| Title: | Draw Geographical Maps |
|---|---|
| Description: | Display of maps. Projection code and larger maps are in separate packages ('mapproj' and 'mapdata'). |
| Authors: | Original S code by Richard A. Becker and Allan R. Wilks. R version by Ray Brownrigg. Enhancements by Thomas P Minka and Alex Deckmyn. |
| Maintainer: | Alex Deckmyn <[email protected]> |
| License: | GPL-2 |
| Version: | 3.4.2.1 |
| Built: | 2024-11-12 08:34:52 UTC |
| Source: | https://github.com/adeckmyn/maps |
Computes the areas of regions in a projected map.
area.map(m, regions = ".", sqmi=TRUE, ...)area.map(m, regions = ".", sqmi=TRUE, ...)
m |
a map object containing named polygons (created with
|
regions |
a character vector naming one of more regions, as in
|
sqmi |
If |
... |
additional arguments to |
The area of each matching region in the map is computed, and
regions which match the same element of regions have their
areas combined. Each region is assumed planar, with vertices
specified by the x and y components of the map object.
The correct use of this function is to first use map to
create polygons and project the coordinates onto a plane, then apply
area.map to compute the area of the projected regions.
If the projection is area-preserving (such as albers),
then these areas will match the area on the globe, up to a
constant. To get an absolute area in square miles, the sqmi
option will scale the result, depending on the projection.
The coordinates from map are affected by
its resolution argument, so use resolution=0 for the most
accurate areas.
a named vector of region areas.
The sqmi option assumes the coordinates have been projected
with the mapproject function.
Tom Minka
area.polygon, apply.polygon
# because the projection is rectangular, these are not true areas on the globe. m = map("state", fill = TRUE, plot = FALSE) area.map(m) area.map(m, ".*dakota") area.map(m, c("North Dakota", "South Dakota")) if(require(mapproj)) { # true areas on the globe m = map("state", proj="bonne", param=45, fill=TRUE, plot=FALSE) # North Dakota is listed as 70,704 square miles area.map(m, "North Dakota") }# because the projection is rectangular, these are not true areas on the globe. m = map("state", fill = TRUE, plot = FALSE) area.map(m) area.map(m, ".*dakota") area.map(m, c("North Dakota", "South Dakota")) if(require(mapproj)) { # true areas on the globe m = map("state", proj="bonne", param=45, fill=TRUE, plot=FALSE) # North Dakota is listed as 70,704 square miles area.map(m, "North Dakota") }
This database is of Canadian cities of population greater than about 1,000. Also included are province capitals of any population size.
A list with 6 components, namely "name", "country.etc", "pop", "lat", "long", and "capital", containing the city name, the province abbreviation, approximate population (as at January 2006), latitude, longitude and capital status indication (0 for non-capital, 1 for capital, 2 for provincial
capital.
Some of the city names may be out of date. Please send any corrections to the package maintainer.
This database produces a map of the counties of the United States mainland generated from US Department of the Census data (see the reference).
data(countyMapEnv)data(countyMapEnv)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
US Department of Commerce, Census Bureau, County Boundary File, computer tape, available from Customer Services, Bureau of the Census, Washingdon DC 20233.
map.
map('county', 'iowa', fill = TRUE, col = palette())map('county', 'iowa', fill = TRUE, col = palette())
A database matching FIPS codes to maps package county and state names.
data(county.fips)data(county.fips)
A list with 2 components, namely "fips" and "polyname", containing the FIPS number and respective state or county polygon name. Note that "fips" is represented as an integer, so any leading zero (which is part of the fips code) is not shown by default.
This france database comes from the NUTS III (Tertiary Administrative Units of the European Community) database of the United Nations Environment Programme (UNEP) GRID-Geneva data sets. These were prepared around 1989, and so may be somewhat out of date.
Users of data sets supplied through UNEP/GRID are requested to incorporate in output products and reports acknowledgements to the originator of the data and to the fact that they were acquired through UNEP/GRID. Appropriate wording may be "UNESCO (1987) through UNEP/GRID-Geneva".
data(franceMapEnv)data(franceMapEnv)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
This map database can now easily be replaced by data taken directly from free sources, e.g. Natural Earth (see example).
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
map('france', fill = TRUE, col = 1:10) # replace by a public domain map at higher resolution: # fr1 <- rnaturalearth::ne_states("france") # this still includes overseas domains, so we remove those: # france2 <- map(fr1, xlim=c(-20, 20), ylim=c(30, 60), lforce="e", # fill=TRUE, plot=FALSE)map('france', fill = TRUE, col = 1:10) # replace by a public domain map at higher resolution: # fr1 <- rnaturalearth::ne_states("france") # this still includes overseas domains, so we remove those: # france2 <- map(fr1, xlim=c(-20, 20), ylim=c(30, 60), lforce="e", # fill=TRUE, plot=FALSE)
Identifies the map regions clicked by the user.
## S3 method for class 'map' identify(x, n = 1, index = FALSE, ...)## S3 method for class 'map' identify(x, n = 1, index = FALSE, ...)
x |
a map object containing named polygons. |
n |
the number of clicks to wait for. |
index |
If TRUE, returns the index of the polygon, rather than its name. |
... |
additional arguments passed to |
The current algorithm is somewhat crude — selects the region whose
centroid is closest to the click. A more sophisticated approach would
use map.where.
a character vector of length n, naming the selected regions.
Tom Minka
identify(map("state", fill = TRUE, col = 0)) if(require(mapproj)) identify(map("world", proj = "lagrange", fill = TRUE, col = 0, wrap=c(-180,180,-90)))identify(map("state", fill = TRUE, col = 0)) if(require(mapproj)) identify(map("world", proj = "lagrange", fill = TRUE, col = 0, wrap=c(-180,180,-90)))
This data set and the simple look-up functions allow to build lists of counrtries for the world map.
iso.expand(a, regex=TRUE) sov.expand(sov, regex=TRUE) iso.alpha(x, n=2)iso.expand(a, regex=TRUE) sov.expand(sov, regex=TRUE) iso.alpha(x, n=2)
a |
A vector of ISO codes. All elements should have the same length, either 2 or 3 letters. Not case sensitive. |
sov |
A vector of country names. The result is a list of all countries that fall under their sovereignty. Case sensitive, must fit completeley. |
regex |
If TRUE (default), the return vector has the same length as the input ( |
x |
Vector of country names, may include colons. |
n |
An integer identitying which ISO code is required. Allowed values are 2 and 3. |
The ISO 3166-1 standard identifies countries by a 2 and 3 letter codes.
iso.expand translates these codes into the country names as used by
the world data base. iso.alpha does the reverse.
Some countries have different ISO codes for different regions
(e.g. China:Hong Kong has ISO code HK). In such cases, iso.alpha will
return the main code, but iso.expand will return a regular expression
that excludes some parts.
iso.expand returns vector of country names. When used as input for map it will plot all the countries as identified either by their sovereignty or by ISO codes. If regex=FALSE the length of the vector may be shorter or longer than the input. If regex=TRUE, the results are concatenated in regular expressions. This format is less readable, but can be used as input e.g. for match.map.
iso.alpha always returns a vector of the same length as the input, containing the 2- or 3-letter codes.
These functions use regular expressions and the results will often not work well with map(...,exact=TRUE).
https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2
# France and all its overseas departments, territories etc. sov.expand("France") # France and all its overseas departments, territories etc. # Canary Islands are not included in map("Spain") iso.expand("ES") map(regions=sov.expand("Spain")) # draw a map with ISO codes as labels: wm <- map("world", fill=TRUE, col=0, xlim=c(-10,40), ylim=c(30,60)) # take out islands, but you loose e.g. UK, New Zealand, small island states nam <- grep(":", wm$names, inv=TRUE, val=TRUE) # ad ISO codes as label map.text(wm, regions=nam, label=iso.alpha(nam), col=2, exact=TRUE, add=TRUE)# France and all its overseas departments, territories etc. sov.expand("France") # France and all its overseas departments, territories etc. # Canary Islands are not included in map("Spain") iso.expand("ES") map(regions=sov.expand("Spain")) # draw a map with ISO codes as labels: wm <- map("world", fill=TRUE, col=0, xlim=c(-10,40), ylim=c(30,60)) # take out islands, but you loose e.g. UK, New Zealand, small island states nam <- grep(":", wm$names, inv=TRUE, val=TRUE) # ad ISO codes as label map.text(wm, regions=nam, label=iso.alpha(nam), col=2, exact=TRUE, add=TRUE)
This data set lists all ISO3166 country codes and the sovereignty for each country in the list. Some entries are regular expressions.
A data frame with 5 columns: "a2", "a3", "name", "mapname", "sovereignty". These contain the 2- and 3-letter ISO code, the official name, the (possibly shorter) name used in the map data base, and the sovereign country.
The ISO 3166-1 standard identifies countries by a 2 and 3 letter codes. This table listst these for all countries on the world map. This data set also serves as basis for the function iso.expand() and its siblings.
Some countries have different ISO codes for some regions. To deal with such particular cases, the "mapname" column may sometimes contain (perl-style) regular expressions rather than simply a country name. For instance, "FI" has mapname "Finland(?!:Aland)", because the Aland islands have a different ISO code. Other codes may appear in two rows if certain parts of countries are not written with the main country as base name. Usually, that is for compatibility with the legacy world data base.
https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2
This italy database comes from the NUTS III (Tertiary Administrative Units of the European Community) database of the United Nations Environment Programme (UNEP) GRID-Geneva data sets. These were prepared around 1989, and so may be somewhat out of date.
Users of data sets supplied through UNEP/GRID are requested to incorporate in output products and reports acknowledgements to the originator of the data and to the fact that they were acquired through UNEP/GRID. Appropriate wording may be "UNESCO (1987) through UNEP/GRID-Geneva".
data(italyMapEnv)data(italyMapEnv)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
map('italy', fill = TRUE, col = 1:10)map('italy', fill = TRUE, col = 1:10)
This database contains a selection of large lakes (and islands within) taken from the Natural Earth 1:50m map, the same data source as the (v3.0) world map. The lake boundaries are consistent with the 'world' database.
data(lakesMapEnv)data(lakesMapEnv)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
The data in this data base is derived from the public domain GIS project Natural Earth, the file "ne_50m_lakes". The Natural Earth data set is available from https://www.naturalearthdata.com.
Natural Earth project https://www.naturalearthdata.com
map.
map('world') map('lakes', add=TRUE, fill=TRUE, col='white', boundary='black')map('world') map('lakes', add=TRUE, fill=TRUE, col='white', boundary='black')
Draw lines and polygons as specified by a map database.
map(database = "world", regions = ".", exact = FALSE, boundary = TRUE, interior = TRUE, projection = "", parameters = NULL, orientation = NULL, fill = FALSE, col = 1, plot = TRUE, add = FALSE, namesonly = FALSE, xlim = NULL, ylim = NULL, wrap = FALSE, resolution = if (plot) 1 else 0, type = "l", bg = par("bg"), mar = c(4.1, 4.1, par("mar")[3], 0.1), myborder = 0.01, namefield="name", lforce="n", ...)map(database = "world", regions = ".", exact = FALSE, boundary = TRUE, interior = TRUE, projection = "", parameters = NULL, orientation = NULL, fill = FALSE, col = 1, plot = TRUE, add = FALSE, namesonly = FALSE, xlim = NULL, ylim = NULL, wrap = FALSE, resolution = if (plot) 1 else 0, type = "l", bg = par("bg"), mar = c(4.1, 4.1, par("mar")[3], 0.1), myborder = 0.01, namefield="name", lforce="n", ...)
database |
character string naming a geographical database, a list of
|
regions |
character vector that names the polygons to draw.
Each database is composed of a collection of polygons, and each polygon has
a unique name.
When a region is composed of more than one polygon, the individual polygons
have the name of the region, followed by a colon and a qualifier,
as in |
exact |
If |
boundary |
If |
interior |
If |
projection |
character string that names a map projection to use.
See |
parameters |
numeric vector of parameters for use with the
|
orientation |
a vector |
fill |
logical flag that says whether to draw lines or fill areas.
If |
col |
vector of colors.
If |
plot |
logical flag that specifies whether plotting
should be done.
If |
add |
logical flag that specifies whether to add to the
current plot.
If |
namesonly |
If |
xlim |
two element numeric
vector giving a range of longitudes, expressed
in degrees, to which drawing
should be restricted.
Longitude is measured in degrees east of Greenwich, so that, in particular,
locations in the USA have negative longitude.
If |
ylim |
two element
numeric vector giving a range of latitudes,
expressed in degrees, to which drawing
should be restricted.
Latitude is measured in degrees north of the
equator, so that, in particular,
locations in the USA have positive latitude.
If |
wrap |
Boolean or a numeric vector. If TRUE, lines that cross too far across the map
(due to a strange projection) are omitted. If wrap is a vector of length 2 or more, it is interpreted as the longitude range to be used for a global map, e.g. |
resolution |
number that specifies the resolution with which
to draw the map.
Resolution 0 is the full resolution of the database.
Otherwise, just before polylines are plotted they are thinned:
roughly speaking, successive points on the polyline that are
within |
type |
character string that controls drawing of the map.
Aside from the default |
bg |
background color. |
mar |
margins, as in |
myborder |
scalar or vector of length 2 specifying the porportion of the plot to add to the defined or computed limits as borders. |
namefield |
A vector of column names to be used as region name if |
lforce |
Limit enforcement. Only taken into account if |
... |
Extra arguments passed to |
The simplest form of use of this function is:
map(mymap)
where mymap is the returned value from a previous call to
map().
If plot = TRUE, a plot is made where
the polygons selected from database, through the
regions, xlim, and ylim arguments, are outlined
(fill is FALSE) or filled (fill is TRUE)
with the colors in col.
The return value is a list with
x, y, range, and names components.
This object can be used as a database for successive calls
to map and functions.
If fill is FALSE, the x and y vectors are
the coordinates of successive polylines, separated by NAs. If
fill is TRUE, the x and y vectors have
coordinates of successive polygons, again separated by NAs.
Thus the return value can be handed directly to lines or
polygon, as appropriate.
When namesonly is TRUE, only the names component is returned.
After a call to map for which the projection argument was
specified there will be a global variable .Last.projection
containing information about the projection used.
This will be consulted in subsequent calls to map which use
projection = ''.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993. https://web.archive.org/web/20050825145143/http://www.research.att.com/areas/stat/doc/93.2.ps
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995. https://web.archive.org/web/20050825145143/http://www.research.att.com/areas/stat/doc/95.2.ps
map.text, map.axes,
map.scale, map.grid (in the mapproj library),
polygon, SpatialPolygons2map
map() # low resolution map of the world map(wrap = c(0,360), fill = TRUE, col = 2) # pacific-centered map of the world map(wrap = c(0, 360, NA), fill = TRUE, col = 2) # idem, without Antarctica map('usa') # national boundaries map('county', 'new jersey') # county map of New Jersey map('state', region = c('new york', 'new jersey', 'penn')) # map of three states map("state", ".*dakota", myborder = 0) # map of the dakotas map.axes() # show the effect of myborder = 0 if(require(mapproj)) map('state', proj = 'bonne', param = 45) # Bonne equal-area projection of states # names of the San Juan islands in Washington state map('county', 'washington,san', names = TRUE, plot = FALSE) # national boundaries in one linetype, states in another # (figure 5 in the reference) map("state", interior = FALSE) map("state", boundary = FALSE, lty = 2, add = TRUE) # plot the ozone data on a base map # (figure 4 in the reference) data(ozone) map("state", xlim = range(ozone$x), ylim = range(ozone$y)) text(ozone$x, ozone$y, ozone$median) box() if(require(mapproj)) { # mapproj is used for projection="polyconic" # color US county map by 2009 unemployment rate # match counties to map using FIPS county codes # Based on J's solution to the "Choropleth Challenge" # http://blog.revolutionanalytics.com/2009/11/choropleth-challenge-result.html # load data # unemp includes data for some counties not on the "lower 48 states" county # map, such as those in Alaska, Hawaii, Puerto Rico, and some tiny Virginia # cities data(unemp) data(county.fips) # define color buckets colors = c("#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043") unemp$colorBuckets <- as.numeric(cut(unemp$unemp, c(0, 2, 4, 6, 8, 10, 100))) leg.txt <- c("<2%", "2-4%", "4-6%", "6-8%", "8-10%", ">10%") # align data with map definitions by (partial) matching state,county # names, which include multiple polygons for some counties cnty.fips <- county.fips$fips[match(map("county", plot=FALSE)$names, county.fips$polyname)] colorsmatched <- unemp$colorBuckets [match(cnty.fips, unemp$fips)] # draw map map("county", col = colors[colorsmatched], fill = TRUE, resolution = 0, lty = 0, projection = "polyconic") map("state", col = "white", fill = FALSE, add = TRUE, lty = 1, lwd = 0.2, projection="polyconic") title("unemployment by county, 2009") legend("topright", leg.txt, horiz = TRUE, fill = colors) # Choropleth Challenge example, based on J's solution, see: # http://blog.revolutionanalytics.com/2009/11/choropleth-challenge-result.html # To see the faint county boundaries, use RGui menu: File/SaveAs/PDF }map() # low resolution map of the world map(wrap = c(0,360), fill = TRUE, col = 2) # pacific-centered map of the world map(wrap = c(0, 360, NA), fill = TRUE, col = 2) # idem, without Antarctica map('usa') # national boundaries map('county', 'new jersey') # county map of New Jersey map('state', region = c('new york', 'new jersey', 'penn')) # map of three states map("state", ".*dakota", myborder = 0) # map of the dakotas map.axes() # show the effect of myborder = 0 if(require(mapproj)) map('state', proj = 'bonne', param = 45) # Bonne equal-area projection of states # names of the San Juan islands in Washington state map('county', 'washington,san', names = TRUE, plot = FALSE) # national boundaries in one linetype, states in another # (figure 5 in the reference) map("state", interior = FALSE) map("state", boundary = FALSE, lty = 2, add = TRUE) # plot the ozone data on a base map # (figure 4 in the reference) data(ozone) map("state", xlim = range(ozone$x), ylim = range(ozone$y)) text(ozone$x, ozone$y, ozone$median) box() if(require(mapproj)) { # mapproj is used for projection="polyconic" # color US county map by 2009 unemployment rate # match counties to map using FIPS county codes # Based on J's solution to the "Choropleth Challenge" # http://blog.revolutionanalytics.com/2009/11/choropleth-challenge-result.html # load data # unemp includes data for some counties not on the "lower 48 states" county # map, such as those in Alaska, Hawaii, Puerto Rico, and some tiny Virginia # cities data(unemp) data(county.fips) # define color buckets colors = c("#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043") unemp$colorBuckets <- as.numeric(cut(unemp$unemp, c(0, 2, 4, 6, 8, 10, 100))) leg.txt <- c("<2%", "2-4%", "4-6%", "6-8%", "8-10%", ">10%") # align data with map definitions by (partial) matching state,county # names, which include multiple polygons for some counties cnty.fips <- county.fips$fips[match(map("county", plot=FALSE)$names, county.fips$polyname)] colorsmatched <- unemp$colorBuckets [match(cnty.fips, unemp$fips)] # draw map map("county", col = colors[colorsmatched], fill = TRUE, resolution = 0, lty = 0, projection = "polyconic") map("state", col = "white", fill = FALSE, add = TRUE, lty = 1, lwd = 0.2, projection="polyconic") title("unemployment by county, 2009") legend("topright", leg.txt, horiz = TRUE, fill = colors) # Choropleth Challenge example, based on J's solution, see: # http://blog.revolutionanalytics.com/2009/11/choropleth-challenge-result.html # To see the faint county boundaries, use RGui menu: File/SaveAs/PDF }
Draws a set of axes on an existing map.
map.axes(...)map.axes(...)
... |
Extra arguments passed to |
x- and y-axes are drawn for the currently displayed map. These will display in longitude and latitude (if no projection= has been specified in the map() call).
map("state") map.axes(cex.axis=0.8)map("state") map.axes(cex.axis=0.8)
Adds city locations and (optionally) names to an existing map using a specified database.
map.cities(x = world.cities, country = "", label = NULL, minpop = 0, maxpop = Inf, capitals = 0, cex = par("cex"), projection = FALSE, parameters = NULL, orientation = NULL, pch = 1, ...)map.cities(x = world.cities, country = "", label = NULL, minpop = 0, maxpop = Inf, capitals = 0, cex = par("cex"), projection = FALSE, parameters = NULL, orientation = NULL, pch = 1, ...)
x |
Name of database. See |
country |
If the string country is specified, limit the displayed cities to be from within the specified country, province or state (depending on how the database has been constructed). |
label |
If |
minpop |
The minimum value of population below which a particular city will not be shown. |
maxpop |
The maximum value of population above which a particular city will not be shown. |
capitals |
Selection of capitals-only display. Capitals may be 1 (country capital),
2 (provincial, state, or regional capital) or 3 (local capital). See
|
cex |
The value of cex acts to override the current value of character size expansion. |
projection |
Boolean or character value. If |
parameters |
numeric vector of parameters for use with the |
orientation |
a vector |
pch |
plotting character to use for marking city location. See
|
... |
Further plotting parameters may be specified as for the commands
|
The database is searched for all cities matching the specified criteria and fitting within the limits of the plot currently displayed. The default database is of all cities that have a population greater than a certain threshold or which are capital cities of a country or island territory. The threshold varies from country to country, but in general is no higher than about 40,000. The data were originally obtained from Stefan Helders' website (http://www.world-gazetteer.com), which now redirects to http://www.populationmondiale.com. There are no recent updates available.
There are three supplied databases, world.cities (the default), us.cities
and canada.cities. The latter two, which need to be made available by
using a 'data()' call, include the state or province name with the
city name (thanks to John Woodruff [email protected]
for the state and province information).
Note that if the underlying map is "Pacific-centric", i.e. longitudes exceed 180 degrees, and a projection is used, then the map.cities data must be transformed appropriately.
No value is returned from map.cities.
All cities within the boundaries of the plot containing the current map are added to the plot. Note that it is possible that the boundaries of the plot exceed the boundaries of the map requested, and so more cities than were expected might be shown.
world.cities, canada.cities,
us.cities
map("world", "China") map.cities(country = "China", capitals = 2) map("state", "New Jersey") data(us.cities) map.cities(us.cities, country="NJ")map("world", "China") map.cities(country = "China", capitals = 2) map("state", "New Jersey") data(us.cities) map.cities(us.cities, country="NJ")
Adds a scale to an existing map, both as a ratio and a distance gauge.
map.scale(x, y, relwidth = 0.15, metric = TRUE, ratio = TRUE, ...)map.scale(x, y, relwidth = 0.15, metric = TRUE, ratio = TRUE, ...)
x |
Horizontal location of left end of distance gauge. If not specified, this will be taken to be near the lower left corner of the map. |
y |
Vertical location of left end of distance gauge. If not specified, this will be taken to be near the lower left corner of the map. |
relwidth |
Proportion of width of display to be used for the scale. The default is 0.15 (15%). |
metric |
If |
ratio |
If |
... |
Further plotting parameters may be specified as for the command text(). |
The scale is calculated from the displayed graph's plotting parameters, and the latitude of the location at which the distance gauge will be displayed.
The exact calculated scale is returned.
This function is meaningful only if no projection= has been specified in the call to map().
A scale is added to the currently displayed map. This takes the form of an approximate 1:n scale (containing 2-3 significant digits), above a distance gauge which is reasonably accurate for the latitude at which it appears. The circumference at the given latitude is interpolated from a radius of 6356.78 km at the pole and 6378.16 km at the equator.
map("world", "China") map.scale()map("world", "China") map.scale()
Like map, but labels the regions.
map.text(database, regions = ".", exact = FALSE, labels, cex = 0.75, add = FALSE, move = FALSE, ...)map.text(database, regions = ".", exact = FALSE, labels, cex = 0.75, add = FALSE, move = FALSE, ...)
database |
character string naming a geographical database, or a list of
|
regions |
character vector that names the polygons to draw. |
exact |
If 'TRUE', only exact matches with 'regions' are selected for drawing. |
labels |
character vector of labels, one for each region selected. Defaults to the names in the database. |
cex |
character expansion factor. |
add |
If |
move |
If |
... |
Other arguments are the same as in |
If add = FALSE, a map is drawn by calling map.
Then the label for each region is placed at the centroid of the region
polygon.
The return value is a map object, as from map.
Tom Minka
map.text("world", "ira") # iran and iraq map.text("state", "penn") map.text("county", "penn") # Pennsylvania counties map.text("county", "new jersey") # New Jersey countiesmap.text("world", "ira") # iran and iraq map.text("state", "penn") map.text("county", "penn") # Pennsylvania counties map.text("county", "new jersey") # New Jersey counties
Returns the region names containing given locations.
map.where(database = "world", x, y, ...)map.where(database = "world", x, y, ...)
database |
character string naming a geographical database, or a list of
|
x |
vector of longitudes. |
y |
vector of latitudes. |
... |
Options for |
.
A list of character strings, naming the map region that each (longitude, latitude) pair falls into.
For points close to a border (polygon boundary), the result may be wrong if the resolution of the database is insufficient. This function may also give erroneous results if the database contains enclaves. For instance, a point in San Marino may also be identified as being in Italy.
Tom Minka
in.polygon
# NYC map.where("state", -73.8, 41) # Auckland map.where("nz", 174.6, -36.92) # find both in the world map.where(x = c(174.6, -73.8), y = c(-36.92, 41)) # with a map object: m = map("state", "new york", fill = TRUE, plot = FALSE) map.where(m, -73.8, 41)# NYC map.where("state", -73.8, 41) # Auckland map.where("nz", 174.6, -36.92) # find both in the world map.where(x = c(174.6, -73.8), y = c(-36.92, 41)) # with a map object: m = map("state", "new york", fill = TRUE, plot = FALSE) map.where(m, -73.8, 41)
Assigns an index to each map region, useful for map coloring.
match.map(database, regions, exact = FALSE, warn = TRUE)match.map(database, regions, exact = FALSE, warn = TRUE)
database |
character string naming a geographical database, or a map object.
See the documentation for |
regions |
a vector of names, or more generally regular expressions to match against the map region names. |
exact |
If |
warn |
If |
Returns an integer vector giving an index to each region in the database.
The index is the index of the string in regions which matches the
region name. Matching is done as in map.
More specifically, all regions r whose name matches
regions[i] will have index i.
Unmatched regions will have index NA.
Overlapping matches cause an error.
This behavior differs from pmatch because a single entry
in regions may match several entries in the map.
Tom Minka
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report, 1991.
# filled map showing Republican vote in 1900 # (figure 6 in the reference) data(state, package = "datasets") data(votes.repub) state.to.map <- match.map("state", state.name) x <- votes.repub[state.to.map, "1900"] gray.colors <- function(n) gray(rev(0:(n - 1))/n) color <- gray.colors(100)[floor(x)] map("state", fill = TRUE, col = color); map("state", add = TRUE)# filled map showing Republican vote in 1900 # (figure 6 in the reference) data(state, package = "datasets") data(votes.repub) state.to.map <- match.map("state", state.name) x <- votes.repub[state.to.map, "1900"] gray.colors <- function(n) gray(rev(0:(n - 1))/n) color <- gray.colors(100)[floor(x)] map("state", fill = TRUE, col = color); map("state", add = TRUE)
This database produce a map of New Zealand at a basic level of detail. The ‘"nz"’ database includes the 3 main Islands and 19 smaller coastal islands.
data(nzMapEnv)data(nzMapEnv)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
map('nz') map('nz', xlim = c(166, 179), ylim = c(-48, -34))map('nz') map('nz', xlim = c(166, 179), ylim = c(-48, -34))
Datasets used to illustrate map functions.
ozone contains the median of daily maxima ozone concentration in 41 US cities for June 1974 through August 1974. Concentrations are in parts per billion (ppb).
unemp Has population and unemployment percentage for US counties.
votes.repub contains the percentage republican votes in the 1900 election.
data(ozone) data(unemp) data(votes.repub)data(ozone) data(unemp) data(votes.repub)
Cleveland, W.S., Kleiner, B., McRae, J.E., Warner, J.L., and Pasceri, P.E. , "The Analysis of Ground-Level Ozone Data from New Jersey, New York, Connecticut, and Massachusetts: Data Quality Assessment and Temporal and Geographical Properties", Bell Laboratories Memorandum, 1975.
Increases the resolution of data aggregated over map regions, by either smoothing or interpolation. Also fills in missing values.
smooth.map(m, z, res = 50, span = 1/10, averages = FALSE, type = c("smooth", "interp"), merge = FALSE)smooth.map(m, z, res = 50, span = 1/10, averages = FALSE, type = c("smooth", "interp"), merge = FALSE)
m |
a map object |
z |
a named vector |
res |
a vector of length two, specifying the resolution of the sampling grid in each dimension. If a single number, it is taken as the vertical resolution, with double taken as the horizontal resolution. |
span |
kernel parameter (larger = smoother).
|
averages |
If |
type |
see details. |
merge |
If |
For type = "smooth", the region totals are first converted
into point measurements on the
sampling grid, by dividing the total for a region among all sample
points inside it. Then it is a regular kernel smoothing
problem. Note that the region totals are not preserved.
The prediction for
location (a vector) is the average of z for
nearby sample points:
is determined from span.
Note that is over the same sampling grid as
, but is not necessarily the same as
.
For type = "interp", the region totals are preserved by the
higher-resolution function.
The function is assumed to come from a
Gaussian process with kernel . The measurement z[r]
is assumed to be the sum of the function over the discrete sample
points inside region r.
This leads to a simple formula for the covariance matrix of z
and the cross-covariance between zo and z.
The prediction is the cross-covariance times the inverse covariance
times z. Unlike Tobler's method, the predictions are not
constrained to live within the original data range,
so there tends to be "ringing" effects.
See the references for more details.
A data frame with columns x, y, and z
giving the smoothed value z for locations (x, y).
Currently the (x, y) values form a grid, but this is not
guaranteed in the future.
Tom Minka
W.F. Eddy and A. Mockus. An example of the estimation and display of a smoothly varying function of time and space - the incidence of disease mumps. Journal of the American Society for Information Science, 45(9):686-693, 1994. https://web.eecs.utk.edu/~audris/papers/jasis.pdf
W. R. Tobler. Smooth pycnophylactic interpolation for geographical regions. Journal of the American Statistical Association 74:519-530, 1979.
# compare to the example for match.map data(state, package = "datasets") data(votes.repub) z = votes.repub[, "1900"] m = map("state", fill = TRUE, plot = FALSE) # use a small span to fill in, but not smooth, the data # increase the resolution to get better results fit = smooth.map(m, z, span = 1/100, merge = TRUE, ave = TRUE) mat = tapply(fit$z, fit[1:2], mean) gray.colors <- function(n) gray(rev(0:(n - 1))/n) par(bg = "blue") filled.contour(mat, color.palette = gray.colors, nlev = 32, asp = 1) # another way to visualize: image(mat, col = gray.colors(100)) # for a higher degree of smoothing: # fit = smooth.map(m, z, merge = TRUE, ave = TRUE) # interpolation, state averages are preserved: # fit = smooth.map(m, z, merge = TRUE, ave = TRUE, type = "interp")# compare to the example for match.map data(state, package = "datasets") data(votes.repub) z = votes.repub[, "1900"] m = map("state", fill = TRUE, plot = FALSE) # use a small span to fill in, but not smooth, the data # increase the resolution to get better results fit = smooth.map(m, z, span = 1/100, merge = TRUE, ave = TRUE) mat = tapply(fit$z, fit[1:2], mean) gray.colors <- function(n) gray(rev(0:(n - 1))/n) par(bg = "blue") filled.contour(mat, color.palette = gray.colors, nlev = 32, asp = 1) # another way to visualize: image(mat, col = gray.colors(100)) # for a higher degree of smoothing: # fit = smooth.map(m, z, merge = TRUE, ave = TRUE) # interpolation, state averages are preserved: # fit = smooth.map(m, z, merge = TRUE, ave = TRUE, type = "interp")
These functions transform some classes provided by the package sp into a simple list that can be used by map().
SpatialPolygons2map(database, namefield=NULL) SpatialLines2map(database, namefield=NULL)SpatialPolygons2map(database, namefield=NULL) SpatialLines2map(database, namefield=NULL)
database |
A |
namefield |
The name of a data column in |
The 'map' list object only preserves co-ordinates and polygon names. All other information available in the original data is lost.
The option namefield is only taken into account if database is class Spatial[]DataFrame. namefield may be a vector of column names, e.g. to get polygons named as 'country:state'.
A list with four components: x, y, names, range, similar to the return value of map(). This data can be used as a database for map(). The lines and polygons are separated by NA.
map,SpatialPolygons (in the sp library).
This database produces a map of the states of the United States mainland generated from US Department of the Census data (see the reference).
data(stateMapEnv)data(stateMapEnv)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
US Department of Commerce, Census Bureau, County Boundary File, computer tape, available from Customer Services, Bureau of the Census, Washingdon DC 20233.
map.
map('state', fill = TRUE, col = palette())map('state', fill = TRUE, col = palette())
This database produces a cartogram of the states of the United States mainland based on CartoDraw, roughly proportional to population (see references).
state.carto.center are coordinates of the state centers
for annotation purposes.
data(stateMapEnv) data(state.carto.center)data(stateMapEnv) data(state.carto.center)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
CartoDraw, http://www.computer.org/csdl/trans/tg/2004/01/v0095-abs.html
map.
map('state.carto', fill = TRUE, col = palette())map('state.carto', fill = TRUE, col = palette())
A database matching FIPS codes to maps package state names.
data(state.fips)data(state.fips)
A list with 6 components, namely "fips", "ssa", "region", "division", "abb" and "polyname", containing the US Census Bureau FIPS, SSA, REGION and DIVISION numbers, the standard state abbreviation and the respective state polygon name. Note that "fips" is represented as an integer, so any leading zero (which is part of the fips code) is not shown by default.
This database produces a map of the states of the United States mainland. The Visibility Base Map was created by Mark Monmonier to provide simplified state shapes with sufficient areas to allow annotations in even the small states.
state.vbm.center are coordinates of the state centers
for annotation purposes. The states are alphabetically ordered, in the same order as the map. So state names can be matched via e.g. map(state.vbm, plot=FALSE)$name.
data(state.vbmMapEnv) data(state.vbm.center)data(state.vbmMapEnv) data(state.vbm.center)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
Mark Monmonier and George Schnell, "The Study of Population", Elements, Patterns, Processes. Charles E. Merrill. Columbus, OH. 1982.
map.
map('state.vbm', fill = TRUE, col = palette())map('state.vbm', fill = TRUE, col = palette())
This database is of us cities of population greater than about 40,000. Also included are state capitals of any population size.
A list with 6 components, namely "name", "country.etc", "pop", "lat", "long", and "capital", containing the city name, the state abbreviation, approximate population (as at January 2006), latitude, longitude and capital status indication (0 for non-capital, 1 for capital, 2 for state capital.
Some of the city names may be out of date. Please send any corrections to the package maintainer.
This database produces a map of the United States mainland generated from US Department of the Census data (see the reference).
data(usaMapEnv)data(usaMapEnv)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
US Department of Commerce, Census Bureau, County Boundary File, computer tape, available from Customer Services, Bureau of the Census, Washingdon DC 20233.
map.
map('usa')map('usa')
This world map (updated in 2013) is imported from the public domain Natural Earth project (the 1:50m resolution version). It replaces a much older version based on the CIA World Data Bank II data.
The old legacy data is still available in the package mapdata (v2.3.0).
data(worldMapEnv)data(worldMapEnv)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR_WORLD) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
As of version 3.1, the world database no longer contains any lakes. These have been moved to a separate database called lakes.
The legacy world map (dating from around 1990) has been removed from the package and is now available from the mapdata package in two different resolutions (worldHires and worldLores).
The Natural Earth data set is in the public domain and available from https://www.naturalearthdata.com.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
# notice how some polygons extend beyond the [-180,180] interval: map('world', fill = TRUE, col = 1:10) # if you wrap at [-180,180], you also can get a clean closure of Antarctica map('world', fill = TRUE, col = 1:10, wrap=c(-180,180) )# notice how some polygons extend beyond the [-180,180] interval: map('world', fill = TRUE, col = 1:10) # if you wrap at [-180,180], you also can get a clean closure of Antarctica map('world', fill = TRUE, col = 1:10, wrap=c(-180,180) )
This database is primarily of world cities of population greater than about 40,000. Also included are capital cities of any population size, and many smaller towns.
data(world.cities)data(world.cities)
A list with 6 components, namely "name", "country.etc", "pop", "lat", "long", and "capital", containing the city name, the country name, approximate population (as at January 2006), latitude, longitude and capital status indication (0 for non-capital, 1 for capital, 2 for China Municipalities, and 3 for China Provincial capitals)
Some of the country names and city names may be out of date. Please send any corrections to the package maintainer.
The data were originally obtained from Stefan Helders' website (http://www.world-gazetteer.com), which now redirects to http://www.populationmondiale.com. There are no recent updates available.
This is an alternative version of the world database
based on latitudes [0, 360), which then has the Pacific
Ocean in the centre of the map.
data(world2MapEnv)data(world2MapEnv)
The data file is merely a character string which
specifies the name of an environment variable which contains the
base location of the binary files used by the map drawing functions.
This environment variable (R_MAP_DATA_DIR_WORLD for the datasets in the
maps package) is set at package load time if it does not
already exist. Hence setting the environment variable before loading
the package can override the default location of the binary datasets.
This data set is in fact largely obsolete. Often the same (more general) result can be obtained by using wrapping:
map("world", wrap=c(0,360))
This will also work fine with fill=TRUE or any other appropriate longitude interval (e.g. c(-90,270)).
However, world2 is useful when setting xlim to an interval crossing the 180 meridian.
The public domain Natural Earth data set is available from https://www.naturalearthdata.com.
Richard A. Becker, and Allan R. Wilks, "Maps in S", AT&T Bell Laboratories Statistics Research Report [93.2], 1993.
Richard A. Becker, and Allan R. Wilks, "Constructing a Geographical Database", AT&T Bell Laboratories Statistics Research Report [95.2], 1995.
map('world2', xlim = c(100, 300)) map.axes() # xlim is performed before wrapping: map('world', wrap=c(0,360), xlim = c(100, 300)) # so to emulate "world2": ww2 <- map('world', wrap=c(0,360), plot=FALSE, fill=TRUE) map(ww2, xlim = c(100, 300), fill=TRUE)map('world2', xlim = c(100, 300)) map.axes() # xlim is performed before wrapping: map('world', wrap=c(0,360), xlim = c(100, 300)) # so to emulate "world2": ww2 <- map('world', wrap=c(0,360), plot=FALSE, fill=TRUE) map(ww2, xlim = c(100, 300), fill=TRUE)