R 为什么ggplot删除了我99%的观察值？

我试图展示我们大学学生的高级瑞典语（SVENSKA2）平均成绩是如何随时间和课程的不同而变化的。我正在使用以下代码：

totdata%>%
group_by(program,ADMISSIONROUND_ID)%>%
mutate(mean=mean(SVENSKA2),na.rm=T)%>%
ungroup%>%
ggplot(aes(x=ADMISSIONROUND_ID,y=mean, group=program, color=program))+geom_line()+scale_y_continuous(limits=c(0,20))

奇怪的是，数据删除了2468行中的2458行，只保留了“Ekonomi COOP”程序中的ONCE

SVENSKA2中只有13.5%的值缺失，所以我认为不应该如此

到底发生了什么，为什么会发生，我该如何补救

数据的小摘录（包含许可证、progam和SVENSKA2）：

数据大摘录（包含许可证、progam和SVENSKA2）：

---

我不确定这是否相关，但您真的希望na.rm在调用之外，在这里表示

mutate（mean=mean（SVENSKA2），na.rm=T）

Ahhh更好：）

structure(list(start_date = structure(c(15585, 15585, 15585, 
15585, 15585, 15585, 15585, 15585, 15585, 15585, 15585, 15585, 
15585, 15585, 15585, 15585, 15585, 15585, 15585, 15585), class = "Date"), 
    ADMISSIONROUND_ID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("HT2012", 
    "HT2013", "HT2014", "HT2015", "HT2016", "HT2017", "HT2018", 
    "HT2019"), class = c("ordered", "factor")), program = c("Ekonom", 
    "Ekonom", "Ekonom", "Ekonom", "Ekonom", "Ekonom", "Ekonom", 
    "Ekonom", "Ekonom", "Ekonom", "Ekonom", "Ekonom", "Ekonom", 
    "Ekonom", "Ekonom", "Ekonom", "Ekonom", "Ekonom", "Ekonom", 
    "Ekonom"), SVENSKA2 = c(20, 15, 15, NA, NA, 10, 15, 10, 10, 
    20, 15, 15, NA, 20, 15, 20, 15, 15, 10, 15)), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -20L), groups = structure(list(
    start_date = structure(15585, class = "Date"), .rows = list(
        1:20)), row.names = c(NA, -1L), class = c("tbl_df", "tbl", 
"data.frame"), .drop = TRUE))

structure(list(start_date = structure(c(15585, 15585, 15585, 
15585, 15585, 15585, 15585, 15585, 15585, 15585, 15585, 15585, 
15585, 15585, 15585, 15585, 15585, 15585, 15585, 15949, 15949, 
15949, 15949, 15949, 15949, 15949, 15949, 15949, 15949, 15949, 
15949, 15949, 15949, 15949, 16313, 16313, 16313, 16313, 16313, 
16313, 16313, 16313, 16313, 16313, 16677, 16677, 16677, 16677, 
16677, 16677, 16677, 16677, 16677, 16677, 17041, 17041, 17041, 
17041, 17041, 17041, 17041, 17041, 17041, 17041, 17041, 17041, 
17041, 17041, 17041, 17041, 17041, 17041, 17041, 17405, 17405, 
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15585, 15585, 15585, 15585, 15585, 15585, 15585, 15949, 15949, 
15949, 15949, 15949, 15949, 15949, 15949, 15949, 15949, 15949, 
15949, 15949, 15949, 15949, 15949, 16313, 16313, 16313, 16313, 
16313, 16313, 16313, 16677, 16677, 16677, 16677, 16677, 16677, 
16677, 16677, 17041, 17041, 17041, 17041, 17041, 17041, 17041, 
17041, 17041, 17041, 17041, 17041, 17041, 17041, 17405, 17405, 
17405, 17405, 17405, 17405, 17405, 17405, 17405, 17405, 17405, 
17405, 17405, 17405, 17405, 17405, 17405, 17405, 17405, 17405, 
17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 
17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 
17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 
17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 
18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 
18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 
18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 
18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 
16677, 16677, 17041, 17041, 17041, 17041, 17041, 17041, 17041, 
17041, 17041, 17041, 17041, 17041, 17405, 17405, 17405, 17405, 
17405, 17405, 17405, 17405, 17405, 17405, 17405, 17405, 17405, 
17405, 17405, 17776, 17776, 17776, 17776, 17776, 18140, 18140, 
18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 
18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 
18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, 16313, 16313, 16677, 16677, 16677, 
17041, 17041, 17041, 17041, 17041, 17041, 17041, 17041, 17041, 
17041, 17405, 17405, 17405, 17405, 17405, 17405, 17405, 17405, 
17405, 17405, 17405, 17405, 17405, 17405, 17405, 17405, 17776, 
17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 
17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 17776, 
17776, 17776, 17776, 18140, 18140, 18140, 18140, 18140, 18140, 
18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140, 
18140, 18140, 18140, 18140, 18140, 18140, 18140, 18140), class = "Date"), 
    ADMISSIONROUND_ID = structure(c(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, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 4L, 4L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
    4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 3L, 3L, 4L, 
    4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("HT2012", 
    "HT2013", "HT2014", "HT2015", "HT2016", "HT2017", "HT2018", 
    "HT2019"), class = c("ordered", "factor")), SVENSKA2 = c(10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
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    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
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    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
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    12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5), program = c("Ekonom", 
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