I'm trying to overlay some spatial data from a bigger SpatialPolygonsDataFrame (world size) to a smaller (country size), by doing these:
x <- c("rgdal", "dplyr",'ggplot2')
apply(x, library, character.only = TRUE)
est<-readOGR(dsn='/estados_2010',layer='estados_2010')
est_f<-fortify(est)
est$id<-row.names(est)
est_f<-left_join(est_f,est#data)
zon<-readOGR(dsn='/Zonas Homogeneas/gyga_ed_poly.shp',layer='gyga_ed_poly')
zon_f<-fortify(zon)
zon$id<-row.names(zon)
zon_f<-left_join(zon_f,zon#data)
t<-ggplot()+geom_polygon(data=zon_f,aes(x=long,y=lat,group=group,fill=GRID_CODE))
t+geom_polygon(data=est_f,aes(x=long,y=lat,group=group),fill=NA,color='red')+coord_fixed(xlim=est_f$long,ylim=est_f$lat,1)
Which is resulting in this:
I'm want to select only what is being plotted inside the polygon with the red lines.
If someone could help me with this issue, I'll appreciate
PS.: For those who want to reproduce the example completely by yourselves, the files are available in the links above to my google drive:
https://drive.google.com/open?id=0B6XKeXRlyyTDakx2cmJORlZqNUE
Thanks in advance.
Since you are using polygons to display the raster values, you can use a spatial selection via [ like in this reproducible example:
library(raster)
library(rgdal)
bra <- getData("GADM", country = "BRA", level = 1)
r <- getData("worldclim", res = 10, var = "bio")
r <- r[[1]]
r <- crop(r, bra)
r <- rasterToPolygons(r)
# bra and raster (now as polygons) have to have the same projection, thusly reproject!
bra <- spTransform(bra, CRSobj = proj4string(r))
here comes the magic!!
r <- r[bra, ]
let's look at the results:
library(ggplot2)
t <- ggplot()+
geom_polygon(data=r,aes(x=long,y=lat,group=group, fill = rep(r$bio1, each = 5)))
t +
geom_polygon(data=bra,aes(x=long,y=lat,group=group),fill=NA,color='red') + coord_map()
Related
I'm doing a research about climate change and I want to extract the CMIP6 information using R but I don't get the results I want. This is the code.
library(raster)
library(ncdf4)
long_lat <- read.csv("C:/Users/USUARIO/Desktop/Tesis/Coordenadas - copia.csv", header = T)
raster_pp <- raster::brick("pr_day_ACCESS-CM2_historical_r1i1p1f1_gn_2000.nc")
sp::coordinates(long_lat) <- ~XX+YY
raster::projection(long_lat) <- raster::projection(raster_pp)
points_long_lat <- raster::extract(raster_pp[[1]], long_lat, cellnumbers = T)[,1]
data_long_lat <- t(raster_pp[points_long_lat])
The cordinates I've used are the following: -77.7754,-9.5352 (latitude, longitude), but I've tried these as well and still doesn't work 102.2246,-9.5352. Thanks for your response.
I have run a factor analysis on a spatial dataset, and I would like to plot the results on a map so that the color of each individual point (location) is a combination in a RGB/HSV space of the scores at that location of the three factors extracted.
I am using base R to plot the locations, which are in a SpatialPointsDataFrame created with the spdep package:
Libraries
library(sp)
library(classInt)
Sample Dataset
fas <- structure(list(MR1 = c(-0.604222013102789, -0.589631093835467,
-0.612647301042234, 2.23360319770647, -0.866779007222414), MR2 = c(-0.492209397489792,
-0.216810726717787, -0.294487678489753, -0.60466348557844, 0.34752411748663
), MR3 = c(-0.510065798219453, -0.61303212834454, 0.194263734935779,
0.347461766159926, -0.756375966467285), x = c(1457543.717, 1491550.224,
1423185.998, 1508232.145, 1521316.942), y = c(4947666.766, 5001394.895,
4948766.5, 4950547.862, 5003955.997)), row.names = c("Acqui Terme",
"Alagna", "Alba", "Albera Ligure", "Albuzzano"), class = "data.frame")
Create spatial object
fas <- SpatialPointsDataFrame(fas[,4:5], fas,
proj4string = CRS("+init=EPSG:3003"))
Plotting function
map <- function(f) {
pal <- colorRampPalette(c("steelblue","white","tomato2"), bias = 1)
collist <- pal(10)
class <- classIntervals(f, 8, style = "jenks")
color <- findColours(class, collist)
plot(fas, pch=21,cex=.8, col="black",bg=color)
}
#example usage
#map(fas$MR1)
The above code works well for producing a separate plot for each factor. What I would like is a way to produce a composite map of the three factors together.
Many thanks in advance for any suggestion.
I found a solution through this post! With the data shown above, it goes like this:
#choose columns to map to color
colors <-fas#data[,c(1:3)]
#set range from 0 to 1
range_col <- function(x){(x-min(x))/(max(x)-min(x))}
colors_norm <- range_col(colors)
print(colors_norm)
#convert to RGB
colors_rgb <- rgb(colors_norm)
print(colors_rgb)
#plot
plot(fas, main="Color Scatterplot", bg=colors_hex,
col="black",pch=21)
I've produced a raster using idw to make predictions across California, and now I'm trying to overlay the raster image with the border/shp file of california.
I've ensured the crs projections are the same for both plots, however I still can't get both to show up.
The following is the code for how I produced the raster:
plot(zip_table)
x.range <- as.double(range(zip_table#coords[,1]))
y.range <- as.double(range(zip_table#coords[,2]))
grd <- expand.grid(x=seq(from=x.range[1], to=x.range[2], by=0.01),
y=seq(from=y.range[1], to=y.range[2], by=0.01))
## convert grid to SpatialPixel class
coordinates(grd) <- ~ x + y
gridded(grd) <- TRUE
idw1 <- gstat::idw(TELERATIO ~ 1, zip_table, grd, idp = 2)
idw.output <- as.data.frame(idw1)
names(idw.output)[1:3] <- c("lng","lat","var1.pred")
r <- raster(idw1)```
And the CA state file I downloaded using a work around of the raster::getData function:
``` CA <- gadm36_USA_1_sp[gadm36_USA_1_sp$NAME_1 =='California',]
When I produce my plots, only the raster shows up. Any help is appreciated:
plot(r)
plot(CA, add = TRUE)
I am a total newbie to R and I would like to draw a line (possibly weighted, e.g., by the number of trips made) between two countries. Currently, I use longitude and latitude for each capital to draw a line, but I would like to do it using the package ggmap. I was looking around, but did not find any solution so far. I would appreciate a quick help.
require(ggmap)
require (rworldmap)
all_content = readLines("ext_lt_intratrd_1_Data.csv")
skip_second = all_content[-2]
dat = read.csv(textConnection(skip_second), header = TRUE, stringsAsFactors =F)
dat[5,2]<- c("Germany") # using a data where the first line is
header, but second line must be skipped as it is EU 27
and not a single country
europe <- read.csv("eulonglat.csv", header = TRUE) # using world capitals to
generate points
myfulldata <- merge(dat, europe)
map <- get_map(location = 'Europe', zoom = 4)
mapPoints <- ggmap(map) + geom_point(aes(x = UNc_longitude, y = UNc_latitude, size
= log(myfulldata$Value)), data = myfulldata, col = "red", alpha= 0.5) # this can
be plotted
# i would continue with drawing line and i searched for references
# i found arrows(42.66,23.34,50.82,4.47) - which did not work
# i tried to look for a reference work more, but could not find
# instead i found it using with the package rworldmap the following
lines(c(4.47, 23.32), c(50.82, 42.66))
# this does not work on ggmap
I stumbled upon fastshp library and according to description (and my quick cursory tests) it really does offer improvements in time of reading large shapefiles compared to three other methods.
I'm using read.shp function to load exemplary dataset from maptools package:
library("maptools")
setwd(system.file("shapes", package="maptools"))
shp <- read.shp("columbus.shp", format="polygon")
I chose 'polygon' format since accordng to docs:
This is typically the preferred format for plotting.
My question is how can I plot these polygons using ggplot2 package?
Since read.shp in the fastshp package returns the polygon data in the form of a list of lists, it is then a matter of reducing it to a single dataframe required for plotting in ggplot2.
library(fastshp)
library(ggplot2)
setwd(system.file("shapes", package="maptools"))
shp <- read.shp("columbus.shp", format="polygon")
shp.list <- sapply(shp, FUN = function(x) do.call(cbind, x[c("id","x","y")]))
shp.df <- as.data.frame(do.call(rbind, shp.list))
shp.gg <- ggplot(shp.df, aes(x = x, y=y, group = id))+geom_polygon()
EDIT: Based on #otsaw's comment regarding polygon holes, the following solution requires a couple of more steps but ensures that the holes are plotted last. It takes advantage that shp.df$hole is logical and polygons with hole==TRUE will be plotted last.
shp.list <- sapply(shp, FUN = function(x) Polygon(cbind(lon = x$x, lat = x$y)))
shp.poly <- Polygons(shp.list, "area")
shp.df <- fortify(shp.poly, region = "area")
shp.gg <- ggplot(shp.df, aes(x = long, y=lat, group = piece, order = hole))+geom_polygon()