I have a column of over 1,000 London full postcodes. I need to convert these postcodes into Latitudes and Longitudes and then plot each point onto a base map of London. I want to show the spatial distribution and concentration of my postcodes as dots on London.
I have attempted this exercise using ggmap, maps and mapdata but to no avail.
install.packages("ggmap")
install.packages(c("maps", "mapdata"))
library(ggmap)
library(mapdata)
library(tidyverse)
library(dplyr)
mapdata <- filter(mapdata, Country--'UK')
Error Message:
Error in as.ts(x) : object 'mapdata' not found
First, I found a shapefile layer of UK water here. You might wish to find a better one. It's best to download all your shapefiles in ONE GIS FOLDER for future use, including all the files that are zipped along with them.
Second, I used the rgdal package to open that shapefile using the readOGR command.
Third, I used the leaflet package to place that data on a map of London.
library(leaflet);library(rgdal)
setwd("/yourpathhere/") #update your path here
ukwater<-readOGR(dsn='.',layer='GBR_water_lines_dcw')
leaflet() %>%
addProviderTiles("Stamen.Toner") %>%
setView(0,51.4,zoom=9) %>% # longitude and latitute, respectively
addPolygons(data=ukwater)
Related
I'd like to export an sf object as a .kml file, with labels for each feature I'm interested in so I can view the data easily in Google Earth. I know you can click on the "info" button in Google Earth, but for hundreds of polygons, this isn't ideal.
For example, I'd like to label each polygon feature below using the column NAME. How can I modify the st_write call below to label the kml polygons so that they appear in the sidebar table of contents in Google Earth?
library(sf)
library(dplyr)
# sf includes this dataset
county_polygons <- st_read(system.file("shape/nc.shp", package="sf")) %>%
st_transform(4326)
st_write(county_polygons , "test.kml", driver = "kml")
Here's a picture showing the lack of labels in Google Earth when this is imported as a kml file:
Consider this code, using a different, although also well known & well loved dataset - the polygons of North Carolina counties from ns.shp shipped with {sf} package:
library(sf)
library(dplyr)
# dataset included with sf package
county_polygons <- st_read(system.file("shape/nc.shp", package="sf")) %>%
st_transform(4326) %>% # just because wgs84...
select(Description = NAME) # see https://gdal.org/drivers/vector/kml.html#creation-options
st_write(county_polygons, "test.kml", driver = "kml", delete_dsn = TRUE)
It is built around the feature of KML export of DescriptionField (which is clickable in Google Earth) defaulting to sf column named Description.
If you want the feature's name in the sidebar instead, you can replace the word Description with Name in the code above.
I am trying to filter specific zones from this shapefile. The shapefile contains traffic zones from the 2016 TTS survey for the entire GTHA. I downloaded the shapefile from here: http://dmg.utoronto.ca/survey-boundary-files#2006_zone.
I am only interested in zones within Hamilton (5001-5252). I am having trouble with the code.
I am able to plot the shapefile, but cannot filter the zones that I am interested in to plot. I have loaded dplyr and sf packages, and tried using the code below:
hamilton <- gtha %>% filter(GTA06 >=5001 & GTA06 <=5252)
I keep getting the following message:
Error: object 'GTA06' not found
I have a shapefile with US counties that I read into r with readOGR(). I need to combine the 3,000 US counties into larger areas, for example the 929 Core Based Statistical Areas (the NBER provides a crosswalk from county FIPS to it). In graphic design, this would be a union of shapes. Can I do this programmatically within R without using a Geographic Information System?
Use the function ms_dissolve() from rmapshaper. For example, to unite the shapes of counties into states:
library(rmapshaper)
# Download the shapefile for US counties from here and save in some/dir
# www2.census.gov/geo/tiger/GENZ2010/gz_2010_us_050_00_5m.zip
counties <- readOGR(dsn="some/dir",layer="gz_2010_us_050_00_5m")
# Drop Alaska, hawaii, Puerto Rico
counties <- counties[!(counties$STATE %in% c("02","15","72")), ]
# Plot counties
plot(counties)
# Unite shapes
library(rmapshaper)
states <- ms_dissolve(counties, field = "STATE")
plot(states)
Map of counties:
Map of states from the union of counties:
The st_union function in the sf package might be what you're looking for. sf ('simple features') makes working with spatial data much much easier than if you were using QGIS or ArcGis.
I was asked whether R can work with shapefiles - I never worked with shapefiles myself before, but I am sure, others must have come across this kind of question!
I have two shapefiles:
a) shapefile 1 (PolygonSamples.shp) contains a list of polygons which are distributed all over Germany (attached is a sample). The polygons might be smaller, equal or larger than the polygon of one postal codes polygon.
b) shapefile 2 lists the german postal codes and can be downloaded from
https://blog.oraylis.de/2010/05/german-map-spatial-data-for-plz-postal-code-regions/
The question is now:
How to 'match' the two shapefiles to get a dataframe that lists which polygon in shapefile 1 matches which postal codes(s) of shapefile 2. The result ideally looks like
Polygon ID (shapefile 1) Postal Code (shapefile 2)
1 80995
2 80997
2 80999
3 81247
Nothing of what I found matches really my question.
For example From a shapefile with polygons/areas, and points (lat,lon), figure out which polygon/area each point belongs to? In R
seems close, but I don't manage to get the desired dataframe (or datatable) output.
library(maps)
library(maptools)
# Polygons
tmp_dir <- "C:/Users/.../"
polygons <- readShapeSpatial(sprintf('%s/polygons.shp', tmp_dir)
plot(polygons)
# Postal codes
dir <- "C:/Users/..../"
postcode <- readShapeSpatial(sprintf('%s/post_pl.shp', dir)
plot(postcode)
The missing codes snipplet would read something like
result_table <- match(polygons_ID, postcode,
data1= polygon, data2 = postcode,
by = "coordinates in the shapefile"
Sample of polygons in a shapefile (.shp) incl. other spatial files (.dbf,.prj, .qpj,.shx) can be send.
Any help is really VERY much appreciated!
PS: R version 3.2.3, 64 bit, RStudio on Windows 7
Unfortunately I did not find an answer in R, but I could figure out how to match the two independent shapefiles in QGIS.
The main problem: The custom shapefile uses in the .prj file the geocoding Google Mercator (EPSG = 900913), while the downloaded postal code shapefile uses EPSG 4326.
QGIS does not automatically recognize these .prj files as projection files. One has to set them by hand.
Most importantly: Google Mercator (EPSG = 900913) was changed to EPSG= 3857. So for the custom shapefile I had to set – by hand! – the CRS to WGS 84/Pseudo-Mercator EPSG = 3857.
Now I could right click on the custom shape layer -> save as …. And Change the CRS to EPSG 4326. Thus the new custom shapefile now has the same projection like the downloaded postal code shapefile, and they can be joined by location.
(PS: Although I have a solution to do the conversion by hand, I would love to do this in R, because I need the resulting file for analysis.)
Check out: https://gis.stackexchange.com/questions/140504/extracting-intersection-areas-in-r?newreg=033544fa0f5349bcb8167d78867c8073
It gives you which shapefiles in dataset B overlap with a shapefile in dataset A as well as how much area in each of B's shapefiles is present in the target shapefile.
Have a Question on Mapping with R, specifically around the choropleth maps in R.
I have a dataset of ZIP codes assigned to an are and some associated data (dataset is here).
My final data format is: Area ID, ZIP, Probability Value, Customer Count, Area Probability and Area Customer Total. I am attempting to present this data by plotting area probability and Area Customer Total on a Map. I have tried to do this by using the census TIGER Shapefiles but I guess R cannot handle the complete country.
I am comfortable with the Statistical capabilities and now I am moving all my Mapping from third party GIS focused applications to doing all my Mapping in R. Does anyone have any pointers to how to achieve this from within R?
To be a little more detailed, here's the point where R stops working -
shapes <- readShapeSpatial("tl_2013_us_zcta510.shp")
(where the shp file is the census/TIGER) shape file.
Edit - Providing further details. I am trying to first read the TIGER shapefiles, hoping to combine this spatial dataset with my data and eventually plot. I am having an issue at the very beginning when attempting to read the shape file. Below is the code with the output
require(maptools)
shapes<-readShapeSpatial("tl_2013_us_zcta510.shp")
Error: cannot allocate vector of size 317 Kb
There are several examples and tutorials on making maps using R, but most are very general and, unfortunately, most map projects have nuances that create inscrutable problems. Yours is a case in point.
The biggest issue I came across was that the US Census Bureau zip code tabulation area shapefile for the whole US is huge: ~800MB. When loaded using readOGR(...) the R SpatialPolygonDataFrame object is about 913MB. Trying to process a file this size, (e.g., converting to a data frame using fortify(...)), at least on my system, resulted in errors like the one you identified above. So the solution is to subset the file based in the zip codes that are actually in your data.
This map:
was made from your data using the following code.
library(rgdal)
library(ggplot2)
library(stringr)
library(RColorBrewer)
setwd("<directory containing shapfiles and sample data>")
data <- read.csv("Sample.csv",header=T) # your sample data, downloaded as csv
data$ZIP <- str_pad(data$ZIP,5,"left","0") # convert ZIP to char(5) w/leading zeros
zips <- readOGR(dsn=".","tl_2013_us_zcta510") # import zip code polygon shapefile
map <- zips[zips$ZCTA5CE10 %in% data$ZIP,] # extract only zips in your Sample.csv
map.df <- fortify(map) # convert to data frame suitable for plotting
# merge data from Samples.csv into map data frame
map.data <- data.frame(id=rownames(map#data),ZIP=map#data$ZCTA5CE10)
map.data <- merge(map.data,data,by="ZIP")
map.df <- merge(map.df,map.data,by="id")
# load state boundaries
states <- readOGR(dsn=".","gz_2010_us_040_00_5m")
states <- states[states$NAME %in% c("New York","New Jersey"),] # extract NY and NJ
states.df <- fortify(states) # convert to data frame suitable for plotting
ggMap <- ggplot(data = map.df, aes(long, lat, group = group))
ggMap <- ggMap + geom_polygon(aes(fill = Probability_1))
ggMap <- ggMap + geom_path(data=states.df, aes(x=long,y=lat,group=group))
ggMap <- ggMap + scale_fill_gradientn(name="Probability",colours=brewer.pal(9,"Reds"))
ggMap <- ggMap + coord_equal()
ggMap
Explanation:
The rgdal package facilitates the creation of R Spatial objects from ESRI shapefiles. In your case we are importing a polygon shapefile into a SpatialPolygonDataFrame object in R. The latter has two main parts: a polygon section, which contains the latitude and longitude points that will be joined to create the polygons on the map, and a data section which contains information about the polygons (so, one row for each polygon). If, e.g., we call the Spatial object map, then the two sections can be referenced as map#polygons and map#data. The basic challenge in making choropleth maps is to associate data from your Sample.csv file, with the relevant polygons (zip codes).
So the basic workflow is as follows:
1. Load polygon shapefiles into Spatial object ( => zips)
2. Subset if appropriate ( => map).
3. Convert to data frame suitable for plotting ( => map.df).
4. Merge data from Sample.csv into map.df.
5. Draw the map.
Step 4 is the one that causes all the problems. First we have to associate zip codes with each polygon. Then we have to associate Probability_1 with each zip code. This is a three step process.
Each polygon in the Spatial data file has a unique ID, but these ID's are not the zip codes. The polygon ID's are stored as row names in map#data. The zip codes are stored in map#data, in column ZCTA5CE10. So first we must create a data frame that associates the map#data row names (id) with map#data$ZCTA5CE10 (ZIP). Then we merge your Sample.csv with the result using the ZIP field in both data frames. Then we merge the result of that into map.df. This can be done in 3 lines of code.
Drawing the map involves telling ggplot what dataset to use (map.df), which columns to use for x and y (long and lat) and how to group the data by polygon (group=group). The columns long, lat, and group in map.df are all created by the call to fortify(...). The call to geom_polygon(...) tells ggplot to draw polygons and fill using the information in map.df$Probability_1. The call to geom_path(...) tells ggplot to create a layer with state boundaries. The call to scale_fill_gradientn(...) tells ggplot to use a color scheme based on the color brewer "Reds" palette. Finally, the call to coord_equal(...) tells ggplot to use the same scale for x and y so the map is not distorted.
NB: The state boundary layer, uses the US States TIGER file.
I would advise the following.
Use readOGR from the rgdal package rather than readShapeSpatial.
Consider using ggplot2 for good-looking maps - many of the examples use this.
Refer to one of the existing examples of creating a choropleth such as this one to get an overview.
Start with a simple choropleth and gradually add your own data; don't try and get it all right at once.
If you need more help, create a reproducible example with a SMALL fake dataset and with links to the shapefiles in question. The idea is that you make it easy to help us help you rather than discourage us by not supplying code and data in your question.