我有一个这样的数据框：
indx country year death value
1 1 Italy 2000 hiv 1
2 1 Italy 2001 hiv 2
3 1 Italy 2005 hiv 3
4 1 Italy 2000 cancer 4
5 1 Italy 2001 cancer 5
6 1 Italy 2002 cancer 6
7 1 Italy 2003 cancer 7
8 1 Italy 2004 cancer 8
9 1 Italy 2005 cancer 9
10 4 France 2000 hiv 10
11 4 France 2004 hiv 11
12 4 France 2005 hiv 12
13 4 France 2001 cancer 13
14 4 France 2002 cancer 14
15 4 France 2003 cancer 15
16 4 France 2004 cancer 16
17 2 Spain 2000 hiv 17
18 2 Spain 2001 hiv 18
19 2 Spain 2002 hiv 19
20 2 Spain 2003 hiv 20
21 2 Spain 2004 hiv 21
22 2 Spain 2005 hiv 22
23 2 Spain ... ... ...
indx是与country相关联的值（相同country =相同indx）。
在这个例子中，我只使用了3个国家（country）和2个疾病（death），在原始数据框中还有更多。
我希望从2000年到2005年，每个国家的每种疾病都有一排。
我想得到的是：
indx country year death value
1 1 Italy 2000 hiv 1
2 1 Italy 2001 hiv 2
3 1 Italy 2002 hiv NA
4 1 Italy 2003 hiv NA
5 1 Italy 2004 hiv NA
6 1 Italy 2005 hiv 3
7 1 Italy 2000 cancer 4
8 1 Italy 2001 cancer 5
9 1 Italy 2002 cancer 6
10 1 Italy 2003 cancer 7
11 1 Italy 2004 cancer 8
12 1 Italy 2005 cancer 9
13 4 France 2000 hiv 10
14 4 France 2001 hiv NA
15 4 France 2002 hiv NA
16 4 France 2003 hiv NA
17 4 France 2004 hiv 11
18 4 France 2005 hiv 12
19 4 France 2000 cancer NA
20 4 France 2001 cancer 13
21 4 France 2002 cancer 14
22 4 France 2003 cancer 15
23 4 France 2004 cancer 16
24 4 France 2005 cancer NA
25 2 Spain 2000 hiv 17
26 2 Spain 2001 hiv 18
27 2 Spain 2002 hiv 19
28 2 Spain 2003 hiv 20
29 2 Spain 2004 hiv 21
30 2 Spain 2005 hiv 22
31 2 Spain ... ... ...
即。我希望在每个国家的每个疾病的缺失年份添加value = NA行。
例如，它在2002年至2004年期间缺乏意大利的艾滋病病毒数据，然后我用value = NA添加了这一行。
我该怎么做？
可重复的例子：
indx <- c(rep(1, times=9), rep(4, times=7), rep(2, times=6))
country <- c(rep("Italy", times=9), rep("France", times=7), rep("Spain", times=6))
year <- c(2000, 2001, 2005, 2000:2005, 2000, 2004, 2005, 2001:2004, 2000:2005)
death <- c(rep("hiv", times=3), rep("cancer", times=6), rep("hiv", times=3), rep("cancer", times=4), rep("hiv", times=6))
value <- c(1:22)
dfl <- data.frame(indx, country, year, death, value)
答案 0 :(得分：4)
使用基数R，你可以这样做：
# setDF(dfl) # run this first if you have a data.table
merge(expand.grid(lapply(dfl[c("country", "death", "year")], unique)), dfl, all.x = TRUE)
首先在country，death和year中创建唯一值的所有组合，然后将其合并到原始数据中，以添加values和其中组合不在原始数据中，它添加了NA s。
在包tidyr中，有一个特殊的功能，可以通过一个命令为您完成此任务：
library(tidyr)
complete(dfl, country, year, death)
答案 1 :(得分：2)
这是一个更长的基础R方法。您创建两个新的data.frames，一个包含country，year和death的所有组合，另一个包含索引键。
# get data.frame with every combination of country, year, and death
dfNew <- with(df, expand.grid("country"=unique(country), "year"=unique(year),
"death"=unique(death)))
# get index key
indexKey <- unique(df[, c("indx", "country")])
# merge these together
dfNew <- merge(indexKey, dfNew, by="country")
# merge onto original data set
dfNew <- merge(df, dfNew, by=c("indx", "country", "year", "death"), all=TRUE)
返回
dfNew
indx country year death value
1 1 Italy 2000 cancer 4
2 1 Italy 2000 hiv 1
3 1 Italy 2001 cancer 5
4 1 Italy 2001 hiv 2
5 1 Italy 2002 cancer 6
6 1 Italy 2002 hiv NA
7 1 Italy 2003 cancer 7
8 1 Italy 2003 hiv NA
9 1 Italy 2004 cancer 8
10 1 Italy 2004 hiv NA
11 1 Italy 2005 cancer 9
12 1 Italy 2005 hiv 3
13 2 Spain 2000 cancer NA
14 2 Spain 2000 hiv 17
15 2 Spain 2001 cancer NA
...
如果df是data.table，则以下是相应的代码行：
# CJ is a cross-join
setkey(df, country, year, death)
dfNew <- df[CJ(country, year, death, unique=TRUE),
.(country, year, death, value)]
indexKey <- unique(df[, .(indx, country)])
dfNew <- merge(indexKey, dfNew, by="country")
dfNew <- merge(df, dfNew, by=c("indx", "country", "year", "death"), all=TRUE)
请注意，它不是使用CJ，而是可以在data.frame版本中使用expand.grid：
dfNew <- df[, expand.grid("country"=unique(country), "year"=unique(year),
"death"=unique(death))]
答案 2 :(得分：0)
tidyr::complete有助于创建您传递的变量的所有组合，但如果您有两列完全相同，则会过度扩展或将NA放在您不想要的位置。作为一种变通方法，您可以使用dplyr分组（df %>% group_by(indx, country) %>% complete(death, year)）或仅将两列合并为一列：
library(tidyr)
# merge indx and country into a single column so they won't over-expand
df %>% unite(indx_country, indx, country) %>%
# fill in missing combinations of new column, death, and year
complete(indx_country, death, year) %>%
# separate indx and country back to how they were
separate(indx_country, c('indx', 'country'))
# Source: local data frame [36 x 5]
#
# indx country death year value
# (chr) (chr) (fctr) (int) (int)
# 1 1 Italy cancer 2000 4
# 2 1 Italy cancer 2001 5
# 3 1 Italy cancer 2002 6
# 4 1 Italy cancer 2003 7
# 5 1 Italy cancer 2004 8
# 6 1 Italy cancer 2005 9
# 7 1 Italy hiv 2000 1
# 8 1 Italy hiv 2001 2
# 9 1 Italy hiv 2002 NA
# 10 1 Italy hiv 2003 NA
# .. ... ... ... ... ...