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Katherine Chau's questions

Martin Hope
Katherine Chau
Asked: 2024-11-17 02:48:35 +0800 CST
  • 5

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，我将具有相同宽度的条和间距。

这是我的数据和代码以及我创建的图表，只是它创建的是虚假数据。

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")

在此处输入图片描述

如您所见，在我的数据中，小（体型）-高（城市强度）组合应该是 0，但无论我做什么，complete都不会添加 0，而是用 1 填充该组合。所以这个图也是错误的，因为它显示的该组合的值应该是空的或至少是空白的，但应该保持条形间距并与 x 轴标签对齐。

Martin Hope
Katherine Chau
Asked: 2024-11-12 00:15:41 +0800 CST
  • 7

鉴于我拥有的字符串是基于来自不同文件的 ASV（如键），我想返回具有最高值的 ASV 匹配项。但是，有时会出现平局，这可能会带来问题，因此我想返回一条消息。

排序后，我能够返回具有最高值的行，但我不知道如何正确解释平局。

以下是我的文件的片段file.txt：

ASV,Kingdom,Phylum,Class,Order,Family,Genus,Species,Hits
29ec61e470705074f483368a70ad18a7,Bacteria,???,???,???,???,???,uncultured bacterium,5
29ec61e470705074f483368a70ad18a7,Bacteria,Chloroflexota,Anaerolineae,???,???,???,uncultured Anaerolineae,2
29ec61e470705074f483368a70ad18a7,Bacteria,Chloroflexota,???,???,???,???,uncultured Chloroflexota,1
29ec61e470705074f483368a70ad18a7,Bacteria,???,???,???,???,???,unidentified marine,1
29ec61e470705074f483368a70ad18a7,Bacteria,Chloroflexota,Chloroflexia,Chloroflexales,Chloroflexaceae,Chloroflexus,uncultured Chloroflexus,1
74627d6dc445e8b5f46a787cf81c4294,Bacteria,Pseudomonadota,Gammaproteobacteria,Legionellales,Legionellaceae,???,uncultured Legionellaceae,2
74627d6dc445e8b5f46a787cf81c4294,Bacteria,???,???,???,???,???,uncultured bacterium,5
74627d6dc445e8b5f46a787cf81c4294,Bacteria,Pseudomonadota,Gammaproteobacteria,Legionellales,Legionellaceae,Legionella,Legionella sp.,3
55b1bec5f8dbe1b58007aee7ede9bae3,Bacteria,Bacteroidota,Cytophagia,Cytophagales,Cytophagaceae,Spirosoma,Spirosoma utsteinense,2
55b1bec5f8dbe1b58007aee7ede9bae3,Bacteria,???,???,???,???,???,uncultured bacterium,2
55b1bec5f8dbe1b58007aee7ede9bae3,Bacteria,Bacteroidota,Cytophagia,Cytophagales,Cytophagaceae,Spirosoma,Spirosoma sp.,2
55b1bec5f8dbe1b58007aee7ede9bae3,Bacteria,Bacteroidota,Cytophagia,Cytophagales,Cytophagaceae,Spirosoma,Spirosoma rigui,6
8964b7d833654ceedbdb6f6f25fb7d6a,Bacteria,???,???,???,???,???,uncultured bacterium,8
8964b7d833654ceedbdb6f6f25fb7d6a,Bacteria,Bacillota,Tissierellia,Tissierellales,Peptoniphilaceae,Finegoldia,Finegoldia magna,1
8964b7d833654ceedbdb6f6f25fb7d6a,???,???,???,???,???,???,uncultured organism,1
9966f0e6e452c31de46d030bab01fdd9,Bacteria,Bacteroidota,Sphingobacteriia,Sphingobacteriales,???,???,uncultured Cytophagales,2
9966f0e6e452c31de46d030bab01fdd9,Bacteria,Bacteroidota,Cytophagia,Cytophagales,Cytophagaceae,Spirosoma,Spirosoma jeollabukense,2
9966f0e6e452c31de46d030bab01fdd9,Bacteria,Bacteroidota,Cytophagia,Cytophagales,Cytophagaceae,Spirosoma,Spirosoma migulaei,2
9966f0e6e452c31de46d030bab01fdd9,Bacteria,Bacteroidota,Cytophagia,Cytophagales,Cytophagaceae,Spirosoma,Spirosoma sp.,1

举个例子，搜索 ASV29ec61e470705074f483368a70ad18a7并返回具有最高值（最后一列）的匹配很容易：

代码：

> grep 29ec61e470705074f483368a70ad18a7 file.txt | sort -t, -nr -k9 | head -n1

# Output
29ec61e470705074f483368a70ad18a7,Bacteria,???,???,???,???,???,uncultured bacterium,5

但是如果我正在搜索诸如的 ASV 9966f0e6e452c31de46d030bab01fdd9，我需要它以某种方式返回或知道可以返回三行（其中 3 行的值为 2）并输出一条消息：

理想输出：

  > grep 9966f0e6e452c31de46d030bab01fdd9 file.txt | does something

  # Output
  CHECK: There are 3 lines tied for top.
bash
Martin Hope
Katherine Chau
Asked: 2024-08-27 22:34:34 +0800 CST
  • 6

我很困惑，为什么如果我在position=position_dodge()代码中保留 ，我的数字总值会全部改变？如果我删除它，总数是正确的，但为什么 dodge 会影响数值？

ggplot(data=check, aes(y=Abundance, x=Urban.Intensity)) +
  geom_bar(stat="identity", position=position_dodge()) + 
  theme_bw() +
  facet_wrap(~Genus, ncol=2) +
  scale_y_continuous(limits=c(0,1500)) +
  theme(text = element_text(size=14),
        axis.title.x = element_text(size=14, color="black"),
        axis.title.y = element_text(size=14, color="black"),
        axis.text.x = element_text(size=14, color="black"),
        axis.text.y = element_text(size=14, color="black"),
        strip.text = element_text(face="italic", size=12, color="black")) +
  ylab("Abundance") + xlab("Urbanization Intensity")

图position_dodge()。Abundance总值不正确，

在此处输入图片描述

图无position_dodge()。Abundance总值正确，

在此处输入图片描述

Martin Hope
Katherine Chau
Asked: 2024-03-20 03:34:49 +0800 CST
  • 7

我正在制作几张图，其中为每种植物物种绘制了 10 种细菌物种，并根据细菌所属的科为条形图着色。虽然我可以做得很好，但我希望在不同的地块上保持不同家族的颜色相同，因为稍后我想将它们组合成一个多面的图形，并且展示属于同一家族的颜色相同的细菌属是有意义的。

这是我的数据和代码的示例：

dput(top10_cintybus)
  structure(list(Order = c("Enterobacterales", "Enterobacterales", 
"Enterobacterales", "Sphingomonadales", "Enterobacterales", "Bacillales", 
"Hyphomicrobiales", "Bacillales", "Xanthomonadales", "Hyphomicrobiales"
), Family = c("Enterobacteriaceae", "Erwiniaceae", "Yersiniaceae", 
"Sphingomonadaceae", "Morganellaceae", "Bacillaceae", "Methylobacteriaceae", 
"Bacillaceae", "Xanthomonadaceae", "Rhizobiaceae"), MKC132 = c(0L, 
0L, 27L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), MKC146 = c(33L, 8L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L), MKC227 = c(11L, 0L, 0L, 0L, 6L, 
2L, 0L, 3L, 0L, 0L), MKC231 = c(37L, 0L, 0L, 20L, 0L, 0L, 3L, 
0L, 2L, 2L), MKC242 = c(0L, 7L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L
), MKC276 = c(9L, 0L, 7L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), MKC351 = c(6L, 
19L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Total = c(96, 34, 34, 20, 
6, 4, 3, 3, 2, 2), Genus = structure(c(10L, 8L, 9L, 7L, 6L, 5L, 
4L, 3L, 2L, 1L), levels = c("Allorhizobium", "Xanthomonas", "Bacillus", 
"Methylobacterium", "Heyndrickxia", "Arsenophonus", "Sphingomonas", 
"Pantoea", "Serratia", "???.1"), class = "factor")), row.names = c(NA, 
10L), class = "data.frame")

dput(top10_alappa)
 structure(list(Order = c("Enterobacterales", "Xanthomonadales", 
"Enterobacterales", "Enterobacterales", "Enterobacterales", "Enterobacterales", 
"Enterobacterales", "Hyphomicrobiales", "Enterobacterales", "Burkholderiales"
), Family = c("Erwiniaceae", "Xanthomonadaceae", "Enterobacteriaceae", 
"Erwiniaceae", "Enterobacteriaceae", "Enterobacteriaceae", "Enterobacteriaceae", 
"Rhizobiaceae", "Morganellaceae", "Oxalobacteraceae"), MKC154 = c(11L, 
0L, 36L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), MKC167A = c(14105L, 0L, 
5810L, 13055L, 1223L, 2316L, 1276L, 0L, 550L, 13L), MKC167B = c(18L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), MKC214 = c(83842L, 5L, 22936L, 
175L, 6828L, 94L, 0L, 7L, 0L, 0L), MKC226 = c(0L, 0L, 11L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L), MKC233 = c(55L, 0L, 13L, 14L, 0L, 0L, 
0L, 0L, 0L, 0L), MKC314 = c(0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 
0L), MKC364 = c(32L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), MKC366 = c(38L, 
46599L, 0L, 10L, 62L, 0L, 0L, 549L, 0L, 512L), Total = c(98101, 
46612, 28806, 13257, 8113, 2410, 1276, 556, 550, 525), Genus = structure(10:1, levels = c("Janthinobacterium", 
"Morganella", "Allorhizobium", "Citrobacter", "Siccibacter", 
"Enterobacter", "Erwinia", "???.1", "Stenotrophomonas", "Pantoea"
), class = "factor")), row.names = c(NA, 10L), class = "data.frame")

我的代码为这些数据帧之一绘制图形作为示例。我可以按家庭对条形进行着色，但如果可能的话，我希望有一种方法可以在数据集中保持家庭相同。请注意，“家庭”是一个因素列。

species="Cintybus"
ggplot(data=top10_cintybus,aes(x=Genus, y=Total, fill=Family)) + 
  geom_bar(stat = "identity", colour="black") +
  coord_flip() +
  theme_bw() +
  scale_y_continuous(trans="log10", limits=c(1,1000000), labels=scales::comma,
                     expand=expansion(mult=c(0,.05))) +
  #theme(text=element_text(size=18)) +
  theme(axis.title.x = element_text(size=18)) +
  theme(axis.text.x = element_text(colour="black", face="bold", size=15)) +
  theme(axis.title.y = element_text(size=18, vjust=2.5)) +
  theme(axis.text.y = element_text(face="bold.italic", colour="black", size=15)) +
  geom_text(aes(label=format(Total, big.mark=",")), hjust=1.5, 
            color="white", fontface="plain", size=5) +
  xlab("Genus") + ylab("Abundance") +
  ggtitle(species) + theme(plot.title = element_text(size=18))

在此输入图像描述

如果我为其他数据帧运行上述代码，这是一个图像。

在此输入图像描述

例如，“Pantoea”属于欧文氏菌科，但该科在两个图中的图例中具有不同的颜色。我想避免编写一个很长的列表，其中我手动为每个系列添加颜色，因为在其他一些数据集中，我有 100 个系列，并且前 10 个系列将跨数据集混合。

Martin Hope
Katherine Chau
Asked: 2024-02-21 05:03:30 +0800 CST
  • 5

如果我的文件夹中不存在该文件，我在获取代码以仅回显文本文件中的文件 ID 时遇到问题。

我有一个目录，其中的文件名为：

ASA11_mod_secun_2011.txt
ASB1133_mod_secun_2013.txt
BC09_mod_secun2_2011.txt
ASA111_mod_secun_2011.txt

以及ids.txtID，例如：

ASA11
ASB1133
BC09
ASA111
MK221

我的目录中缺少一个文件，但因为我正在处理数百个文件和数百个 ID，所以我只想回显该 ID，ids.txt该 ID 不会作为文件名的一部分出现在当前目录中。我希望MK221得到回应。

我尝试了 grep 和 for 循环的一些变体，并且得到了类似的结果，但我不断收到错误：

for i in $(cat ids.txt); do grep -v $i .; done

但这是打开我的目录中的每个文件，而不是仅仅打印$i我的目录中没有出现的文件。

我认为 if then 语句可能就是我在这里需要的，但我什至无法正确地提出一个。任何帮助，将不胜感激！

bash