我正在尝试创建一个堆叠的条形图,该条形图将在y轴上表示平均丰度,在x轴上表示主要营养组,每个条形图均由特定的营养组填充(主要营养组被进一步细分)
我创建了一个数据示例,您应该可以将其直接放入R中:
Example<-structure(list(Species = c("Fish1", "Fish2", "Fish3", "Fish4",
"Fish5", "Fish6", "Fish7", "Fish1", "Fish2", "Fish3", "Fish4",
"Fish5", "Fish6", "Fish7", "Fish1", "Fish2", "Fish3", "Fish4",
"Fish5", "Fish6", "Fish7"), Trophic = c("Herbivore", "Omnivore",
"Herbivore", "Predator", "Predator", "Omnivore", "Omnivore",
"Herbivore", "Omnivore", "Herbivore", "Predator", "Predator",
"Omnivore", "Omnivore", "Herbivore", "Omnivore", "Herbivore",
"Predator", "Predator", "Omnivore", "Omnivore"), Trophic_Specific = c("Grazer",
"Generalist_Omnivore", "Browser", "Micro-invertebrate_Predator",
"Micro-invertebrate_Predator", "Generalist_Omnivore", "Benthic_Omnivore",
"Grazer", "Generalist_Omnivore", "Browser", "Micro-invertebrate_Predator",
"Micro-invertebrate_Predator", "Generalist_Omnivore", "Benthic_Omnivore",
"Grazer", "Generalist_Omnivore", "Browser", "Micro-invertebrate_Predator",
"Micro-invertebrate_Predator", "Generalist_Omnivore", "Benthic_Omnivore"
), Transect = c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3,
3, 3, 3, 3, 3, 3), Count = c(1, 2, 34, 0, 4, 2, 1, 0, 2, 25,
1, 4, 2, 1, 1, 4, 50, 3, 6, 7, 3)), class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -21L), spec = structure(list(
cols = list(Species = structure(list(), class = c("collector_character",
"collector")), Trophic = structure(list(), class = c("collector_character",
"collector")), Trophic_Specific = structure(list(), class = c("collector_character",
"collector")), Transect = structure(list(), class = c("collector_double",
"collector")), Count = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))
如果我在Excel中手动算出平均数量(即3个样带中每个物种/营养组的平均数量),我知道如何使用ggplots在条形图中进行绘制(但后来我不知道如何获取我的数量)错误条)。
如何在R中汇总这些原始数据,以便我可以使用样线1-3作为重复来获得每个特定营养组的平均丰度,然后可以如上所述将其绘制在条形图中?
我不是100%确信这是您要寻找的东西,但我想我会尝试一下。
library(tidyverse)
Example %>%
group_by(Trophic, Trophic_Specific) %>%
summarise(Mean = mean(Count),
SD = sd(Count),
n = n(),
SE = SD/n)
# A tibble: 5 x 6
# Groups: Trophic [3]
Trophic Trophic_Specific Mean SD n SE
<chr> <chr> <dbl> <dbl> <int> <dbl>
1 Herbivore Browser 36.3 12.7 3 4.22
2 Herbivore Grazer 0.667 0.577 3 0.192
3 Omnivore Benthic_Omnivore 1.67 1.15 3 0.385
4 Omnivore Generalist_Omnivore 3.17 2.04 6 0.340
5 Predator Micro-invertebrate_Predator 3 2.19 6 0.365
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