我的数据集如下所示:
unique.id abx.1 start.1 stop.1 abx.2 start.2 stop.2 abx.3 start.3 stop.3 abx.4 start.4
1 1 Moxi 2014-01-01 2014-01-07 PenG 2014-01-01 2014-01-07 Vanco 2014-01-01 2014-01-07 Moxi 2014-01-01
2 2 Moxi 2014-01-01 2014-01-02 Cipro 2014-01-01 2014-01-02 PenG 2014-01-01 2014-01-02 Vanco 2014-01-01
3 3 Cipro 2014-01-01 2014-01-05 Vanco 2014-01-01 2014-01-05 Cipro 2014-01-01 2014-01-05 Vanco 2014-01-01
4 4 Vanco 2014-01-02 2014-01-03 Cipro 2014-01-02 2014-01-03 Cipro 2014-01-02 2014-01-03 PenG 2014-01-02
5 5 Vanco 2014-01-01 2014-01-02 PenG 2014-01-01 2014-01-02 PenG 2014-01-01 2014-01-02 Cipro 2014-01-01
stop.4 intervention
1 2014-01-07 0
2 2014-01-02 0
3 2014-01-05 1
4 2014-01-03 1
5 2014-01-02 0
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用一些代码创建:
abxoptions <- c("Cipro", "Moxi", "PenG", "Vanco")
df3 <- data.frame(
unique.id = 1:5,
abx.1 = sample(abxoptions,5, replace=TRUE),
start.1 = as.Date(c('2014-01-01', '2014-01-01', '2014-01-01', '2014-01-02', '2014-01-01')),
stop.1 = as.Date(c('2014-01-07', '2014-01-02', '2014-01-05', '2014-01-03', '2014-01-02')),
abx.2 = sample(abxoptions,5, replace=TRUE),
start.2 = as.Date(c('2014-01-01', '2014-01-01', '2014-01-01', '2014-01-02', '2014-01-01')),
stop.2 = as.Date(c('2014-01-07', '2014-01-02', '2014-01-05', '2014-01-03', '2014-01-02')),
abx.3 = sample(abxoptions,5, replace=TRUE),
start.3 = as.Date(c('2014-01-01', '2014-01-01', '2014-01-01', '2014-01-02', '2014-01-01')),
stop.3 = as.Date(c('2014-01-07', '2014-01-02', '2014-01-05', '2014-01-03', '2014-01-02')),
abx.4 = sample(abxoptions,5, replace=TRUE),
start.4 = as.Date(c('2014-01-01', '2014-01-01', '2014-01-01', '2014-01-02', '2014-01-01')),
stop.4 = as.Date(c('2014-01-07', '2014-01-02', '2014-01-05', '2014-01-03', '2014-01-02')),
intervention = c(0,0,1,1,0)
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)
我想整理这些数据看起来像这样:
unique.id abx start stop intervention
1 Moxi 2014-01-10 2014-01-07 0
1 Pen G 2014-01-01 2014-01-07 0
1 Vanco 2014-01-01 2014-01-07 0
1 Moxi 2014-01-01 2014-01-07 0 etc etc
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以下解决方案无法满足我的需求: 收集多组列并将 多列组合成一列
我怀疑Hadley令人惊奇的tidyr pakcage是要走的路......只是想不出来.任何帮助将不胜感激.
had*_*ley 10
几乎每个数据整理问题都可以通过三个步骤解决:
(通常你只需要其中的一个或两个,但我认为它们几乎总是按照这个顺序).
对于您的数据:
unique.id这看起来像:
library(tidyr)
library(dplyr)
df3 %>%
gather(col, value, -unique.id, -intervention) %>%
separate(col, c("variable", "number")) %>%
spread(variable, value, convert = TRUE) %>%
mutate(start = as.Date(start, "1970-01-01"), stop = as.Date(stop, "1970-01-01"))
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你的情况有点复杂,因为你有两种类型的变量,所以你需要在最后恢复类型.
你可以尝试reshape从base R
reshape(df3, direction='long', varying=2:ncol(df3), sep=".")
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或者使用merged.stack从splitstackshape
library(splitstackshape)
merged.stack(df3, var.stubs=c('abx', 'start', 'stop'), sep='.')[,
c('start', 'stop') := lapply(.SD, as.Date,
origin='1970-01-01'), .SDcols=4:5][]
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