spplot question in sp_0.9-98
Thanks for notifying this. Removing the cex=.7 solves this, as does
replicating cex, as in
spplot(meuse, c("cadmium", "copper", "lead", "zinc"), do.log = TRUE,
key.space = "right", as.table = TRUE,
sp.layout=list(rv, scale, text1, text2, arrow),
main = "Heavy metals (top soil), ppm", cex=rep(.7,4*length(meuse)), cuts
= cuts)
This, and returning the color vector, are obviously bugs, and will be
addressed in the next version of sp.
On 04/03/2012 08:37 PM, Mauricio Zambrano-Bigiarini wrote:
Dear List, I just notice an strange behaviour of the 'spplot' function, and I would like your help to figure out if it is something related to my system or is something related to the 'spplot' function. Below goes a reproducible example taken from http://r-spatial.sourceforge.net/gallery/ : # fig06.R multi-panel plot, scales + north arrow only in last plot. ----------------------START----------------------------------- library(sp) library(lattice) # required for trellis.par.set(): trellis.par.set(sp.theme()) # sets color ramp to bpy.colors() data(meuse) coordinates(meuse)=~x+y data(meuse.riv) meuse.sr = SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv"))) rv = list("sp.polygons", meuse.sr, fill = "lightblue") ## multi-panel plot, scales + north arrow only in last plot: ## using the "which" argument in a layout component ## (if which=4 was set as list component of sp.layout, the river ## would as well be drawn only in that (last) panel) scale = list("SpatialPolygonsRescale", layout.scale.bar(), offset = c(180500,329800), scale = 500, fill=c("transparent","black"), which = 4) text1 = list("sp.text", c(180500,329900), "0", cex = .5, which = 4) text2 = list("sp.text", c(181000,329900), "500 m", cex = .5, which = 4) arrow = list("SpatialPolygonsRescale", layout.north.arrow(), offset = c(181300,329800), scale = 400, which = 4) cuts = c(.2,.5,1,2,5,10,20,50,100,200,500,1000,2000) spplot(meuse, c("cadmium", "copper", "lead", "zinc"), do.log = TRUE, key.space = "right", as.table = TRUE, sp.layout=list(rv, scale, text1, text2, arrow), main = "Heavy metals (top soil), ppm", cex = .7, cuts = cuts) ----------------------END----------------------------------- After the last spplot command, I only get one point in the upper left figure, which is very different from the Figure 6 shown in http://r-spatial.sourceforge.net/gallery/ In addition, I get the following output after the spplot command: [1] "#A714EBFF" "#6500FFFF" "#6500FFFF" "#2400FFFF" "#2400FFFF" "#2400FFFF" [7] "#2400FFFF" "#2400FFFF" "#2400FFFF" "#0000DAFF" "#0000DAFF" "#0000DAFF" [13] "#A714EBFF" "#2400FFFF" "#0000DAFF" "#6500FFFF" "#6500FFFF" "#6500FFFF" [19] "#6500FFFF" "#A714EBFF" "#6500FFFF" "#2400FFFF" "#2400FFFF" "#0000DAFF" [25] "#0000DAFF" "#0000DAFF" "#0000DAFF" "#0000DAFF" "#0000DAFF" "#0000DAFF" [31] "#0000DAFF" "#0000DAFF" "#2400FFFF" "#0000DAFF" "#0000DAFF" "#2400FFFF" [37] "#6500FFFF" "#6500FFFF" "#6500FFFF" "#A714EBFF" "#2400FFFF" "#0000DAFF" [43] "#0000DAFF" "#0000DAFF" "#2400FFFF" "#2400FFFF" "#2400FFFF" "#0000DAFF" [49] "#0000DAFF" "#2400FFFF" "#0000DAFF" "#6500FFFF" "#A714EBFF" "#A714EBFF" [55] "#6500FFFF" "#6500FFFF" "#2400FFFF" "#2400FFFF" "#A714EBFF" "#6500FFFF" [61] "#6500FFFF" "#6500FFFF" "#6500FFFF" "#6500FFFF" "#6500FFFF" "#6500FFFF" [67] "#6500FFFF" "#000086FF" "#2400FFFF" "#2400FFFF" "#2400FFFF" "#2400FFFF" [73] "#2400FFFF" "#2400FFFF" "#2400FFFF" "#2400FFFF" "#2400FFFF" "#2400FFFF" [79] "#6500FFFF" "#6500FFFF" "#A714EBFF" "#A714EBFF" "#6500FFFF" "#2400FFFF" [85] "#0000DAFF" "#2400FFFF" "#2400FFFF" "#2400FFFF" "#2400FFFF" "#0000DAFF" [91] "#0000DAFF" "#2400FFFF" "#2400FFFF" "#000086FF" "#000086FF" "#000033FF" [97] "#000086FF" "#000033FF" "#000033FF" "#000033FF" "#000086FF" "#000033FF" [103] "#000033FF" "#000033FF" "#000033FF" "#000033FF" "#000033FF" "#000033FF" [109] "#000033FF" "#000033FF" "#000033FF" "#000033FF" "#000033FF" "#000033FF" [115] "#000033FF" "#000033FF" "#000033FF" "#2400FFFF" "#000033FF" "#000033FF" [121] "#000033FF" "#000033FF" "#0000DAFF" "#2400FFFF" "#000033FF" "#000033FF" [127] "#000033FF" "#000033FF" "#000033FF" "#0000DAFF" "#000086FF" "#0000DAFF" [133] "#000033FF" "#000033FF" "#000086FF" "#000086FF" "#000086FF" "#0000DAFF" [139] "#0000DAFF" "#0000DAFF" "#0000DAFF" "#000086FF" "#000086FF" "#2400FFFF" [145] "#2400FFFF" "#2400FFFF" "#2400FFFF" "#2400FFFF" "#0000DAFF" "#0000DAFF" [151] "#000086FF" "#2400FFFF" "#2400FFFF" "#000086FF" "#2400FFFF" "#FF6798FF" [157] "#FF6798FF" "#FF6798FF" "#FF6798FF" "#E83EC1FF" "#FF6798FF" "#E83EC1FF" [163] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#FF6798FF" [169] "#E83EC1FF" "#E83EC1FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" [175] "#FF6798FF" "#FF6798FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [181] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [187] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [193] "#FF6798FF" "#FF6798FF" "#FF916EFF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [199] "#E83EC1FF" "#FF6798FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [205] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#FF916EFF" "#FF916EFF" "#FF916EFF" [211] "#FF6798FF" "#E83EC1FF" "#E83EC1FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" [217] "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" [223] "#E83EC1FF" "#FF6798FF" "#E83EC1FF" "#E83EC1FF" "#FF6798FF" "#E83EC1FF" [229] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#FF6798FF" [235] "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#E83EC1FF" "#E83EC1FF" [241] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [247] "#E83EC1FF" "#E83EC1FF" "#A714EBFF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [253] "#E83EC1FF" "#E83EC1FF" "#A714EBFF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [259] "#E83EC1FF" "#E83EC1FF" "#A714EBFF" "#A714EBFF" "#E83EC1FF" "#E83EC1FF" [265] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [271] "#E83EC1FF" "#E83EC1FF" "#FF6798FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" [277] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#A714EBFF" "#A714EBFF" [283] "#A714EBFF" "#E83EC1FF" "#E83EC1FF" "#A714EBFF" "#E83EC1FF" "#A714EBFF" [289] "#A714EBFF" "#E83EC1FF" "#E83EC1FF" "#A714EBFF" "#E83EC1FF" "#E83EC1FF" [295] "#E83EC1FF" "#E83EC1FF" "#A714EBFF" "#A714EBFF" "#E83EC1FF" "#E83EC1FF" [301] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#A714EBFF" "#A714EBFF" [307] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#FFBB44FF" "#FFBB44FF" [313] "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FF916EFF" [319] "#FF916EFF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FFBB44FF" "#FF916EFF" [325] "#FF916EFF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#FFBB44FF" [331] "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FF6798FF" "#FF6798FF" "#FF6798FF" [337] "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" [343] "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF916EFF" "#FFBB44FF" [349] "#FFBB44FF" "#FFBB44FF" "#FF916EFF" "#E83EC1FF" "#FF6798FF" "#FF6798FF" [355] "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" "#FF916EFF" [361] "#FF6798FF" "#FF916EFF" "#FFBB44FF" "#FFE51AFF" "#FFBB44FF" "#FFBB44FF" [367] "#FF916EFF" "#FFBB44FF" "#FFE51AFF" "#FFBB44FF" "#FFBB44FF" "#FF916EFF" [373] "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#E83EC1FF" [379] "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" [385] "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" [391] "#FFBB44FF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" "#FF6798FF" "#FF916EFF" [397] "#FFBB44FF" "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" [403] "#FFBB44FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" [409] "#FF6798FF" "#FF6798FF" "#FF6798FF" "#E83EC1FF" "#FF6798FF" "#E83EC1FF" [415] "#FF6798FF" "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#FF6798FF" "#FF6798FF" [421] "#FF6798FF" "#E83EC1FF" "#E83EC1FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" [427] "#FF6798FF" "#FFBB44FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF6798FF" [433] "#FFBB44FF" "#FFBB44FF" "#FF6798FF" "#FF6798FF" "#E83EC1FF" "#E83EC1FF" [439] "#FF916EFF" "#FF916EFF" "#E83EC1FF" "#FF6798FF" "#FF6798FF" "#E83EC1FF" [445] "#E83EC1FF" "#E83EC1FF" "#E83EC1FF" "#FF6798FF" "#FF916EFF" "#FF6798FF" [451] "#FF6798FF" "#FF6798FF" "#FF6798FF" "#FF916EFF" "#FF916EFF" "#FF916EFF" [457] "#FF916EFF" "#FFBB44FF" "#FF6798FF" "#FF6798FF" "#E83EC1FF" "#FF916EFF" [463] "#FF916EFF" "#FF6798FF" "#FF916EFF" "#FFFF60FF" "#FFFF60FF" "#FFE51AFF" [469] "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" [475] "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#FFFF60FF" "#FFE51AFF" "#FFBB44FF" [481] "#FFFF60FF" "#FFE51AFF" "#FFE51AFF" "#FFE51AFF" "#FFFF60FF" "#FFE51AFF" [487] "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" [493] "#FF916EFF" "#FFBB44FF" "#FF916EFF" "#FFBB44FF" "#FF916EFF" "#FFBB44FF" [499] "#FF916EFF" "#FFBB44FF" "#FFBB44FF" "#FFE51AFF" "#FFE51AFF" "#FFE51AFF" [505] "#FFFF60FF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#FFE51AFF" [511] "#FFE51AFF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" [517] "#FFE51AFF" "#FFFF60FF" "#FFFF60FF" "#FFFF60FF" "#FFE51AFF" "#FFBB44FF" [523] "#FFE51AFF" "#FFFF60FF" "#FFFF60FF" "#FFE51AFF" "#FFE51AFF" "#FFE51AFF" [529] "#FFE51AFF" "#FFE51AFF" "#FFE51AFF" "#FFFF60FF" "#FF916EFF" "#FFE51AFF" [535] "#FFE51AFF" "#FFE51AFF" "#FFE51AFF" "#FFE51AFF" "#FFE51AFF" "#FFE51AFF" [541] "#FFE51AFF" "#FFE51AFF" "#FFE51AFF" "#FFFF60FF" "#FFFF60FF" "#FFFF60FF" [547] "#FFFF60FF" "#FFE51AFF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFE51AFF" [553] "#FFE51AFF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FFE51AFF" "#FFE51AFF" [559] "#FF916EFF" "#FFBB44FF" "#FF916EFF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" [565] "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" [571] "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#FFBB44FF" [577] "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#FFBB44FF" [583] "#FFE51AFF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#FFE51AFF" [589] "#FFE51AFF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" "#FF916EFF" "#FFBB44FF" [595] "#FFBB44FF" "#FF916EFF" "#FFBB44FF" "#FFBB44FF" "#FF916EFF" "#FF916EFF" [601] "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#FFBB44FF" "#FFBB44FF" "#FF916EFF" [607] "#FF916EFF" "#FF916EFF" "#FFBB44FF" "#FFBB44FF" "#FFE51AFF" "#FFE51AFF" [613] "#FFE51AFF" "#FFBB44FF" "#FFBB44FF" "#FF916EFF" "#FFBB44FF" "#FFBB44FF" [619] "#FF916EFF" "#FFBB44FF" ------------------- sessionInfo() R version 2.14.1 (2011-12-22) Platform: x86_64-redhat-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_GB.utf8 LC_NUMERIC=C [3] LC_TIME=en_GB.utf8 LC_COLLATE=en_GB.utf8 [5] LC_MONETARY=en_GB.utf8 LC_MESSAGES=en_GB.utf8 [7] LC_PAPER=C LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_GB.utf8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] lattice_0.20-6 sp_0.9-98 loaded via a namespace (and not attached): [1] grid_2.14.1 tools_2.14.1 ----------- Thanks in advance for any help, Mauricio Zambrano-Bigiarini
Edzer Pebesma Institute for Geoinformatics (ifgi), University of M?nster Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251 8333081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de http://www.52north.org/geostatistics e.pebesma at wwu.de