问题描述
我有一个数据框，其中几列可能具有相同的名称。在这个小例子中，列 A和 G都出现两次：
I have a data frame where several columns may have the same name. In this small example, both column "A" and "G" occur twice:
A C G A G T
1 1 NA NA NA 1 NA
2 1 NA 5 3 1 NA
3 NA 1 NA NA NA 1
4 NA NA 1 2 NA NA
5 NA NA 1 1 NA NA
6 NA 1 NA NA NA 1
7 NA 1 NA NA NA 1
我希望创建一个数据集，每个列名称的 one 列。对于每一行，应将各个列的值替换为每个列名称中的值的总和（ sum（...，na.rm = TRUE））。例如，在第二行中，两个单独的 A值（ 1 和 3 ）应替换为 4 。我事先不知道哪个列名会出现几次。
I wish to create a data set with one column per column name. For each row, the individual column values should be replaced with the sum (sum(..., na.rm = TRUE)) of the values within each column name. For example, in row two, the two individual "A" values (1 and 3) should be replaced with 4. I don't know in advance which column names that occur several times.
那么预期的输出将是：
# A C G T
# 1 1 0 1 0
# 2 4 0 6 0
# 3 0 1 0 1
# 4 2 0 1 0
# 5 1 0 1 0
# 6 0 1 0 1
# 7 0 1 0 1
所以我想我可以做类似的事情：
So I guess I could do something like:
noms = colnames(dat)
for(x in noms[duplicated(noms)]) {
dat[ , x] = rowSums(dat[ , x == noms], na.rm = TRUE)
}
dat = dat[,!duplicated(noms)]
但这有点笨重for循环注定是邪恶的。有什么方法可以更简单地做到这一点？
But this is a bit clunky and for loops are meant to be evil. Is there any way to do this more simply?
推荐答案
我们可以转换 dat ，计算每组行总数（原始 dat 姓氏 c>），然后将结果转回原始结构。
We can transpose dat , calculate rowsum per group (colnames of the original dat), then transpose the result back to original structure.
t(rowsum(t(dat), group = colnames(dat), na.rm = T))
# A C G T
#1 1 0 1 0
#2 4 0 6 0
#3 0 1 0 1
#4 2 0 1 0
#5 1 0 1 0
#6 0 1 0 1
#7 0 1 0 1
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