在R中重塑数据帧：从宽到长，但'；变化的'；列的长度不相等

我的问题在下面的代码中描述。我已经在这里和其他论坛上寻找过类似的问题，但还没有找到一个完全符合我这里要求的解决方案。如果仅仅依靠基本R就可以解决这个问题，那将是更好的选择，但是使用包也可以

id1 <- c("A", "A", "A", "B", "B", "C", "C", "C")
id2 <- c(10, 20, 30, 10, 30, 10, 20, 30)
x.1 <- ceiling(runif(8)*80) + 20
y.1 <- ceiling(runif(8)*15) + 200
x.2 <- ceiling(runif(8)*90) + 20
y.2 <- ceiling(runif(8)*20) + 200
x.3 <- ceiling(runif(8)*80) + 40

# The data frame contains to kinds of data values, x and y, repeated by a suffix number. In my example both
# the id-part and the data-part are not structured in a completely uniform manner.
mywidedata <- data.frame(id1, id2, x.1, y.1, x.2, y.2, x.3)

# If I wanted to make the data frame even wider, this would work. It generates NAs for the missing combination (B,20).
reshape(mywidedata, idvar = "id1", timevar = "id2", direction = "wide")

# What I want is "long", and this fails.
reshape(mywidedata, varying = c(3:7), direction = "long")

# I could introduce the needed column. This works.
mywidecopy <- mywidedata
mywidecopy$y.3 <- NA
mylongdata <- reshape(mywidecopy, idvar=c(1,2), varying = c(3:8), direction = "long", sep = ".")
# (sep-argument not needed in this case - the function can figure out the system)
names(mylongdata)[(names(mylongdata)=="time")] <- "id3"

# I want to reach the same outcome without manual manipulation. Is it possible with the just the
# built-in 'reshape'?

# Trying 'melt'. Not what I want.
reshape::melt(mywidedata, id.vars = c(1,2))

---

id1您可以从
tidyr
使用
pivot\u更长的时间
：

tidyr::pivot_longer(mywidedata, 
                    cols = -c(id1, id2), 
                    names_to = c('.value', 'id3'), 
                    names_sep = '\\.')

# A tibble: 24 x 5
#   id1     id2 id3       x     y
#   <chr> <dbl> <chr> <dbl> <dbl>
# 1 A        10 1        66   208
# 2 A        10 2        95   220
# 3 A        10 3        89    NA
# 4 A        20 1        34   208
# 5 A        20 2        81   219
# 6 A        20 3        82    NA
# 7 A        30 1        23   201
# 8 A        30 2        80   204
# 9 A        30 3        75    NA
#10 B        10 1        52   210
# … with 14 more rows

tidyr:：pivot_更长（mywidedata，
cols=-c（id1，id2），
名称_to=c（'.value'，id3'），
名称（sep=“\\”）
#一个tibble:24x5
#id1 id2 id3 x y
#       
#1A10166208
#2 A 10 2 95 220
#3 A 10 3 89 NA
#4 A 20 1 34 208
#5 A 20 2 81 219
#6 A 20 3 82 NA
#7 A 30 1 23 201
#8 A 30 2 80 204
#9 A 30 3 75 NA
#10B1152210
#…还有14行
仅
cbind
缺少的级别为
NA

reshape(cbind(mywidedata, y.2=NA), varying=3:8, direction="long")
#     id1 id2 time   x   y id
# 1.1   A  10    1  98 215  1
# 2.1   A  20    1  38 208  2
# 3.1   A  30    1  97 205  3
# 4.1   B  10    1  61 207  4
# 5.1   B  30    1  73 201  5
# 6.1   C  10    1  96 202  6
# 7.1   C  20    1 100 202  7
# 8.1   C  30    1  94 202  8
# 1.2   A  10    2  73 208  1
# 2.2   A  20    2  69 218  2
# 3.2   A  30    2  64 219  3
# 4.2   B  10    2 104 213  4
# 5.2   B  30    2  99 203  5
# 6.2   C  10    2  92 206  6
# 7.2   C  20    2  49 206  7
# 8.2   C  30    2  59 209  8
# 1.3   A  10    3  63 208  1
# 2.3   A  20    3  91 218  2
# 3.3   A  30    3  42 219  3
# 4.3   B  10    3  67 213  4
# 5.3   B  30    3  90 203  5
# 6.3   C  10    3  74 206  6
# 7.3   C  20    3  86 206  7
# 8.3   C  30    3  83 209  8

我们可以从
数据表中使用
melt

library(data.table)
melt(setDT(mywidedata), measure = patterns("^x", "^y"), value.name = c('x', 'y'))
#    id1 id2 variable   x   y
# 1:   A  10        1  97 215
# 2:   A  20        1  75 202
# 3:   A  30        1  87 213
# 4:   B  10        1  51 206
# 5:   B  30        1  75 203
# 6:   C  10        1  41 210
# 7:   C  20        1  58 211
# 8:   C  30        1  50 207
# 9:   A  10        2  92 204
#10:   A  20        2  60 207
#11:   A  30        2  35 201
#12:   B  10        2  83 202
#13:   B  30        2  81 202
#14:   C  10        2  55 216
#15:   C  20        2  68 204
#16:   C  30        2  70 218
#17:   A  10        3  89  NA
#18:   A  20        3 108  NA
#19:   A  30        3  47  NA
#20:   B  10        3  78  NA
#21:   B  30        3  43  NA
#22:   C  10        3 106  NA
#23:   C  20        3  92  NA
#24:   C  30        3  96  NA

重塑
功能对宽变长时的不平衡数据有限制。您唯一的选择是使用另一个软件包，如tidyr或添加缺少的列。@Edward适时地指出，谢谢，很好。而且似乎没有办法使用软件包。答案已通过。我忘了单击。完成。