I am creating a map using the zip code information from the census data together with data that I have for each zip code.
I obtained the shapefiles for the zip codes from https://www.census.gov/geo/maps-data/data/tiger-cart-boundary.html.
I successfully read in the shapefiles using readOGR with the following code:
Zip = readOGR("C:\\...pathname....\\cb_2013_us_zcta510_500k.shp")
I am running into a problem when I try to use fortify so that I can combine the information from the shapefile with my data.
names(Zip)
[1] "ZCTA5CE10" "AFFGEOID10" "GEOID10" "ALAND10" "AWATER10"
test = ggplot2::fortify(Zip, region="AWATER10")
Error in fortify.SpatialPolygonsDataFrame(Zip, region = "AWATER10") :
`region' not supported
The error is replicated when I try all of the results from names(Zip). I am not sure which column contains the zip code information that I am interested in, so I just tried all of them.
My ultimate goal is to use the zip code information with ggmap to fill in the zip codes with different colors based on information in my data.
Related
Is it possible to plot/map xml data from arc GIS in R? I have an XML file with a basemap I would like to map in R.
I dont know how to add my XML file to my question but if someone could show me how to plot using this type of file that would be awesome. I usually make my maps in r using ggplot and geom_sf.
#How I loaded my XML file into r
# Load the package required to read XML files.
library("XML")
# Also load the other required package.
library("methods")
# Give the input file name to the function.
result <- xmlParse(file = "Grey background.xml")
# Print the result.
print(result)
Thanks for reading my post. I am trying to create one map in Plotly using R by layering data from 2 data sources and the function plot_mapbox. The map will show locations of stores in zoned business districts.
test is a geoJSON file of zoning districts
test2 is a csv file of business locations using longitude and latitude coordinates
I've tried layering the data and combining two geoJSON files. The first file is a geoJSON file (business zones)and the second file is a .csv (store locations) with longitude and latitude. I converted the csv file to a geoJSON file and then tried to merge them. I would really need to append them since they don't have a common key.
library(plotly)
library(geojsonR)
library(sf)
test<-st_read("D:/SPB/Zoning_Detailed.geojson", quiet=FALSE, , geometry_column="SHAPE_Area")
test2<-read.csv("D:/SPB/Pet_Bus.csv")
One layering exampe
plot_mapbox(data=test, color=~ZONING) %>%
add_markers(data=test2, x=~Longitude, y=~Latitude)
layout(mapbox=list(style = "streets"))
One merge example (only the first file is added in merge)
files Zoning_Detailed.geojson and Pet_Bus.geojson are in the Merge folder. I
converted Pet_Bus.csv to a geojson file.
This should really be append since test and test2 are independent of each other, but in same city.
merge_files("D:/SPB/Merge/", "D:/SPB/Merge/test7.geojson")
library(raster)
france<-getData('GADM', country='FRA', level=1)
However, the command is leading me to this error.
trying URL 'http://biogeo.ucdavis.edu/data/gadm2.8/rds/FRA_adm1.rds'
Error in utils::download.file(url = aurl, destfile = fn, method = "auto", :
cannot open URL 'http://biogeo.ucdavis.edu/data/gadm2.8/rds/FRA_adm1.rds'
First, download the country data you want from the GADM database, and save it to your local directory. Be sure that you have chosen the R (SpatialPolygonsDataFrame) format. There are five levels available for France (from level 0 to level 5). You can choose what you need.
Second, read the .rds file downloaded from GADM with readRDS() function and transform it into a data.frame with ggplot2::fortify().
library(ggplot2)
library(sp)
# assumed that you downloaded into a such path: '~/Downloads/FRA_adm1.rds':
path <- file.path(Sys.getenv("HOME"), "Downloads", "FRA_adm1.rds")
# FR map (Level 1) from GADM version 2.8
frRDS <- readRDS(path)
# Region names 1 in data frame
frRDS_df <- ggplot2::fortify(frRDS, region = "NAME_1")
head(frRDS_df)
I am going to improve upon the previous answer to the OP's question.
To answer the OP's question directly and correctly, there is nothing wrong with the OP's code. The issue was likely a temporary internet connection issue because the OP's code works and retrieves the gadm.org data without issue. Note, the getData() function retrieves the gadm.org website's geodata that is stored and retrieved from the http://biogeo.ucdavis.edu/ website.
The raster package provides the getData() function which is very useful for automatically retrieving the geodata from the internet. This function can also be used to retrieve geodata that is kept locally on a PC.
In years past, the way to use geodata was to first download a file from the gadm.org website, and then to move that file from the download folder and save the file in a folder on the pc. These files then needed to be unpackaged/unzipped before the geodata was available to be used by R.
Using the getData() makes life simpler because this method directly retrieves the desired geodata and then makes the geodata available to use with R.
The gadm.org website clearly states:
"Downloading by country is the recommended approach"
Even though downloading the large world geodata file directly from the website can be done, it is unnecessary and resource intensive. Unless there is some specific reason for doing so, there is absolutely no need to download and keep the large worldwide geodata database on the PC.
And one last thing about the getData() function. This function is currently generating a warning when it is used in R nowadays. The warning reads:
Warning message in getData("GADM", country = "USA", level = 1):
"getData will be removed in a future version of raster.
Please use the geodata package instead"
I have a script to create a randomly distributed square polygons in KML format which takes in shapefile with a single polygon as an input which works absolutely well. The problem arise when I tried to create the user defined function out of it. I used readShapePoly() function to read the shapefile and it works well when used out of the function. But when the function is created in which shapefile should be given as an input, it simply wont take. It shows this error message
Error in getinfo.shape(filen) : Error opening SHP file
I avoid writing extensions and I do have all the extension files to create the shapefile.
Part of the script to read the shapefile using it as the input file:
library(maptools)
library(sp)
library(ggplot2)
Polytokml <- function(shapefile_name){
###Input Files
file1 <- "shapefile_name"
Shape_file <- readShapePoly(file1) #requires maptools
return(Shape_file)
}
The function is created but it doesn't work if the function is called.
>Polytokml(HKH.shp)
Error in getinfo.shape(filen) : Error opening SHP file
This works well out of the function.
###Input Files
file1 <- "shapefile.shp"
Shape_file <- readShapePoly(file1) #requires maptools
This is just an example out of the whole script in which different arguments are taken as an input. So just to make things simple I have added script to read the shapefile which has been a problem now. Do let me know if it is not clear.
Thank you so much in advance :)
I'm trying to create a shapefile in R that I will later import to either Fusion Table or some other GIS application.
To start,I imported a blank shapefile containing all the census tracts in Canada. I have attached other data (in tabular format) to the shapefile based on the unique ID of the CTs, and I have mapped my results. At the moment, I only need the ones in Vancouver and I would like to export a shapefile that contains only the Vancouver CTs as well as my newly attached attribute data.
Here is my code (some parts omitted due to privacy reasons):
shape <- readShapePoly('C:/TEST/blank_ct.shp') #Load blank shapefile
shape#data = data.frame(shape#data, data2[match(shape#data$CTUID, data2$CTUID),]) #data2 is my created attributes that I'm attaching to blank file
shape1 <-shape[shape$CMAUID == 933,] #selecting the Vancouver CTs
I've seen other examples using this: writePolyShape to create the shapefile. I tried it, and it worked to an extent. It created the .shp, .dbf, and .shx files. I'm missing the .prj file and I'm not sure how to go about creating it. Are there better methods out there for creating shapefiles?
Any help on this matter would be greatly appreciated.
Use rgdal and writeOGR. rgdal will preserve the projection information
something like
library(rdgal)
shape <- readOGR(dsn = 'C:/TEST', layer = 'blank_ct')
# do your processing
shape#data = data.frame(shape#data, data2[match(shape#data$CTUID, data2$CTUID),]) #data2 is my created attributes that I'm attaching to blank file
shape1 <-shape[shape$CMAUID == 933,]
writeOGR(shape1, dsn = 'C:/TEST', layer ='newstuff', driver = 'ESRI Shapefile')
Note that the dsn is the folder containing the .shp file, and the layer is the name of the shapefile without the .shp extension. It will read (readOGR) and write (writeOGR) all the component files (.dbf, .shp, .prj etc)
Problem solved! Thank you again for those who help!
Here is what I ended up doing:
As Mnel wrote, this line will create the shapefile.
writeOGR(shape1, dsn = 'C:/TEST', layer ='newstuff', driver = 'ESRI Shapefile')
However, when I ran this line, it came back with this error:
Can't convert columns of class: AsIs; column names: ct2,mprop,mlot,mliv
This is because my attribute data was not numeric, but were characters. Luckily, my attribute data is all numbers so I ran transform() to fix this problem.
shape2 <-shape1
shape2#data <- transform(shape1#data, ct2 = as.numeric(ct2),
mprop = as.numeric(mprop),
mlot = as.numeric(mlot),
mliv = as.numeric(mliv))
I tried the writeOGR() command again, but I still didn't get the .prj file that I was looking for. The problem was I didn't specified the coordinate systems for the shapefile when I was importing the file. Since I already know what the coordinate system is, all I had to do was define it when importing.
readShapePoly('C:/TEST/blank_ct.shp',proj4string=CRS("+proj=longlat +datum=WGS84")
After that, I re-ran all the things I wanted to do with the shapefile, and the writeOGR line for exporting. And that's it!