问题描述
对于我的数据类型,一个非常常见的操作是对所有列应用归一化因子.这可以使用 sweep
或 scale
有效地完成:
An incredibly common operation for my type of data is applying a normalisation factor to all columns. This can be done efficiently using sweep
or scale
:
normalized = scale(data, center = FALSE, scale = factors)
# or
normalized = sweep(data, 2, factors, `/`)
哪里
data = structure(list(A = c(3L, 174L, 6L, 1377L, 537L, 173L),
B = c(1L, 128L, 2L, 1019L, 424L, 139L),
C = c(3L, 66L, 2L, 250L, 129L, 40L),
D = c(4L, 57L, 4L, 251L, 124L, 38L)),
.Names = c("A", "B", "C", "D"),
class = c("tbl_df", "data.frame"), row.names = c(NA, -6L))
factors = c(A = 1, B = 1.2, C = 0.8, D = 0.75)
但是,当我的数据前面有附加列时,如何使用 dplyr 执行此操作?我可以在单独的语句中完成,但我希望在 one 管道中完成.这是我的数据:
However, how do I do this with dplyr, when my data has additional columns in front? I can do it in separate statements, but I’d like doing it in one pipeline. This is my data:
data = structure(list(ID = c(1, 2, 3, 4, 5, 6),
Type = c("X", "X", "X", "Y", "Y", "Y"),
A = c(3L, 174L, 6L, 1377L, 537L, 173L),
B = c(1L, 128L, 2L, 1019L, 424L, 139L),
C = c(3L, 66L, 2L, 250L, 129L, 40L),
D = c(4L, 57L, 4L, 251L, 124L, 38L)),
.Names = c("ID", "Type", "A", "B", "C", "D"),
class = c("tbl_df", "data.frame"), row.names = c(NA, -6L))
我想在不触及前两列的情况下改变数据列.通常我可以用 mutate_each
做到这一点;但是,我如何无法将标准化因子传递给该函数:
And I’d like to mutate the data columns without touching the first two columns. Normally I can do this with mutate_each
; however, how I cannot pass my normalisation factors to that function:
data %>% mutate_each(funs(. / factors), A:D)
不出所料,这假设我想将每一列除以factors
,而不是每一列除以其匹配因数.
This, unsurprisingly, assumes that I want to divide each column by factors
, rather than each column by its matching factor.
推荐答案
从dplyr 1.0.0
开始,你可以:
data %>%
rowwise() %>%
mutate(across(A:D)/factors)
ID Type A B C D
<dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 X 3 0.833 3.75 5.33
2 2 X 174 107. 82.5 76
3 3 X 6 1.67 2.5 5.33
4 4 Y 1377 849. 312. 335.
5 5 Y 537 353. 161. 165.
6 6 Y 173 116. 50 50.7
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