Question

我以前有一篇文章，关于在特定的y轴值（plotting longer ticks at particular y-axis values）上绘制更长的刻度线。

我的y轴是对数刻度。y值分别标记为0.01、0.1、1、10和20。我还希望与这些中断相对应的报价比其他报价更长。 jaySF建议使用annotation_logticks，但是，刻度线出现在绘图区域内，我希望刻度线在外部。

ggplot2: Have shorter tick marks for tick marks without labels中的桑迪·穆斯普拉特（Sandy Muspratt）的回答似乎有所帮助，但我尝试了类似的代码，但未成功。在我之前的post中可以看到我遵循Sandy方法的代码。

我想知道要么应该遵循Sandy的方法，要么使用一些新方法来使0.01、0.1、1、10和20的刻度线变长，而图形中的其他位置不变。

Answer 1

您的代码（来自here）已关闭。 ticks包含两个选项：刻度线和刻度线标签。在x轴上，先是刻度线，然后是标签；但在y轴上，标签排在最前面，然后是刻度线。因此，要选择y轴上的刻度线，您需要：marks = ticks$grobs[[2]]

此外，长和短刻度线的顺序并不像您所跟随的示例中那么简单；特别是最后两个刻度线很长。在下面的代码中，我列出了所有刻度线的起点和终点的x坐标。（可能有一些使用ggplot_build信息来自动执行此步骤的方法，但是如果您的绘图是一次性的，则手动设置x坐标同样容易。）

此外，我将刻度线标签向左移动一点。（您的数据在帖子的最后。）

library(ggplot2)
library(scales)
library(grid)


y_breaks <- c(seq(0.01, 0.1, 0.01), seq(0.2, 1, 0.1), seq(2, 10, 1), 20)
y_labels <- y_breaks
y_labels[c(F, rep(T, 8), F, rep(T, 8), F, rep(T, 8), F, F)] <- ''

x_breaks <- seq(1970, 2015, 5)
x_labels <- x_breaks
x_labels[c(F, T)] <- ''

p <- ggplot(data, aes(year, rate)) + geom_point(aes(col = size)) +   
     scale_y_continuous(breaks = y_breaks,
                        labels = y_labels,
                        limits = c(0.01, 20),
                        trans = 'log10',
                        expand = c(0, 0)) +
     scale_x_continuous(name = "Year of Diagnosis", 
                        breaks = x_breaks, 
                        labels = x_labels,
                        limits = c(1970, 2015),
                        expand = c(0, 0)) +
     theme(panel.background = element_blank(),
           axis.line = element_line(colour = "black"),
           panel.grid.minor = element_blank(),
           panel.grid.major = element_blank(),
           # Move the tick mark labels a little to the left
           axis.text.y = element_text(margin = margin(r = 5, unit = "pt")) 
           )

p

# Get the ggplot grob
g = ggplotGrob(p)

# Get the x axis
yaxis <- g$grobs[[which(g$layout$name == "axis-l")]]  

# Get the tick marks and tick mark labels   
ticks <- yaxis$children[[2]]

# Get the tick marks
marks = ticks$grobs[[2]]

# Edit the x positions of the end points of the tick marks
# The '5.5' and the '2.75' in the code below 
# are the lengths in pts of the major and minor tick marks respectively. 
marks$x = unit.c(rep(
                     unit.c(unit(1, "npc") - unit(5.5, "pt"), unit(1, "npc"),   # 1st long tick mark
                     rep(unit.c(unit(1, "npc") - unit(2.75, "pt"), unit(1, "npc")), 8)), # 8 shorter tick marks
                 3), # repeat that sequence of tick marks 3 times
             rep(unit.c(unit(1, "npc") - unit(5.5, "pt"), unit(1, "npc")), 2)) # top 2 long tick marks

# Put the tick marks back into the plot
ticks$grobs[[2]] = marks
yaxis$children[[2]] = ticks
g$grobs[[which(g$layout$name == "axis-l")]]  = yaxis

# Draw the plot
grid.newpage()
grid.draw(g)

但是，知道我现在所知道的，我不推荐这种方法。我认为很难找到从列表中的列表序列到适当插槽的路径。我建议使用grid编辑工具进行此编辑。

# Get the ggplot grob
g= ggplotGrob(p)

# Get a list of the grobs that make up the plot
grid.ls(grid.force(g))

寻找与左轴有关的grob及其子代。
这是相关的序列（尾随数字可能有所不同）：

axis-l.7-4-7-4
  axis.line.y.left..polyline.106
  轴
  轴.1-1-1-1
  GRID.text.102
  轴。1-2-1-2

axis.1-2-1-2是刻度线grob； GRID.text.102是刻度标记grob。

# Edit the x-coordinates of the tick marks
g1 = editGrob(grid.force(g), gPath("axis-l", "axis", "axis.1-2"), grep = TRUE,
     x = unit.c(rep(
                     unit.c(unit(1, "npc") - unit(5.5, "pt"), unit(1, "npc"),   # 1st long tick mark
                     rep(unit.c(unit(1, "npc") - unit(2.75, "pt"), unit(1, "npc")), 8)), # 8 shorter tick marks
                 3), # repeat that sequence of tick marks 3 times
             rep(unit.c(unit(1, "npc") - unit(5.5, "pt"), unit(1, "npc")), 2))) # top 2 long tick marks

# Draw the edited plot
grid.newpage()
grid.draw(g1)

您的数据

data = structure(list(size = structure(c(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, 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, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L), .Label = c("(1,2]", "(2,4]", "<=1", ">4", "Unknown"
), class = "factor"), year = c(1983L, 1984L, 1985L, 1986L, 1987L, 
1988L, 1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 
1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 1983L, 
1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L, 1992L, 
1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 
2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 
2011L, 2012L, 2013L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 
1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 
1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 
2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 1983L, 1984L, 
1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L, 1992L, 1993L, 
1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 
2012L, 2013L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 
1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 
1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 
2008L, 2009L, 2010L, 2011L, 2012L, 2013L), rate = c(0.53, 0.53, 
0.63, 0.62, 0.6, 0.58, 0.63, 0.82, 0.79, 0.94, 0.88, 1.03, 1.17, 
1.22, 1.32, 1.47, 1.71, 1.69, 2.11, 2.36, 2.61, 2.91, 3.48, 3.49, 
4.26, 4.44, 4.92, 4.87, 5.09, 5.23, 5.39, 0.87, 0.92, 1.06, 1, 
1.04, 1.08, 1.18, 1.13, 1.22, 1.16, 1.12, 1.17, 1.33, 1.29, 1.47, 
1.57, 1.41, 1.68, 1.75, 2.12, 2.18, 2.31, 2.5, 2.78, 3.02, 3.18, 
3.64, 3.43, 3.87, 3.78, 3.92, 0.8, 0.81, 0.87, 0.99, 0.87, 0.78, 
0.98, 1.02, 1.04, 1.04, 1.04, 1, 1.02, 1.22, 1.2, 1.26, 1.34, 
1.38, 1.49, 1.75, 1.82, 1.95, 1.8, 1.95, 2.07, 2.31, 2.58, 2.52, 
2.62, 2.83, 2.7, 0.22, 0.24, 0.18, 0.25, 0.24, 0.17, 0.24, 0.19, 
0.22, 0.23, 0.25, 0.34, 0.38, 0.38, 0.35, 0.36, 0.44, 0.4, 0.51, 
0.58, 0.53, 0.55, 0.73, 0.69, 0.67, 0.72, 0.79, 0.95, 0.88, 0.93, 
0.96, 0.74, 0.91, 0.85, 1.02, 0.81, 0.88, 0.82, 1.05, 0.88, 1.07, 
0.97, 1.2, 1.08, 1.01, 1.07, 0.94, 0.98, 1.16, 1.02, 0.98, 0.96, 
0.74, 0.66, 0.66, 0.64, 0.62, 0.61, 0.52, 0.55, 0.55, 0.47), 
    se = c(0.05, 0.05, 0.06, 0.06, 0.05, 0.05, 0.05, 0.06, 0.06, 
    0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.09, 
    0.09, 0.1, 0.1, 0.11, 0.11, 0.12, 0.12, 0.13, 0.13, 0.13, 
    0.13, 0.13, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 
    0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.07, 0.08, 
    0.08, 0.09, 0.09, 0.09, 0.09, 0.1, 0.1, 0.11, 0.11, 0.11, 
    0.12, 0.11, 0.12, 0.06, 0.06, 0.06, 0.07, 0.06, 0.06, 0.07, 
    0.07, 0.07, 0.07, 0.07, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 
    0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.1, 
    0.09, 0.09, 0.1, 0.1, 0.03, 0.03, 0.03, 0.04, 0.03, 0.03, 
    0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 
    0.04, 0.04, 0.04, 0.05, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05, 
    0.05, 0.06, 0.05, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.06, 
    0.06, 0.06, 0.07, 0.06, 0.07, 0.06, 0.07, 0.07, 0.06, 0.06, 
    0.06, 0.06, 0.07, 0.06, 0.06, 0.06, 0.05, 0.05, 0.05, 0.05, 
    0.05, 0.05, 0.04, 0.04, 0.04, 0.04), lci = c(0.43, 0.44, 
    0.52, 0.51, 0.5, 0.48, 0.53, 0.7, 0.68, 0.82, 0.77, 0.91, 
    1.04, 1.09, 1.18, 1.32, 1.56, 1.53, 1.94, 2.18, 2.43, 2.71, 
    3.27, 3.28, 4.02, 4.2, 4.67, 4.62, 4.84, 4.97, 5.13, 0.75, 
    0.8, 0.93, 0.87, 0.91, 0.95, 1.04, 0.99, 1.08, 1.03, 0.99, 
    1.04, 1.19, 1.15, 1.32, 1.43, 1.27, 1.53, 1.6, 1.96, 2.01, 
    2.13, 2.32, 2.59, 2.82, 2.97, 3.42, 3.22, 3.64, 3.56, 3.7, 
    0.68, 0.69, 0.75, 0.86, 0.75, 0.67, 0.86, 0.89, 0.91, 0.92, 
    0.92, 0.88, 0.9, 1.09, 1.07, 1.13, 1.21, 1.24, 1.35, 1.59, 
    1.67, 1.79, 1.64, 1.79, 1.91, 2.13, 2.4, 2.34, 2.44, 2.65, 
    2.51, 0.16, 0.17, 0.12, 0.18, 0.18, 0.12, 0.18, 0.14, 0.16, 
    0.17, 0.19, 0.27, 0.31, 0.31, 0.28, 0.29, 0.36, 0.33, 0.42, 
    0.5, 0.45, 0.47, 0.63, 0.6, 0.58, 0.63, 0.69, 0.84, 0.78, 
    0.83, 0.85, 0.63, 0.79, 0.72, 0.88, 0.7, 0.76, 0.71, 0.92, 
    0.76, 0.94, 0.85, 1.06, 0.96, 0.89, 0.95, 0.82, 0.86, 1.04, 
    0.9, 0.86, 0.85, 0.65, 0.57, 0.57, 0.55, 0.54, 0.53, 0.44, 
    0.46, 0.47, 0.4), uci = c(0.64, 0.65, 0.74, 0.74, 0.71, 0.69, 
    0.74, 0.95, 0.92, 1.07, 1.01, 1.17, 1.32, 1.37, 1.46, 1.62, 
    1.88, 1.85, 2.29, 2.55, 2.81, 3.12, 3.71, 3.71, 4.5, 4.69, 
    5.18, 5.13, 5.36, 5.5, 5.66, 1.01, 1.06, 1.21, 1.15, 1.19, 
    1.23, 1.33, 1.27, 1.37, 1.31, 1.27, 1.32, 1.48, 1.44, 1.62, 
    1.73, 1.56, 1.84, 1.92, 2.31, 2.36, 2.49, 2.69, 2.99, 3.23, 
    3.39, 3.86, 3.65, 4.1, 4, 4.15, 0.93, 0.94, 1, 1.14, 1.01, 
    0.91, 1.12, 1.16, 1.18, 1.18, 1.18, 1.14, 1.15, 1.37, 1.35, 
    1.41, 1.49, 1.53, 1.65, 1.91, 1.99, 2.13, 1.96, 2.12, 2.25, 
    2.49, 2.78, 2.71, 2.82, 3.03, 2.89, 0.3, 0.31, 0.24, 0.33, 
    0.32, 0.23, 0.32, 0.25, 0.28, 0.3, 0.32, 0.42, 0.47, 0.47, 
    0.43, 0.44, 0.53, 0.49, 0.6, 0.68, 0.63, 0.65, 0.84, 0.8, 
    0.78, 0.83, 0.9, 1.07, 1, 1.05, 1.08, 0.88, 1.06, 0.98, 1.16, 
    0.95, 1.01, 0.95, 1.19, 1.01, 1.21, 1.11, 1.34, 1.22, 1.14, 
    1.21, 1.06, 1.11, 1.3, 1.15, 1.1, 1.08, 0.85, 0.76, 0.76, 
    0.74, 0.72, 0.71, 0.61, 0.64, 0.64, 0.55), count = structure(c(15L, 
    16L, 21L, 20L, 19L, 18L, 22L, 35L, 36L, 53L, 48L, 63L, 79L, 
    83L, 88L, 96L, 101L, 100L, 112L, 118L, 121L, 126L, 136L, 
    1L, 7L, 8L, 10L, 9L, 11L, 12L, 13L, 38L, 44L, 56L, 51L, 55L, 
    60L, 75L, 68L, 78L, 76L, 73L, 78L, 89L, 86L, 95L, 98L, 93L, 
    101L, 102L, 113L, 115L, 117L, 120L, 125L, 130L, 132L, 3L, 
    2L, 4L, 5L, 6L, 30L, 32L, 42L, 49L, 40L, 34L, 52L, 57L, 61L, 
    65L, 67L, 62L, 67L, 85L, 84L, 87L, 90L, 92L, 97L, 103L, 105L, 
    108L, 105L, 110L, 114L, 119L, 123L, 122L, 124L, 129L, 127L, 
    104L, 106L, 91L, 106L, 111L, 94L, 107L, 99L, 106L, 109L, 
    116L, 128L, 134L, 135L, 131L, 133L, 17L, 14L, 21L, 27L, 23L, 
    25L, 43L, 41L, 39L, 47L, 54L, 77L, 71L, 77L, 81L, 26L, 40L, 
    31L, 50L, 31L, 41L, 37L, 58L, 45L, 65L, 57L, 80L, 72L, 64L, 
    73L, 59L, 66L, 82L, 74L, 70L, 69L, 46L, 34L, 35L, 36L, 33L, 
    35L, 26L, 28L, 29L, 24L), .Label = c("1,010", "1,022", "1,064", 
    "1,147", "1,157", "1,204", "1,234", "1,303", "1,462", "1,465", 
    "1,570", "1,612", "1,684", "107", "110", "114", "115", "130", 
    "131", "132", "135", "140", "145", "149", "153", "155", "158", 
    "167", "173", "177", "178", "179", "183", "184", "185", "186", 
    "187", "190", "191", "195", "196", "198", "201", "202", "205", 
    "206", "210", "214", "216", "218", "224", "226", "227", "230", 
    "234", "235", "236", "238", "244", "245", "250", "251", "253", 
    "255", "256", "258", "259", "260", "264", "266", "267", "268", 
    "274", "275", "280", "282", "283", "291", "293", "294", "295", 
    "308", "310", "313", "316", "334", "335", "337", "342", "356", 
    "37", "372", "375", "38", "381", "383", "406", "418", "44", 
    "451", "453", "478", "479", "48", "500", "51", "54", "542", 
    "55", "557", "56", "573", "580", "593", "598", "60", "636", 
    "643", "668", "703", "720", "741", "750", "785", "789", "813", 
    "816", "84", "857", "869", "90", "919", "92", "95", "98", 
    "984"), class = "factor"), pop = structure(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, 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, 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, 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, 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
    ), .Label = c("21,998,396", "22,197,735", "22,423,982", "22,644,373", 
    "22,872,669", "23,111,066", "23,349,445", "23,657,474", "23,998,620", 
    "24,368,037", "24,713,120", "25,022,087", "25,339,972", "25,652,964", 
    "25,969,420", "26,263,552", "26,520,657", "26,787,544", "27,018,187", 
    "27,165,850", "27,298,693", "27,458,357", "27,662,860", "27,954,662", 
    "28,212,877", "28,496,587", "28,777,105", "29,014,912", "29,276,092", 
    "29,546,129", "29,806,864"), class = "factor")), class = "data.frame", row.names = c(NA, 
-155L))

以对数刻度绘制y轴的较长刻度，刻度指向外部

1 个答案: