如何在重塑R之前扩展数据

Question

我有一个如下所示的数据框:

as.is <- data.frame(Project = c('Proj A', 'Proj B', 'Proj C', 'Proj D'), 
               Start.Date = c('16.02.2015', '02.03.2015', '16.02.2015', '09.03.2015'), 
               Duration = c(3, 2, 2, 4),
               No.Of.Resources = c(3, 5, 2, 6))

我需要更改格式,使其看起来像这样:

to.be <- data.frame(Project = c('Proj A', 'Proj B', 'Proj C', 'Proj D'),
                '16.02.2015' = c(3, NA, 2, NA),
                '23.02.2015' = c(3, NA, 2, NA),
                '02.03.2015' = c(3, 5, NA, NA),
                '09.03.2015' = c(NA, 5, NA, 6),
                '16.03.2015' = c(NA, NA, NA, 6),
                '23.03.2015' = c(NA, NA, NA, 6),
                '30.03.2015' = c(NA, NA, NA, 6))

我无法弄清楚如何扩展日期,所以我每行得到一个,所以我可以在数据上使用reshape2.我可以获得一个列表,列出我希望成为我的标题的日期,但是看不出如何将各个部分组合在一起.

什么是解决这个问题的正确方法？

编辑:为了澄清,持续时间是周数,所以我需要生成标题为x,x + 7,x + 14的列...

Answer 1

这是一种似乎有效的方法.它使用expandRows和getanID来自我的"splitstackshape"包,然后dcast.data.table从"data.table"中将值扩展为宽泛的形式:

as.is$Start.Date <- as.Date(as.character(as.is$Start.Date), "%d.%m.%Y")

library(splitstackshape)
dcast.data.table(
  getanID(
    expandRows(as.is, "Duration"), 
    c("Project", "Start.Date"))[
      , Start.Date := Start.Date + (.id-1) * 7], 
  Project ~ Start.Date, value.var = "No.Of.Resources")
#    Project 2015-02-16 2015-02-23 2015-03-02 2015-03-09 2015-03-16 2015-03-23 2015-03-30
# 1:  Proj A          3          3          3         NA         NA         NA         NA
# 2:  Proj B         NA         NA          5          5         NA         NA         NA
# 3:  Proj C          2          2         NA         NA         NA         NA         NA
# 4:  Proj D         NA         NA         NA          6          6          6          6

这是"dplyr"确实能够更好地读取解决方案的情况:

library(splitstackshape)
library(dplyr)
library(tidyr)

as.is$Start.Date <- as.Date(as.character(as.is$Start.Date), "%d.%m.%Y")
expandRows(as.is, "Duration") %>%                   # expand the data
  getanID(c("Project", "Start.Date")) %>%           # add an "id" column
  mutate(Start.Date = Start.Date + (.id-1) * 7) %>% # recalculate start dates
  select(-.id) %>%                                  # drop the "id" column
  spread(Start.Date, No.Of.Resources)               # reshape long to wide

Answer 2

我会以不同的方式做到这一点data.table.更新了新解决方案:

library(data.table)
dt = as.data.table(as.is)
dt[, Start.Date := as.Date(Start.Date, '%d.%m.%Y')]

# use dcast.data.table before version 1.9.5
dcast(dt[, list(seq(Start.Date, length.out = Duration, by = '1 week'), No.Of.Resources)
         , by = Project], Project ~ V1)

旧的(和不必要的复杂)解决方案:

# expand out Start.Date by Project
dates.all = dt[, seq(Start.Date, length.out = Duration, by = '1 week'), by = Project]

# set the key and do a rolling join, then dcast
# (can use just dcast in version 1.9.5+, have to use dcast.data.table before that)
setkey(dt, Project, Start.Date)
dcast(dt[dates.all, roll = TRUE], Project ~ Start.Date)
#   Project 2015-02-16 2015-02-23 2015-03-02 2015-03-09 2015-03-16 2015-03-23 2015-03-30
#1:  Proj A          3          3          3         NA         NA         NA         NA
#2:  Proj B         NA         NA          5          5         NA         NA         NA
#3:  Proj C          2          2         NA         NA         NA         NA         NA
#4:  Proj D         NA         NA         NA          6          6          6          6