Question

如果我在R

中有以下数据框

date <- rep(seq(as.Date("2013-11-1"),as.Date("2014-6-1"), by = "months"),2)
category <- c(rep("Group 1","8"),rep("Group 2","8"))
x <- c(rnorm(8), rnorm(8))
data.frame(date,category,x)

         date category          x
1  2013-11-01  Group 1 -0.5511129
2  2013-12-01  Group 1 -0.6640636
3  2014-01-01  Group 1  0.6348586
4  2014-02-01  Group 1  0.2673702
5  2014-03-01  Group 1  0.9949441
6  2014-04-01  Group 1  0.4077544
7  2014-05-01  Group 1  1.8395109
8  2014-06-01  Group 1 -0.4685328
9  2013-11-01  Group 2 -0.7624855
10 2013-12-01  Group 2 -1.1774081
11 2014-01-01  Group 2 -2.5409333
12 2014-02-01  Group 2  0.5013774
13 2014-03-01  Group 2  0.6504688
14 2014-04-01  Group 2  0.2582353
15 2014-05-01  Group 2  0.6385828
16 2014-06-01  Group 2 -0.4358158

如何生成以下输出：

  date         group 1      group 2
1  2013-11-01  -0.5511129g   
2  2013-12-01  -0.6640636    
3  2014-01-01  0.6348586
4  2014-02-01  0.2673702     group 2 variable x values here
5  2014-03-01  0.9949441     
6  2014-04-01  0.4077544
7  2014-05-01  1.8395109
8  2014-06-01 -0.4685328

我想要的是跨时间的数据帧，第一列是日期，其余列对应于不同组的变量x。 Assumne将会有更多的组和变量。我希望这是有道理的！

Answer 1

这是您的标准票价＆＃34;长＆＃34;到＆＃34;宽＆＃34;重塑问题。

从＆＃34; reshape2＆＃34;：

查看dcast

set.seed(1)
date <- rep(seq(as.Date("2013-11-1"),as.Date("2014-6-1"), by = "months"),2)
category <- c(rep("Group 1","8"),rep("Group 2","8"))
x <- c(rnorm(8), rnorm(8))
mydf <- data.frame(date,category,x)

library(reshape2)
dcast(mydf, date ~ category)
# Using x as value column: use value.var to override.
#         date    Group 1     Group 2
# 1 2013-11-01 -0.6264538  0.57578135
# 2 2013-12-01  0.1836433 -0.30538839
# 3 2014-01-01 -0.8356286  1.51178117
# 4 2014-02-01  1.5952808  0.38984324
# 5 2014-03-01  0.3295078 -0.62124058
# 6 2014-04-01 -0.8204684 -2.21469989
# 7 2014-05-01  0.4874291  1.12493092
# 8 2014-06-01  0.7383247 -0.04493361

或者，在基数R中，reshape：

reshape(mydf, direction = "wide", idvar = "date", timevar = "category")
#         date  x.Group 1   x.Group 2
# 1 2013-11-01 -0.6264538  0.57578135
# 2 2013-12-01  0.1836433 -0.30538839
# 3 2014-01-01 -0.8356286  1.51178117
# 4 2014-02-01  1.5952808  0.38984324
# 5 2014-03-01  0.3295078 -0.62124058
# 6 2014-04-01 -0.8204684 -2.21469989
# 7 2014-05-01  0.4874291  1.12493092
# 8 2014-06-01  0.7383247 -0.04493361

而且，对于多样性，还有另一种方法： - ）

library(dplyr)
library(tidyr)
mydf %>% spread(category, x)

按R中的因子创建新数据帧

1 个答案: