关于plot：如何在R :: lattice :: layerplot上显示投影地图？


how to display a projected map on an R::lattice::layerplot?

摘要：我可以在lattice::layerplot上显示lon-lat映射，并且可以在spam::image上显示Lambert保形圆锥(LCC)映射。如何在lattice::layerplot上显示LCC映射？以下是如何不执行此操作的示例-赞赏了修复方面的帮助(甚至只是调试)。

详细信息：

我一直在成功使用网格图形(通过latticeExtra和rasterVis)来显示未投影的经纬度的全球大气数据，效果很好(尽管我对ggplot / ggmap确实很感兴趣) 。特别是，我能够用地图覆盖这些图，这对于我正在做的工作是必不可少的。但是，我目前无法对某些区域数据投影的LCC使用lattice::layerplot：数据图，但是我无法覆盖地图。我可以通过更粗略的方式来执行此操作，但希望知道如何使用lattice/rasterVis或类似方法(例如ggplot/ggmap)执行此操作。接下来是两个几乎独立的示例...但是，如果您已经知道如何执行此操作，请告诉我，我将跳过调试。 (我在一个项目上落后了。)

netCDF数据ozone_lcc.nc随CRAN软件包= M3一起提供...除了M3将其提供为

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system.file("extdata/ozone_lcc.ncf", package="M3")

，该文件扩展名(.ncf)当前导致CRAN package = raster出现问题(请参阅此文章)。您可以重命名该文件(并将其放在当前工作目录中)，也可以仅下载该文件(270 kB)，也可以从M3 tarball中获取它(但请记住重命名！)。

然后，您可以运行以下任何示例(如果未运行Windows，则提供的(IIRC)，不会在其中构建package = M3(但为ICBW))，并根据需要更改常量以适应您的系统。示例1生成了一种类型的映射，我知道(根据以前的经验)该类型将与levelplot中的raster一起使用；但是，在这种情况下，地图的坐标值与数据/栅格的坐标值不匹配。示例2使用了较旧样式的基础图形，并同时绘制了数据和地图。不幸的是，我不知道如何制作在levelplot上生成的地图。正如我想使此代码与其他使用raster和levelplot的代码一起使用一样，这是一个问题。

示例1：产生类似this

的输出

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##### 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::map@projection (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)
o3.raster@crs <- 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 #####

示例2：产生类似this

的输出

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##### 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 #####

但是我看不到如何从M3::get.map.lines.M3.proj返回的简单矩阵(等，但不多)到sp::sp.lines想要的SpatialLines，更不用说latticeExtra::layer想要的东西了。 (我是一个新手，足以发现难以理解的网格文档。)此外，我更希望避免手动进行上述IOAPI转换(尽管我当然愿意这样做，而不是跳过旧式图形的框架) )。

执行此操作的一种方法(尽管很丑)是使用线性IOAPI变换"固定"从maps::map获得的state.map中的坐标。例如，

示例3：产生类似this

的输出

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##### start example 3 #####

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")
# See notes in question regarding unfortunate problem with raster::raster,
# and remember to download or rename the file (symlinking alone will not work).
lcc.file <-"./ozone_lcc.nc"

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::map@projection (below)
lcc.crs <- sp::CRS(lcc.proj4)
lcc.pdf <-"./ozone_lcc.example3.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)
o3.raster@crs <- 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

# replace its coordinates with
metadata.coords.IOAPI.list <- M3::get.grid.info.M3(lcc.file)
metadata.coords.IOAPI.x.orig <- metadata.coords.IOAPI.list$x.orig
metadata.coords.IOAPI.y.orig <- metadata.coords.IOAPI.list$y.orig
metadata.coords.IOAPI.x.cell.width <- metadata.coords.IOAPI.list$x.cell.width
metadata.coords.IOAPI.y.cell.width <- metadata.coords.IOAPI.list$y.cell.width
map.lines <- M3::get.map.lines.M3.proj(
  file=lcc.file, database="state", units="m")
map.lines.coords.IOAPI.x <-
  (map.lines$coords[,1] - metadata.coords.IOAPI.x.orig)/metadata.coords.IOAPI.x.cell.width
map.lines.coords.IOAPI.y <-
  (map.lines$coords[,2] - metadata.coords.IOAPI.y.orig)/metadata.coords.IOAPI.y.cell.width
map.lines.coords.IOAPI <-
  cbind(map.lines.coords.IOAPI.x, map.lines.coords.IOAPI.y)
# start debugging
class(map.lines.coords.IOAPI)
summary(map.lines.coords.IOAPI)
#   end debugging

# >     class(map.lines.coords.IOAPI)
# [1]"matrix"
# >     summary(map.lines.coords.IOAPI)
#  map.lines.coords.IOAPI.x map.lines.coords.IOAPI.y
#  Min.   : 12.88           Min.   :14.47          
#  1st Qu.: 78.62           1st Qu.:39.28          
#  Median :101.37           Median :57.25          
#  Mean   : 95.17           Mean   :55.65          
#  3rd Qu.:124.47           3rd Qu.:72.57          
#  Max.   :140.51           Max.   :93.16          
#  NA's   :168              NA's   :168        

coordinates(state.map.shp) <- map.lines.coords.IOAPI # FAIL:
> Error in `coordinates<-`(`*tmp*`, value = c(99.0242231482288, 99.1277727928691,  :
>   setting coordinates cannot be done on Spatial objects, where they have already been set

state.map.IOAPI <- state.map # copy
state.map.IOAPI$x <- map.lines.coords.IOAPI.x
state.map.IOAPI$y <- map.lines.coords.IOAPI.y
state.map.IOAPI$range <- c(
  min(map.lines.coords.IOAPI.x),
  max(map.lines.coords.IOAPI.x),
  min(map.lines.coords.IOAPI.y),
  max(map.lines.coords.IOAPI.y))
state.map.IOAPI.shp <-
  maptools::map2SpatialLines(state.map.IOAPI, proj4string=lcc.crs)
# start debugging
# thanks, Felix Andrews!
class(state.map.IOAPI.shp)
summary(do.call("rbind",
  unlist(coordinates(state.map.IOAPI.shp), recursive=FALSE)))
#   end debugging

# > class(state.map.IOAPI.shp)
# [1]"SpatialLines"
# attr(,"package")
# [1]"sp"

# > summary(do.call("rbind",
# +   unlist(coordinates(state.map.IOAPI.shp), recursive=FALSE)))
#        V1               V2      
#  Min.   : 12.88   Min.   :14.47  
#  1st Qu.: 78.62   1st Qu.:39.28  
#  Median :101.37   Median :57.25  
#  Mean   : 95.17   Mean   :55.65  
#  3rd Qu.:124.47   3rd Qu.:72.57  
#  Max.   :140.51   Max.   :93.16  

pdf(file=lcc.pdf)
rasterVis::levelplot(o3.raster, margin=FALSE
) + latticeExtra::layer(
  sp::sp.lines(state.map.IOAPI.shp, lwd=0.8, col='darkgray'))
dev.off()
# change this for viewing PDF on your system
system(sprintf("xpdf %s", lcc.pdf))

#####   end example 3 #####