问题

I can help you with the translation of the provided code. Here's the translated code:

我想使用ggplot生成散点图，该图显示了我的研究中每个属（Genus）的d13C的均值和标准差（sd）在x轴上，d15N在y轴上。我可以分别计算每个维度中的均值和误差栏，但无法将它们同时显示在一起。希望能够按Class对点进行编码。

这些类型的图在同位素研究中非常常见，所以我很惊讶找不到相应的代码。

我尝试了一些不同的方法，但这是我能够得到的最接近的代码：

N_mean_std <- NescoA %>%
  group_by(Genus) %>%
  summarise_at(vars(d15N), list(meanN=mean, sdN=sd)) %>%
  as.data.frame()

C_mean_std <- NescoA %>%
  group_by(Genus) %>%
  summarise_at(vars(d13C), list(meanC=mean, sdC=sd)) %>%
  as.data.frame()

#查看结果
N_mean_std
C_mean_std

#到目前为止还不错，但是
ggplot(N_mean_std, aes(x=Genus, y=meanN)) +
  geom_errorbar(aes(ymin=meanN-sdN, ymax=meanN+sdN), width=.3) +
  geom_point(size=2) +
  geom_errorbar(aes(ymin=meanC-sdC, ymax=meanC+sdC), width=0.3)

这不起作用，因为ggplot只调用氮数据。如何将这两者合并到单个图表中？

这里是数据：

结构（列表（Class = c（“Aves”，“Arachnida”，“Arachnida”，“Arachnida”，
“Diplopoda”，“Insecta”，“Insecta”，“Insecta”，“Insecta”，“Insecta”，
“Insecta”，“Insecta”，“Insecta”，“Insecta”，“Insecta”，“Insecta”，
“Insecta”，“Insecta”，“Insecta”），Genus = c（“Melospiza”，
“(Lycosidae)”，NA，NA，“Cambala”，NA，“Oulema”，“Oulema”，NA，
NA，“（Scolytidae）”，NA，“Forficula”，NA，NA，NA，NA，“Anasa”，
NA，NA），site.code = c（“NESCO_A”，“NESCO_A”，“NESCO_A”，“NESCO_A”，
“NESCO_A”，“NESCO_A”，“NESCO_A”，“NESCO_A”，“NESCO_A”，“NESCO_A”，
“NESCO_A”，“NESCO_A”，“NESCO_A”，“NESCO_A”，“NESCO_A”，“NESCO_A”，
“NESCO_A”，“NESCO_A”，“NESCO_A”，“NESCO_A”），d13C = c（-24.61，
-25.07，-25.27，-22.84，-19.95，-28.04，-19.57，-24.3，-20.16，
-18.99，-18.54，-27.66，-24.75，-30.18，-28.59，-19.78，-14.52，
-26.18，-25.02，-26.32），d15N = c（4.39，4.32，6.27，4.2，2.31，
1.22，-0.27，0.91，0.08，4.32，2.46，-0.63，-0.9，7.46，6.15，
1.2，1.13，0.94，0.23，4.6）），row.names = c（NA，20L），class = “data.frame”）

<details>
<summary>英文:</summary>

I would like to produce a scatterplot using ggplot that shows the means and sds of d13C on the x-axis and d15N on the y-axis for each Genus in my study.  I can do the calculations and get the means and error bars in each dimension separately but I cannot not show them together. Would be great to code the points by Class. 

These types of plots are very common in isotope studies so I was surprised not to find the code for it. 
  
I tried a few different things but this is the closest I was able to get: 

N_mean_std <- NescoA %>%
group_by(Genus) %>%
summarise_at(vars(d15N), list(meanN=mean, sdN=sd)) %>%
as.data.frame()

C_mean_std <- NescoA %>%
group_by(Genus) %>%
summarise_at(vars(d13C), list(meanC=mean, sdC=sd)) %>%
as.data.frame()

#view results
N_mean_std
C_mean_std

*so far so good but* 

ggplot(N_mean_std , aes(x=Genus, y=meanN)) +
geom_errorbar(aes(ymin=meanN-sdN, ymax=meanN+sdN), width=.3) +
geom_point(size=2) +
geom_errorbar(aes(ymin=meanC-sdC, ymax=meanC+sdC), width=0.3)

*This doesn't work because the ggplot only calls up the nitrogen data. How do I merge these two together on a single graph?* 

Here are the data 

structure(list(Class = c("Aves", "Arachnida", "Arachnida", "Arachnida",
"Diplopoda", "Insecta", "Insecta", "Insecta", "Insecta", "Insecta",
"Insecta", "Insecta", "Insecta", "Insecta", "Insecta", "Insecta",
"Insecta", "Insecta", "Insecta", "Insecta"), Genus = c("Melospiza",
"(Lycosidae)", NA, NA, "Cambala", NA, "Oulema", "Oulema", NA,
NA, "(Scolytidae)", NA, "Forficula", NA, NA, NA, NA, "Anasa",
NA, NA), site.code = c("NESCO_A", "NESCO_A", "NESCO_A", "NESCO_A",
"NESCO_A", "NESCO_A", "NESCO_A", "NESCO_A", "NESCO_A", "NESCO_A",
"NESCO_A", "NESCO_A", "NESCO_A", "NESCO_A", "NESCO_A", "NESCO_A",
"NESCO_A", "NESCO_A", "NESCO_A", "NESCO_A"), d13C = c(-24.61,
-25.07, -25.27, -22.84, -19.95, -28.04, -19.57, -24.3, -20.16,
-18.99, -18.54, -27.66, -24.75, -30.18, -28.59, -19.78, -14.52,
-26.18, -25.02, -26.32), d15N = c(4.39, 4.32, 6.27, 4.2, 2.31,
1.22, -0.27, 0.91, 0.08, 4.32, 2.46, -0.63, -0.9, 7.46, 6.15,
1.2, 1.13, 0.94, 0.23, 4.6)), row.names = c(NA, 20L), class = "data.frame")

</details>


# 答案1
**得分**: 2

这应该帮助您朝正确的方向前进：

首先，我认为将汇总数据分成两个数据框没有必要：

```R
df_summary <- df %>%
  group_by(Class, Genus) %>%
  summarize(
    d15N_mean = mean(d15N),
    d13C_mean = mean(d13C),
    d15N_sd = sd(d15N),
    d13C_sd = sd(d13C)
  )

然后，您可以轻松地绘制带有误差线的数据点，以任何您想要的方式分组。我选择按类别进行颜色编码，然后在类别上进行分面包装：

ggplot(data = df_summary, aes(x = d13C_mean, y = d15N_mean, color = Genus)) + geom_point() +
  geom_errorbar(aes(ymin=d15N_mean-d15N_sd, ymax=d15N_mean+d15N_sd)) +
  geom_errorbarh(aes(xmin=d13C_mean-d13C_sd, xmax=d13C_mean+d13C_sd)) +
  facet_wrap(~Class)

在仔细考虑后，分面包装可能没有太多意义，因为每个属只能属于一个类，所以也许可以使用属的颜色和类的形状：

ggplot(data = df_summary, aes(
  x = d13C_mean,
  y = d15N_mean,
  color = Genus,
  shape = Class)) + 
  geom_point(stroke=2) +
  geom_errorbar(aes(ymin = d15N_mean - d15N_sd, ymax = d15N_mean + d15N_sd)) +
  geom_errorbarh(aes(xmin = d13C_mean - d13C_sd, xmax = d13C_mean + d13C_sd))

希望我能帮助到您！

df_summary <- df %>% group_by(Class,Genus) %>%
  summarize(
    d15N_mean = mean(d15N),
    d13C_mean = mean(d13C),
    d15N_sd = sd(d15N),
    d13C_sd = sd(d13C)
  )

ggplot(data = df_summary, aes(x = d13C_mean, y = d15N_mean, color = Genus)) + geom_point() +
  geom_errorbar(aes(ymin=d15N_mean-d15N_sd,ymax=d15N_mean+d15N_sd)) +
  geom_errorbarh(aes(xmin=d13C_mean-d13C_sd,xmax=d13C_mean+d13C_sd)) +
  facet_wrap(~Class)

ggplot(data = df_summary, aes(
  x = d13C_mean,
  y = d15N_mean,
  color = Genus,
  shape = Class)) + 
  geom_point(stroke=2) +
  geom_errorbar(aes(ymin = d15N_mean - d15N_sd, ymax = d15N_mean + d15N_sd)) +
  geom_errorbarh(aes(xmin = d13C_mean - d13C_sd, xmax = d13C_mean + d13C_sd))