对于这项研究，我们记录了每棵树的胸径（dbh）的种类和直径>在100m 2 圆形图中，高度为1.5m，直径> 1.8cm。有100多个圆形地块被采样。
我想确定四种直径等级中最具代表性的树种。直径等级是幼树（2.5-8厘米），极（> 8-18厘米），成熟（> 18-28厘米）和大（> 28厘米）。
如果可能，我想了一个例子，说明如何根据密度（每个类别中每个物种的数量）以及体积（基础面积）选择每个类别中最具代表性的物种。
我在crossvalidated.com上发布了一个关于哪种方法（密度或体积）更合适的问题......
https://stats.stackexchange.com/q/148734/57117
请注意，每个网站的名称（location）都不遵循任何模式。根据从子样本中采样的数据集中的位置来分配名称。此外，如果某个站点的某个直径类别中没有物种，NA将起作用。
这是一个示例数据集，其中15个位置被采样，1200个树被测量。我的数据与以下示例数据非常相似。
tree.species<-c("PSME", "PIEN", "LAOC", "POTR", "SALIX")
tree.diameters<- data.frame(location= paste0(sample(LETTERS[c(2,4,6,8)], 4, replace=TRUE),sample(seq(006,250,57), 1200, replace = TRUE)),
dbh= c(rep(3.81,200),rnorm(350, mean = 6.32, sd=1.5),rnorm(50, mean = 75, sd=6),
runif(550, min=20, max=100),rnorm(50, mean = 150, sd=2.3)), #Units in cm
species = factor(sample(tree.species, 1200, replace=TRUE)))
#Add basal area (m sq. per ha)
tree.diameters$basal.area=100*(pi*((tree.diameters$dbh/2)^2)/(4*10000))
#Order the data
tree.diameters<-tree.diameters[order(tree.diameters$location, tree.diameters$dbh,tree.diameters$species),]
> head(tree.diameters, n=15)
location dbh species basal.area
426 B120 3.303363 PSME 0.02142607
358 B120 3.657538 SALIX 0.02626682
450 B120 3.667190 PSME 0.02640565
150 B120 3.810000 PIEN 0.02850230
94 B120 3.810000 POTR 0.02850230
10 B120 3.810000 PSME 0.02850230
90 B120 3.810000 PSME 0.02850230
18 B120 3.810000 SALIX 0.02850230
134 B120 3.810000 SALIX 0.02850230
194 B120 3.810000 SALIX 0.02850230
274 B120 3.979974 SALIX 0.03110214
290 B120 5.345510 SALIX 0.05610586
310 B120 5.480217 POTR 0.05896921
254 B120 5.625061 SALIX 0.06212757
478 B120 5.852126 LAOC 0.06724456
我一直致力于通过密度进行选择的一般方法类似于location=="B120"＆amp; location=="B177"，但我不确定如何迭代它并将所有结果放入列表或data.frame中。我也不确定如何计算音量（basal.area）的相同内容
> names(which(table(subset(tree.diameters,
+ location == "B120" & dbh < 8,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B120" & dbh < 8,
+ select=species)))))
[1] "SALIX"
>
> names(which(table(subset(tree.diameters,
+ location == "B120" & dbh >= 8 | dbh < 18,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B120" & dbh >= 8 | dbh < 18,
+ select=species)))))
[1] "POTR"
>
> names(which(table(subset(tree.diameters,
+ location == "B120" & dbh >= 18 | dbh < 28,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B120" & dbh >= 18 | dbh < 28,
+ select=species)))))
[1] "POTR"
> names(which(table(subset(tree.diameters,
+ location == "B120" & dbh > 28,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B120" & dbh > 28,
+ select=species)))))
[1] "PIEN"
> #Location "B120"
> names(which(table(subset(tree.diameters,
+ location == "B120" & dbh < 8,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B120" & dbh < 8,
+ select=species)))))
[1] "SALIX"
>
> names(which(table(subset(tree.diameters,
+ location == "B120" & dbh >= 8 | dbh < 18,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B120" & dbh >= 8 | dbh < 18,
+ select=species)))))
[1] "POTR"
>
> names(which(table(subset(tree.diameters,
+ location == "B120" & dbh >= 18 | dbh < 28,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B120" & dbh >= 18 | dbh < 28,
+ select=species)))))
[1] "POTR"
>
> names(which(table(subset(tree.diameters,
+ location == "B120" & dbh > 28,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B120" & dbh > 28,
+ select=species)))))
[1] "PIEN"
>
> #Location "B177"
> names(which(table(subset(tree.diameters,
+ location == "B177" & dbh < 8,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B177" & dbh < 8,
+ select=species)))))
[1] "POTR"
>
> names(which(table(subset(tree.diameters,
+ location == "B177" & dbh >= 8 | dbh < 18,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B177" & dbh >= 8 | dbh < 18,
+ select=species)))))
[1] "POTR"
>
> names(which(table(subset(tree.diameters,
+ location == "B177" & dbh >= 18 | dbh < 28,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B177" & dbh >= 18 | dbh < 28,
+ select=species)))))
[1] "POTR"
>
> names(which(table(subset(tree.diameters,
+ location == "B177" & dbh > 28,
+ select=species))
+ ==max(table(subset(tree.diameters,
+ location == "B177" & dbh > 28,
+ select=species)))))
[1] "PSME"
我希望输出类似于这样（除了为每个类填充vol.i）。
location den.sap den.pole den.mat den.lrg vol.sap vol.pole vol.mat vol.lrg
B120 SALIX POTR POTR PIEN ? ? ? ?
B177 POTR POTR POTR PSME ? ? ? ?
答案 0 :(得分：1)
这个问题要求很多 - 但它有很好的文档记录，它提供了展示dplyr和tidyr包的绝佳机会。所以这里。
首先按大小对树进行分类：
data flatMap Function.tupled(f)计算每个位置/物种/大小组合的计数和总体积：
library(dplyr) ; library(tidyr)
tree.diameters <- tree.diameters %>%
mutate(size = ifelse(dbh <= 8, "sapling",
ifelse(dbh <= 18, "pole",
ifelse(dbh <= 28, "mature", "large"))))
最后，为每个位置/大小组合选择最大种类，并将该长表扩展为宽格式。重复音量并将两个结果表连接在一起。
treesummary <- tree.diameters %>%
group_by(location, species, size) %>%
summarise(vol = sum(basal.area), count = n())
收率：
result <- inner_join(
treesummary %>%
group_by(location, size) %>%
arrange(-count) %>%
slice(1) %>%
select(-count, -vol) %>%
spread(size, species) %>%
setNames(c("location", paste0("den.", names(.)[-1]))),
treesummary %>%
group_by(location, size) %>%
arrange(-vol) %>%
slice(1) %>%
select(-count, -vol) %>%
spread(size, species) %>%
setNames(c("location", paste0("vol.", names(.)[-1]))),
by = "location"
)