错误：Aesthetics 必须是长度为 1 或与数据 (13) 相同：ymax

Question

我正在尝试绘制一个图表，显示三种不同模型 wap、lg 和 reg 的物种优化和容差，但是我不断收到错误，Error: Aesthetics must be either length 1 or the same as the data (13): ymax我读到它与填充有关，但我确定我已经正确定义了填充？我还尝试保持所有三个模型共有的物种，以便数据框中没有任何 NA，但我遇到了同样的错误。任何人都可以解决这个问题吗？

specoptima<-read.csv("regspecopt.csv",header=TRUE)

ggplot()+
geom_point(data = specoptima, aes(x = Species, y = wapopt), color = "red",pch=21,size=3)+
geom_point(data = specoptima, aes(x = Species, y = lgopt), color = "blue",pch=23,size=3)+
geom_point(data = specoptima, aes(x = Species, y = regopt), color = "green",pch=23,size=3)+
  xlab('Species') +
  ylab('Height m AHD')+theme_classic()+
  geom_errorbar(aes(x=specoptima$Species,ymin=specoptima$wapopt-specoptima$waptol, ymax=specoptima$waopt+specoptima$waptol), width=0)+
  geom_errorbar(aes(x=specoptima$Species,ymin=specoptima$lgopt-specoptima$lgtol, ymax=specoptima$lgopt+spectopima$lgtol), width=0)+
  geom_errorbar(aes(x=specoptima$Species,ymin=specoptima$regopt-specoptima$regtol, ymax=specoptima$regopt+specoptima$regtol), width=0)

structure(list(Species = structure(c(13L, 12L, 4L, 9L, 11L, 5L
), .Label = c("A.agglutinans", "A.exiguus", "A.subcatenulatus", 
"H.wilberti", "J.macrescens", "M.fusca", "P.hyperhalina", "P.ipohalina", 
"S.lobata", "T.inflata", "T.irregularis", "T.salsa", "Textularia"
), class = "factor"), wapopt = c(NA, 178.2315, 177.5775, 177.1053, 
169.4055, 167.8907), waptol = c(NA, 15.21344, 6.385151, 8.477989, 
10.844778, 9.444243), lgopt = c(190.3974, 187.1097, 177.6777, 
170.332, 173.4925, 174.8782), lgtol = c(8.236862, 4.204461, 12.198399, 
9.714885, 10.590835, 8.939749), regopt = c(190.3974, 186.8404, 
177.6699, 174.0947, 173.2112, 172.8087), regtol = c(8.609964, 
4.767529, 11.754856, 9.363322, 10.508812, 9.539666)), row.names = c(NA, 
6L), class = "data.frame")
  
  

Answer 1

就像 Duck 说的，你有几个错别字。但这就是为什么你有几个错别字 - 在你的代码中不必要的重复。您必须键入数据框名称的次数越多，您打错字的可能性就越大。长而笨重的代码行以及运算符之间的最小间距也使这些代码更难看到。

您的 geom 都可以从ggplot调用中继承它们的数据，并且您不需要specoptima$variable在 geom 或 stat 调用中进行，因此您的代码简化为：

ggplot(data = specoptima, aes(x = Species)) +
  geom_point(aes(y = wapopt), color = "red", pch = 21, size = 3) +
  geom_point(aes(y = lgopt), color = "blue", pch = 23, size = 3) +
  geom_point(aes(y = regopt), color = "green", pch = 23, size = 3) +
  geom_errorbar(aes(ymin = wapopt - waptol, ymax = wapopt + waptol), width = 0) +
  geom_errorbar(aes(ymin = lgopt - lgtol, ymax = lgopt + lgtol), width = 0) +
  geom_errorbar(aes(ymin = regopt - regtol, ymax = regopt + regtol), width = 0) +
  xlab('Species') +
  ylab('Height m AHD') +
  theme_classic()

或者更好的是，重塑您的数据以避免重复的 geom 调用：

tidyr::pivot_longer(specoptima, cols = -1,
                    names_sep = -3, names_to = c("type", "b")) %>% 
  tidyr::pivot_wider(names_from = b) %>%
  mutate(ymin = opt - tol, ymax = opt + tol) %>%
  ggplot(aes(x = Species, color = type)) +
  geom_point(aes(y = ymin), pch = 23) + 
  geom_point(aes(y = ymax), pch = 23) +
  geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0, color = "black")