I have a graph with 12 variables divided into two groups. I can't use facets, but using colour and shape, I have been able to make the visualization easy to understand. However, there are some points that overlap (partially or wholly). I am using jitter to deal with these, but as you can see from the attached graph, this leads to all points being moved around, not just those with overlap.
有没有办法有条件地使用抖动或闪避？更好的是，有没有办法将部分重叠的点并排放置？正如您所看到的，我的 x 轴是离散类别，向左/向右轻微移动并不重要。我尝试使用点图binaxis='y'，但这完全破坏了 x 轴。
Edit: 这张图 http://www.biomedcentral.com/1471-2180/7/56/figure/F1?highres=y已经成功地完成了我正在寻找的事情。
进一步编辑：添加此可视化背后的代码。
disciplines <- c("Comp. Sc.\n(17.2%)", "Physics\n(19.6%)", "Maths\n(29.4%)", "Pol.Sc.\n(40.4%)", "Psychology\n(69.8%)")
# To stop ggplot from imposing alphabetical ordering on x-axis
disciplines <- factor(disciplines, levels=disciplines, ordered=T)
# involved aspects
intensive <- c( 0.660, 0.438, 0.515, 0.028, 0.443)
comparative <- c( 0.361, 0.928, 0.270, 0.285, 0.311)
wh_adverbs <- c( 0.431, 0.454, 0.069, 0.330, 0.577)
past_tense <- c(0.334, 0.229, 0.668, 0.566, 0.838)
present_tense <- c(0.680, 0.408, 0.432, 0.009, 0.996)
conjunctions <- c( 0.928, 0.207, 0.162, -0.299, -0.045)
personal <- c(0.498, 0.521, 0.332, 0.01, 0.01)
interrogative <- c(0.266, 0.202, 0.236, 0.02, 0.02)
sbj_objective <- c(0.913, 0.755, 0.863, 0.803, 0.913)
possessive <- c(0.896, 0.802, 0.960, 0.611, 0.994)
thrd_person <- c(-0.244, -0.265, -0.310, -0.008, -0.384)
nouns <- c(-0.602, -0.519, -0.388, -0.244, -0.196)
df1 <- data.frame(disciplines,
"Intensive Adverbs"=intensive,
"Comparative Adverbs"=comparative,
"Wh-adverbs (WRB)"=wh_adverbs,
"Verb: Past Tense"=past_tense,
"Verb: Present Tense"=present_tense,
"Conjunctions"=conjunctions,
"Personal Pronouns"=personal,
"Interrogative Pronouns"=interrogative,
"Subjective/Objective Pronouns"=sbj_objective,
"Possessive Pronouns"=possessive,
"3rd-person verbs"=thrd_person,
"Nouns"=nouns,
check.names=F)
df1.m <- melt(df1)
grp <- ifelse(df1.m$variable %in% c('3rd-person verbs','Nouns'), 'Informational Features', 'Involved Features')
g <- ggplot(df1.m, aes(group=grp, disciplines, value, shape=grp, colour=variable))
g <- g + geom_hline(yintercept=0, size=9, color="white")
g <- g + geom_smooth(method=loess, span=0.75, level=0.95, alpha=I(0.16), linetype="dashed")
g <- g + geom_point(size=4, alpha=I(0.7), position=position_jitter(width=0.1, height=0))
g <- g + scale_shape_manual(values=c(17,19))
我很好奇其他人可能会建议什么，但为了获得并排效果，您可以将主要的 x 轴类别编码为数字 (10, 20,..50) 加/减少量，例如 (0.. 10)/2 基于您使用的颜色类别。因此，您可以获得 x 轴为 9.6、9.8、10.0、10.2 ...，然后为 20.0、20.2、20.4。这可以创建一个有组织的图，而不是随机分配这些分数调整。
这是针对您的数据集的该想法的快速实现。它抵消了主 x 变量disciplines子类别的六分之一variable并将其用于无抖动的 x 值...
M = df1.m
ScaleFactor = 6
xadj = as.numeric(M$variable)/ScaleFactor
xadj = xadj - mean(xadj) # shift it to center around zero
x10 = as.numeric(M$disciplines) * 10
M$x = x10 + xadj
g = ggplot(M, aes(group=grp, x, value, shape=grp, colour=variable))
g + geom_point(size=4,alpha=I(0.7)) + scale_x_discrete(breaks=x10,labels=disciplines)
请注意，每个类别中的值均匀分布且顺序相同。 （此代码不包括图中所示的所有曲线拟合等）。
变化：如果“量化” y 值，您可以更清楚地看到效果，因此更多的值并排绘制。
M$valmod = M$value - M$value %% 0.2 + .1
然后使用valmod代替value in the aes()声明看看效果。
要恢复类别标签，请手动设置scale_x_discrete。这个版本使用了不同的ScaleFactor对于更宽的间距和量化的 y 轴：
M=df1.m
ScaleFactor = 3
# Note this could just be xadj instead of adding to data frame
M$xadj = as.numeric(M$variable)/ScaleFactor
M$xadj = M$xadj - mean(M$xadj) # shift it to center around zero
M$x10 = as.numeric(M$disciplines) * 10
M$x = M$x10 + M$xadj
Qfact = 0.2 # resolution to quantize y values
M$valmod = M$value - M$value %% Qfact + Qfact/2 # clump y to given resolution
g = ggplot(M, aes(group=grp, x, valmod, shape=grp, colour=variable)) +
scale_x_discrete(limits = M$x10, breaks=unique(M$x10),labels=levels(M$disciplines))
g + geom_point(size=3,alpha=I(0.7))