删除了包含缺失值的 N 行，但没有缺失值或超出范围的值答案

【问题标题】：Removed N rows containing missing values BUT there are no missing values nor values out of range删除了包含缺失值的 N 行，但没有缺失值或超出范围的值
【发布时间】：2021-06-14 06:53:59
【问题描述】：

我一周前发布了一个类似的问题，但我未能确定真正的问题。因此，这个问题远非正确。

现在，我清楚地知道发生了什么，但我不明白为什么会这样。我还查看了与相同错误相关的类似问题，但这些问题的解决方案不适用于我的案例。

我正在绘制调查现场工作过程中变量的频率分布。因此，它显示了这些变量的比例如何随时间变化。

所以，我有一个变量 (Startday)，它告诉受访者在哪一天参加了调查，如果他/她没有参加，那么它就是 NA。然后，我有典型的变量，如性别或婚姻状况。

这是绘制此类图表的代码

    df %>% 
  mutate(date = lubridate::mdy(startday)) %>% 
  arrange(date) %>% 
  mutate(Rs = cumsum(sf_sex %in% c("Male", "Female")),
         female_Rs = cumsum(sf_sex == "Female")) %>% 
  group_by(date) %>% 
  slice(n()) %>% 
  select(date, Rs, female_Rs) %>% 
  mutate(female_prop = female_Rs/Rs) %>% 
  ggplot(aes(x = date, y = female_prop)) + 
  geom_point() +
  geom_line()

这就是我得到的。

正是我想要的。当我将婚姻状况用作变量时，问题就出现了（并且该变量与另一个变量具有相同的性质：虚拟和字符）。这就是我使用以下代码得到的结果：

df %>% 
  mutate(date = lubridate::mdy(startday)) %>% 
  arrange(date) %>% 
  mutate(Rs = cumsum(Maritaldummy %in% c("Not married", "Married")),
         Married_Rs = cumsum(Maritaldummy == "Married")) %>% 
  group_by(date) %>% 
  slice(n()) %>% 
  select(date, Rs, Married_Rs) %>% 
  mutate(Married_prop = Married_Rs/Rs) %>% 
  ggplot(aes(x = date, y = Married_prop)) + 
  geom_point() +
  geom_line()

随后出现此错误：

警告信息： 1：删除了包含缺失值的 34 行 (geom_point)。 2：删除了包含缺失值的 34 行 (geom_path)。

如您所见，观测在 6 月 5 日左右停止。

需要考虑的事项：

它没有超出范围，因为我尝试使用 ylim() 和 xlim() 更改图形的范围
没有缺失值

当此代码适用于实验组 2 和 3（每个 n = 350）但不适用于实验组 1（n = 2050）时，奇怪的部分就出现了。我确实相信错误必须来自这里，因为当我为第 1 组随机抽样少于 1300 个观察值时......它有效！！！这是第 2 组的相同代码示例。

我给你一个可重现的例子，但我担心这个错误只有在与完整样本一起使用时才有效，但也许你会发现它有什么问题？

非常感谢您的关注、时间和帮助。

df <- structure(list(startday = c("06/02/2019", "05/22/2019", "05/28/2019", 
                            "05/26/2019", "06/03/2019", "06/10/2019", "05/22/2019", "05/30/2019", 
                            "05/31/2019", "06/18/2019", "05/22/2019", "05/25/2019", "05/25/2019", 
                            "05/22/2019", "06/14/2019", "06/14/2019", "05/20/2019", "05/27/2019", 
                            "05/20/2019", "05/21/2019", "05/20/2019", "05/20/2019", "06/09/2019", 
                            "06/12/2019", "05/24/2019", "05/20/2019", "05/20/2019", "05/28/2019", 
                            "06/09/2019", "05/20/2019", "06/21/2019", "06/03/2019", "06/07/2019", 
                            "05/26/2019", "05/28/2019", "06/03/2019", "06/06/2019", "06/05/2019", 
                            "05/27/2019", "06/10/2019", "05/20/2019", "06/05/2019", "05/20/2019", 
                            "06/04/2019", "05/23/2019", "05/20/2019", "06/11/2019", "05/28/2019", 
                            "06/09/2019", "06/15/2019", "05/25/2019", "06/14/2019", "05/20/2019", 
                            "06/05/2019", "06/04/2019", "06/10/2019", "06/16/2019", "06/05/2019", 
                            "06/29/2019", "05/30/2019", "06/03/2019", "06/09/2019", "05/20/2019", 
                            "05/25/2019", "06/16/2019", "06/14/2019", "05/21/2019", "05/28/2019", 
                            "06/09/2019", "06/07/2019", "05/25/2019", "05/20/2019", "05/27/2019", 
                            "05/20/2019", "05/21/2019", "05/20/2019", "06/17/2019", "06/26/2019", 
                            "06/07/2019", "05/22/2019", "06/19/2019", "06/04/2019", "05/21/2019", 
                            "05/21/2019", "05/21/2019", "06/14/2019", "05/25/2019", "06/19/2019", 
                            "05/20/2019", "06/03/2019", "05/20/2019", "06/04/2019", "05/20/2019", 
                            "05/27/2019", "05/22/2019", "05/20/2019", "06/02/2019", "05/21/2019", 
                            "05/23/2019", "06/03/2019", "06/14/2019", "06/14/2019", "06/07/2019", 
                            "05/20/2019", "05/23/2019", "06/24/2019", "06/03/2019", "05/20/2019", 
                            "06/06/2019", "06/15/2019", "06/06/2019", "05/27/2019", "05/24/2019", 
                            "05/22/2019", "05/20/2019", "05/30/2019", "06/23/2019", "05/21/2019", 
                            "05/20/2019", "06/16/2019", "05/20/2019", "05/24/2019", "05/21/2019", 
                            "05/21/2019", "06/20/2019", "05/20/2019", "05/22/2019", "06/06/2019", 
                            "05/20/2019", "05/21/2019", "06/15/2019", "05/27/2019", "05/26/2019", 
                            "06/06/2019", "05/20/2019", "06/05/2019", "06/02/2019", "06/20/2019", 
                            "05/22/2019", "05/20/2019", "06/03/2019", "05/20/2019", "06/03/2019", 
                            "05/20/2019", "06/03/2019", "05/22/2019", "05/20/2019", "05/22/2019", 
                            "05/22/2019", "05/20/2019", "05/20/2019", "05/23/2019", "05/23/2019", 
                            "05/23/2019", "06/05/2019", "06/08/2019", "06/03/2019", "05/24/2019", 
                            "06/05/2019", "06/02/2019", "05/20/2019", "05/29/2019", "06/04/2019", 
                            "05/21/2019", "06/08/2019", "06/12/2019", "05/30/2019", "06/05/2019", 
                            "06/12/2019", "05/20/2019", "05/20/2019", "06/26/2019", "05/20/2019", 
                            "06/04/2019", "05/20/2019", "06/06/2019", "05/24/2019", "05/24/2019", 
                            "06/06/2019", "06/22/2019", "05/26/2019", "05/29/2019", "05/27/2019", 
                            "05/20/2019", "05/23/2019", "05/21/2019", "05/22/2019", "05/22/2019", 
                            "06/11/2019", "06/05/2019", "06/05/2019", "05/28/2019", "05/23/2019", 
                            "06/13/2019", "05/20/2019", "06/07/2019", "05/28/2019", "06/12/2019", 
                            "06/28/2019", "06/15/2019"), sf_sex = c("Female", "Male", "Male", 
                                                                    "Male", "Male", "Female", "Female", "Female", "Female", "Female", 
                                                                    "Female", "Male", "Female", "Male", "Female", "Female", "Female", 
                                                                    "Male", "Female", "Female", "Male", "Male", "Female", "Male", 
                                                                    "Male", "Female", "Male", "Female", "Female", "Male", "Male", 
                                                                    "Male", "Female", "Female", "Male", "Male", "Female", "Male", 
                                                                    "Female", "Male", "Female", "Female", "Female", "Male", "Male", 
                                                                    "Female", "Male", "Male", "Male", "Female", "Male", "Female", 
                                                                    "Male", "Male", "Male", "Female", "Female", "Female", "Female", 
                                                                    "Male", "Female", "Male", "Male", "Female", "Female", "Male", 
                                                                    "Male", "Male", "Male", "Female", "Male", "Male", "Female", "Female", 
                                                                    "Male", "Male", "Male", "Male", "Female", "Female", "Male", "Male", 
                                                                    "Female", "Male", "Male", "Male", "Female", "Female", "Female", 
                                                                    "Female", "Male", "Female", "Female", "Female", "Male", "Female", 
                                                                    "Female", "Female", "Male", "Female", "Female", "Female", "Female", 
                                                                    "Female", "Female", "Female", "Male", "Female", "Male", "Male", 
                                                                    "Female", "Male", "Female", "Female", "Male", "Female", "Male", 
                                                                    "Male", "Female", "Female", "Female", "Male", "Female", "Female", 
                                                                    "Male", "Female", "Male", "Female", "Female", "Male", "Female", 
                                                                    "Female", "Male", "Female", "Male", "Male", "Female", "Female", 
                                                                    "Female", "Female", "Female", "Male", "Female", "Female", "Female", 
                                                                    "Female", "Female", "Male", "Female", "Male", "Female", "Female", 
                                                                    "Female", "Female", "Female", "Female", "Female", "Male", "Male", 
                                                                    "Male", "Female", "Female", "Female", "Female", "Female", "Male", 
                                                                    "Male", "Female", "Female", "Female", "Male", "Female", "Male", 
                                                                    "Female", "Male", "Female", "Female", "Male", "Female", "Male", 
                                                                    "Male", "Female", "Male", "Female", "Female", "Male", "Female", 
                                                                    "Female", "Male", "Female", "Female", "Female", "Male", "Male", 
                                                                    "Male", "Female", "Female", "Female", "Female", "Male"), Maritaldummy = c("Not married", 
                                                                                                                                               "Married", "Married", "Not married", "Not married", "Married", 
                                                                                                                                               "Married", "Married", "Not married", "Not married", "Not married", 
                                                                                                                                               "Married", "Married", "Married", "Married", "Married", "Not married", 
                                                                                                                                               "Not married", "Not married", "Married", "Not married", "Not married", 
                                                                                                                                               "Not married", "Not married", "Not married", "Married", "Married", 
                                                                                                                                               "Not married", "Married", "Not married", "Married", "Not married", 
                                                                                                                                               "Not married", "Not married", "Not married", "Not married", "Married", 
                                                                                                                                               "Not married", "Married", "Married", "Not married", "Not married", 
                                                                                                                                               "Married", "Not married", "Married", "Not married", "Not married", 
                                                                                                                                               "Not married", "Married", "Married", "Married", "Not married", 
                                                                                                                                               "Not married", "Married", "Married", "Not married", "Not married", 
                                                                                                                                               "Married", "Married", "Not married", "Married", "Married", "Married", 
                                                                                                                                               "Not married", "Married", "Not married", "Not married", "Married", 
                                                                                                                                               "Not married", "Married", "Not married", "Not married", "Not married", 
                                                                                                                                               "Married", "Not married", "Not married", "Married", "Married", 
                                                                                                                                               "Not married", "Married", "Married", "Married", "Married", "Married", 
                                                                                                                                               "Married", "Not married", "Married", "Not married", "Not married", 
                                                                                                                                               "Not married", "Not married", "Not married", "Married", "Not married", 
                                                                                                                                               "Married", "Married", "Not married", "Not married", "Married", 
                                                                                                                                               "Not married", "Married", "Married", "Married", "Married", "Not married", 
                                                                                                                                               "Married", "Married", "Married", "Not married", "Married", "Not married", 
                                                                                                                                               "Not married", "Married", "Not married", "Married", "Not married", 
                                                                                                                                               "Not married", "Married", "Not married", "Married", "Not married", 
                                                                                                                                               "Married", "Married", "Not married", "Married", "Married", "Married", 
                                                                                                                                               "Not married", "Married", "Married", "Married", "Married", "Married", 
                                                                                                                                               "Married", "Married", "Married", "Not married", "Not married", 
                                                                                                                                               "Not married", "Married", "Married", "Married", "Not married", 
                                                                                                                                               "Married", "Not married", "Married", "Not married", "Married", 
                                                                                                                                               "Married", "Married", "Married", "Married", "Not married", "Married", 
                                                                                                                                               "Not married", "Not married", "Married", "Married", "Married", 
                                                                                                                                               "Married", "Married", "Married", "Married", "Married", "Not married", 
                                                                                                                                               "Married", "Married", "Married", "Not married", "Not married", 
                                                                                                                                               "Married", "Not married", "Married", "Not married", "Married", 
                                                                                                                                               "Married", "Not married", "Not married", "Married", "Not married", 
                                                                                                                                               "Married", "Not married", "Not married", "Married", "Not married", 
                                                                                                                                               "Not married", "Married", "Married", "Married", "Not married", 
                                                                                                                                               "Not married", "Not married", "Married", "Married", "Married", 
                                                                                                                                               "Married", "Not married", "Not married", "Married", "Not married")), row.names = c("3564", "2999", "20144", "17281", "11917", 
                                                                                                                                                                                                                                  "14549", "5116", "10553", "23108", "19521", "277", "24312", "5449", 
                                                                                                                                                                                                                                  "19006", "9171", "21265", "20494", "11961", "15556", "12237", 
                                                                                                                                                                                                                                  "10959", "23460", "14050", "13996", "16222", "21852", "5593", 
                                                                                                                                                                                                                                  "18871", "18770", "776", "24913", "7813", "25079", "1063", "22878", 
                                                                                                                                                                                                                                  "13638", "19169", "7226", "14895", "8088", "19789", "22835", 
                                                                                                                                                                                                                                  "14196", "13816", "7124", "10394", "8290", "16807", "732", "3130", 
                                                                                                                                                                                                                                  "16033", "14958", "7500", "15039", "1538", "12532", "2890", "18907", 
                                                                                                                                                                                                                                  "21581", "3120", "20198", "22943", "8468", "3128", "24153", "22911", 
                                                                                                                                                                                                                                  "6225", "8489", "13040", "17506", "14855", "1500", "11955", "24484", 
                                                                                                                                                                                                                                  "17625", "19888", "10351", "19210", "22946", "14699", "1959", 
                                                                                                                                                                                                                                  "6770", "23286", "11842", "12811", "22197", "5899", "10138", 
                                                                                                                                                                                                                                  "20505", "16090", "17835", "20512", "12271", "9152", "12767", 
                                                                                                                                                                                                                                  "25244", "16865", "6970", "10036", "22531", "12329", "15366", 
                                                                                                                                                                                                                                  "2", "9440", "2100", "23166", "11421", "18912", "4441", "25202", 
                                                                                                                                                                                                                                  "20599", "411", "12584", "1586", "4543", "1307", "10044", "25033", 
                                                                                                                                                                                                                                  "5005", "25122", "16236", "9653", "16194", "14393", "7512", "10059", 
                                                                                                                                                                                                                                  "12010", "1619", "3136", "24088", "14641", "19564", "9568", "18815", 
                                                                                                                                                                                                                                  "21079", "22010", "9553", "20380", "20416", "15745", "7000", 
                                                                                                                                                                                                                                  "7735", "24924", "15286", "20403", "4680", "13714", "13302", 
                                                                                                                                                                                                                                  "12508", "17514", "4480", "7446", "3723", "24069", "25317", "14607", 
                                                                                                                                                                                                                                  "12274", "21715", "8983", "23488", "9228", "7265", "18192", "16475", 
                                                                                                                                                                                                                                  "11760", "15530", "18177", "11535", "18839", "17908", "9789", 
                                                                                                                                                                                                                                  "18045", "1025", "21645", "11853", "22453", "18052", "22763", 
                                                                                                                                                                                                                                  "9", "12286", "15329", "3306", "13215", "16533", "18385", "23784", 
                                                                                                                                                                                                                                  "10131", "4894", "14154", "3365", "8648", "17325", "21219", "16689", 
                                                                                                                                                                                                                                  "9969", "10621", "24206", "19621", "8440", "19889"), class = "data.frame")

【问题讨论】：

我刚刚用代码编辑了
sum(is.na(df$Maritaldummy)) 在您的原始数据中返回什么？
它给出了 2.... 问题可能是因为这个原因无法计算这些值吗？

标签： r ggplot2

【解决方案1】：

如果您将列中的任何一个值更改为NA，我们可以重现该错误。

library(dplyr)
library(ggplot2)

df$Maritaldummy[195] <- NA

df %>% 
  mutate(date = lubridate::mdy(startday)) %>% 
  arrange(date) %>% 
  mutate(Rs = cumsum(Maritaldummy %in% c("Not married", "Married")),
         Married_Rs = cumsum(Maritaldummy == "Married")) %>% 
  group_by(date) %>% 
  slice(n()) %>% 
  select(date, Rs, Married_Rs) %>% 
  mutate(Married_prop = Married_Rs/Rs) %>% 
  ggplot(aes(x = date, y = Married_prop)) + 
  geom_point() +
  geom_line()

警告信息： 1：删除了包含缺失值的 38 行 (geom_point)。 2：删除了包含缺失值的 38 行（geom_path）。

由于一个或多个值为NA cumsum 失败并为之后的所有值返回NA。一个简单的解决方法是使用%in% 而不是==，与NA 相比，它返回FALSE。

df %>% 
  mutate(date = lubridate::mdy(startday)) %>% 
  arrange(date) %>% 
  mutate(Rs = cumsum(Maritaldummy %in% c("Not married", "Married")),
         Married_Rs = cumsum(Maritaldummy %in% "Married")) %>% 
  group_by(date) %>% 
  slice(n()) %>% 
  select(date, Rs, Married_Rs) %>% 
  mutate(Married_prop = Married_Rs/Rs) %>% 
  ggplot(aes(x = date, y = Married_prop)) + 
  geom_point() +
  geom_line()

【讨论】：