使用 dplyr 将行添加到分组数据中? [英] Add rows to grouped data with dplyr?
问题描述
我的数据采用 data.frame 格式,如以下示例数据:
My data is in a data.frame format like this sample data:
data <-
structure(list(Article = structure(c(1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L
), .Label = c("10004", "10006", "10007"), class = "factor"),
Demand = c(26L, 780L, 2L, 181L, 228L, 214L, 219L, 291L, 104L,
72L, 155L, 237L, 182L, 148L, 52L, 227L, 2L, 355L, 2L, 432L,
1L, 156L), Week = c("2013-W01", "2013-W01", "2013-W01", "2013-W01",
"2013-W01", "2013-W02", "2013-W02", "2013-W02", "2013-W02",
"2013-W02", "2013-W03", "2013-W03", "2013-W03", "2013-W03",
"2013-W03", "2013-W04", "2013-W04", "2013-W04", "2013-W04",
"2013-W04", "2013-W04", "2013-W04")), .Names = c("Article",
"Demand", "Week"), class = "data.frame", row.names = c(NA, -22L))
我想按周和文章总结需求栏.为此,我使用:
I would like to summarize the demand column by week and article. To do this, I use:
library(dplyr)
WeekSums <-
data %>%
group_by(Article, Week) %>%
summarize(
WeekDemand = sum(Demand)
)
但由于某些文章在某些周内未售出,因此每篇文章的行数有所不同(WeekSums 数据框中仅显示销售周数).如何调整我的数据,使每篇文章的行数相同(每周一个),包括需求为 0 的周?
But because some articles were not sold in certain weeks, the number of rows per article differs (only weeks with sales are shown in the WeekSums dataframe). How could I adjust my data so that each article has the same number of rows (one for each week), including weeks with 0 demand?
输出应该如下所示:
Article Week WeekDemand
1 10004 2013-W01 1215
2 10004 2013-W02 900
3 10004 2013-W03 774
4 10004 2013-W04 1170
5 10006 2013-W01 0
6 10006 2013-W02 0
7 10006 2013-W03 0
8 10006 2013-W04 5
9 10007 2013-W01 2
10 10007 2013-W02 0
11 10007 2013-W03 0
12 10007 2013-W04 0
我试过了
WeekSums %>%
group_by(Article) %>%
if(n()< 4) rep(rbind(c(Article,NA,NA)), 4 - n() )
但这不起作用.在我最初的方法中,我通过将第 1-4 周的数据框与每篇文章的原始数据文件合并来解决这个问题.这样,我每篇文章有 4 周(行),但是使用 for 循环的实现效率非常低,所以我试图用 dplyr(或任何其他更有效的包/函数)做同样的事情.任何建议将不胜感激!
but this doesn’t work. In my original approach, I resolved this problem by merging a dataframe of week numbers 1-4 with my rawdata file for each article. That way, I got 4 weeks (rows) per article, but the implementation with a for loop is very inefficient and so I’m trying to do the same with dplyr (or any other more efficient package/function). Any suggestions would be much appreciated!
推荐答案
没有 dplyr 可以这样做:
Without dplyr it can be done like this:
as.data.frame(xtabs(Demand ~ Week + Article, data))
给予:
Week Article Freq
1 2013-W01 10004 1215
2 2013-W02 10004 900
3 2013-W03 10004 774
4 2013-W04 10004 1170
5 2013-W01 10006 0
6 2013-W02 10006 0
7 2013-W03 10006 0
8 2013-W04 10006 5
9 2013-W01 10007 2
10 2013-W02 10007 0
11 2013-W03 10007 0
12 2013-W04 10007 0
这可以重写为 magrittr 或 dplyr 管道,如下所示:
and this can be rewritten as a magrittr or dplyr pipeline like this:
data %>% xtabs(formula = Demand ~ Week + Article) %>% as.data.frame()
如果需要宽格式解决方案,可以省略末尾的 as.data.frame()
.
The as.data.frame()
at the end could be omitted if a wide form solution were desired.
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