WGS_1 <- gIntersection(spgeom1 = v_WGS, spgeom2 = bc,byid=TRUE)
u_WGS_2 <- intersect(bc, v_WGS)
When I apply the intersect function, the variable returned u_WGS_2 is a spatial polygon data frame with only 4 features, instead of 5. The Voronoi object v_WGS has 5 features as well.
By other hand, when I apply the gIntesection function, I get 5 features. However, the u_WGS_1 object is a spatial polygon only and I loss the rainfall data.
I'd like to know if I am committing any mistake or if there is any way to get the 5 features aggregated with the rainfall data in a spatial polygon data frame through the intersect function.
My objective is to transform this spatial polygon data frame with the rainfall data for each Voronoi polygon in a raster through the rasterize function later to compare with other interpolating results and satellite data.
Look these results. The first one is when I get the SPDF (Spatial Polygon Data Frame) I want, but missing the 5º feature. The second is the one I get with all the features I want, but missing the rainfall data.
spplot(u_WGS_2, 'JAN')
plot(u_WGS_1)
2. What I've tried
I look into the ws_polygon_2 shape searching for any other unwanted polygon who would pollute the shape and guide to this results. The shape is composed by only one polygon feature, the correct watershed feature.
I tried to use the aggregate function, as above, and as I saw in this tutorial. But I got the same result.
I tried to create a SPDF with de u_WGS_1 and the d_precSpatial Point Data Frame object. Actually, I'm working on it. And if it is the correct answer to my trouble, please help me with some code.
Thank you!
This is not an issue when using st_intersection() from sf, which retains the data from both data sets. Mind that dismo::voronoi() is compatible with sp objects only, so the precipitation data needs to be available in that format, at least temporarily. If you do not feel comfortable with sf and prefer to continue working with Spatial* objects after the actual intersection, simply invoke the as() method upon the output sf object as shown below.
library(sf)
#[1] Montagem da tabela de coordenadas dos postos pluviométricos
dados_precipitacao_1985 <- readxl::read_excel(path="data/DATA.xlsx")
dados_precipitacao_1985 <- st_as_sf(dados_precipitacao_1985, coords = c("x", "y"), crs = 4326)
dados_precipitacao_1985_sp <- as(dados_precipitacao_1985, "Spatial")
#[2] Coleta dos dados espaciais da bacia hidrográfica
bacia_Caio_Prado <- st_read(dsn="data/SHAPE_CORRIGIDO", layer="ws_polygon_2")
#[3] Poligonos de Thiessen - 1 INTERPOLAÇÃO
limits_voronoi_WGS <- c(-40.00,-38.90,-5.00,-4.50)
v_WGS <- dismo::voronoi(dados_precipitacao_1985_sp, ext=limits_voronoi_WGS)
v_WGS_sf <- st_as_sf(v_WGS)
u_WGS_3 <- st_intersection(bacia_Caio_Prado, v_WGS_sf)
plot(u_WGS_3[, 6], key.pos = 1)
The missing polygon is removed because it is invalid
library(raster)
bacia <- shapefile("SHAPE_CORRIGIDO/ws_polygon_2.shp")
rgeos::gIsValid(bacia)
#[1] FALSE
#Warning message:
#In RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid") :
# Ring Self-intersection at or near point -39.070555560000003 -4.8419444399999998
The self-intersection is here:
zoom(bacia, ext=extent(-39.07828, -39.06074, -4.85128, -4.83396))
points(cbind( -39.070555560000003, -4.8419444399999998))
Invalid polygons are removed as they are assumed to have been produced by intersect. In this case, the invalid data was already there and should have been retained. I will see if I can fix that.
I am trying to get the intersection of two shapefiles (census tracts that fall within the boundaries of certain metropolitan areas). I am able to successfully get the intersecting features, however when I try to convert the output of sf_intersect to a SpatialPolygonsDataframe I get the error:
"Error in as_Spatial(from) : conversion from feature type
sfc_GEOMETRY to sp is not supported"
This is my code:
library(sf)
library(dplyr)
library(tigris)
library(sp)
#download shapefiles corresponding to metro areas
metro_shapefiles<-core_based_statistical_areas(cb = FALSE, year = 2016)
#convert to sf and filter
metro_shapefiles<-st_as_sf(metro_shapefiles)%>%filter(GEOID==31080 )
#Data for California
census_tracts_california<-tracts(state="CA",year=2016)
census_tracts_california<-st_as_sf(census_tracts_california)
#INTERSECT AND CONVERT BACK TO SP
census_tracts_intersected1<-st_intersection(census_tracts_california,
metro_shapefiles)
#back to spatial
census_tracts_intersected1<-as(census_tracts_intersected1,"Spatial")
The error message is telling you you can't convert an sfc_GEOMETRY to a Spatial object. There is no sp equivalent object.
In your intersection result you have a mixture of geometries (and hence, you're returned an sfc_GEOMETRY as your 'geometry'). You can see all the geometries here:
types <- vapply(sf::st_geometry(census_tracts_intersected1), function(x) {
class(x)[2]
}, "")
unique(types)
# [1] "POLYGON" "MULTILINESTRING" "MULTIPOLYGON"
If you want, you can extract each type of geometry, and convert those to SP individually:
lines <- census_tracts_intersected1[ grepl("*LINE", types), ]
polys <- census_tracts_intersected1[ grepl("*POLYGON", types), ]
spLines <- as(lines, "Spatial")
spPolys <- as(polys, "Spatial")
Additional Information
I mentioned in the comments you could use st_join. However, this may not give you the result you want. Within sf library there are the geometric binary predicates, such as ?st_intersects, and geometric operations such as ?st_intersection
The predicates return a sparse (default) or dense matrix telling you with which geometry of y each geometry of x intersects. If you use this within st_join, it will return the (original) geometries that intersect, rather than the sparse matrix.
Whereas the operations (e.g. st_intersection) will compute the intersection, and return new geometries.
Example use
The predicates (st_intersects) can be used inside st_join, and they will return the original geometries which 'intersect'
sf_join <- sf::st_join(census_tracts_california, metro_shapefiles, join = st_intersects)
In this case this gives a single type of object
types <- vapply(sf::st_geometry(sf_join), function(x) {
class(x)[2]
}, "")
unique(types)
# [1] "MULTIPOLYGON"
## so you can convert to a Spatial object
spPoly <- as(sf_join, "Spatial")
But you need to decide if the result of st_intersect is what you're after, or whether you need the new geometries given by st_intersection.
Further reading
information on each join is on the sf blog.
spatial predicates and examples of what the different operations do are on wikipedia (with some good illustrations)
Credit to user #lbussett for their description on the difference between st_intersect and st_intersection
Conversion to a Spatial object can't handle mixed lines and polygons. After the intersection you can extract just the polygons (and discard any lines) using:
st_collection_extract("POLYGON")
Your example doesn't seem to fail anymore, so I've created a new example that intersects two polygons, with a shared side. This results in an intersection output of a polygon and a line.
On the second attempt I've piped the intersection through the st_collection_extract() function prior to successful conversion to a Spatial object.
library(sf)
library(dplyr)
library(sp)
#Create some boxes
BoxA <- st_polygon(list(cbind(c(0,0,2,2,0),c(0,2,2,0,0))))
BoxB <- st_polygon(list(cbind(c(1,1,3,3,1),c(1,3,3,1,1))))
BoxC <- st_polygon(list(cbind(c(2,2,4,4,2),c(0,2,2,0,0))))
#Create a funny shaped union to help demonstrate the intersection issue
BoxAB <- st_union(BoxA,BoxB)
plot(BoxAB)
plot(BoxC,add=TRUE,border="blue")
Example polygons to intersect
#Intersect of BoxAB with BoxC results in a line and a polygon
BoxIntersects<-st_intersection(BoxAB,BoxC)
plot(BoxIntersects)
Intersection made up of a polygon and a line
#back to spatial fails
SpatialVersionOfIntersects<-as(BoxIntersects,"Spatial")
Error in .as_Spatial(from, cast, IDs) :
conversion from feature type sfc_GEOMETRY to sp is not supported
#Intersect again, but this time extract only the polygons
BoxIntersects<-st_intersection(BoxAB,BoxC) %>% st_collection_extract("POLYGON")
#back to spatial suceeds!
SpatialVersionOfIntersects<-as(BoxIntersects,"Spatial")
I'm trying to use R to perform a map of interpolated frequencies of data collected from Iberian Peninsula. (something like this https://gis.stackexchange.com/questions/147660/strange-spatial-interpolation-results-from-ordinary-kriging )
My problem is that the plot is not showing the interpolated data, due to some kind of error in the atributte new_data of the autokrige function.
https://cran.r-project.org/web/packages/automap/automap.pdf
new_data:
A sp object containing the prediction locations. new_data can be a points set, a
grid or a polygon. Must not contain NA’s. If this object is not provided a default is calculated. This is done by taking the convex hull of input_data and placing around 5000 gridcells in that convex hull.
I think the problem is that R is not reading well the map transformed to poligons because if I avoid this new_data attribute I get a propper plot of the krigging values. but I do not obtain a good shape of the iberian peninsula.
Can someone help me please? I would be truly grateful
here you can see my data: http://pastebin.com/QHjn4qjP
Actual code:
now since I transform my data coordinates to UTM projection i do not get error messages but the last plot is not interpolated, the whole map appear of one single color :(
setwd("C:/Users/Ruth/Dropbox/R/pruebas")
#Libraries
library(maps)
library(mapdata)
library(automap)
library(sp)
library(maptools)
library(raster)
library(rgdal)
####################MAPA#############
#obtain the grid of the desired country
map<-getData('GADM', country='ESP', level=0)
#convert the grid to projected data
mapa.utm<-spTransform(mapa3,CRSobj =CRS(" +proj=utm +zone=29 +zone=30 +zone=31 +ellps=WGS84"))
###############################Datos#######################
#submit the data
data1<-read.table("FRECUENCIASH.txt",header=T)
head(data1)
attach(data1)
#convert longlat coordinates to UTM
coordinates(data1)<-c("X","Y")
proj4string(data1) = CRS("+proj=longlat +datum=WGS84")
data1.utm=spTransform(data1, CRS("+proj=utm +zone=29 +zone=30 +zone=31 +ellps=WGS84 "))
######################Kriging interpolation #####################
#Performs an automatic interpolation
kriging_result<-autoKrige(Z~1,data1.utm,mapa.utm,data_variogram = data1.utm)
#Plot the kriging
result1<-automapPlot(kriging_result$krige_output,"var1.pred",sp.layout = list("sp.points", data1.utm));result1
result2<-plot(kriging_result);result2
The error you get is related to the fact you are using unprojected data as input for automap. Automap can only deal with projected data. Googling for map projections should give you some background information. To project your data to a suitable projection, you can use spTransform from the sp package.
The fact that it works without newdata is because in that object the projection is not set, so automap cannot warn you. However, the results of automap with latlon input data is not reliable.
I have a spatial polygon object and a spatial points object. The latter was created from xy latlong data vectors (called latitude and longitude, respectively) in a dataframe, while the former was simply read into R directly using rgdal. My code is as follows:
boros <- readOGR(dsn = ".", "nybb")
rats <- read.csv("nycrats_missing_latlong_removed_4.2.14.csv", header = TRUE)
coordinates(rats) <- ~longitude + latitude
At this point neither spatial object is projected. If I project these objects as follows:
proj4string(boros) <- CRS("+proj=lcc")
proj4string(rats) <- CRS("+proj=lcc")
Both objects are now projected, and both will successfully map with the plot() function as follows:
plot(boros)
plot(rats)
However when I try to plot them together:
plot(boros)
plot(rats, add = TRUE)
I get the first plot only, without the rats object superimposed over boros. However, and this is the big problem, I get NO error message, so I have been unable to determine where the disconnection is between these two spatial objects being able to speak to each other. Both commands run smoothly without error or warning, yet I am left with just the single plot. And when I check the projections of each object with proj4string() I get the same projection returned for each object.
I have now spent many, many hours over several days trying various ways of creating two spatial objects whose CRS and projections match such that they can be mapped together using plot(). Incidentally, one approach I took was to create a shapefile in ArcGIS for the rats object, which worked fine to create the file. But I am still left with the same inability of the two spatial objects to work together in R. I have tried many different projections for both objects, spTransform on both objects, nothing seems to work.
Any help would be most appreciated. I have also included a dropbox link with the 2 data files I have described above:
https://www.dropbox.com/sh/x0urdo6guprnw8y/tQdfzSZ384
So, as some of the comments point out, the problem is that your data and your maps have different projections.
It looks like your map comes from the NYC Department of City Planning. The shapefile is definitely not in WGS84 (longlat), but the CRS is not included in the file (which is very disappointing by the way...). Nevertheless, there is a metadata file which indicates that the shapefile is projected as EPSG 2263.
In order to make use of this in R we need a projection string. The idiomatic way to get this in R is:
library(rgdal)
EPSG <- make_EPSG()
NY <- with(EPSG,EPSG[grepl("New York",note) & code==2263,]$prj4)
NY
# [1] "+proj=lcc +lat_1=41.03333333333333 +lat_2=40.66666666666666 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000.0000000001 +y_0=0 +datum=NAD83 +units=us-ft +no_defs"
Now we can either take your map and reproject that into WGS84, or take your data and reproject that into the map CRS.
setwd("< directory with all your files >")
data <- read.csv("nycrats_missing_latlong_removed_4.2.14.csv")
# First approach: reproject map into long-lat
wgs.84 <- "+proj=longlat +datum=WGS84"
map <- readOGR(dsn=".",layer="nybb",p4s=NY)
map.wgs84 <- spTransform(map,CRS(wgs.84))
map.wgs84.df <- fortify(map.wgs84)
library(ggplot2)
ggplot(map.wgs84.df, aes(x=long,y=lat,group=group))+
geom_path()+
geom_point(data=data, aes(x=longitude,y=latitude, group=NULL),
colour="red", alpha=0.2, size=1)+
ggtitle("Projection: WGS84 longlat")+
coord_fixed()
# Second approach: reproject data
map.df <- fortify(map)
rats <- SpatialPoints(data[,c("longitude","latitude")],proj4string=CRS(wgs.84))
rats <- spTransform(rats,CRS(NY))
rats.df <- data.frame(coordinates(rats))
ggplot(map.df, aes(x=long,y=lat,group=group))+
geom_path()+
geom_point(data=rats.df, aes(x=longitude,y=latitude, group=NULL),
colour="red", alpha=0.2, size=1)+
ggtitle("Projection: NAD83.2263")+
coord_fixed()
No rats in Central Park?