我已经查看了一些与我类似的问题，但无法理解它们如何或是否适用于我的数据集。
我有一些学生在 11 个不同时间点对 5 个问题的答案的数据。我希望对学生对问题的回答进行平均，这样他们的平均回答就可以随着时间的推移而绘制出来。
有没有一种简单的方法来平均每 5 行？
我是初学者，如有任何帮助，我们将不胜感激!
我在下面提供了我的数据:
> dput(pulse)
structure(list(ï..Question = structure(c(1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L, 1L, 9L, 10L, 2L, 8L,
1L, 9L, 10L, 2L, 8L), .Label = c("Q", "Q10", "Q11_1", "Q11_2",
"Q11_3", "Q11_4", "Q11_5", "Q12", "Q2", "Q8"), class = "factor"),
Type = structure(c(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, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 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, 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
), .Label = c("FYS", "SNR"), class = "factor"), Student = c(789331L,
789331L, 789331L, 789331L, 789331L, 805933L, 805933L, 805933L,
805933L, 805933L, 826523L, 826523L, 826523L, 826523L, 826523L,
832929L, 832929L, 832929L, 832929L, 832929L, 838607L, 838607L,
838607L, 838607L, 838607L, 841903L, 841903L, 841903L, 841903L,
841903L, 843618L, 843618L, 843618L, 843618L, 843618L, 852125L,
852125L, 852125L, 852125L, 852125L, 876406L, 876406L, 876406L,
876406L, 876406L, 879972L, 879972L, 879972L, 879972L, 879972L,
885650L, 885650L, 885650L, 885650L, 885650L, 888712L, 888712L,
888712L, 888712L, 888712L, 903303L, 903303L, 903303L, 903303L,
903303L, 796882L, 796882L, 796882L, 796882L, 796882L, 827911L,
827911L, 827911L, 827911L, 827911L, 830271L, 830271L, 830271L,
830271L, 830271L, 831487L, 831487L, 831487L, 831487L, 831487L,
834598L, 834598L, 834598L, 834598L, 834598L, 836364L, 836364L,
836364L, 836364L, 836364L, 839802L, 839802L, 839802L, 839802L,
839802L, 855524L, 855524L, 855524L, 855524L, 855524L, 873527L,
873527L, 873527L, 873527L, 873527L, 885409L, 885409L, 885409L,
885409L, 885409L, 894218L, 894218L, 894218L, 894218L, 894218L,
928026L, 928026L, 928026L, 928026L, 928026L, 932196L, 932196L,
932196L, 932196L, 932196L, 955389L, 955389L, 955389L, 955389L,
955389L, 956952L, 956952L, 956952L, 956952L, 956952L, 957206L,
957206L, 957206L, 957206L, 957206L, 957759L, 957759L, 957759L,
957759L, 957759L, 959200L, 959200L, 959200L, 959200L, 959200L,
962490L, 962490L, 962490L, 962490L, 962490L, 968728L, 968728L,
968728L, 968728L, 968728L, 969005L, 969005L, 969005L, 969005L,
969005L, 971179L, 971179L, 971179L, 971179L, 971179L, 976863L,
976863L, 976863L, 976863L, 976863L, 981621L, 981621L, 981621L,
981621L, 981621L, 952797L, 952797L, 952797L, 952797L, 952797L,
965873L, 965873L, 965873L, 965873L, 965873L, 967416L, 967416L,
967416L, 967416L, 967416L, 975424L, 975424L, 975424L, 975424L,
975424L), Rt1 = c(4, 3, 4, 4, 3, 5, 4, 5, 5, 5, 4, 4, 4,
5, 5, 4, 4, 4, 4, 3, 5, 5, 5, 5, 5, 2, 3, 4, 3, 4, 4, 5,
5, 4, 4, 3, 3, 3, 4, 3, 3, 3, 4, 4, 4, 3, 4, 5, 4, 3, 4,
4, 4, 3, 5, 4, 4, 4, 5, 5, 3, 4, 4, 4, 3, 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, 4, 5, 3, 4, 4,
4, 3, 3, 5, 4, 4, 2, 2, 3, 4, NA, NA, NA, NA, NA, 3, 4, 4,
4, 3, NA, NA, NA, NA, NA, 5, 4, 5, 4, 4, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 4, 4, 3, 3, 4, 1, 3, 4, 5, 4, 4,
4, 5, 4, 4, NA, NA, NA, NA, NA), Rt2 = c(4, 4, 4, 4, 3, 4,
4, 4, 4, 4, 3, 4, 4, 5, 5, 4, 4, 4, 4, 3, 5, 5, 5, 5, 5,
4, 4, 4, 4, 5, 4, 4, 5, 5, 4, NA, NA, NA, NA, NA, 4, 4, 4,
4, 4, 3, 4, 4, 5, 3, 4, 4, 4, 5, 5, 4, 4, 4, 4, 4, 1, 5,
5, 5, 3, 3, 5, 5, 5, 4, 5, 4, 3, 4, 5, 4, 5, 5, 5, 4, 4,
5, 4, 5, 4, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 3, 4, 3, 4, 3,
5, 5, 5, 5, 5, 3, 5, 4, 4, 3, 4, 5, 5, 5, 5, 4, 4, 4, 5,
5, 4, 5, 5, 5, 4, 4, 2, 2, 4, 4, 5, 5, 5, 5, 5, 3, 4, 4,
5, 5, 5, 5, 3, 5, 4, 5, 4, 4, 5, 4, 5, 2, 3, 4, 3, 4, 3,
4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3, 4, 3, 5, 5, 5, 5, 4, 5,
5, 5, 3, 4, 4, 5, 5, 5, 5, NA, NA, NA, NA, NA, NA, 4, 5,
5, 5, NA, NA, NA, NA, NA, NA, 4, 4, 4, 4), Rt3 = c(4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 5, 5, 4, 4, 4, 4, 3, 5,
5, 5, 5, 5, 4, 5, 4, 4, 4, 5, 4, 5, 5, 4, 4, 4, 4, 4, 3,
4, 3, 4, 5, 5, 3, 4, 4, 4, 4, 3, 4, 4, 4, 5, NA, NA, NA,
NA, NA, 3, 5, 5, 5, 5, 3, 4, 5, 5, 3, 4, 3, 3, 4, 4, 4, 5,
5, 5, 5, 4, 5, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 1,
3, 1, 4, 1, 4, 5, 5, 5, 4, 4, 4, 4, 4, 3, 4, 5, 5, 5, 4,
4, 5, 5, 4, 4, 5, 5, 5, 4, 5, NA, NA, NA, NA, NA, 4, 4, 5,
5, 5, NA, NA, NA, NA, NA, 5, 4, 4, 4, 3, 5, 4, 4, 5, 4, NA,
NA, NA, NA, NA, 5, 4, 3, 5, 4, 3, 4, 4, 4, 3, 5, 5, 4, 4,
5, 5, 4, 4, 5, 4, NA, 5, 5, 5, 5, 5, 4, 4, 5, 5, NA, NA,
NA, NA, NA, 5, 5, 5, 5, 5, 5, 5, 4, 3, 4, 3, 4, 3, 3, 4),
Rt4 = c(5, 4, 4, 4, 4, 4, 4, 3, 4, 3, 4, 4, 4, 5, 5, 4, 4,
4, 4, 4, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA, 5, 4, 4, 4, 5,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, NA, NA, NA, NA, NA, 4, 4, 4,
3, 5, 4, 4, 4, 4, 5, 3, 4, 4, 4, 5, 3, 4, 5, 5, 3, NA, NA,
NA, NA, NA, 5, 5, 5, 5, 5, 5, 5, 4, 4, 5, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 1, 1, 2, 3, 2, 4, 5, 5, 5, 4, 4, 4, 4, 4,
5, 4, 5, 5, 5, 5, 5, 5, 4, 4, 5, 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, 5, 4, 4, 5, 4, NA, NA, NA, NA, NA, 4, 4,
5, 4, 4, 4, 3, 3, 4, 3, 5, 4, 4, 4, 5, NA, NA, NA, NA, NA,
5, 4, 3, 3, 4, NA, NA, NA, NA, NA, 4, 4, 4, 4, 4, 5, 5, 5,
5, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Rt5 = c(3,
3, 3, 4, 4, 4, 3, 3, 3, 3, 4, 5, 4, 5, 5, 2, 4, 4, 4, 4,
5, 5, 5, 5, 5, 4, 4, 4, 3, 3, 5, 4, 4, 4, 5, 4, 3, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 5, 3, 4, 4, 4, 5, 4, 4, 4, 4, 5,
4, 5, NA, NA, NA, NA, NA, 3, 2, 4, 4, 1, 3, 2, 3, 5, 4, 5,
5, 5, 5, 5, 4, 5, 4, 5, 4, 4, 4, 4, 4, 5, 3, 4, 3, 4, 4,
5, 4, 3, 4, 5, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 4,
4, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
4, 3, 3, 5, 5, NA, NA, NA, NA, NA, 4, 4, 4, 4, 4, 4, 4, 4,
4, 3, 4, 2, 2, 4, 4, 5, 4, 4, 4, 4, 3, 3, 4, 4, 3, NA, NA,
NA, NA, NA, 5, 5, 4, 4, 4, NA, NA, NA, NA, NA, 5, 5, 5, 5,
5, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, NA, NA, NA,
NA, NA), Rt6 = c(4, 2, 2, 1, 3, 4, 3, 3, 3, 3, 4, 5, 5, 4,
5, NA, NA, NA, NA, NA, 5, 4, 4, 4, 5, NA, NA, NA, NA, NA,
5, 4, 4, 4, 5, 3, 3, 4, 4, 4, 4, 3, 2, 1, 2, 4, 4, 4, 5,
4, 4, 5, 4, 3, 4, 4, 5, 5, 4, 4, 3, 4, 4, 3, 3, 5, 3, 2,
3, 5, 4, 3, 3, 4, 3, 5, 4, 4, 4, 5, NA, NA, NA, NA, NA, 4,
4, 4, 4, 4, 3, 4, 3, 3, 3, 2, 2, 3, 2, 2, 4, 4, 5, 4, 5,
NA, NA, NA, NA, NA, 4, 5, 5, 4, 4, 5, 5, 5, 5, 5, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, 2,
4, 3, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 2, 4, 4,
5, 4, 5, 5, 3, 3, 3, 3, 3, NA, NA, NA, NA, NA, NA, 5, 4,
4, 4, NA, NA, NA, NA, NA, 5, 3, 4, 4, 5, 4, 3, 4, 4, 3, 4,
4, 4, 3, 4, 4, 4, 5, 4, 5, NA, NA, NA, NA, NA), Rt7 = c(5,
2, 2, 3, 3, 4, 3, 3, 3, 3, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 5, 4, 4, 4, 4, 4, 4, 3, 4, 5, 5, 4, 4, 4, 5, 3, 4,
3, 4, 4, 4, 3, 2, 2, 3, 4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 5,
4, 5, 4, 5, 3, 4, 4, 4, 4, 4, 3, 1, 1, 5, NA, NA, NA, NA,
NA, 5, 5, 4, 5, 5, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3,
4, 4, 3, 3, 3, 3, 3, 5, 5, 5, 5, 4, 4, 4, 4, 4, 5, 4, 5,
5, 3, 4, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 3, 5, 5, 4, 5, 5, 5, 3, 4, 5, 4, 4, 4, 4, 4, 4, 3, 3,
3, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1,
1, 5, 4, 4, 4, 5, 5, 4, 4, 4, 4, 4, 3, 3, 4, 4, 5, 3, 4,
3, 4, 4, 4, 4, 4, 4, 3, 1, 1, 1, 1, 5, 5, 5, 4, 4, 3, 2,
2, 3, 4), Rt8 = c(4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 5, 5, 5,
4, 4, NA, NA, NA, NA, NA, 5, 4, 4, 5, 4, 3, 4, 3, 3, 4, 5,
4, 4, 3, 5, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4,
5, 4, 4, 3, 5, 4, 4, 4, 3, 4, 3, 4, 4, 3, 4, 1, 1, 1, 1,
3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, 5, 4, 4, 5,
NA, NA, NA, NA, NA, 3, 4, 3, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4,
4, 4, 4, 5, 4, 4, 5, 5, 5, 4, 3, 5, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, 5, 5, 5, 5, 4, 4,
4, 5, 4, 5, 5, 4, 4, 3, 4, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4,
4, 2, 4, 4, 3, 3, 3, 3, 3, 5, 5, 4, 4, 5, 5, 5, 4, 5, 5,
4, 3, 3, 4, 4, 5, 5, 5, 3, 3, 5, 4, 4, 4, 4, 3, 2, 2, 2,
2, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA), Rt9 = c(4, 3, 3, 3,
3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 4, 3, 4, 4, 4, 4, 4, 4, 4, 5, 4, 3, 3, 4, 4, NA, NA,
NA, NA, NA, 3, 3, 3, 2, 4, 4, 4, 4, 4, 4, 5, 4, 4, 3, 3,
5, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3, 1, 1, 1, 5, NA, NA, NA,
NA, NA, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, NA, NA, NA, NA, NA,
3, 4, 3, 3, 4, 3, 3, 3, 2, 3, 5, 5, 5, 5, 5, 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, 4, 5, 5, 4, 4, NA, NA, NA, NA,
NA, 5, 4, 3, 4, 4, 4, 3, 3, 3, 2, NA, NA, NA, NA, NA, 1,
1, 1, 1, 1, 2, 3, 4, 4, 2, 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, 4, 1, 1, 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA), Rt10 = c(5, 3, 3, 3, 4, NA, NA, NA, NA, NA, 5, 4, 4,
4, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 5, 4, 4, 3, 4, 4, 3, 3, 3, 4, 4, 3, 2, 3, 4, 4, 4,
4, 4, 4, 5, 5, 4, 3, 3, 5, 4, 4, 3, 4, 3, 4, 4, 4, 3, 3,
1, 1, 1, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, 5,
4, 3, 5, 4, 4, 4, 4, 4, 3, 4, 3, 3, 4, 1, 1, 2, 2, 3, 4,
5, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 2, 5, 4, 4, 4, 3, 5,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
5, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4,
5, 4, 2, 2, 4, 4, 1, 1, 3, 1, 2, 5, 5, 4, 4, 5, NA, NA, NA,
NA, NA, 4, 5, 3, 4, 4, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 5, 3,
3, 2, 4, NA, NA, NA, NA, NA, 3, 4, 3, 4, 4), Rt11 = c(5,
3, 4, 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 5, NA, NA, NA, NA,
NA, 4, 4, 3, 3, 4, 3, 5, 5, 5, 5, 5, 4, 4, 4, 5, 3, 5, 5,
5, 5, 4, 4, 4, 4, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 5, 5, 5, 4, 4, 4, 5, 5, 4, 5, 5, 3, 4, 5, 4, NA, NA,
NA, NA, NA, 5, 5, 5, 5, 5, 5, 5, 4, 4, 5, 4, 4, 4, 4, 5,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 5, 4, 5, 4, 4,
5, 4, 4, 4, 3, 3, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5,
4, 4, 4, 5, 5, 4, 5, 5, 4, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1, 1, 1, 2, 3, 5, 5, 4, 4, 5, 5, 5, 5, 5, 5, NA,
NA, NA, NA, NA, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), .Names = c("ï..Question",
"Type", "Student", "Rt1", "Rt2", "Rt3", "Rt4", "Rt5", "Rt6",
"Rt7", "Rt8", "Rt9", "Rt10", "Rt11"), row.names = c(1L, 2L, 3L,
4L, 5L, 11L, 12L, 13L, 14L, 15L, 21L, 22L, 23L, 24L, 25L, 31L,
32L, 33L, 34L, 35L, 41L, 42L, 43L, 44L, 45L, 51L, 52L, 53L, 54L,
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325L, 331L, 332L, 333L, 334L, 335L, 341L, 342L, 343L, 344L, 345L,
351L, 352L, 353L, 354L, 355L, 361L, 362L, 363L, 364L, 365L, 371L,
372L, 373L, 374L, 375L, 381L, 382L, 383L, 384L, 385L, 391L, 392L,
393L, 394L, 395L, 401L, 402L, 403L, 404L, 405L), class = "data.frame")
最佳答案
使用 dplyr :
library(dplyr)
df %>%
group_by(Student) %>%
summarise_each(funs(mean(., na.rm = TRUE)), -`ï..Question`, -Type, -Student)
输出
# A tibble: 41 × 12
Student Rt1 Rt2 Rt3 Rt4 Rt5 Rt6 Rt7 Rt8 Rt9 Rt10 Rt11
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 789331 3.6 3.8 4.0 4.2 3.4 2.4 3.0 3.2 3.2 3.6 4.0
2 796882 NaN 4.4 4.0 4.0 2.8 3.6 2.8 1.4 2.2 2.0 4.2
3 805933 4.8 4.0 4.0 3.6 3.2 3.2 3.2 3.2 NaN NaN 3.2
4 826523 4.4 4.2 4.2 4.4 4.6 4.6 NaN 4.6 NaN 4.2 4.2
5 827911 NaN 4.2 3.6 NaN 3.4 3.4 NaN NaN NaN NaN NaN
6 830271 NaN 4.6 4.8 5.0 5.0 4.4 4.8 NaN 5.0 NaN 5.0
7 831487 NaN 4.4 4.2 4.6 4.4 NaN 4.2 4.6 4.8 4.4 4.6
8 832929 3.8 3.8 3.8 4.0 3.6 NaN NaN NaN NaN NaN NaN
9 834598 NaN 5.0 3.8 4.0 4.2 4.0 4.0 NaN NaN 4.0 4.2
10 836364 NaN 4.0 4.0 4.0 3.6 3.2 3.6 3.6 3.4 3.4 4.0
# ... with 31 more rows
更新:修复了所有 NaN 输出
library(dplyr)
# Custom `mean` function to return NA if the mean is NaN
# This occurs if you try to take the mean of an empty set
# (i.e. when all of the elements are NA and na.rm = TRUE is selected
fmean <- function(x) {
m <- mean(x, na.rm = TRUE)
ifelse(is.nan(m), NA, m)
}
df %>%
group_by(Student) %>%
summarise_each(funs(fmean), -`ï..Question`, -Type, -Student)
关于r - 如何平均数据集中每 5 行 - R，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/44248041/