我对此非常陌生,我实际上并不了解绘图方法之间的差异,但loess似乎给了我最丰富的图表,考虑到我有一个小数据集(n = ~300).我试图按性别分割我的数据facet_wrap,并且loess对男性来说很好,但不适合女性.
这是我用来绘制图形的代码:
ggplot(data = df, aes(x = STM, y = ATTRACTcomp, color=Harasser_Attractiveness)) +
geom_point(position="jitter", size=0.5) +
facet_wrap( ~Participant_Gender,
labeller = as_labeller(c("Female" = "Female Participants", "Male" = "Male Participants"))) +
geom_smooth(method = "loess") +
labs(title = paste(strwrap("Interaction of Harasser Attractiveness, Participant Gender
and SOI on Attraction/Flattery", 50), collapse="\n"),
x = "Participant Short-term Mating Orientation", y = "Participant Attraction/Flattery",
color="Harasser:") +
theme(plot.title = element_text(hjust = 0.5),
plot.caption = element_text(hjust=0, margin=margin(t=15,0,0,0)),
legend.position="top", legend.margin = margin(1,0,0,0), legend.title = element_text(size=10),
legend.text = element_text(size=9), legend.key.size=unit(c(12), "pt")) +
scale_color_grey(start = .6, end = .1)
问题是我得到了男性图表的平滑条件平均线,但不是女性图表.
这是我的错误消息:
Warning messages:
1: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
at 0.97
2: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
radius 0.0009
3: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
all data on boundary of neighborhood. make span bigger
4: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
pseudoinverse used at 0.97
5: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
neighborhood radius 0.03
6: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
reciprocal condition number 1
7: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
zero-width neighborhood. make span bigger
8: In simpleLoess(y, x, w, span, degree = degree, parametric = parametric, :
There are other near singularities as well. 1
9: Computation failed in `stat_smooth()`:
NA/NaN/Inf in foreign function call (arg 5)
有趣的是,这发生在多个y变量中:女性图总是缺少线条而且我总是得到类似的错误.当我忽略facet_wrap并尝试仅绘制女性参与者的数据框子集时,也会发生这种情况.
从我所理解的读取类似错误消息的线程,内部的一些计算geom_smooth(或者stat_smooth我认为它在引擎盖下调用)返回无限值.(我相当肯定这里的相关变量中没有NAs/NaNs.)问题是,关于此错误的所有线程都假设您可以访问生成无限值的进程,而我没有.
有些人一直说当你的值恰好等于1时就会发生这种情况.我确实有很多ATTRACTcomp(我的y变量)等于1的值,但它们都是男性和女性,所以我不知道为什么我能够为男性而不是女性获得正确的线条.
另外的绘图方法同样有用,也会有所帮助.
我不确定重现此错误所需的最小数据量是多少,所以我只想包含一个只包含图中使用的变量的数据帧:
> dput(df)
structure(list(STM = c(6L, 4L, 7L, 3L, 6L, 7L, 3L, 1L, 4L, 6L,
1L, 1L, 6L, 4L, 6L, 3L, 5L, 2L, 5L, 5L, 4L, 1L, 1L, 4L, 4L, 1L,
1L, 2L, 3L, 4L, 3L, 4L, 6L, 6L, 1L, 1L, 1L, 5L, 1L, 1L, 2L, 4L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 7L, 2L, 1L, 6L, 4L, 1L, 1L, 1L,
1L, 1L, 4L, 1L, 4L, 5L, 1L, 1L, 7L, 4L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 4L, 1L, 1L, 2L, 1L, 1L, 2L, 4L, 5L, 1L,
1L, 1L, 1L, 4L, 1L, 2L, 1L, 7L, 5L, 4L, 1L, 1L, 1L, 1L, 1L, 4L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
7L, 3L, 1L, 1L, 1L, 1L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 7L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 5L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 5L, 5L, 4L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 7L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 5L, 2L,
1L, 1L, 6L, 2L, 1L, 1L, 1L, 1L, 5L, 2L, 1L, 1L, 1L, 1L, 4L, 1L,
1L, 1L, 1L, 1L, 2L, 4L, 1L, 1L, 1L, 6L, 1L, 1L, 1L, 3L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L,
4L, 5L, 5L, 1L, 1L, 4L, 4L, 1L, 7L, 1L, 1L, 4L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 5L, 1L, 1L, 1L, 1L, 5L, 2L, 1L, 4L, 7L, 1L,
1L, 2L, 1L, 1L, 4L, 5L, 5L, 2L, 1L, 4L, 7L, 3L, 5L, 4L, 5L, 4L,
5L, 7L, 7L, 3L), ATTRACTcomp = c(6.53125, 4.25, 5.84375, 4.21875,
5.4375, 2.15625, 3.96875, 4.71875, 3.875, 5.875, 2, 1.87096774193548,
5.65625, 4.5625, 5.65625, 4.53125, 5.375, 1, 5.125, 3.5625, 4.71875,
3.96875, 4.03125, 4.15625, 4.28125, 4.6875, 3.53125, 2.40625,
4.15625, 2.8125, 4.54838709677419, 3.40625, 4.09677419354839,
4.625, 4.53125, 1.90625, 2.32258064516129, 3.53125, 1.90625,
3.46666666666667, 2.2258064516129, 3.625, 4.40625, 4.625, 2.125,
4.3125, 1.9375, 2.4375, 3.96875, 4.875, 5.16129032258065, 2.1875,
1.0625, 3.34375, 3.40625, 1.90625, 1, 3.75, 3.45161290322581,
1.93548387096774, 3.53125, 1.84375, 2.71875, 3.40625, 2.59375,
4.09375, 4.125, 3.96875, 4.34375, 1, 2.6875, 3.6875, 1.09375,
1.0625, 1.375, 1.96875, 2.25, 1.28125, 1.03125, 3.8125, 4.0625,
2.09375, 1.25, 2.34375, 2.90625, 1, 1.5625, 1.25, 1.5625, 1.34375,
2.46875, 1.96875, 1.15625, 1.59375, 1.09375, 2.03125, 1, 5.40625,
3.59375, 1.1875, 1.90625, 1.8125, 1.56666666666667, 1.0625, 3.58064516129032,
4.90625, 6.28125, 1.0625, 2.9375, 1.09375, 1.78125, 1, 2.09375,
1.03125, 4.75, 2.71875, 1, 5.96875, 1.42307692307692, 1, 1.0625,
1.0625, 1.03125, 1.90625, 1.28125, 1.15625, 1.03125, 1.09375,
6.53125, 2.15625, 1.03125, 1.59375, 2, 1.1875, 1.1875, 1.34375,
2.25, 1.03125, 1.0625, 1.3125, 1, 1.5, 1, 2.375, 1.1875, 1.0625,
1.35483870967742, 1, 1.09375, 1.15625, 1, 1, 1.5625, 2, 1, 1.03125,
1.03125, 1, 1.125, 1, 6.6875, 1.1875, 1.51612903225806, 1.0625,
1.125, 1, 1.15625, 1.4375, 1.25, 1.0625, 1.03125, 1.41935483870968,
1, 1, 2.09375, 1.15625, 1, 1, 1, 3.06451612903226, 1, 1, 1, 1,
1, 1, 1, 1.03125, 1.1875, 1.875, 1, 1, 1.5625, 3.25, 1.3125,
1.46875, 2.375, 3.78125, 3.25, 1.21875, 1.25, 1, 1.65625, 1,
1, 6.0625, 1.90625, 6.80645161290323, 1.21875, 1.65625, 1, 1.28125,
1.26666666666667, 1.03125, 1, 2.3125, 4.125, 3.59375, 2.40625,
5.34375, 4.84375, 3.65625, 1.28125, 1.5625, 3.6875, 1.53125,
1.09375, 1.21875, 2.15625, 1.25, 1, 1.375, 1.3125, 1.125, 1.5625,
1.25, 1.5, 1.28125, 2.21875, 3.09375, 3.15625, 1, 1.15625, 4.75,
1, 1.61290322580645, 1.90322580645161, 1.74193548387097, 1.46875,
1, 1.1875, 1.1875, 1.03125, 1.34375, 1.78125, 1, 1.8125, 1, 1,
1.2258064516129, 1.0625, 1.25, 1.59375, 1.09375, 1, 1.03125,
3.9375, 1.46875, 2.71875, 7, 3.875, 3.40625, 2.4375, 2.53125,
2.09677419354839, 1.28125, 1, 1.8125, 1, 1.78125, 1.0625, 1,
1, 1.03125, 1.09375, 1.4375, 1, 1.625, 1.03125, 1.03125, 1.40625,
1.84375, 3.40625, 3.21875, 1, 1, 6.6875, 2.71875, 2.5625, 3.96875,
2.8125, 2.125, 4.21875, 3.65625, 3.25, 1.53125, 5.8125, 3.5625,
4.78125, 1.625, 5.875, 3.21875, 3.41935483870968, 3.21875, 6,
6.34375, 6, 1.40625), Harasser_Attractiveness = structure(c(1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 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, 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, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L), .Label = c("Attractive",
"Unattractive"), class = "factor"), Participant_Gender = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L), .Label = c("Female",
"Male"), class = "factor")), .Names = c("STM", "ATTRACTcomp",
"Harasser_Attractiveness", "Participant_Gender"), row.names = c(NA,
-318L), class = "data.frame")
正如joran所指出的，我认为您的数据过于倾斜，无法适应这种平滑功能。我会重新考虑使用这种方法来处理这种不平衡且如此集中的数据STM == 1。可以使它显示一些行，例如通过删除前 50 个女性观察，在下面STM == 1，但这并不是离散 x 轴的正确显示（即使理论上变量是连续的，您的实际数据是作为离散测量）。确实，平滑的线条具有误导性（女性线条真的在 处下降了这么远STM == 7，还是真的只是因为你在那里只有两个点？
我更喜欢下面的 boxplot 方法，factor(STM)在aes()调用中使用。在这里，我们可以使用geom_text图表底部的箱线图添加计数，使每个箱线图所基于的内容更加清晰。我们仍然得到更高STM与更高相关联的一般情况ATTRACTComp，变化为Harasser_Attractiveness，但我们不再建议基于极少数数据点的平滑关系线。
library(tidyverse)
df <- structure(list(STM = c(6L, 4L, 7L, 3L, 6L, 7L, 3L, 1L, 4L, 6L,
1L, 1L, 6L, 4L, 6L, 3L, 5L, 2L, 5L, 5L, 4L, 1L, 1L, 4L, 4L, 1L,
1L, 2L, 3L, 4L, 3L, 4L, 6L, 6L, 1L, 1L, 1L, 5L, 1L, 1L, 2L, 4L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 7L, 2L, 1L, 6L, 4L, 1L, 1L, 1L,
1L, 1L, 4L, 1L, 4L, 5L, 1L, 1L, 7L, 4L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 4L, 1L, 1L, 2L, 1L, 1L, 2L, 4L, 5L, 1L,
1L, 1L, 1L, 4L, 1L, 2L, 1L, 7L, 5L, 4L, 1L, 1L, 1L, 1L, 1L, 4L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
7L, 3L, 1L, 1L, 1L, 1L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 7L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 5L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 5L, 5L, 4L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 7L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 5L, 2L,
1L, 1L, 6L, 2L, 1L, 1L, 1L, 1L, 5L, 2L, 1L, 1L, 1L, 1L, 4L, 1L,
1L, 1L, 1L, 1L, 2L, 4L, 1L, 1L, 1L, 6L, 1L, 1L, 1L, 3L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L,
4L, 5L, 5L, 1L, 1L, 4L, 4L, 1L, 7L, 1L, 1L, 4L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 5L, 1L, 1L, 1L, 1L, 5L, 2L, 1L, 4L, 7L, 1L,
1L, 2L, 1L, 1L, 4L, 5L, 5L, 2L, 1L, 4L, 7L, 3L, 5L, 4L, 5L, 4L,
5L, 7L, 7L, 3L), ATTRACTcomp = c(6.53125, 4.25, 5.84375, 4.21875,
5.4375, 2.15625, 3.96875, 4.71875, 3.875, 5.875, 2, 1.87096774193548,
5.65625, 4.5625, 5.65625, 4.53125, 5.375, 1, 5.125, 3.5625, 4.71875,
3.96875, 4.03125, 4.15625, 4.28125, 4.6875, 3.53125, 2.40625,
4.15625, 2.8125, 4.54838709677419, 3.40625, 4.09677419354839,
4.625, 4.53125, 1.90625, 2.32258064516129, 3.53125, 1.90625,
3.46666666666667, 2.2258064516129, 3.625, 4.40625, 4.625, 2.125,
4.3125, 1.9375, 2.4375, 3.96875, 4.875, 5.16129032258065, 2.1875,
1.0625, 3.34375, 3.40625, 1.90625, 1, 3.75, 3.45161290322581,
1.93548387096774, 3.53125, 1.84375, 2.71875, 3.40625, 2.59375,
4.09375, 4.125, 3.96875, 4.34375, 1, 2.6875, 3.6875, 1.09375,
1.0625, 1.375, 1.96875, 2.25, 1.28125, 1.03125, 3.8125, 4.0625,
2.09375, 1.25, 2.34375, 2.90625, 1, 1.5625, 1.25, 1.5625, 1.34375,
2.46875, 1.96875, 1.15625, 1.59375, 1.09375, 2.03125, 1, 5.40625,
3.59375, 1.1875, 1.90625, 1.8125, 1.56666666666667, 1.0625, 3.58064516129032,
4.90625, 6.28125, 1.0625, 2.9375, 1.09375, 1.78125, 1, 2.09375,
1.03125, 4.75, 2.71875, 1, 5.96875, 1.42307692307692, 1, 1.0625,
1.0625, 1.03125, 1.90625, 1.28125, 1.15625, 1.03125, 1.09375,
6.53125, 2.15625, 1.03125, 1.59375, 2, 1.1875, 1.1875, 1.34375,
2.25, 1.03125, 1.0625, 1.3125, 1, 1.5, 1, 2.375, 1.1875, 1.0625,
1.35483870967742, 1, 1.09375, 1.15625, 1, 1, 1.5625, 2, 1, 1.03125,
1.03125, 1, 1.125, 1, 6.6875, 1.1875, 1.51612903225806, 1.0625,
1.125, 1, 1.15625, 1.4375, 1.25, 1.0625, 1.03125, 1.41935483870968,
1, 1, 2.09375, 1.15625, 1, 1, 1, 3.06451612903226, 1, 1, 1, 1,
1, 1, 1, 1.03125, 1.1875, 1.875, 1, 1, 1.5625, 3.25, 1.3125,
1.46875, 2.375, 3.78125, 3.25, 1.21875, 1.25, 1, 1.65625, 1,
1, 6.0625, 1.90625, 6.80645161290323, 1.21875, 1.65625, 1, 1.28125,
1.26666666666667, 1.03125, 1, 2.3125, 4.125, 3.59375, 2.40625,
5.34375, 4.84375, 3.65625, 1.28125, 1.5625, 3.6875, 1.53125,
1.09375, 1.21875, 2.15625, 1.25, 1, 1.375, 1.3125, 1.125, 1.5625,
1.25, 1.5, 1.28125, 2.21875, 3.09375, 3.15625, 1, 1.15625, 4.75,
1, 1.61290322580645, 1.90322580645161, 1.74193548387097, 1.46875,
1, 1.1875, 1.1875, 1.03125, 1.34375, 1.78125, 1, 1.8125, 1, 1,
1.2258064516129, 1.0625, 1.25, 1.59375, 1.09375, 1, 1.03125,
3.9375, 1.46875, 2.71875, 7, 3.875, 3.40625, 2.4375, 2.53125,
2.09677419354839, 1.28125, 1, 1.8125, 1, 1.78125, 1.0625, 1,
1, 1.03125, 1.09375, 1.4375, 1, 1.625, 1.03125, 1.03125, 1.40625,
1.84375, 3.40625, 3.21875, 1, 1, 6.6875, 2.71875, 2.5625, 3.96875,
2.8125, 2.125, 4.21875, 3.65625, 3.25, 1.53125, 5.8125, 3.5625,
4.78125, 1.625, 5.875, 3.21875, 3.41935483870968, 3.21875, 6,
6.34375, 6, 1.40625), Harasser_Attractiveness = structure(c(1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 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, 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, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L), .Label = c("Attractive",
"Unattractive"), class = "factor"), Participant_Gender = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L), .Label = c("Female",
"Male"), class = "factor")), .Names = c("STM", "ATTRACTcomp",
"Harasser_Attractiveness", "Participant_Gender"), row.names = c(NA,
-318L), class = "data.frame")
ggplot(
data = df %>%
arrange(Participant_Gender, STM) %>%
slice(50:nrow(.)),
mapping = aes(x = STM, y = ATTRACTcomp, color = Harasser_Attractiveness)
) +
geom_jitter() +
geom_smooth() +
facet_wrap(~ Participant_Gender,
labeller = as_labeller(c("Female" = "Female Participants", "Male" = "Male Participants"))
) +
labs(
title = paste(strwrap("Interaction of Harasser Attractiveness, Participant Gender
and SOI on Attraction/Flattery", 50), collapse = "\n"),
x = "Participant Short-term Mating Orientation",