删除了包含缺失值的 N 行，但没有缺失值或超出范围的值
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，我现在清楚发生了什么，但我不明白为什么会发生。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是如何随时间变化的。
因此，我有一个变量 (Startday) 来说明受访者参加调查的日期，如果他/她没有参加，则为 NA。然后，我有典型的变量，比如性别或婚姻状况。
这是绘制此类图形的代码
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（并且该变量具有与另一个相同的性质：虚拟和字符）。这是我使用以下代码得到的：
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 (geom_path) 的 34 行。
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（n = 350 每个）但不适用于实验组 1（n = 2050）时，奇怪的部分出现了。我确实相信错误一定来自这里，因为当我为第 1 组随机抽样少于 1300 个观察值时……它起作用了！！！这是第 2 组相同代码的示例。
我给您提供了一个可重现的示例，但恐怕该错误仅在将其与完整示例一起使用时才有效，但也许您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")
1 个答案:
答案 0 :(得分：1)
如果您将列中的任何一个值更改为 NA，我们都可以重现该错误。
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()
退货
<块引用>警告信息： 1：删除了 38 行包含缺失值 (geom_point)。 2：删除了包含缺失值的 38 行（geom_path）。
由于一个或多个值是 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 FALSE 相比返回 NA。
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()
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