ggpairs错误：cor.test.default（x，y，方法=方法，使用=使用）中的错误：没有足够的有限观察答案

【问题标题】：ggpairs error: Error in cor.test.default(x, y, method = method, use = use) : not enough finite observationsggpairs错误：cor.test.default（x，y，方法=方法，使用=使用）中的错误：没有足够的有限观察
【发布时间】：2023-03-14 00:35:01
【问题描述】：

我正在尝试使用包 GGally 和 ggpairs 创建散点图矩阵。在我的数据集tol 中，我有几个分类的人口统计变量，还有几个是连续的。我用我想要的变量创建了一个数据框，并尝试省略 NA 值，因为我不断收到此错误：

cor.test.default(x, y, method = method, use = use) 中的错误：不是足够有限的观察"

当我不包括美学映射时，散点图可以正常工作。即使我弄乱了我的 csv 文件以确保没有空单元格，我仍然会收到此错误。

代码如下：

cs <- tol[c("location","comp_sat_avg","burnout_avg","sec_stress_avg","burnout_ee_avg","burnout_dp_avg","burnout_pa_avg","obs_avg","desc_avg","aware_avg","nonjudg_avg","nonreac_avg","wkplre_wc_avg","Efficacy_avg","Lotr_avg","hsecontrol_avg","hsemsupport_avg","hsepsupport_avg","hserole_avg","hsedemands_avg")]
csdata <- na.omit(cs)

ggpairs(csdata,lower=list(continuous="smooth"),mapping=ggplot2::aes(color= location)) +
  theme_bw()

我还有其他三个分类变量需要单独分组，因此非常感谢任何帮助。

根据 stefan 的评论，这里是我的数据集的一个示例：

tol  <- structure(list(location = c("Mukono Health Center IV", "Mukono Health Center IV", 
"Goma Health Center III", "Goma Health Center III", "Goma Health Center III", 
"Kawolo General Hospital", "Kawolo General Hospital", "Mukono Health Center IV", 
"Mukono Health Center IV", "Lwanyonyi VHT", "Mukono Health Center IV", 
"Goma Health Center III", "Mukono Health Center IV", "Mukono Health Center IV", 
"Goma Health Center III", "Mukono Health Center IV", "Mukono Health Center IV", 
"Mukono Health Center IV", "Mukono Health Center IV", "Lwanyonyi VHT"
), comp_sat_avg = c(4.6, 4.9, 4.4, 4.2, 3.7, 4.2, 3, 4.3, 3.8, 
4.4, 2.8, 3.9, 4.7, 4.4, 3.22, 4.6, 1.8, 4.67, 3, 4.8), burnout_avg = c(2.2, 
3.2, 2.1, 2.7, 3.4, 2.1, 3.11, 2.4, 2.6, 2.5, 2.89, 2, 1.8, 1.8, 
2.78, 2.6, 3.5, 2.7, 2.56, 2.1), sec_stress_avg = c(2.6, 1.4, 
2.44, 3.1, 3.5, 2.8, 3.1, 2.4, 3.1, 3.33, 2.56, 1.8, 2.8, 1.9, 
3.1, 2.8, 1.5, 3.8, 3.9, 2.6), burnout_ee_avg = c(2.11, 2.33, 
2.78, 2.67, 4.67, 1.22, 1, 3.33, 1.78, 4.33, 3.33, 1.78, 2.78, 
1.11, 1.67, 2.89, 5.89, 1.78, 3, 0.78), burnout_dp_avg = c(1.6, 
0.4, 1.2, 2.4, 1.8, 0.75, 1.2, 2.8, 0.6, 2.4, 4.2, 2.4, 1.2, 
0.6, 3.8, 3.2, 5.6, 1, 1.6, 0.4), burnout_pa_avg = c(5.13, 5.75, 
4.75, 2.88, 5.25, 4.67, 5.75, 5, 5.5, 5.25, 4.88, 4.5, 3.75, 
4.13, 3.13, 4, 4, 3, 4.88, 5.88), obs_avg = c(3.63, 3.25, 2, 
4.38, 2.88, 4, 3.75, 2.38, 2.13, 2.75, 4.63, 3.88, 3, 2.14, 3.83, 
3.5, 2.25, 2.63, 4.13, 3.88), desc_avg = c(3, 3.38, 4.5, 3.88, 
3.38, 3.13, 3.63, 2.63, 3.75, 4.25, 3.5, 4.38, 2.57, 3.63, 3.25, 
3.63, 3.13, 4.13, 4.25, 3.38), aware_avg = c(2.5, 4.25, 4.63, 
4.25, 4.13, 3.5, 4.13, 3.25, 3.25, 4.75, 4.13, 4.75, 3.5, 3.88, 
2.13, 4.13, 3.5, 4.13, 3.57, 3.25), nonjudg_avg = c(1.88, 3.63, 
4.38, 1.88, 2.63, 3.25, 3, 3, 3.25, 4, 2, 3, 3, 4.88, 1.86, 2.88, 
3.25, 2.5, 2.38, 1.63), nonreac_avg = c(3.71, 3.57, 2.43, 4.29, 
3, 3.43, 3.86, 3.86, 2.86, 4.29, 3.86, 3, 3, 3.14, 4.43, 3.43, 
2.8, 3.71, 3.57, 3.43), wkplre_wc_avg = c(5.07, 6.13, 5.8, 5.27, 
4.33, 6.2, 4.07, 7, 6.27, 2.29, 5.14, 4.4, 4.73, 5.47, 5.07, 
4.93, 3.07, 5.6, 5.73, 4.8), Efficacy_avg = c(4, 1.4, 3.6, 3.1, 
3.1, 2.9, 3.6, 2, 2.5, 3.3, 3.7, 3.6, 1.9, 3.7, 3.5, 3.6, 3.2, 
3.6, 3.5, 3.9), Lotr_avg = c(2.17, 2.33, 3.6, 0.5, 2.67, 1.67, 
3.2, 2.17, 2.5, 3.67, 2.33, 3.67, 1.17, 1.83, 2, 2.67, 1.83, 
2.67, 2.83, 3.5), hsecontrol_avg = c(3.67, 4.5, 3.5, 3.5, 3.17, 
3.83, 4.5, 4.33, 3.83, 3.83, 3.67, 4.67, 4.5, 3.67, 3.83, 3.17, 
3, 4.17, 3.83, 3.17), hsemsupport_avg = c(3.6, 4, 3.2, 3.6, 3.2, 
4.2, 3.6, 4, 3.8, 3.6, 3, 4.2, 3.4, 4.2, 3.8, 3.2, 2.4, 4, 4, 
3.8), hsepsupport_avg = c(3.25, 4, 3.75, 3.5, 3, 4.75, 4.25, 
4.75, 3.75, 3.5, 4.67, 4.25, 3.75, 4, 4, 3.25, 1.5, 4, 4, 4), 
    hserole_avg = c(4.8, 5, 4.4, 4.2, 5, 4, 4, 4.2, 4, 4.6, 4.6, 
    4.8, 4.2, 4.2, 3.2, 4.4, 2.8, 4, 4.2, 5), hsedemands_avg = c(2, 
    3.29, 3.29, 4, 1.86, 3.57, 3.29, 1.71, 3.14, 1.71, 3.71, 
    3.71, 3.43, 3.86, 1.86, 2.71, 4, 3.29, 3.57, 2.57)), row.names = c(NA, 
-20L), class = c("tbl_df", "tbl", "data.frame"), na.action = structure(c(`1` = 1L, 
`5` = 5L, `11` = 11L, `15` = 15L, `19` = 19L, `24` = 24L, `27` = 27L, 
`30` = 30L, `46` = 46L, `47` = 47L), class = "omit"))

【问题讨论】：

欢迎来到 SO！为了帮助我们帮助您，您能否通过分享您的数据样本来重现您的问题？只需在控制台中输入dput(head(NAME_OF_DATASET, 20))（这将给出前20行）并将以structure(....开头的输出复制并粘贴到您的帖子中。另请参阅how to make a minimal reproducible example。

标签： r ggally ggpairs

【解决方案1】：

您需要采取两个步骤来完成这项工作。有两个位置只有两个观察值，这不适用于 cor.test.default。对您的数据进行子集化以删除这些观察结果：

csdata <- 
  csdata %>%
  filter(
    location != "Kawolo General Hospital"
  , location != "Lwanyonyi VHT"
  )

但是，现在您的数据集将保留这些因子水平，但每个因子水平为 0。使用以下方法将变量 locations 转换为因子：

csdata$location <- factor(csdata$location)

现在你的 ggpairs 与美学映射将没有问题：

ggpairs(csdata,lower=list(continuous="smooth"),mapping=ggplot2::aes(color= location)) +
  theme_bw()

【讨论】：