Difficulty plotting data with ggplot2 geom_smooth method=loess?

Question

I'm very new at this and I don't actually understand the differences between the plotting methods, but loess seems to be giving me the most informative graphs, considering I have a small-ish data set (n=~300). I'm trying to split my data by gender using facet_wrap, and loess is working fine for men, but not for women.

Here's the code I'm using to plot the graph:

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)

The problem is that I'm getting a smoothed conditional mean line for the male graph, but not for the female graph.

Here are my error messages:

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)

The interesting thing is this happens for multiple y variables: the female graph is always missing the lines and I always get similar errors. It also happens when I ignore facet_wrap and try to just plot a subset of the dataframe with only female participants.

From what I understand reading threads about similar error messages, some computation within geom_smooth (or stat_smooth as I think it's called under the hood) is returning infinite values. (I am fairly certain there are no NAs/NaNs in the relevant variables here.) The problem is, all the threads about this error assume that you have access to the process producing the infinite values, and I don't.

Some people have been saying this can occur when you have values equal to exactly 1. I do have quite a few values of ATTRACTcomp (my y variable) equal to exactly 1, but they are both men and women, so I don't know why I'm able to get the correct lines for men but not women.

Alternative plotting methods that would be equally informative would also be helpful.

I'm not sure what the minimal amount of data necessary to reproduce this error is, so I'm just going to include a dataframe with only the variables used in the graph:

> 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")

What a great first question - reproducible example, good explanation, nontrivial problem - welcome to the site! — Gregor Thomas, Apr 16 '18 at 21:36
I think the issue here is not that changing the code will somehow make loess "work" but that your data are just fundamentally not amenable to this kind of smoothing. Your x axis is essentially discrete and only spans 7 unique values. And large majorities of your data are piled up all on one x value (1). — joran, Apr 16 '18 at 21:40
For example, one category has data distributed 66/7/7/3/1 points on each x value. My expectations for data like that would be that **any** naive "regression" will at best perform poorly and at worst just be entirely inappropriate. Personally, I might lean more towards displays that show slightly aggregated versions of the data that highlight the sample size discrepancies. — joran, Apr 16 '18 at 21:46

score 7 · Answer 1 · answered Apr 16 '18 at 22:22

As noted by joran, I think your data is too skewed to be amenable to this smoothing function. I would reconsider using this method with data this unbalanced and so concentrated on STM == 1. It is possible to make it display some lines, for example below by removing the first 50 female observations where STM == 1, but this is not really the right display for a discrete x-axis (even if in theory the variable is continuous, your actual data is measured as discrete). Indeed, the smoothed lines are misleading (does the female line really go down so far at STM == 7, or is that really just because you have just two points there?

I would instead prefer a boxplot method as below, using factor(STM) in the aes() call. Here, we can use geom_text to add the counts by boxplot at the bottom of the graph, making it clearer what each boxplot is based on. We still get the general picture that higher STM is associated with higher ATTRACTComp, varying by Harasser_Attractiveness, but we no longer suggest a smoothed relationship line based on very few points of data.

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",
    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")
  )
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : pseudoinverse used at 0.97
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : neighborhood radius 1.03
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : reciprocal condition number 0
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : There are other near singularities as well. 1
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
#> at 0.97
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
#> 1.03
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : reciprocal
#> condition number 0
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : There are other
#> near singularities as well. 1
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : pseudoinverse used at 0.97
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : neighborhood radius 2.03
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : reciprocal condition number 0
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : There are other near singularities as well. 4
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
#> at 0.97
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
#> 2.03
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : reciprocal
#> condition number 0
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : There are other
#> near singularities as well. 4

ggplot(
  data = df, 
  mapping = aes(x = factor(STM), y = ATTRACTcomp, color = Harasser_Attractiveness)
) +
  geom_boxplot() +
  facet_wrap(
    ~ Participant_Gender,
    labeller = as_labeller(c("Female" = "Female Participants", "Male" = "Male Participants"))
  ) +
  geom_text(
    data = df %>%
      count(Participant_Gender, STM, Harasser_Attractiveness) %>%
      mutate(label = str_c(n), yloc = 0.75),
    mapping = aes(y = yloc, label = label),
    position = position_dodge(width = 0.75)
  ) +
  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")
  )

Created on 2018-04-16 by the reprex package (v0.2.0).

Difficulty plotting data with ggplot2 geom_smooth method=loess?

1 Answers1