江阴网站建设,江阴网站制作,江阴网站设计,江阴SEO优化,江阴小程序开发-江阴雨辰互联

  1. JustNews 首页
  2. questions

ggplot2 - Dplyr complete is not filling empty combinations with zeros, but instead with 1s - Stack Overflow

admin••questions•阅读7

I have an empty combination in my dataset that I want to fill with 0, so that when I use geom_bar I wil

I have an empty combination in my dataset that I want to fill with 0, so that when I use geom_bar I will have equal width bars and spacing.

Here is my data and code and the plot I create, except that it is creating false data.

Code and data (figure is below):

# 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")

As you can see, in my data there should be 0 for the Small (body size) - High (urban intensity) combo but no matter what I do, complete isn't adding 0s and instead is filling that combination with a 1. So the figure is also erroneous because it is showing a value for that combination which should be empty or at least blank, but should keep the bar spacing and alignment with the x-axis labels.

I have an empty combination in my dataset that I want to fill with 0, so that when I use geom_bar I will have equal width bars and spacing.

Here is my data and code and the plot I create, except that it is creating false data.

Code and data (figure is below):

# 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")

As you can see, in my data there should be 0 for the Small (body size) - High (urban intensity) combo but no matter what I do, complete isn't adding 0s and instead is filling that combination with a 1. So the figure is also erroneous because it is showing a value for that combination which should be empty or at least blank, but should keep the bar spacing and alignment with the x-axis labels.

  • ggplot2
  • dplyr
Share Improve this question asked Nov 16, 2024 at 18:48 Katherine ChauKatherine Chau 4432 silver badges7 bronze badges
Add a comment  | 

1 Answer 1

Reset to default 1

By using complete(), you are forcing the creation of a row with Urban.intensity == High and Body.Size == Small. I think all you have to do is use the count() function with .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.

Created on 2024-11-16 with reprex v2.1.1

发布者：admin，转转请注明出处：http://www.yc00.com/questions/1745646966a4638027.html

adminadmin
0

相关推荐

  • Windows下配置Golang开发环境，并安装配置GoLand IDE网站建设

    Windows下配置Golang开发环境，并安装配置GoLand IDE

    作者&#xff1a;非妃是公主 专栏&#xff1a;《Golang》 博客地址&#xff1a;https:blog.csdnmyf_666 个性签&#xff1a;顺境不惰&#xff0c;逆境不馁

    adminadmin
    1小时前
    00
  • 论文检测，文章检测，降AI率的工具网站建设

    论文检测，文章检测，降AI率的工具

    1：文字滚筒鸭文字滚筒鸭作为市面上少见的全免费平台，文字滚筒鸭支持文章 AI 率检测、头条文章原创度分析、作文质量评估等多元场景，甚至能一键对比原文与改写后的相似度，所有功能统统 0 收费！精准算法秒出检测结果，不用充值会员，也无需分享裂变

    adminadmin
    1小时前
    20
  • windows 配置 upx网站建设

    windows 配置 upx

    ​1、下载：2、解压：解压后​3、配置环境变量，右键我的电脑-——》属性——》高级属性：在Path中添加： D:ProgramFileupx-5.0.0-win644、验证cmd中输入：代码语言：txt复制upx --version

    adminadmin
    1小时前
    20
  • 怎么用html写出哆啦A梦？网站建设

    怎么用html写出哆啦A梦？

    用HTML和CSS来画哆啦A梦（Doraemon）是一项有趣且具有挑战性的任务。虽然HTML和CSS主要用于网页布局和样式，但通过巧妙的组合和定位，可以创建出相对简单的图形和图案。下面是一个简单的示例，展示了如何用HTML和CSS绘制哆啦A

    adminadmin
    1小时前
    00
  • 谷歌云第三方SSH工具登录与一键重装系统网站建设

    谷歌云第三方SSH工具登录与一键重装系统

    一&#xff0c;设置 root 密码 先选择从浏览器打开 ssh 连接服务器切换到 root 账号&#xff0c;输入代码&#xff1a;sudo -i设置 root 密码&#xff0c;输入代码&

    adminadmin
    1小时前
    00
  • 文章降 AI 痕迹方法与工具速览网站建设

    文章降 AI 痕迹方法与工具速览

    文章降AI的方法和工具汇总‌如下： 这几天又认真研究类了一下，想让 AI 生成的文章更自然，摆脱程式化痕迹，可尝试以下方法。借助 GPT、文字滚筒鸭，朱雀大模型检测器、豆包、kimi 等大模型，输入文本后，它们能通过调整结构、替换同义词等操

    adminadmin
    1小时前
    00
  • MySQL 8.4 配置复制网站建设

    MySQL 8.4 配置复制

    参考文档:.4enreplication-configuration.html1.先在源数据库主机的myf添加这几项代码语言：javascript代码运行次数：0运行复制[mysqld]server-id = 2binlog_forma

    adminadmin
    1小时前
    00
  • MySQL8.4 Enterprise安装Firewall及测试网站建设

    MySQL8.4 Enterprise安装Firewall及测试

    参考:.4enfirewall.html1.首先执行安装SQL，路径在baseshare目录下代码语言：javascript代码运行次数：0运行复制cd u01mysql3308baseshare[root@mysql8_3

    adminadmin
    1小时前
    00
  • 用安信可Ai网站建设

    用安信可Ai

    以下作品由安信可社区用户业余菜狗制作前言自从接触智能家居之后,笔者就变得很依赖智能家居(绝对不是懒!)比如卧室灯,就在进门的地方,进门开灯很方便,但是晚上睡觉关灯就很不方便。之前是买了一款Wi-Fi灯,是用手机APP操作,刚开始用的时候感觉

    adminadmin
    1小时前
    00
  • MySQL8.4创建keyring给InnoDB表进行静态数据加密网站建设

    MySQL8.4创建keyring给InnoDB表进行静态数据加密

    参考文档:.4enkeyring-plugin-installation.html.4enkeyring-hashicorp-plugin.html#keyring-hashicorp-vault-configuration1.首先

    adminadmin
    1小时前
    10
  • Go项目实战网站建设

    Go项目实战

    上节课我给大家介绍了怎么给Go项目做单元测试的规划，当然这里仅限于跟咱们课程里的实战项目一样分层架构设计做的还可以的项目哦，要是所有逻辑都耦合在Controller里，那这个规划就不适用了。。。，所有逻辑都耦合在Controller里还做个

    adminadmin
    1小时前
    00
  • 电脑密码在哪里设置win11,win11电脑开机密码怎么设置网站建设

    电脑密码在哪里设置win11,win11电脑开机密码怎么设置

    Win11系统由于许多设置和以前系统不一样了&#xff0c;所以很多用户们操作非常不习惯&#xff0c;有很多的小伙伴不知道win11系统怎么设置开机密码。给电脑设置密码&#xff0c;只有自己能打开进入系统桌面&a

    adminadmin
    47分钟前
    00
  • HLS最全知识库网站建设

    HLS最全知识库

    HLS最全知识库副标题-FPGA高层次综合HLS(二)-Vitis HLS知识库高层次综合（High-level Synthesis）简称HLS，指的是将高层次语言描述的逻辑结构，自动转换成低抽象级语言描述的电路模型的过程。对于AMD Xi

    adminadmin
    43分钟前
    00
  • 面试官:从三万英尺角度谈一下Ceph架构设计(1)网站建设

    面试官:从三万英尺角度谈一下Ceph架构设计(1)

    把面试官当陪练，在找工作中才会越战越勇大家好我是小义同学，这是大厂面试拆解——项目实战系列的第3篇文章，如果有误，请指正。本文主要解决的一个问题，Ceph为例子 如何描述项目的架构。一句话描述：主要矛盾发生变化10年前的技术和方案，放到10

    adminadmin
    38分钟前
    00
  • ascend pytorch 踩坑.网站建设

    ascend pytorch 踩坑.

    在910b上安装pytorch 和 pytorch_npu, 因为后续准备装vllm, 所以torch_npu是特殊的版本.代码语言：shell复制pip install torch==2.5.1 --extra-index pip in

    adminadmin
    22分钟前
    00
  • 开源在线考试系统网站建设

    开源在线考试系统

    看到调问已经开始扩展在线考试的场景，试了一下，发现在线考试的基本能力都已经支持了。主要是考试中的各种计分功能，包括对每道题的选项设置分值计算、考试时间限制等，和官方了解了一下，考试中的其他各项能力也在逐步完善，有需求可以随时

    adminadmin
    20分钟前
    00
  • MongoDB “升级项目” 大型连续剧(2)网站建设

    MongoDB “升级项目” 大型连续剧(2)

    上期写的是非必要不升级，想到这个事情，有一些事情的仔细琢磨琢磨，为什么数据库升级的事情在很多公司都是一个困扰，从一个技术人的观点，升级是一件好事，功能提升了，性能提升了，开发效率和一些数据库使用的痛点也被解决了，为什么就不愿意升级呢？如果只

    adminadmin
    14分钟前
    00
  • 深度学习在DOM解析中的应用：自动识别页面关键内容区块网站建设

    深度学习在DOM解析中的应用：自动识别页面关键内容区块

    爬虫代理摘要本文介绍了如何在爬取东方财富吧（）财经新闻时，利用深度学习模型对 DOM 树中的内容区块进行自动识别和过滤，并将新闻标题、时间、正文等关键信息分类存储。文章聚焦爬虫整体性能瓶颈，通过指标对比、优化策略、压测数据及改进结果，展示了

    adminadmin
    11分钟前
    10
  • UCB的硅光MEMS OCS控制技术网站建设

    UCB的硅光MEMS OCS控制技术

    昨天写的旭创与nEye合作了一个64端口硅光OCS一、光电路交换技术概述（一）电交换与分组交换电路交换的概念源于早期的电话交换机，通过物理连接实现设备间的通信，就像早期打电话时，接线员手动连接线路一样。而分组交换则是

    adminadmin
    7分钟前
    00
  • 重装系统只影响C盘吗？深入解析系统重装的全过程网站建设

    重装系统只影响C盘吗？深入解析系统重装的全过程

    重装系统只影响C盘吗？深入解析系统重装的全过程 在计算机的日常使用中，重装系统是一个常见的操作，尤其是在系统出现故障、感染病毒或需要优化系统性能时。然而，许多用户对于重装系统的具体过程和影响存在误解，认为重装系统仅仅是对C盘进行清空和重置

    adminadmin
    2分钟前
    00

发表回复

评论列表（0条）

  • 暂无评论

联系我们

400-800-8888

在线咨询： QQ交谈

邮件：admin@example.com

工作时间：周一至周五，9:30-18:30，节假日休息

关注微信