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我正在尝试使用 dcast 重塑我的数据。我正在处理每个样本有 10-30 个样本单位的样本。我无法汇总我的数据。

我的数据是这种格式：

ID  total
sample_1    1
sample_1    0
sample_1    2
sample_1    1
sample_1    0
sample_1    0
sample_1    2
sample_1    1
sample_1    0
sample_1    2
sample_1    1
sample_1    4
sample_2    2
sample_2    1
sample_2    2
sample_2    0
sample_2    0
sample_2    0
sample_2    1
sample_2    2
sample_2    1
sample_2    4
sample_2    5
sample_2    2
sample_2    1
sample_3    0
sample_3    0
sample_3    1
sample_3    2
sample_3    1
sample_3    0
sample_3    2
sample_3    1
sample_3    4
sample_3    5
sample_3    1
sample_3    1
sample_3    0
sample_3    0
sample_3    1

我希望它看起来像这样：

sample_1    sample_2    sample_3
1           2           0
0           1           0
2           2           1
1           0           2
0           0           1
0           0           0
2           1           2
1           2           1
0           1           4
2           4           5
1           5           1
4           2           1
            1           0
                        0
                        1

我的样本 ID 变成不同的列。

我尝试了几种方法，但 R 一直在聚合它。

4

1 回答 1

3

您可以这样做，dcast()但您必须为每个ID.

该data.table包是另一个包，除了reshape2它实现dcast()。data.table有一个方便的rowid()功能，可以在每个组中生成唯一的行 ID。这样，我们得到：

library(data.table)
dcast(setDT(DF), rowid(ID) ~ ID, value.var = "total")
#    ID sample_1 sample_2 sample_3
# 1:  1        1        2        0
# 2:  2        0        1        0
# 3:  3        2        2        1
# 4:  4        1        0        2
# 5:  5        0        0        1
# 6:  6        0        0        0
# 7:  7        2        1        2
# 8:  8        1        2        1
# 9:  9        0        1        4
#10: 10        2        4        5
#11: 11        1        5        1
#12: 12        4        2        1
#13: 13       NA        1        0
#14: 14       NA       NA        0
#15: 15       NA       NA        1

但是，我建议继续以长格式处理任何数据并使用分组。这比在单个列上工作要容易得多。例如，

# count observations by group
DF[, .N, by = ID]
#         ID  N
#1: sample_1 12
#2: sample_2 13
#3: sample_3 15

# compute mean by group
DF[, mean(total), by = ID]
#         ID       V1
#1: sample_1 1.166667
#2: sample_2 1.615385
#3: sample_3 1.266667

# get min and max by group
DF[, .(min = min(total), max = max(total)), by = ID]
#         ID min max
#1: sample_1   0   4
#2: sample_2   0   5
#3: sample_3   0   5

# the same using range()
DF[, as.list(range(total)), by = ID]
#         ID V1 V2
#1: sample_1  0  4
#2: sample_2  0  5
#3: sample_3  0  5

数据

DF <- structure(list(ID = c("sample_1", "sample_1", "sample_1", "sample_1", 
"sample_1", "sample_1", "sample_1", "sample_1", "sample_1", "sample_1", 
"sample_1", "sample_1", "sample_2", "sample_2", "sample_2", "sample_2", 
"sample_2", "sample_2", "sample_2", "sample_2", "sample_2", "sample_2", 
"sample_2", "sample_2", "sample_2", "sample_3", "sample_3", "sample_3", 
"sample_3", "sample_3", "sample_3", "sample_3", "sample_3", "sample_3", 
"sample_3", "sample_3", "sample_3", "sample_3", "sample_3", "sample_3"
), total = c(1L, 0L, 2L, 1L, 0L, 0L, 2L, 1L, 0L, 2L, 1L, 4L, 
2L, 1L, 2L, 0L, 0L, 0L, 1L, 2L, 1L, 4L, 5L, 2L, 1L, 0L, 0L, 1L, 
2L, 1L, 0L, 2L, 1L, 4L, 5L, 1L, 1L, 0L, 0L, 1L)), .Names = c("ID", 
"total"), row.names = c(NA, -40L), class = "data.frame")
于 2017-04-14T08:24:22.397 回答