缩小绘图宽度，为 ggrepel 标签腾出更多空间

Question

我想缩小绘图区域，以便为当前被截断的ggrepel 标签留出更多空间。我似乎无法再通过nudge_x() 偏移标签，而且我不想缩小文本大小。

我正在尝试找到一种方法来压缩图表，以便所有组都更靠近中心，从而为 x 轴极端的标签留出更多空间。

具体来说，我正在尝试将这个人物编织成一个肖像 PDF。我尝试在块选项中控制fig.width，但这只会使整个图表变小。

我想最大化纵向页面的宽度，但相对于标签区域缩小绘图区域。

---
title             : "The title"
shorttitle        : "Title"

author: 
  - name          : "Me"
    affiliation   : "1"
    corresponding : yes    # Define only one corresponding author
    address       : "Address"
    email         : "email"

affiliation:
  - id            : "1"
    institution   : "Company"

authornote: |
  Note here

abstract: |
  Abstract here.


floatsintext      : yes
figurelist        : no
tablelist         : no
footnotelist      : no
linenumbers       : no
mask              : no
draft             : no
note              : "\\clearpage"

documentclass     : "apa6"
classoption       : "man,noextraspace"
header-includes:
  - \usepackage{pdfpages}
  - \usepackage{setspace}
  - \AtBeginEnvironment{tabular}{\singlespacing}
  - \makeatletter\let\expandableinput\@@input\makeatother
  - \interfootnotelinepenalty=10000
  - \usepackage{float} #use the 'float' package
  - \floatplacement{figure}{H} #make every figure with caption = h
  - \raggedbottom
output            : papaja::apa6_pdf
---


```{r test, fig.cap="Caption.", fig.height=8, include=TRUE, echo=FALSE}
library("papaja")
library(tidyverse)
library(ggrepel)

ageGenderF <- structure(list(genAge = structure(c(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, 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, 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, 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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("Women, 15-19", 
"Women, 20-24", "Women, 25-35", "Women, 36+"), class = "factor"), 
    word_ = c("this is label 2", "this is label 3", "this is label 4", 
    "this is label 1", "this is label 7", "this is label 5", 
    "this is label 8", "this is label 10", "this is label 11", 
    "this is label 20", "this is label 12", "this is label 6", 
    "this is label 17", "this is label 9", "this is label 15", 
    "this is label 21", "this is label 31", "this is label 25", 
    "this is label 26", "this is label 19", "this is label 24", 
    "this is label 28", "this is label 29", "this is label 30", 
    "this is label 14", "this is label 22", "this is label 18", 
    "this is label 54", "this is label 32", "this is label 44", 
    "this is label 52", "this is label 34", "this is label 59", 
    "this is label 48", "this is label 23", "this is label 47", 
    "this is label 38", "this is label 35", "this is label 61", 
    "this is label 56", "this is label 39", "this is label 72", 
    "this is label 42", "this is label 16", "this is label 66", 
    "this is label 37", "this is label 51", "this is label 27", 
    "this is label 40", "this is label 73", "this is label 60", 
    "this is label 113", "this is label 50", "this is label 45", 
    "this is label 81", "this is label 84", "this is label 53", 
    "this is label 49", "this is label 67", "this is label 68", 
    "this is label 46", "this is label 65", "this is label 41", 
    "this is label 57", "this is label 1", "this is label 2", 
    "this is label 3", "this is label 4", "this is label 5", 
    "this is label 6", "this is label 7", "this is label 8", 
    "this is label 9", "this is label 10", "this is label 11", 
    "this is label 12", "this is label 13", "this is label 14", 
    "this is label 15", "this is label 16", "this is label 17", 
    "this is label 18", "this is label 19", "this is label 20", 
    "this is label 21", "this is label 22", "this is label 23", 
    "this is label 24", "this is label 25", "this is label 26", 
    "this is label 27", "this is label 28", "this is label 29", 
    "this is label 30", "this is label 31", "this is label 32", 
    "this is label 33", "this is label 34", "this is label 35", 
    "this is label 36", "this is label 37", "this is label 38", 
    "this is label 39", "this is label 40", "this is label 41", 
    "this is label 42", "this is label 43", "this is label 44", 
    "this is label 45", "this is label 46", "this is label 47", 
    "this is label 48", "this is label 49", "this is label 50", 
    "this is label 51", "this is label 52", "this is label 53", 
    "this is label 54", "this is label 55", "this is label 56", 
    "this is label 57", "this is label 58", "this is label 59", 
    "this is label 60", "this is label 61", "this is label 62", 
    "this is label 63", "this is label 64", "this is label 1", 
    "this is label 2", "this is label 3", "this is label 6", 
    "this is label 4", "this is label 5", "this is label 12", 
    "this is label 7", "this is label 8", "this is label 9", 
    "this is label 10", "this is label 14", "this is label 11", 
    "this is label 18", "this is label 29", "this is label 45", 
    "this is label 27", "this is label 15", "this is label 26", 
    "this is label 71", "this is label 37", "this is label 13", 
    "this is label 25", "this is label 23", "this is label 22", 
    "this is label 41", "this is label 42", "this is label 55", 
    "this is label 52", "this is label 36", "this is label 34", 
    "this is label 17", "this is label 63", "this is label 24", 
    "this is label 19", "this is label 28", "this is label 38", 
    "this is label 32", "this is label 21", "this is label 30", 
    "this is label 35", "this is label 16", "this is label 64", 
    "this is label 20", "this is label 31", "this is label 53", 
    "this is label 77", "this is label 39", "this is label 70", 
    "this is label 57", "this is label 48", "this is label 43", 
    "this is label 132", "this is label 51", "this is label 66", 
    "this is label 58", "this is label 85", "this is label 120", 
    "this is label 65", "this is label 40", "this is label 121", 
    "this is label 78", "this is label 59", "this is label 141", 
    "this is label 1", "this is label 12", "this is label 6", 
    "this is label 2", "this is label 3", "this is label 5", 
    "this is label 4", "this is label 45", "this is label 52", 
    "this is label 26", "this is label 77", "this is label 8", 
    "this is label 7", "this is label 10", "this is label 14", 
    "this is label 31", "this is label 59", "this is label 178", 
    "this is label 18", "this is label 27", "this is label 42", 
    "this is label 70", "this is label 29", "this is label 37", 
    "this is label 330", "this is label 78", "this is label 25", 
    "this is label 34", "this is label 21", "this is label 450", 
    "this is label 83", "this is label 185", "this is label 57", 
    "this is label 16", "this is label 50", "this is label 126", 
    "this is label 895", "this is label 63", "this is label 402", 
    "this is label 19", "this is label 724", "this is label 40", 
    "this is label 11", "this is label 43", "this is label 758", 
    "this is label 1099", "this is label 73", "this is label 62", 
    "this is label 46", "this is label 183", "this is label 819", 
    "this is label 295", "this is label 1100", "this is label 17", 
    "this is label 282", "this is label 153", "this is label 1101", 
    "this is label 41", "this is label 1102", "this is label 446", 
    "this is label 216", "this is label 13", "this is label 109", 
    "this is label 20"), n = c(774L, 635L, 618L, 495L, 329L, 
    284L, 259L, 217L, 197L, 181L, 163L, 163L, 162L, 160L, 138L, 
    124L, 114L, 112L, 110L, 107L, 99L, 98L, 97L, 92L, 85L, 84L, 
    84L, 78L, 74L, 72L, 68L, 67L, 66L, 66L, 65L, 60L, 60L, 60L, 
    58L, 57L, 55L, 51L, 51L, 51L, 50L, 50L, 48L, 47L, 47L, 46L, 
    46L, 44L, 44L, 44L, 43L, 43L, 43L, 43L, 42L, 41L, 41L, 41L, 
    41L, 41L, 1568L, 1366L, 1220L, 1012L, 687L, 682L, 633L, 516L, 
    464L, 374L, 372L, 326L, 326L, 304L, 293L, 292L, 274L, 261L, 
    259L, 257L, 236L, 232L, 229L, 223L, 223L, 221L, 221L, 213L, 
    210L, 205L, 198L, 191L, 189L, 167L, 165L, 164L, 146L, 142L, 
    140L, 140L, 139L, 136L, 134L, 129L, 122L, 121L, 115L, 115L, 
    115L, 113L, 112L, 110L, 110L, 109L, 107L, 104L, 103L, 102L, 
    99L, 99L, 99L, 97L, 96L, 93L, 426L, 332L, 310L, 290L, 197L, 
    166L, 147L, 134L, 125L, 113L, 105L, 104L, 97L, 83L, 78L, 
    77L, 77L, 74L, 69L, 69L, 69L, 69L, 68L, 61L, 61L, 59L, 59L, 
    58L, 58L, 58L, 57L, 57L, 56L, 54L, 51L, 48L, 47L, 46L, 43L, 
    42L, 38L, 38L, 36L, 34L, 34L, 33L, 32L, 32L, 32L, 32L, 31L, 
    29L, 29L, 28L, 28L, 27L, 27L, 27L, 27L, 27L, 26L, 26L, 25L, 
    24L, 37L, 26L, 26L, 20L, 19L, 18L, 17L, 15L, 14L, 12L, 12L, 
    12L, 12L, 12L, 11L, 10L, 9L, 9L, 9L, 9L, 8L, 7L, 7L, 7L, 
    7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L), rank = c(1L, 2L, 
    3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
    16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 
    28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 
    40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 
    52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 
    64L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 
    14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 
    26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 
    38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 
    50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 
    62L, 63L, 64L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
    12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 
    24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 
    36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 
    48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 
    60L, 61L, 62L, 63L, 64L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
    9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 
    21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 
    33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 
    45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 
    57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L)), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -256L), groups = structure(list(
    genAge = structure(1:4, .Label = c("Women, 15-19", "Women, 20-24", 
    "Women, 25-35", "Women, 36+"), class = "factor"), .rows = list(
        1:64, 65:128, 129:192, 193:256)), row.names = c(NA, -4L
), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))

ageGenderFLow <- 
  ageGenderF %>%
  filter(genAge=="Women, 15-19") %>%
  filter(rank<=10)

ageGenderFHigh <- 
  ageGenderF %>%
  filter(genAge=="Women, 36+") %>%
  filter(rank<=10)

ageGenderF_ <-
  ageGenderF %>%
  filter(word_ %in% ageGenderFLow$word_ |
         word_ %in% ageGenderFHigh$word_)

# get rank order of words for low set
ageGenderFLowRank <- 
  ageGenderF_ %>%
  filter(genAge=="Women, 15-19") %>%
  arrange(rank) %>%
  mutate(order = 1:n()) 

ageGenderF_ %>%
  mutate(word = factor(word_, ordered=TRUE, levels=ageGenderFLowRank$word_)) %>%
  # https://ibecav.github.io/slopegraph/
  ggplot(., aes(x = genAge, y = reorder(rank, -rank), group = word_)) +
  geom_line(aes(color = word_, alpha = 1), size = 1.5) +
  #geom_line(size = 0.5, color="lightgrey") +
  geom_text_repel(data = . %>% filter(genAge == "Women, 15-19"), 
                  aes(label = word) , 
                  hjust = "left", 
                  #fontface = "bold", 
                  size = 3, 
                  nudge_x = -3, 
                  direction = "y") +
  geom_text_repel(data = . %>% filter(genAge == "Women, 36+"), 
                  aes(label = word) , 
                  hjust = "right", 
                  #fontface = "bold", 
                  size = 3, 
                  nudge_x = 3, 
                  direction = "y") +
  geom_label(aes(label = rank), 
             size = 2.5, 
             label.padding = unit(0.15, "lines"), 
             label.size = 0.0) +
  scale_x_discrete(position = "top") +
  theme_bw() +
  # Remove the legend
  theme(legend.position = "none") +
  # Remove the panel border
  theme(panel.border     = element_blank()) +
  # Remove just about everything from the y axis
  theme(axis.title.y     = element_blank()) +
  theme(axis.text.y      = element_blank()) +
  theme(panel.grid.major.y = element_blank()) +
  theme(panel.grid.minor.y = element_blank()) +
  # Remove a few things from the x axis and increase font size
  theme(axis.title.x     = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(axis.text.x.top      = element_text(size=10)) +
  # Remove x & y tick marks
  theme(axis.ticks       = element_blank()) +
  # Format title & subtitle
  theme(plot.title       = element_text(size=10, face = "bold", hjust = 0.5)) +
  theme(plot.subtitle    = element_text(hjust = 0.5))
```

Answer 1

如果你愿意改变你的方法，你可以做一个大的转变，把你用作标签的文本用作轴标签。您可以利用辅助轴为绘图的每一侧制作单独的标签，因此事情看起来很像您现在正在做的事情。

我看到的优点是文本适合，因为它现在是轴的一部分。

首先这是一个使用rank 作为因素的示例。您必须通过as.numeric() 将因子转换为数字才能获得重复轴（到目前为止，离散轴没有辅助轴）。然后需要做一些工作来获取每一侧的轴的中断和标签，所以我将数据操作移到第二步（并将rank2 作为重新排序的因素，以便稍后执行breaks）。

还要注意在scale_x_discrete() 中使用expand 来移除面板区域边缘周围的空间。

ageGenderF_ = ageGenderF_ %>%
    ungroup() %>%
    mutate(word = factor(word_, ordered = TRUE, levels = ageGenderFLowRank$word_),
           rank2 = reorder(rank, -rank) )

ageGenderF_ %>%
    # https://ibecav.github.io/slopegraph/
    ggplot(., aes(x = genAge, y = as.numeric(rank2), group = word_)) +
    geom_line(aes(color = word_, alpha = 1), size = 1.5) +
    geom_label(aes(label = rank), 
           size = 2.5, 
           label.padding = unit(0.15, "lines"), 
           label.size = 0.0) +
    scale_x_discrete(position = "top", expand = c(0, .05) ) +
    scale_y_continuous(breaks = filter(ageGenderF_, genAge == "Women, 15-19") %>% pull(rank2) %>% as.numeric(), 
                    labels = filter(ageGenderF_, genAge == "Women, 15-19") %>% pull(word),
                    sec.axis = dup_axis(~., 
                                        breaks = filter(ageGenderF_, genAge == "Women, 36+") %>% pull(rank2) %>% as.numeric(), 
                                        labels = filter(ageGenderF_, genAge == "Women, 36+") %>% pull(word) ) ) +
    theme_bw() +
    # Remove the legend
    theme(legend.position = "none",
          # Remove the panel border
          panel.border     = element_blank(),
          # Remove just about everything from the y axis
          axis.title.y     = element_blank(),
          panel.grid.major.y = element_blank(),
          panel.grid.minor.y = element_blank(),
          # Remove a few things from the x axis and increase font size
          axis.title.x     = element_blank(),
          panel.grid.major.x = element_blank(),
          axis.text.x.top      = element_text(size=10),
          # Remove x & y tick marks
          axis.ticks       = element_blank(),
          axis.ticks.length = unit(0, "cm"),
          # Format title & subtitle
          plot.title       = element_text(size=10, face = "bold", hjust = 0.5),
          plot.subtitle    = element_text(hjust = 0.5) )

从一个简单的 r markdown 文档来看，这看起来与您的示例相似（尽管不准确）：

您可以使用 rank 作为数字执行完全相同的操作，使用 scale_y_reverse() 来反转 y 轴。

ageGenderF_ = ageGenderF_ %>%
    ungroup() %>%
    mutate(word = factor(word_, ordered = TRUE, levels = ageGenderFLowRank$word_))

ageGenderF_ %>%
    # https://ibecav.github.io/slopegraph/
    ggplot(., aes(x = genAge, y = rank, group = word_)) +
    geom_line(aes(color = word_, alpha = 1), size = 1.5) +
    geom_label(aes(label = rank), 
               size = 2.5, 
               label.padding = unit(0.15, "lines"), 
               label.size = 0.0) +
    scale_x_discrete(position = "top", expand = c(0, .05) ) +
    scale_y_reverse(breaks = filter(ageGenderF_, genAge == "Women, 15-19") %>% pull(rank), 
                    labels = filter(ageGenderF_, genAge == "Women, 15-19") %>% pull(word),
                    sec.axis = dup_axis(~., 
                                        breaks = filter(ageGenderF_, genAge == "Women, 36+") %>% pull(rank), 
                                        labels = filter(ageGenderF_, genAge == "Women, 36+") %>% pull(word) ) ) +
    theme_bw() +
    # Remove the legend
    theme(legend.position = "none",
          # Remove the panel border
          panel.border     = element_blank(),
          # Remove just about everything from the y axis
          axis.title.y     = element_blank(),
          panel.grid.major.y = element_blank(),
          panel.grid.minor.y = element_blank(),
          # Remove a few things from the x axis and increase font size
          axis.title.x     = element_blank(),
          panel.grid.major.x = element_blank(),
          axis.text.x.top      = element_text(size=10),
          # Remove x & y tick marks
          axis.ticks       = element_blank(),
          axis.ticks.length = unit(0, "cm"),
          # Format title & subtitle
          plot.title       = element_text(size=10, face = "bold", hjust = 0.5),
          plot.subtitle    = element_text(hjust = 0.5) )

Answer 2

一种选择是将绘图保存为对象 (p)，然后使用 egg 包中的 set_panel_size 参数显式设置面板的高度和宽度（如 this answer 中所做的那样）。这样的事情会让你接近：

library(egg)
library(grid)

p2 <- set_panel_size(p, width=unit(7,"in"), height=unit(10, "in"))

grid.draw(p2)