如何将多个变量的重复测量扩展为宽格式？

Question

我正在尝试使用长格式的列并将它们扩展为宽格式,如下所示.我想用tidyr用我正在投资的数据处理工具来解决这个问题,但为了使这个答案更加通用,请提供其他解决方案.

这就是我所拥有的:

library(dplyr); library(tidyr)

set.seed(10)
dat <- data_frame(
    Person = rep(c("greg", "sally", "sue"), each=2),
    Time = rep(c("Pre", "Post"), 3),
    Score1 = round(rnorm(6, mean = 80, sd=4), 0),
    Score2 = round(jitter(Score1, 15), 0),
    Score3 = 5 + (Score1 + Score2)/2
)

##   Person Time Score1 Score2 Score3
## 1   greg  Pre     80     78   84.0
## 2   greg Post     79     80   84.5
## 3  sally  Pre     75     74   79.5
## 4  sally Post     78     78   83.0
## 5    sue  Pre     81     78   84.5
## 6    sue Post     82     81   86.5

所需的宽幅格式:

  Person Pre.Score1 Pre.Score2 Pre.Score3  Post.Score1 Post.Score2 Post.Score3
1   greg         80         78       84.0           79          80        84.5
2  sally         75         74       79.5           78          78        83.0
3    sue         81         78       84.5           82          81        86.5

我可以通过为每个分数做这样的事情来做到这一点:

spread(dat %>% select(Person, Time, Score1), Time, Score1) %>% 
    rename(Score1_Pre = Pre, Score1_Post = Post)

然后使用_join但看起来很冗长,并且必须有更好的方法.

相关问题:
tidyr从长到两个重复的措施
是否有可能在tidyr的多个列上使用传播类似于dcast？

Answer 1

如果你想坚持下去 tidyr/dplyr

dat %>% 
  gather(temp, score, starts_with("Score")) %>% 
  unite(temp1, Time, temp, sep = ".") %>% 
  spread(temp1, score)

Answer 2

使用reshape2:

library(reshape2)
dcast(melt(dat), Person ~ Time + variable)

生产:

Using Person, Time as id variables
  Person Post_Score1 Post_Score2 Post_Score3 Pre_Score1 Pre_Score2 Pre_Score3
1   greg          79          78        83.5         83         81       87.0
2  sally          82          81        86.5         75         74       79.5
3    sue          78          78        83.0         82         79       85.5

Answer 3

dcast从data.table包中使用.

library(data.table)#v1.9.5+
dcast(setDT(dat), Person~Time, value.var=paste0("Score", 1:3))
#     Person Score1_Post Score1_Pre Score2_Post Score2_Pre Score3_Post Score3_Pre
#1:   greg          79         80          80         78        84.5       84.0
#2:  sally          78         75          78         74        83.0       79.5
#3:    sue          82         81          81         78        86.5       84.5

或reshape来自baseR

reshape(as.data.frame(dat), idvar='Person', timevar='Time',direction='wide')