Dplyr Complete 不是用零填充空组合，而是用 1 填充

Question

我的数据集中有一个空组合，我想用 0 填充，这样当我使用 geom_bar 时，我将拥有相等宽度的条形和间距。

这是我的数据和代码以及我创建的绘图，只是它创建了错误数据。

代码和数据（如下图）：

# Fill empty case (e.g., no small sized individuals from high urban sites)
my.data <- data_ordered %>% 
           arrange(factor(Urban.Intensity,levels=c("Low","Medium","High"))) %>% 
           complete(Urban.Intensity = unique(Urban.Intensity), 
                    Body.Size=unique(Body.Size), 
                    fill = list(n=0))

urban <- my.data %>%
         select(Body.Size, Urban.Intensity) %>%
         count() %>% 
         arrange(Urban.Intensity, desc(freq)); urban

ggplot(urban, aes(x=fct_inorder(Urban.Intensity), y=freq, fill=Body.Size, width=1)) +
      theme_bw() +
      geom_bar(stat = "identity", color="black", size=0.7, position='dodge') +
      theme(axis.text.x = element_text(size=12, face="bold"),
            axis.text.y = element_text(size=12, face="bold"),
            axis.title = element_text(size=13, face="bold"),
            legend.text = element_text(size=13),
            legend.title = element_text(size=13, face="bold")) +
      ylab("Frequency\n") + xlab("\nUrban Intensity") +
      guides(fill=guide_legend("Body Size")) +
      scale_y_continuous(expand=c(0,0), limits=c(0,125), breaks=seq(0,125,25)) +
      scale_fill_manual(values=col)


> dput(data_ordered)
structure(list(Urban.Intensity = structure(c(3L, 3L, 2L, 2L, 
2L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 
1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 
1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 
2L, 2L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), levels = c("Low", "Medium", 
"High"), class = "factor"), Body.Size = structure(c(3L, 3L, 2L, 
2L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 
2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 
2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 
2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 3L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 
2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 
2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 
3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 
2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), levels = c("Small", 
"Medium", "Large"), class = "factor")), row.names = c(NA, -510L
), class = "data.frame")

如您所见，在我的数据中，小（体型）-高（城市强度）组合应该为 0，但无论我做什么，

complete

都不会添加 0，而是用 填充该组合1. 所以该图也是错误的，因为它显示该组合的值应该为空或至少为空白，但应保持条形间距并与 x 轴标签对齐。

Answer 1

通过使用complete()，您将强制创建Urban.intensity == High且Body.Size == Small的行。我认为您所要做的就是使用 count() 函数和 .drop = FALSE。

data_ordered <- structure(list(Urban.Intensity = structure(c(3L, 3L, 2L, 2L, 
                                                             2L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
                                                             1L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 
                                                             1L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 
                                                             1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 
                                                             2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 
                                                             1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                             1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                             1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                             1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                             2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
                                                             1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                             2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
                                                             1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                             1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                             1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
                                                             3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 
                                                             1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                             1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 
                                                             2L, 2L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                             2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 
                                                             2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                             2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                             2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                             2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                             1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
                                                             3L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                             2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                             2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
                                                             2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
                                                             3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
                                                             3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 1L, 1L, 1L, 
                                                             2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), levels = c("Low", "Medium","High"), class = "factor"), 
                               Body.Size = structure(c(3L, 3L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 
                                                       2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 2L, 
                                                       2L, 2L, 1L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 
                                                       2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 
                                                       2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 
                                                       1L, 1L, 1L, 2L, 3L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 
                                                       2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                                       3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                                       3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 
                                                       2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
                                                       3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 
                                                       2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                                                       1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 
                                                       2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 
                                                       3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                                       3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 
                                                       3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 
                                                       2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 
                                                       3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
                                                       3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
                                                       3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 
                                                       1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                                       3L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
                                                       3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 
                                                       3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), levels = c("Small", 
                                                                                                               "Medium", "Large"), class = "factor")), row.names = c(NA, -510L
                                                                                                               ), class = "data.frame")

library(tidyverse)

urban <- data_ordered |>  
  count(Urban.Intensity, Body.Size, .drop = FALSE)

ggplot(urban, aes(x=Urban.Intensity, y=n, fill=Body.Size, width=1)) +
  theme_bw() +
  geom_bar(stat = "identity", color="black", size=0.7, position='dodge') +
  theme(axis.text.x = element_text(size=12, face="bold"),
        axis.text.y = element_text(size=12, face="bold"),
        axis.title = element_text(size=13, face="bold"),
        legend.text = element_text(size=13),
        legend.title = element_text(size=13, face="bold")) +
  ylab("Frequency\n") + xlab("\nUrban Intensity") +
  guides(fill=guide_legend("Body Size")) +
  scale_y_continuous(expand=c(0,0), limits=c(0,125), breaks=seq(0,125,25))
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

^{创建于 2024 年 11 月 16 日，使用 reprex v2.1.1}