使用dplyr向分组数据添加行？

Question

我的数据采用data.frame格式,如此示例数据:

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一下需求列.为此,我使用:

library(dplyr)
WeekSums <- 
  data %>%
   group_by(Article, Week) %>%
   summarize(
    WeekDemand = sum(Demand)
   )

但由于某些文章在某些星期没有销售,因此每篇文章的行数不同(仅周数与销售额在WeekSums数据框中显示).我如何调整我的数据,以便每篇文章的行数相同(每周一行),包括需求为0的周数？

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,我通过将每周数字1-4的数据帧与我的rawdata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循环的实现非常低效,所以我试图用dplyr(或任何其他更有效的包/函数)来做同样的事情.我们欢迎所有的建议!

Answer 1

如果没有dplyr,可以这样做:

as.data.frame(xtabs(Demand ~ Week + Article, data))

赠送:

       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:

data %>% xtabs(formula = Demand ~ Week + Article) %>% as.data.frame()

所述as.data.frame()如果宽形式的溶液中所需的在端部可以省略.

Answer 2

由于dplyr正在积极开发中,我想我会发布一个更新,其中包含tidyr:

library(dplyr)
library(tidyr)

data %>%
  expand(Article, Week) %>%
  left_join(data) %>%
  group_by(Article, Week) %>%
  summarise(WeekDemand = sum(Demand, na.rm=TRUE))

哪个产生:

   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

使用tidyr> = 0.3.1现在可以写成:

data %>% 
  complete(Article, Week) %>%  
  group_by(Article, Week) %>% 
  summarise(Demand = sum(Demand, na.rm = TRUE))