使用ggplot2在多面图中右对齐y轴标签问题

Question

我正在尝试准备一个多面图，其中 y 轴标签根据特定条件着色。我已经设法将 y 轴标签映射到各自的颜色，但标签的对齐似乎是一个问题。我希望标签与 y 轴右对齐。我尝试实施here和here建议的解决方案，但似乎都不起作用。

数据如下：

> dput(df)
structure(list(Term = structure(c(103L, 98L, 97L, 94L, 68L, 65L, 
62L, 59L, 56L, 55L, 99L, 96L, 95L, 93L, 72L, 70L, 66L, 86L, 79L, 
64L, 63L, 58L, 57L, 54L), levels = c("GO:1901642~nucleoside transmembrane transport", 
"GO:0046033~AMP metabolic process", "GO:1990544~mitochondrial ATP transmembrane transport", 
"GO:0140021~mitochondrial ADP transmembrane transport", "GO:0005471~ATP:ADP antiporter activity", 
"GO:0003876~AMP deaminase activity", "GO:0047555~3',5'-cyclic-GMP phosphodiesterase activity", 
"GO:0005324~long-chain fatty acid transmembrane transporter activity", 
"GO:0005337~nucleoside transmembrane transporter activity", "GO:0042761~very long-chain fatty acid biosynthetic process", 
"GO:0019367~fatty acid elongation, saturated fatty acid", "GO:0034625~fatty acid elongation, monounsaturated fatty acid", 
"GO:0034626~fatty acid elongation, polyunsaturated fatty acid", 
"GO:0032264~IMP salvage", "GO:0006006~glucose metabolic process", 
"GO:0090263~positive regulation of canonical Wnt signaling pathway", 
"GO:0005319~lipid transporter activity", "GO:0009922~fatty acid elongase activity", 
"GO:0010608~post-transcriptional regulation of gene expression", 
"GO:0007288~sperm axoneme assembly", "GO:0030148~sphingolipid biosynthetic process", 
"GO:0005952~cAMP-dependent protein kinase complex", "GO:0097729~9+2 motile cilium", 
"GO:0034620~cellular response to unfolded protein", "GO:0004198~calcium-dependent cysteine-type endopeptidase activity", 
"GO:0008569~minus-end-directed microtubule motor activity", "GO:0018107~peptidyl-threonine phosphorylation", 
"GO:0046835~carbohydrate phosphorylation", "GO:0003730~mRNA 3'-UTR binding", 
"GO:0006096~glycolytic process", "GO:0005871~kinesin complex", 
"GO:0006869~lipid transport", "GO:0051287~NAD binding", "GO:0031514~motile cilium", 
"GO:0140359~ABC-type transporter activity", "GO:0018105~peptidyl-serine phosphorylation", 
"GO:0031072~heat shock protein binding", "GO:0006099~tricarboxylic acid cycle", 
"GO:0005516~calmodulin binding", "GO:0003333~amino acid transmembrane transport", 
"GO:0030286~dynein complex", "GO:0051959~dynein light intermediate chain binding", 
"GO:0007165~signal transduction", "GO:0015171~amino acid transmembrane transporter activity", 
"GO:0004672~protein kinase activity", "GO:0005975~carbohydrate metabolic process", 
"GO:0051787~misfolded protein binding", "GO:0007018~microtubule-based movement", 
"GO:0004722~protein serine/threonine phosphatase activity", "GO:0004674~protein serine/threonine kinase activity", 
"GO:0140662~ATP-dependent protein folding chaperone", "GO:0051085~chaperone cofactor-dependent protein refolding", 
"GO:0006468~protein phosphorylation", "GO:0016301~kinase activity", 
"GO:0042026~protein refolding", "GO:0035556~intracellular signal transduction", 
"GO:0003777~microtubule motor activity", "GO:0017018~myosin phosphatase activity", 
"GO:0016310~phosphorylation", "GO:0005874~microtubule", "GO:0009507~chloroplast", 
"GO:0016311~dephosphorylation", "GO:0008017~microtubule binding", 
"GO:0000287~magnesium ion binding", "GO:0055085~transmembrane transport", 
"GO:0005929~cilium", "GO:0003729~mRNA binding", "GO:0006508~proteolysis", 
"GO:0005524~ATP binding", "GO:0005886~plasma membrane", "GO:0016887~ATP hydrolysis activity", 
"GO:0005829~cytosol", "GO:0005737~cytoplasm", "GO:0016020~membrane", 
"GO:0005739~mitochondrion", "GO:0015031~protein transport", "GO:0000398~mRNA splicing, via spliceosome", 
"GO:0045505~dynein intermediate chain binding", "GO:0003755~peptidyl-prolyl cis-trans isomerase activity", 
"GO:0030134~COPII-coated ER to Golgi transport vesicle", "GO:0005868~cytoplasmic dynein complex", 
"GO:0016226~iron-sulfur cluster assembly", "GO:0071013~catalytic step 2 spliceosome", 
"GO:0007017~microtubule-based process", "GO:0046540~U4/U6 x U5 tri-snRNP complex", 
"GO:0051537~2 iron, 2 sulfur cluster binding", "GO:0005665~RNA polymerase II, core complex", 
"GO:0005686~U2 snRNP", "GO:0005682~U5 snRNP", "GO:0005689~U12-type spliceosomal complex", 
"GO:0004792~thiosulfate sulfurtransferase activity", "GO:0071011~precatalytic spliceosome", 
"GO:0016272~prefoldin complex", "GO:0000387~spliceosomal snRNP assembly", 
"GO:0005687~U4 snRNP", "GO:0005685~U1 snRNP", "GO:0140647~P450-containing electron transport chain", 
"GO:0006367~transcription initiation at RNA polymerase II promoter", 
"GO:0000811~GINS complex", "GO:0034719~SMN-Sm protein complex", 
"GO:0097526~spliceosomal tri-snRNP complex", "GO:0034715~pICln-Sm protein complex", 
"GO:0045842~positive regulation of mitotic metaphase/anaphase transition"
), class = c("ordered", "factor")), Category = c("Biological process", 
"Biological process", "Biological process", "Biological process", 
"Biological process", "Biological process", "Biological process", 
"Biological process", "Biological process", "Biological process", 
"Cellular component", "Cellular component", "Cellular component", 
"Cellular component", "Cellular component", "Cellular component", 
"Cellular component", "Molecular function", "Molecular function", 
"Molecular function", "Molecular function", "Molecular function", 
"Molecular function", "Molecular function"), PValue = c(0.0173149736221496, 
0.0022211250550039, 0.0378179166640749, 0.00127796539857151, 
0.00798138530033138, 0.00366564486671723, 0.0473204717333284, 
2.22454619848845e-16, 0.000422682592906967, 0.0192436873377812, 
0.0232976965541856, 0.000899326018158991, 0.000899326018158991, 
0.0138812532009331, 0.00368570740043085, 0.0133201149734851, 
0.000113258109494652, 0.0216207725534343, 0.0192092227496835, 
0.0359035412306864, 0.000661709893356406, 0.0216873994434862, 
0.00146360693566605, 0.00145820771302719), Fold.Enrichment = c(5.8453947368421, 
5.01033834586466, 4.67631578947368, 4.54641812865497, -1.5614892923452, 
-1.63674178836183, -1.81334240401378, -1.92576126700771, -1.96445427101492, 
-1.96445427101492, 5.27043269230769, 4.61162860576923, 4.61162860576923, 
4.39202724358974, -1.24768223226522, -1.37019016159272, -1.61838520486908, 
2.93721286370597, 2.33112132040157, -1.76124885215794, -1.78745791245791, 
-1.92748917748918, -1.94326241134752, -1.98140495867769), Regulation = c("Upregulated", 
"Upregulated", "Upregulated", "Upregulated", "Downregulated", 
"Downregulated", "Downregulated", "Downregulated", "Downregulated", 
"Downregulated", "Upregulated", "Upregulated", "Upregulated", 
"Upregulated", "Downregulated", "Downregulated", "Downregulated", 
"Upregulated", "Upregulated", "Downregulated", "Downregulated", 
"Downregulated", "Downregulated", "Downregulated"), Label = c("orange", 
"red", "red", "orange", "blue", "blue", "cyan4", "blue", "cyan4", 
"cyan4", "orange", "orange", "orange", "orange", "blue", "cyan4", 
"blue", "orange", "orange", "blue", "cyan4", "cyan4", "cyan4", 
"blue")), row.names = c(1L, 6L, 7L, 10L, 36L, 39L, 42L, 45L, 
48L, 49L, 5L, 8L, 9L, 11L, 32L, 34L, 38L, 18L, 25L, 40L, 41L, 
46L, 47L, 50L), class = "data.frame")

和代码：

library(ggh4x) 
library(ggplot2)


strip <- strip_themed(background_x = elem_list_rect(fill = c("#0C6291", "#A63446")))

ggplot(data = df, 
   aes(x = Fold.Enrichment, 
       y = Term)) + 
geom_bar(aes(fill = PValue),
         color = "black",
         stat = "identity",
         width = 0.4) +
facet_grid2(df$Category ~ df$Regulation,
            scales = "free",
            strip = strip) +
theme_bw() + 
labs(x = "Fold enrichment",
     y = "") +
scale_fill_gradient2(name = "p-value\n", 
                     low = "red", 
                     mid = "yellow",
                     high = "blue",
                     midpoint = 0.025,
                     limits = c(0, 0.05)) +
geom_text(data = df,
          aes(color = Label,
              label = Term,
              y = Term),
          x = -2.2,
          size = 5/.pt,
          hjust = 1,
          vjust = 0.5,
          show.legend = FALSE) +
scale_color_manual(values = c("red" = "red", 
                              "blue" = "blue", 
                              "orange" = "orange", 
                              "cyan4" = "cyan4")) +
coord_cartesian(clip = "off") +
theme(axis.text.y = element_blank())

Answer 1

恕我直言，您可以通过使用

ggtext

轻松实现您的愿望，如下所示：

library(ggh4x)
library(ggplot2)

strip <- strip_themed(background_x = elem_list_rect(fill = c("#0C6291", "#A63446")))

p <- ggplot(
  data = df,
  aes(
    x = Fold.Enrichment,
    y = Term
  )
) +
  geom_bar(aes(fill = PValue),
    color = "black",
    stat = "identity",
    width = 0.4
  ) +
  facet_grid2(
    Category ~ Regulation,
    scales = "free",
    strip = strip
  ) +
  theme_bw() +
  labs(
    x = "Fold enrichment",
    y = NULL
  ) +
  scale_fill_gradient2(
    name = "p-value\n",
    low = "red",
    mid = "yellow",
    high = "blue",
    midpoint = 0.025,
    limits = c(0, 0.05)
  )

p +
  aes(y = glue::glue("<span style='color: {Label}'>{Term}</span>")) +
  theme(axis.text.y.left = ggtext::element_markdown(size = 5))

但是，如果您想使用

geom_text

使用假轴文本方法，那么您必须确保通过第一列面添加标签：

df_axis_text <- df |>
  dplyr::distinct(Term, Category, Label) |>
  dplyr::mutate(Regulation = "Downregulated")

p +
  geom_text(
    data = df_axis_text,
    aes(
      color = I(Label),
      label = Term,
      y = Term
    ),
    x = I(-0.05),
    size = 5 / .pt,
    hjust = 1,
    vjust = 0.5,
    show.legend = FALSE
  ) +
  coord_cartesian(clip = "off") +
  theme(
    axis.text.y.left = element_blank(),
    plot.margin = margin(t = 5.5, r = 5.5, b = 5.5, l = 180, unit = "pt")
  )