我正在运行一个循环，以在我的数据集的每个子设置中获取地图并相应地应用给定的调色板（和相应的图例）。
人们倾向于不喜欢使用 for() 循环并最大限度地利用他们的方法。我不知道使用此特定数据集对流程进行矢量化的最佳方法。
在这种特殊情况下，我正在处理一个相对较大的数据集（分布物种地图集），该数据集特别复杂，因为使用了不同的方法，并且必须为每个物种传递不同的选项，考虑到特定的季节、不同的观察集等。物种可能在一个季节出现而错过另一个季节（它们可能是繁殖者、居民或移民）。应为所有情况（季节）创建地图，缺席时为空。可能会提供和使用其他数据（除了来自现场工作的数据）。Map Legend 必须适应所有变化，除了以自定义离散比例呈现感兴趣的变量（丰度）。
通过运行循环，我觉得（以我有限的专业知识）我可以更轻松地保留和控制几个需要的对象，同时进入我创建的通量以产生感兴趣的部分并最终创建一组物种分布图。
我的问题是我将每个生成的 ggplot 存储在 list() 对象中。每个季节的每个物种都将存储在一个列表中。我面临的问题与在循环中使用时 的scale_fill_manual有关。
这种行为很奇怪，因为我完成了地图，但颜色仅应用于最后一个 ggplot 输出。尽管如此，所有值仍然在图例中正确识别。
举例说明：
套餐
if (!require(ggplot2)) install.packages("ggplot2",
repos = "http://cran.r-project.org"); library(ggplot2)
if (!require(grid)) install.packages("grid",
repos = "http://cran.r-project.org"); library(grid)
if (!require(RColorBrewer)) install.packages("RColorBrewer",
repos = "http://cran.r-project.org"); library(RColorBrewer)
if (!require(reshape)) install.packages("reshape",
repos = "http://cran.r-project.org"); library(reshape)
先举个简单的例子
#Create a list of colors to be used with scale_manual
palette.l <- list()
palette.l[[1]] <- c('red', 'blue', 'green')
palette.l[[2]] <- c('pink', 'blue', 'yellow')
# Store each ggplot in a list object
plot.l <- list()
#Loop it
for(i in 1:2){
plot.l[[i]] <- qplot(mpg, wt, data = mtcars, colour = factor(cyl)) +
scale_colour_manual(values = palette.l[[i]])
}
我的特殊情况
功能
排列地块
ArrangeGraph <- function(..., nrow=NULL, ncol=NULL, as.table=FALSE) {
dots <- list(...)
n <- length(dots)
if(is.null(nrow) & is.null(ncol)) { nrow = floor(n/2) ; ncol = ceiling(n/nrow)}
if(is.null(nrow)) { nrow = ceiling(n/ncol)}
if(is.null(ncol)) { ncol = ceiling(n/nrow)}
## NOTE see n2mfrow in grDevices for possible alternative
grid.newpage()
pushViewport(viewport(layout=grid.layout(nrow,ncol)))
ii.p <- 1
for(ii.row in seq(1, nrow)) {
ii.table.row <- ii.row
if(as.table) {ii.table.row <- nrow - ii.table.row + 1}
for(ii.col in seq(1, ncol)) {
ii.table <- ii.p
if(ii.p > n) break
print(dots[[ii.table]], vp=VPortLayout(ii.table.row, ii.col))
ii.p <- ii.p + 1
}
}
}
视口
VPortLayout <- function(x, y) viewport(layout.pos.row=x, layout.pos.col=y)
物种数据集
bd.aves.1 <- structure(list(quad = c("K113", "K114", "K114", "K114", "K114",...
due to limited body character number limit, please download entire code from
https://docs.google.com/open?id=0BxSZDr4eTnb9R09iSndzZjBMS28
物种清单
list.esp.1 <- c("Sylv mela", "Saxi rube","Ocea leuc")#
# download from the above link
一些分类和其他数据
txcon.1 <- structure(list(id = c(156L, 359L, 387L), grupo = c("Aves", "Aves",#
# download from the above link
四季
kSeason.1 <- c("Inverno", "Primavera", "Outono")
示例网格
grid500.df.1 <- structure(list(id = c("K113", "K113", "K113", "K113", "K113",#...
# download from the above link
其他映射元素
海岸线
coastline.df.1 <- structure(list(long = c(182554.963670234, 180518, 178865.39,#...
# download from the above link
标签位置调整
kFacx1 <- c(9000, -13000, -10000, -12000)
R代码
for(i in listsp.1) { # LOOP 1 - Species
# Set up objects
sist.i <- list() # Sistematic observations
nsist.i <- list() # Non-Sistematic observations
breaks.nind.1 <- list() # Breaks on abundances
## Grid and merged dataframe
spij.1 <- list() # Stores a dataframe for sp i at season j
## Palette build
classes.1 <- list()
cllevels.1 <- list()
palette.nind.1 <- list() # Color palette
## Maps
grid500ij.1 <- list() # Grid for species i at season j
map.dist.ij.1 <- NULL
for(j in 1:length(kSeason.1)) { # LOOP 2 - Seasons
# j assume each season: Inverno, Primavera, Outono
# Sistematic occurences ===================================================
sist.i.tmp <- nrow(subset(bd.aves.1, esp == i & cod_tipo %in% sistematica &
periodo == kSeason.1[j]))
if (sist.i.tmp!= 0) { # There is sistematic entries, Then:
sist.i[[j]]<- ddply(subset(bd.aves.1,
esp == i & cod_tipo %in% sistematica &
periodo == kSeason.1[j]),
.(periodo, quad), summarise, nind = sum(n_ind),
codnid = max(cod_nidi))
} else { # No Sistematic entries, Then:
sist.i[[j]] <- data.frame('quad' = NA, 'periodo' = NA, 'nind' = NA,
'codnid' = NA, stringsAsFactors = F)
}
# Additional Entries (RS1) e other non-sistematic entries (biblio) =======
nsist.tmp.i = nrow(subset(bd.aves.1, esp == i & !cod_tipo %in% sistematica &
periodo == kSeason.1[j]))
if (nsist.tmp.i != 0) { # RS1 and biblio entries, Then:
nsist.i[[j]] <- subset(bd.aves.1,
esp == i & !cod_tipo %in% sistematica &
periodo == kSeason.1[j] &
!quad %in% if (nrow(sist.i[[j]]) != 0) {
subset(sist.i[[j]],
select = quad)$quad
} else NA,
select = c(quad, periodo, cod_tipo, cod_nidi)
)
names(nsist.i[[j]])[4] <- 'codnid'
} else { # No RS1 and biblio entries, Then:
nsist.i[[j]] = data.frame('quad' = NA, 'periodo' = NA, 'cod_tipo' = NA,
'codnid' = NA, stringsAsFactors = F)
}
# Quantile breaks =========================================================
if (!is.na(sist.i[[j]]$nind[1])) {
breaks.nind.1[[j]] <- c(0,
unique(
ceiling(
quantile(unique(
subset(sist.i[[j]], is.na(nind) == F)$nind),
q = seq(0, 1, by = 0.25)))))
} else {
breaks.nind.1[[j]] <- 0
}
# =========================================================================
# Build Species dataframe and merge to grid
# =========================================================================
if (!is.na(sist.i[[j]]$nind[1])) { # There are Sistematic entries, Then:
spij.1[[j]] <- merge(unique(subset(grid500df.1, select = id)),
sist.i[[j]],
by.x = 'id', by.y = 'quad', all.x = T)
# Adjust abundances when equals to NA ===================================
spij.1[[j]]$nind[is.na(spij.1[[j]]$nind) == T] <- 0
# Break abundances to create discrete variable ==========================
spij.1[[j]]$cln <- if (length(breaks.nind.1[[j]]) > 2) {
cut(spij.1[[j]]$nind, breaks = breaks.nind.1[[j]],
include.lowest = T, right = F)
} else {
cut2(spij.1[[j]]$nind, g = 2)
}
# Variable Abundance ====================================================
classes.1[[j]] = nlevels(spij.1[[j]]$cln)
cllevels.1[[j]] = levels(spij.1[[j]]$cln)
# Color Palette for abundances - isolated Zero class (color #FFFFFF) ====
if (length(breaks.nind.1[[j]]) > 2) {
palette.nind.1[[paste(kSeason.1[j])]] = c("#FFFFFF", brewer.pal(length(
cllevels.1[[j]]) - 1, "YlOrRd"))
} else {
palette.nind.1[[paste(kSeason.1[j])]] = c(
"#FFFFFF", brewer.pal(3, "YlOrRd"))[1:classes.1[[j]]]
}
names(palette.nind.1[[paste(kSeason.1[j])]])[1 : length(
palette.nind.1[[paste(kSeason.1[j])]])] <- cllevels.1[[j]]
# Add RS1 and bilbio values to palette ==================================
palette.nind.1[[paste(kSeason.1[j])]][length(
palette.nind.1[[paste(kSeason.1[j])]]) + 1] <- '#CCC5AF'
names(palette.nind.1[[paste(kSeason.1[j])]])[length(
palette.nind.1[[paste(kSeason.1[j])]])] <- 'Suplementar'
palette.nind.1[[paste(kSeason.1[j])]][length(
palette.nind.1[[paste(kSeason.1[j])]]) + 1] <- '#ADCCD7'
names(palette.nind.1[[paste(kSeason.1[j])]])[length(
palette.nind.1[[paste(kSeason.1[j])]])] <- 'Bibliografia'
# Merge species i dataframe to grid map =================================
grid500ij.1[[j]] <- subset(grid500df.1, select = c(id, long, lat, order))
grid500ij.1[[j]]$cln = merge(grid500ij.1[[j]],
spij.1[[j]],
by.x = 'id', by.y = 'id', all.x = T)$cln
# Adjust factor levels of cln variable - Non-Sistematic data ============
levels(grid500ij.1[[j]]$cln) <- c(levels(grid500ij.1[[j]]$cln), 'Suplementar',
'Bibliografia')
if (!is.na(nsist.i[[j]]$quad[1])) {
grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
nsist.i[[j]], cod_tipo == 'RS1', select = quad)$quad] <- 'Suplementar'
grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
nsist.i[[j]], cod_tipo == 'biblio', select = quad)$quad] <- 'Bibliografia'
}
} else { # No Sistematic entries, Then:
if (!is.na(nsist.i[[j]]$quad[1])) { # RS1 or Biblio entries, Then:
grid500ij.1[[j]] <- grid500df
grid500ij.1[[j]]$cln <- '0'
grid500ij.1[[j]]$cln <- factor(grid500ij.1[[j]]$cln)
levels(grid500ij.1[[j]]$cln) <- c(levels(grid500ij.1[[j]]$cln),
'Suplementar', 'Bibliografia')
grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
nsist.i[[j]], cod_tipo == 'RS1',
select = quad)$quad] <- 'Suplementar'
grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
nsist.i[[j]],cod_tipo == 'biblio',
select = quad)$quad] <- 'Bibliografia'
} else { # No entries, Then:
grid500ij.1[[j]] <- grid500df
grid500ij.1[[j]]$cln <- '0'
grid500ij.1[[j]]$cln <- factor(grid500ij.1[[j]]$cln)
levels(grid500ij.1[[j]]$cln) <- c(levels(grid500ij.1[[j]]$cln),
'Suplementar', 'Bibliografia')
}
} # End of Species dataframe build
# Distribution Map for species i at season j =============================
if (!is.na(sist.i[[j]]$nind[1])) { # There is sistematic entries, Then:
map.dist.ij.1[[paste(kSeason.1[j])]] <- ggplot(grid500ij.1[[j]],
aes(x = long, y = lat)) +
geom_polygon(aes(group = id, fill = cln), colour = 'grey80') +
coord_equal() +
scale_x_continuous(limits = c(100000, 180000)) +
scale_y_continuous(limits = c(-4000, 50000)) +
scale_fill_manual(
name = paste("LEGEND",
'\nSeason: ', kSeason.1[j],
'\n% of Occupied Cells : ',
sprintf("%.1f%%", (length(unique(
grid500ij.1[[j]]$id[grid500ij.1[[j]]$cln != levels(
grid500ij.1[[j]]$cln)[1]]))/12)*100), # percent
sep = ""
),
# Set Limits
limits = names(palette.nind.1[[j]])[2:length(names(palette.nind.1[[j]]))],
values = palette.nind.1[[j]][2:length(names(palette.nind.1[[j]]))],
drop = F) +
opts(
panel.background = theme_rect(),
panel.grid.major = theme_blank(),
panel.grid.minor = theme_blank(),
axis.ticks = theme_blank(),
title = txcon.1$especie[txcon.1$esp == i],
plot.title = theme_text(size = 10, face = 'italic'),
axis.text.x = theme_blank(),
axis.text.y = theme_blank(),
axis.title.x = theme_blank(),
axis.title.y = theme_blank(),
legend.title = theme_text(hjust = 0,size = 10.5),
legend.text = theme_text(hjust = -0.2, size = 10.5)
) +
# Shoreline
geom_path(inherit.aes = F, aes(x = long, y = lat),
data = coastline.df.1, colour = "#997744") +
# Add localities
geom_point(inherit.aes = F, aes(x = x, y = y), colour = 'grey20',
data = localidades, size = 2) +
# Add labels
geom_text(inherit.aes = F, aes(x = x, y = y, label = c('Burgau',
'Sagres')),
colour = "black",
data = data.frame(x = c(142817 + kFacx1[1], 127337 + kFacx1[4]),
y = c(11886, 3962), size = 3))
} else { # NO sistematic entries,then:
map.dist.ij.1[[paste(kSeason.1[j])]] <- ggplot(grid500ij.1[[j]],
aes(x = long, y = lat)) +
geom_polygon(aes.inherit = F, aes(group = id, fill = cln),
colour = 'grey80') +
#scale_color_manual(values = kCorLimiteGrid) +
coord_equal() +
scale_x_continuous(limits = c(100000, 40000)) +
scale_y_continuous(limits = c(-4000, 180000)) +
scale_fill_manual(
name = paste('LEGENDA',
'\nSeason: ', kSeason.1[j],
'\n% of Occupied Cells :',
sprintf("%.1f%%", (length(unique(
grid500ij.1[[j]]$id[grid500ij.1[[j]]$cln != levels(
grid500ij.1[[j]]$cln)[1]]))/12 * 100)), # percent
sep = ''),
limits = names(kPaletaNsis)[2:length(names(kPaletaNsis))],
values = kPaletaNsis[2:length(names(kPaletaNsis))],
drop = F) +
opts(
panel.background = theme_rect(),
panel.grid.major = theme_blank(),
panel.grid.minor = theme_blank(),
title = txcon.1$especie[txcon.1$esp == i],
plot.title = theme_text(size = 10, face = 'italic'),
axis.ticks = theme_blank(),
axis.text.x = theme_blank(),
axis.text.y = theme_blank(),
axis.title.x = theme_blank(),
axis.title.y = theme_blank(),
legend.title = theme_text(hjust = 0,size = 10.5),
legend.text = theme_text(hjust = -0.2, size = 10.5)
) +
# Add Shoreline
geom_path(inherit.aes = F, data = coastline.df.1,
aes(x = long, y = lat),
colour = "#997744") +
# Add Localities
geom_point(inherit.aes = F, aes(x = x, y = y),
colour = 'grey20',
data = localidades, size = 2) +
# Add labels
geom_text(inherit.aes = F, aes(x = x, y = y,
label = c('Burgau', 'Sagres')),
colour = "black",
data = data.frame(x = c(142817 + kFacx1[1],
127337 + kFacx1[4],),
y = c(11886, 3962)),
size = 3)
} # End of Distribution map building for esp i and j seasons
} # Fim do LOOP 2: j Estacoes
# Print Maps
png(file = paste('panel_species',i,'.png', sep = ''), res = 96,
width = 800, height = 800)
ArrangeGraph(map.dist.ij.1[[paste(kSeason.1[3])]],
map.dist.ij.1[[paste(kSeason.1[2])]],
map.dist.ij.1[[paste(kSeason.1[1])]],
ncol = 2, nrow = 2)
dev.off()
graphics.off()
} # End of LOOP 1
map.dist.ij.1[[paste(kSeason.1[3])]] 是唯一一个将调色板应用于多边形的，但每个 j 地图的图例项都得到了很好的定义。
使用 R 代码输出
如我们所见， Legends 是 OK 但不是 coloured。
希望不要遗漏任何东西。很抱歉丢失了一些葡萄牙语术语。