标题名称作为r中的日期

我试图计算用户的“死亡”，这意味着我想确定用户注册一个程序到他们不再在程序中活动之间的持续时间。我有两个文件，我使用

read.csv（“filename”，header=TRUE）

读取它们：

和文件2：

> df2
       names X04.16.2013 X04.17.2013 X04.18.2014  X04.19.2013
2001 Allison           5           5           0           0
2002  Andrew           0           0           0           0
2003    Carl           8           8           11          10
2004    Dora           6           4           9           3
2005  Hilary           2           0           0           0
2006   Louis           18         10           8           3
2007    Mary           4           7           7           0
2008  Mickey           9           5           0           0

我想做的是将df2的标题名转换为日期，这样我就可以创建一个新的数据框，其中包含用户名、开始日期和“死亡天数”，即当用户在df2中的条目为0时：

      name   start.date   days.to.death
1  Allison   2013-03-16   33
2   Andrew   2013-03-16   0
3     Carl   2013-03-16   NA
4     Dora   2013-03-17   NA
5   Hilary   2013-03-17   31
6    Louis   2013-03-19   NA
7     Mary   2013-03-20   30
8   Mickey   2013-03-20   28

---

请注意，安德鲁从未“活着”，卡尔、多拉和路易斯还没有“死”。我对R还是比较陌生，所以非常感谢您的任何意见

格式正确的简单

as.Date

将把列名转换为日期。首先，数据的可复制可复制形式

df<-structure(list(name = structure(1:8, .Label = c("Allison", "Andrew", 
"Carl", "Dora", "Hilary", "Louis", "Mary", "Mickey"), class = "factor"), 
    start.date = structure(c(15780, 15780, 15780, 15781, 15781, 
    15783, 15784, 15784), class = "Date")), .Names = c("name", 
"start.date"), row.names = c("1", "2", "3", "4", "5", "6", "7", 
"8"), class = "data.frame")

df2<-structure(list(names = structure(1:8, .Label = c("Allison", "Andrew", 
"Carl", "Dora", "Hilary", "Louis", "Mary", "Mickey"), class = "factor"), 
    X04.16.2013 = c(5L, 0L, 8L, 6L, 2L, 18L, 4L, 9L), X04.17.2013 = c(5L, 
    0L, 8L, 4L, 0L, 10L, 7L, 5L), X04.18.2014 = c(0L, 0L, 11L, 
    9L, 0L, 8L, 7L, 0L), X04.19.2013 = c(0L, 0L, 10L, 3L, 0L, 
    3L, 0L, 0L)), .Names = c("names", "X04.16.2013", "X04.17.2013", 
"X04.18.2014", "X04.19.2013"), class = "data.frame", row.names = c("2001", 
"2002", "2003", "2004", "2005", "2006", "2007", "2008"))

假设

start.date

也是正确的

date

类，则将给出以下内容

     name start.date days.to.death
1 Allison 2013-03-16      398 days
2  Andrew 2013-03-16       31 days
3    Carl 2013-03-16       NA days
4    Dora 2013-03-17       NA days
5  Hilary 2013-03-17       31 days
6   Louis 2013-03-19       NA days
7    Mary 2013-03-20       30 days
8  Mickey 2013-03-20      394 days

---

假设df2的列标题中有一个输入错误，下面使用dplyr和tidyr的解决方案可以让您在大多数情况下都做到这一点

  library(tidyr)
  library(dplyr)

  colnames(df)<-c("names", "start") #  To join dfs, the first column header needs to be identical to df2
  df$start<-as.Date(df$start, format="%m/%d/%Y") #formatting date

给这个

    names      start       date daydiff
1  Hilary 2013-03-17 2013-04-17 31 days
2 Allison 2013-03-16 2013-04-18 33 days
3  Mickey 2013-03-20 2013-04-18 29 days
4    Mary 2013-03-20 2013-04-19 30 days

---

把NAs和那些从未生活过的人放进去应该很容易。也许这有点帮助？

以下简单代码可能有用：

names(df2)[1] = 'name'
merge(df, ddf2)
dfm$days.to.death = ifelse(dfm[,3]==0,0,ifelse(dfm[,4]==0,31, ifelse(dfm[,5]==0,33,ifelse(dfm[,6]==0,28,NA))))

dfm[,c(1,2,7)]
     name start.date days.to.death
1 Allison 2013-03-16            33
2  Andrew 2013-03-16             0
3    Carl 2013-03-16            NA
4    Dora 2013-03-17            NA
5  Hilary 2013-03-17            31
6   Louis 2013-03-19            NA
7    Mary 2013-03-20            28
8  Mickey 2013-03-20            33

---

这是df2列标题中的输入错误吗？他们都应该是2013年吗？我道歉。是的，df2中的所有日期应为2013年

     name start.date days.to.death
1 Allison 2013-03-16      398 days
2  Andrew 2013-03-16       31 days
3    Carl 2013-03-16       NA days
4    Dora 2013-03-17       NA days
5  Hilary 2013-03-17       31 days
6   Louis 2013-03-19       NA days
7    Mary 2013-03-20       30 days
8  Mickey 2013-03-20      394 days

  library(tidyr)
  library(dplyr)

  colnames(df)<-c("names", "start") #  To join dfs, the first column header needs to be identical to df2
  df$start<-as.Date(df$start, format="%m/%d/%Y") #formatting date

  df2 %>% 
  filter(X04.16.2013!=0) %>%   #removes Andrew who has 0 in first date col
  gather(key,value,2:5) %>%     
  mutate(date=as.Date(key, format="X%m.%d.%Y")) %>%
  left_join(df) %>%
  filter(value==0) %>%
  group_by(names) %>%
  filter(date == nth(date, 1)) %>% 
  select(names, start, date) %>%
  mutate (daydiff=difftime(date,start, unit="days"))

    names      start       date daydiff
1  Hilary 2013-03-17 2013-04-17 31 days
2 Allison 2013-03-16 2013-04-18 33 days
3  Mickey 2013-03-20 2013-04-18 29 days
4    Mary 2013-03-20 2013-04-19 30 days

names(df2)[1] = 'name'
merge(df, ddf2)
dfm$days.to.death = ifelse(dfm[,3]==0,0,ifelse(dfm[,4]==0,31, ifelse(dfm[,5]==0,33,ifelse(dfm[,6]==0,28,NA))))

dfm[,c(1,2,7)]
     name start.date days.to.death
1 Allison 2013-03-16            33
2  Andrew 2013-03-16             0
3    Carl 2013-03-16            NA
4    Dora 2013-03-17            NA
5  Hilary 2013-03-17            31
6   Louis 2013-03-19            NA
7    Mary 2013-03-20            28
8  Mickey 2013-03-20            33