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  3. 如何平均數據集中的每5行 - R

如何平均數據集中的每5行 - R

2017-05-29 67 views
2

我已經回顧了一些與我相似的問題，並且無法理解它們如何或是否適用於我的數據集。如何平均數據集中的每5行 - R

我有大量學生對十一個不同時間點的五個問題的答案的數據。我希望平均學生對這些問題的答案，以便他們的平均答案可以隨時間變化。

有沒有簡單的方法來平均每5行？

我是初學者，任何幫助將不勝感激！

我提供我下面的數據：

> 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, 
55L, 61L, 62L, 63L, 64L, 65L, 71L, 72L, 73L, 74L, 75L, 81L, 82L, 
83L, 84L, 85L, 91L, 92L, 93L, 94L, 95L, 101L, 102L, 103L, 104L, 
105L, 111L, 112L, 113L, 114L, 115L, 121L, 122L, 123L, 124L, 125L, 
131L, 132L, 133L, 134L, 135L, 141L, 142L, 143L, 144L, 145L, 151L, 
152L, 153L, 154L, 155L, 161L, 162L, 163L, 164L, 165L, 171L, 172L, 
173L, 174L, 175L, 181L, 182L, 183L, 184L, 185L, 191L, 192L, 193L, 
194L, 195L, 201L, 202L, 203L, 204L, 205L, 211L, 212L, 213L, 214L, 
215L, 221L, 222L, 223L, 224L, 225L, 231L, 232L, 233L, 234L, 235L, 
241L, 242L, 243L, 244L, 245L, 251L, 252L, 253L, 254L, 255L, 261L, 
262L, 263L, 264L, 265L, 271L, 272L, 273L, 274L, 275L, 281L, 282L, 
283L, 284L, 285L, 291L, 292L, 293L, 294L, 295L, 301L, 302L, 303L, 
304L, 305L, 311L, 312L, 313L, 314L, 315L, 321L, 322L, 323L, 324L, 
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")

來源

2017-05-29 Bailey

+0

你想要平均'Rt'列嗎？ – akrun

+0

@akrun 嗨！我想平均Q，Q2，Q8，Q10，Q12行。 – Bailey

+0

你在描述中說'平均每5行'。目前還不清楚你是否說要平均Q，Q2等。 – akrun

回答

3

我們可以嘗試

t(sapply(split(pulse[4:ncol(pulse)], as.integer(gl(nrow(pulse), 5, nrow(pulse)))), colMeans, na.rm = TRUE))

或者，如果我們需要組基於第一列

t(sapply(split(pulse[4:ncol(pulse)], cumsum(pulse[[1]]=="Q")), colMeans, na.rm = TRUE))

或者，如果我們分組依據 '學生'

library(data.table) 
setDT(pulse)[, lapply(.SD, mean, na.rm = TRUE) , by = .(Type, Student), .SDcols = 4:ncol(pulse)] 
# Type Student Rt1 Rt2 Rt3 Rt4 Rt5 Rt6 Rt7 Rt8 Rt9 Rt10 Rt11 
# 1: SNR 789331 3.6 3.80 4.0 4.2 3.4 2.40 3.0 3.2 3.2 3.6 4.0 
# 2: SNR 805933 4.8 4.00 4.0 3.6 3.2 3.20 3.2 3.2 NaN NaN 3.2 
# 3: SNR 826523 4.4 4.20 4.2 4.4 4.6 4.60 NaN 4.6 NaN 4.2 4.2 
# 4: SNR 832929 3.8 3.80 3.8 4.0 3.6 NaN NaN NaN NaN NaN NaN 
# 5: SNR 838607 5.0 5.00 5.0 5.0 5.0 4.40 4.2 4.4 3.8 NaN 3.6 
# 6: SNR 841903 3.2 4.20 4.2 NaN 3.6 NaN 4.0 3.4 4.2 NaN 4.6 
# 7: SNR 843618 4.4 4.40 4.6 4.4 4.4 4.40 4.4 4.2 3.6 4.0 4.4 
# 8: SNR 852125 3.2 NaN 3.8 4.0 3.8 3.60 3.6 4.0 NaN 3.4 4.6 
# 9: SNR 876406 3.6 4.00 4.2 4.0 4.0 2.40 2.8 3.8 3.0 3.2 4.2 
#10: SNR 879972 3.8 3.80 3.8 NaN 4.0 4.20 4.0 4.0 4.0 4.0 NaN 
#11: SNR 885650 4.0 4.40 4.0 4.0 4.2 4.00 4.4 4.2 3.8 4.0 NaN 
#12: SNR 888712 4.4 4.00 NaN 4.2 4.4 4.40 4.6 3.8 4.2 4.0 4.6 
#13: SNR 903303 3.6 3.80 4.6 4.0 NaN 3.40 3.8 3.6 3.8 3.6 4.6 
#14: SNR 796882 NaN 4.40 4.0 4.0 2.8 3.60 2.8 1.4 2.2 2.0 4.2 
#15: SNR 827911 NaN 4.20 3.6 NaN 3.4 3.40 NaN NaN NaN NaN NaN 
#16: SNR 830271 NaN 4.60 4.8 5.0 5.0 4.40 4.8 NaN 5.0 NaN 5.0 
#17: SNR 831487 NaN 4.40 4.2 4.6 4.4 NaN 4.2 4.6 4.8 4.4 4.6 
#18: SNR 834598 NaN 5.00 3.8 4.0 4.2 4.00 4.0 NaN NaN 4.0 4.2 
#19: SNR 836364 NaN 4.00 4.0 4.0 3.6 3.20 3.6 3.6 3.4 3.4 4.0 
#20: SNR 839802 NaN 3.40 2.0 1.8 4.2 2.20 3.0 4.2 2.8 1.8 4.0 
#21: SNR 855524 NaN 5.00 4.6 4.6 4.2 4.40 4.8 4.0 5.0 4.2 4.6 
#22: SNR 873527 NaN 3.80 3.8 4.2 4.0 NaN 4.2 4.4 NaN 3.8 4.4 
#23: SNR 885409 NaN 4.80 4.6 4.8 4.4 4.40 4.2 4.4 NaN 3.8 3.6 
#24: SNR 894218 NaN 4.40 4.4 4.6 5.0 5.00 5.0 NaN NaN 4.0 5.0 
#25: FYS 928026 NaN 4.60 4.8 NaN NaN NaN NaN NaN NaN NaN NaN 
#26: FYS 932196 NaN 3.20 NaN NaN NaN NaN NaN NaN NaN NaN NaN 
#27: FYS 955389 NaN 5.00 4.6 NaN 4.0 NaN 4.4 4.6 4.4 NaN NaN 
#28: FYS 956952 NaN 4.20 NaN NaN NaN 3.20 4.4 4.2 NaN 4.2 NaN 
#29: FYS 957206 4.0 4.40 4.0 NaN 4.0 4.40 4.0 4.2 4.0 3.8 4.4 
#30: FYS 957759 3.8 4.40 4.4 4.4 3.8 4.00 3.2 3.2 3.0 4.2 4.6 
#31: FYS 959200 3.0 3.40 NaN NaN 3.2 3.00 NaN 4.0 NaN 4.2 NaN 
#32: FYS 962490 NaN 3.80 4.2 4.2 4.2 4.60 NaN 3.6 1.0 3.2 NaN 
#33: FYS 968728 3.6 3.80 3.6 3.4 3.4 3.00 1.0 3.0 3.0 1.6 1.6 
#34: FYS 969005 NaN 3.60 4.6 4.4 NaN NaN 4.4 4.6 NaN 4.6 4.6 
#35: FYS 971179 4.4 4.80 4.4 NaN 4.4 4.25 4.2 4.8 NaN NaN 5.0 
#36: FYS 976863 NaN 4.40 5.0 3.8 NaN NaN 3.6 3.6 NaN 4.0 NaN 
#37: FYS 981621 NaN 4.80 4.6 NaN 5.0 4.20 3.8 4.2 NaN 5.0 5.0 
#38: FYS 952797 3.6 NaN NaN 4.0 4.4 3.60 4.0 4.2 NaN 4.0 4.0 
#39: FYS 965873 3.4 4.75 5.0 5.0 4.0 3.80 1.4 2.2 1.6 3.4 NaN 
#40: FYS 967416 4.2 NaN 4.2 NaN 4.2 4.40 4.6 5.0 NaN NaN NaN 
#41: FYS 975424 NaN 4.00 3.4 NaN NaN NaN 2.8 NaN NaN 3.6 NaN

來源

2017-05-29 17:52:35 akrun

+1

非常感謝你的幫助！ 有什麼方法可以將學生類型也保留爲列？它告訴我學生在哪一年（高級= SNR，第一年= FYR。 – Bailey

+1

@貝利也補充說 – akrun

6

使用dplyr：

library(dplyr) 

df %>% 
    group_by(Student) %>% 
    summarise_each(funs(mean(., na.rm = TRUE)), -`ï..Question`, -Type, -Student)

輸出

# 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：固定NaN輸出所有

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)

來源

2017-05-29 17:55:58 Paolo

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