我正在尝试重新安排我的数据：
Type Student Rt1 Rt2 Rt3 Rt4 Rt5 Rt6 Rt7 Rt8 Rt9 Rt10 Rt11
1: SNR 789331 3.6 3.8 4.0 4.2 3.4 2.4 3.0 3.2 3.2 3.6 4.0
2: SNR 805933 4.8 4.0 4.0 3.6 3.2 3.2 3.2 3.2 NaN NaN 3.2
3: SNR 826523 4.4 4.2 4.2 4.4 4.6 4.6 NaN 4.6 NaN 4.2 4.2
4: SNR 832929 3.8 3.8 3.8 4.0 3.6 NaN NaN NaN NaN NaN NaN
5: SNR 838607 5.0 5.0 5.0 5.0 5.0 4.4 4.2 4.4 3.8 NaN 3.6
6: SNR 841903 3.2 4.2 4.2 NaN 3.6 NaN 4.0 3.4 4.2 NaN 4.6
到此：
Student Type timePeriod week Rating
1 789331 SNR Rt1 Jan11 3.6
2 805933 SNR Rt1 Jan11 4.8
3 826523 SNR Rt1 Jan11 4.4
4 832929 SNR Rt1 Jan11 3.8
5 838607 SNR Rt1 Jan11 5.0
6 841903 SNR Rt1 Jan11 3.2
以下是我一直在尝试使用的代码。它使列和一切都很好，但它分配的评级是不正确的。我不确定我做错了什么。
pulse1<-NULL
timePeriods<-c("Rt1", "Rt2", "Rt3", "Rt4", "Rt5", "Rt6", "Rt7", "Rt8", "Rt9", "Rt10", "Rt11")
weeks<-c("Jan11","Jan25","Feb1","Feb8", "Feb15", "Mar1", "Mar8", "Mar15","Mar22", "Mar29", "Apr5")
measureType<-c("Time", 11)
for (columnNumber in 1:11)
{
temp.data<-data.frame(Student=pulse$Student, Type=pulse$Type,
timePeriod=timePeriods[columnNumber], week=weeks[columnNumber],
Rating=pulse[, columnNumber+2])
pulse1<-rbind(pulse1, temp.data)
}
此代码产生的输出是：
Student Type timePeriod week Rating
1 789331 SNR Rt1 Jan11 3
2 805933 SNR Rt1 Jan11 3
3 826523 SNR Rt1 Jan11 3
4 832929 SNR Rt1 Jan11 3
5 838607 SNR Rt1 Jan11 3
6 841903 SNR Rt1 Jan11 3
谢谢!!! 我在下面附上了我的数据：
> dput (pulse)
structure(list(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, 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,
805933L, 826523L, 832929L, 838607L, 841903L, 843618L, 852125L,
876406L, 879972L, 885650L, 888712L, 903303L, 796882L, 827911L,
830271L, 831487L, 834598L, 836364L, 839802L, 855524L, 873527L,
885409L, 894218L, 928026L, 932196L, 955389L, 956952L, 957206L,
957759L, 959200L, 962490L, 968728L, 969005L, 971179L, 976863L,
981621L, 952797L, 965873L, 967416L, 975424L), Rt1 = c(3.6, 4.8,
4.4, 3.8, 5, 3.2, 4.4, 3.2, 3.6, 3.8, 4, 4.4, 3.6, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
4, 3.8, 3, NaN, 3.6, NaN, 4.4, NaN, NaN, 3.6, 3.4, 4.2, NaN),
Rt2 = c(3.8, 4, 4.2, 3.8, 5, 4.2, 4.4, NaN, 4, 3.8, 4.4,
4, 3.8, 4.4, 4.2, 4.6, 4.4, 5, 4, 3.4, 5, 3.8, 4.8, 4.4,
4.6, 3.2, 5, 4.2, 4.4, 4.4, 3.4, 3.8, 3.8, 3.6, 4.8, 4.4,
4.8, NaN, 4.75, NaN, 4), Rt3 = c(4, 4, 4.2, 3.8, 5, 4.2,
4.6, 3.8, 4.2, 3.8, 4, NaN, 4.6, 4, 3.6, 4.8, 4.2, 3.8, 4,
2, 4.6, 3.8, 4.6, 4.4, 4.8, NaN, 4.6, NaN, 4, 4.4, NaN, 4.2,
3.6, 4.6, 4.4, 5, 4.6, NaN, 5, 4.2, 3.4), Rt4 = c(4.2, 3.6,
4.4, 4, 5, NaN, 4.4, 4, 4, NaN, 4, 4.2, 4, 4, NaN, 5, 4.6,
4, 4, 1.8, 4.6, 4.2, 4.8, 4.6, NaN, NaN, NaN, NaN, NaN, 4.4,
NaN, 4.2, 3.4, 4.4, NaN, 3.8, NaN, 4, 5, NaN, NaN), Rt5 = c(3.4,
3.2, 4.6, 3.6, 5, 3.6, 4.4, 3.8, 4, 4, 4.2, 4.4, NaN, 2.8,
3.4, 5, 4.4, 4.2, 3.6, 4.2, 4.2, 4, 4.4, 5, NaN, NaN, 4,
NaN, 4, 3.8, 3.2, 4.2, 3.4, NaN, 4.4, NaN, 5, 4.4, 4, 4.2,
NaN), Rt6 = c(2.4, 3.2, 4.6, NaN, 4.4, NaN, 4.4, 3.6, 2.4,
4.2, 4, 4.4, 3.4, 3.6, 3.4, 4.4, NaN, 4, 3.2, 2.2, 4.4, NaN,
4.4, 5, NaN, NaN, NaN, 3.2, 4.4, 4, 3, 4.6, 3, NaN, 4.25,
NaN, 4.2, 3.6, 3.8, 4.4, NaN), Rt7 = c(3, 3.2, NaN, NaN,
4.2, 4, 4.4, 3.6, 2.8, 4, 4.4, 4.6, 3.8, 2.8, NaN, 4.8, 4.2,
4, 3.6, 3, 4.8, 4.2, 4.2, 5, NaN, NaN, 4.4, 4.4, 4, 3.2,
NaN, NaN, 1, 4.4, 4.2, 3.6, 3.8, 4, 1.4, 4.6, 2.8), Rt8 = c(3.2,
3.2, 4.6, NaN, 4.4, 3.4, 4.2, 4, 3.8, 4, 4.2, 3.8, 3.6, 1.4,
NaN, NaN, 4.6, NaN, 3.6, 4.2, 4, 4.4, 4.4, NaN, NaN, NaN,
4.6, 4.2, 4.2, 3.2, 4, 3.6, 3, 4.6, 4.8, 3.6, 4.2, 4.2, 2.2,
5, NaN), Rt9 = c(3.2, NaN, NaN, NaN, 3.8, 4.2, 3.6, NaN,
3, 4, 3.8, 4.2, 3.8, 2.2, NaN, 5, 4.8, NaN, 3.4, 2.8, 5,
NaN, NaN, NaN, NaN, NaN, 4.4, NaN, 4, 3, NaN, 1, 3, NaN,
NaN, NaN, NaN, NaN, 1.6, NaN, NaN), Rt10 = c(3.6, NaN, 4.2,
NaN, NaN, NaN, 4, 3.4, 3.2, 4, 4, 4, 3.6, 2, NaN, NaN, 4.4,
4, 3.4, 1.8, 4.2, 3.8, 3.8, 4, NaN, NaN, NaN, 4.2, 3.8, 4.2,
4.2, 3.2, 1.6, 4.6, NaN, 4, 5, 4, 3.4, NaN, 3.6), Rt11 = c(4,
3.2, 4.2, NaN, 3.6, 4.6, 4.4, 4.6, 4.2, NaN, NaN, 4.6, 4.6,
4.2, NaN, 5, 4.6, 4.2, 4, 4, 4.6, 4.4, 3.6, 5, NaN, NaN,
NaN, NaN, 4.4, 4.6, NaN, NaN, 1.6, 4.6, 5, NaN, 5, 4, NaN,
NaN, NaN)), row.names = c(NA, -41L), class = c("data.table",
"data.frame"), .Names = c("Type", "Student", "Rt1", "Rt2", "Rt3",
"Rt4", "Rt5", "Rt6", "Rt7", "Rt8", "Rt9", "Rt10", "Rt11"), .internal.selfref = <pointer: 0x0000000007b30788>)
答案 0 :(得分：3)
一种可能的解决方案是融合数据集，然后合并到weeks：
weeks_time <- data.frame(timePeriod=c("Rt1", "Rt2", "Rt3", "Rt4", "Rt5", "Rt6", "Rt7", "Rt8", "Rt9", "Rt10", "Rt11"),
weeks=c("Jan11","Jan25","Feb1","Feb8", "Feb15", "Mar1", "Mar8", "Mar15","Mar22", "Mar29", "Apr5"))
pulse_m <- melt(pulse, id.vars = c("Student", "Type"), variable.name = "timePeriod", value.name = "Rating")
merge(pulse_m, weeks_time)
# timePeriod Student Type Rating weeks
#1: Rt1 789331 SNR 3.6 Jan11
#2: Rt1 805933 SNR 4.8 Jan11
#3: Rt1 826523 SNR 4.4 Jan11
#4: Rt1 832929 SNR 3.8 Jan11
#5: Rt1 838607 SNR 5.0 Jan11
#---
#447: Rt11 981621 FYS 5.0 Apr5
#448: Rt11 952797 FYS 4.0 Apr5
#449: Rt11 965873 FYS NaN Apr5
#450: Rt11 967416 FYS NaN Apr5
#451: Rt11 975424 FYS NaN Apr5
这可以避免任何循环。
答案 1 :(得分：1)
以下是使用dplyr和tidyr
library(dplyr)
library(tidyr)
df.weeks <- data.frame(timePeriod = c("Rt1", "Rt2", "Rt3", "Rt4", "Rt5", "Rt6", "Rt7", "Rt8", "Rt9", "Rt10", "Rt11"),
week = c("Jan11","Jan25","Feb1","Feb8", "Feb15", "Mar1", "Mar8", "Mar15","Mar22", "Mar29", "Apr5"))
pulse <- pulse %>%
gather(timePeriod, Rating, Rt1:Rt11) %>%
merge(df.weeks) %>%
select(Student, Type, timePeriod, week, Rating)