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摘要：我可以在lattice::layerplot上顯示lon-lat圖，我可以在spam::image上顯示Lambert conformal conic（LCC）圖。如何在lattice::layerplot上顯示LCC地圖？如何不這樣做的例子 - 幫助修理是讚賞（甚至只是調試）。如何在R :: lattice :: layerplot上顯示投影映射？
細節：
我已經使用了網格圖形（通過latticeExtra和rasterVis）成功地顯示未投影LON-LAT全球大氣數據，其作品不夠好（雖然我肯定通過ggplot/ggmap感興趣）。特別是，我可以用地圖覆蓋這些地塊，這對我正在做的這類工作很重要。然而，我目前無法使用lattice::layerplot對於某些區域數據投影LCC：數據圖，但我無法獲得覆蓋圖。我可以通過更爲粗魯的方式來做到這一點，但寧願知道如何在lattice/rasterVis或類似方法中執行此操作（例如，ggplot/ggmap）。兩個幾乎自成一體的例子遵循...但如果你知道如何做到這一點，請讓我知道，我會跳過調試。 （我waayyy背後的一個項目。）
的NetCDF數據，ozone_lcc.nc，自帶CRAN package=M3 ...除了M3提供它作爲
system.file("extdata/ozone_lcc.ncf", package="M3")
和文件擴展名（.ncf）當前導致問題對於CRAN package=raster（見this post）。您可以重命名該文件（並將其放置在當前的工作目錄中），或者您可以下載just that file（270 kB），或者從M3 tarball（但請記住重命名它！）獲取它。
然後，您可以運行以下任何示例（提供（IIRC）您沒有運行Windows，其中package = M3將不會生成（但是ICBW）），根據需要更改常量以適應您的系統。示例1生成一個我知道的類型的地圖（來自以前的經驗）將與中的raster一起使用;但是，在這種情況下，地圖和數據/光柵的座標值不匹配。示例2使用舊式基本圖形，並實際繪製數據和地圖;不幸的是，我不知道如何製作這種在levelplot上生成覆蓋圖的地圖。由於我想使這個代碼與一堆使用raster和levelplot的其他代碼一起工作，這是一個問題。
例1：產生輸出等
##### start example 1 #####
library("M3") # http://cran.r-project.org/web/packages/M3/
library("rasterVis") # http://cran.r-project.org/web/packages/rasterVis/
## Use an example file with projection=Lambert conformal conic.
# lcc.file <- system.file("extdata/ozone_lcc.ncf", package="M3")
lcc.file <- "./ozone_lcc.nc" # unfortunate problem with raster::raster
lcc.proj4 <- M3::get.proj.info.M3(lcc.file)
lcc.proj4 # debugging
# [1] "+proj=lcc +lat_1=33 +lat_2=45 +lat_0=40 +lon_0=-97 +a=6370000 +b=6370000"
# Note +lat_0=40 +lat_1=33 +lat_2=45 for maps::[email protected] (below)
lcc.crs <- sp::CRS(lcc.proj4)
lcc.pdf <- "./ozone_lcc.example1.pdf" # for output
## Read in variable with daily ozone.
# o3.raster <- raster::raster(x=lcc.file, varname="O3", crs=lcc.crs)
# ozone_lcc.nc has 4 timesteps, choose 1 at random
o3.raster <- raster::raster(x=lcc.file, varname="O3", crs=lcc.crs, level=1)
[email protected] <- lcc.crs # why does the above assignment not take?
# start debugging
o3.raster
summary(coordinates(o3.raster)) # thanks, Felix Andrews!
M3::get.grid.info.M3(lcc.file)
# end debugging
# > o3.raster
# class : RasterLayer
# band : 1
# dimensions : 112, 148, 16576 (nrow, ncol, ncell)
# resolution : 1, 1 (x, y)
# extent : 0.5, 148.5, 0.5, 112.5 (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=lcc +lat_1=33 +lat_2=45 +lat_0=40 +lon_0=-97 +a=6370000 +b=6370000
# data source : .../ozone_lcc.nc
# names : O3
# z-value : 1
# zvar : O3
# level : 1
# > summary(coordinates(o3.raster))
# x y
# Min. : 1.00 Min. : 1.00
# 1st Qu.: 37.75 1st Qu.: 28.75
# Median : 74.50 Median : 56.50
# Mean : 74.50 Mean : 56.50
# 3rd Qu.:111.25 3rd Qu.: 84.25
# Max. :148.00 Max. :112.00
# > M3::get.grid.info.M3(lcc.file)
# $x.orig
# [1] -2736000
# $y.orig
# [1] -2088000
# $x.cell.width
# [1] 36000
# $y.cell.width
# [1] 36000
# $hz.units
# [1] "m"
# $ncols
# [1] 148
# $nrows
# [1] 112
# $nlays
# [1] 1
# The grid (`coordinates(o3.raster)`) is integers, because this
# data is stored using the IOAPI convention. IOAPI makes the grid
# Cartesian by using an (approximately) equiareal projection (LCC)
# and abstracting grid positioning to metadata stored in netCDF
# global attributes. Some of this spatial metadata can be accessed
# by `M3::get.grid.info.M3` (above), and some can be accessed via
# the coordinate reference system (`CRS`, see `lcc.proj4` above)
## Get US state boundaries in projection units.
state.map <- maps::map(
database="state", projection="lambert", par=c(33,45), plot=FALSE)
# parameters to lambert: ^^^^^^^^^^^^
# see mapproj::mapproject
state.map.shp <-
maptools::map2SpatialLines(state.map, proj4string=lcc.crs)
# start debugging
# thanks, Felix Andrews!
class(state.map.shp)
summary(do.call("rbind",
unlist(coordinates(state.map.shp), recursive=FALSE)))
# end debugging
# > class(state.map.shp)
# [1] "SpatialLines"
# attr(,"package")
# [1] "sp"
# > summary(do.call("rbind",
# + unlist(coordinates(state.map.shp), recursive=FALSE)))
# V1 V2
# Min. :-0.234093 Min. :-0.9169
# 1st Qu.:-0.000333 1st Qu.:-0.8289
# Median : 0.080378 Median :-0.7660
# Mean : 0.058492 Mean :-0.7711
# 3rd Qu.: 0.162993 3rd Qu.:-0.7116
# Max. : 0.221294 Max. :-0.6343
# I can't see how to relate these coordinates to `coordinates(o3.raster)`
pdf(file=lcc.pdf)
rasterVis::levelplot(o3.raster, margin=FALSE
) + latticeExtra::layer(
sp::sp.lines(state.map.shp, lwd=0.8, col='darkgray'))
dev.off()
# change this for viewing PDF on your system
system(sprintf("xpdf %s", lcc.pdf))
# data looks correct, map invisible
## Try again, with lambert(40,33)
state.map <- maps::map(
database="state", projection="lambert", par=c(40,33), plot=FALSE)
# parameters to lambert: ^^^^^^^^^^^^
# see mapproj::mapproject
state.map.shp <-
maptools::map2SpatialLines(state.map, proj4string=lcc.crs)
# start debugging
summary(do.call("rbind",
unlist(coordinates(state.map.shp), recursive=FALSE)))
# end debugging
# > summary(do.call("rbind",
# + unlist(coordinates(state.map.shp), recursive=FALSE)))
# V1 V2
# Min. :-0.2226344 Min. :-0.9124
# 1st Qu.:-0.0003149 1st Qu.:-0.8295
# Median : 0.0763322 Median :-0.7706
# Mean : 0.0553948 Mean :-0.7752
# 3rd Qu.: 0.1546465 3rd Qu.:-0.7190
# Max. : 0.2112617 Max. :-0.6458
# no real change from previous `coordinates(state.map.shp)`
pdf(file=lcc.pdf)
rasterVis::levelplot(o3.raster, margin=FALSE
) + latticeExtra::layer(
sp::sp.lines(state.map.shp, lwd=0.8, col='darkgray'))
dev.off()
system(sprintf("xpdf %s", lcc.pdf))
# as expected, same as before: data looks correct, map invisible
##### end example 1 #####
例2：產生像
##### start example 2 #####
# Following adapted from what is installed in my
# .../R/x86_64-pc-linux-gnu-library/2.14/m3AqfigExampleScript.r
# (probably by my sysadmin), which also greatly resembles
# https://wiki.epa.gov/amad/index.php/R_packages_developed_in_AMAD
# which is behind a firewall :-(
## EXAMPLE WITH LAMBERT CONIC CONFORMAL FILE.
library("M3")
library("aqfig") # http://cran.r-project.org/web/packages/aqfig/
## Use an example file with LCC projection: either local download ...
lcc.file <- "./ozone_lcc.nc"
## ... or as installed by package=M3:
# lcc.file <- system.file("extdata/ozone_lcc.ncf", package="M3")
## Choose the one that works for you.
lcc.pdf <- "./ozone_lcc.example2.pdf" # for output
## READ AND PLOT OZONE FROM FILE WITH LCC PROJECTION.
## Read in variable with daily ozone. Note that we can give dates
## rather than date-times, and that will include all time steps
## anytime during those days. Or, we can give lower and upper bounds
## and all time steps between these will be taken.
o3 <- M3::get.M3.var(
file=lcc.file, var="O3", hz.units="m",
ldatetime=as.Date("2001-07-01"), udatetime=as.Date("2001-07-04"))
# start debugging
class(o3)
summary(o3)
summary(o3$x.cell.ctr)
# end debugging
# > class(o3)
# [1] "list"
# > summary(o3)
# Length Class Mode
# data 66304 -none- numeric
# data.units 1 -none- character
# x.cell.ctr 148 -none- numeric
# y.cell.ctr 112 -none- numeric
# hz.units 1 -none- character
# rows 112 -none- numeric
# cols 148 -none- numeric
# layers 1 -none- numeric
# datetime 4 POSIXct numeric
# > summary(o3$x.cell.ctr)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# -2718000 -1395000 -72000 -72000 1251000 2574000
# Note how these grid coordinates relate to the IOAPI metadata above:
# min(o3$x.cell.ctr) == -2718000
# == -2736000 + (36000/2) == x.orig + (x.cell.width/2)
## Get colors and map boundaries for plot.
library("fields")
col.rng <- tim.colors(20)
detach("package:fields")
## Get state boundaries in projection units.
map.lines <- M3::get.map.lines.M3.proj(
file=lcc.file, database="state", units="m")
# start debugging
class(map.lines)
summary(map.lines)
summary(map.lines$coords)
# end debugging
# > class(map.lines)
# [1] "list"
# > summary(map.lines)
# Length Class Mode
# coords 23374 -none- numeric
# units 1 -none- character
# > summary(map.lines$coords)
# x y
# Min. :-2272238 Min. :-1567156
# 1st Qu.: 94244 1st Qu.: -673953
# Median : 913204 Median : -26948
# Mean : 689997 Mean : -84644
# 3rd Qu.: 1744969 3rd Qu.: 524531
# Max. : 2322260 Max. : 1265778
# NA's :168 NA's :168
## Set color boundaries to encompass the complete data range.
z.rng <- range(as.vector(o3$data))
## Make a color tile plot of the ozone for the first day (2001-07-01).
pdf(file=lcc.pdf)
image(o3$x.cell.ctr, o3$y.cell.ctr, o3$data[,,1,1],
col=col.rng, zlim=z.rng,
xlab="x-coord (m)", ylab="y-coord (m)")
## Put date-time string and chemical name (O3) into a format I can use
## to label the actual figure.
date.str <- format(o3$datetime[1], "%Y-%m-%d")
title(main=bquote(paste(O[3], " on ", .(date.str), sep="")))
## Put the state boundaries on the plot.
lines(map.lines$coords)
## Add legend to right of plot. NOTE: YOU CANNOT ADD TO THE PLOT
## AFTER USING vertical.image.legend(). Before making a new plot,
## open a new device or turn this device off.
vertical.image.legend(zlim=z.rng, col=col.rng)
dev.off() # close the plot if you haven't already, ...
# ... and change the following to display PDFs on your system.
system(sprintf("xpdf %s", lcc.pdf))
# data displays with state map
##### end example 2 #####
輸出但我不能看到如何從簡單矩陣得到（但不是太多）由M3::get.map.lines.M3.proj返回即sp::sp.lines想要的，更不用說latticeExtra::layer想要。 （我足夠的新手來查找網格文檔相當難以理解。）此外，我寧願避免手動進行上面的IOAPI轉換（儘管我肯定會這樣做比跳過舊式圖形）。
