我想“取消融化”具有多个ID和多个融化变量并且被卡住的data.frame。
这是我的数据框的样子:
这是理想的结果
我尝试过:
unmelted <- dcast(setDT(melted), Id, Date, Type ~ Score, Time,
value.var = c("Score","Time"), sep = "")
和
unmelted <- melted %>%
group_by(Id, Date, Type) %>%
unite(variable, Score, Time)%>%
spread(Score, Time, -Id, Date, Type)
我不能使用pivot_wider
。
答案 0 :(得分:3)
我们可以使用
library(data.table)
dcast(setDT(df1), id + Date + Type ~ rowid(id, Date, Type),
value.var = c("Score","Time"), sep = "")
# id Date Type Score1 Score2 Score3 Score4 Time1 Time2 Time3 Time4
#1: 1 2001-01-13 aaa 123 456 789 NA 12:12 13:12 14:12 <NA>
#2: 2 2001-01-16 ddd 113 145 NA NA 15:12 16:12 <NA> <NA>
#3: 3 2001-01-18 bbb 789 145 113 145 17:12 18:12 19:12 20:12
使用来自@M的数据-
答案 1 :(得分:2)
一个touch test
解决方案,使用tidyverse
的{{1}}和gather
>
spread
您可以更方便地使用tidyr
进行相同操作:
library(dplyr)
library(tidyr) #version 1.0.0 which has pivot_wider
df1 %>%
group_by(Type) %>%
mutate(name_x = row_number()) %>%
gather(key=var, value=val, c(Score, Time)) %>%
mutate(var = paste(var, name_x, sep="_")) %>%
select(-name_x) %>%
spread(key=var, value=val)
#> # A tibble: 3 x 11
#> # Groups: Type [3]
#> id Date Type Score_1 Score_2 Score_3 Score_4 Time_1 Time_2 Time_3 Time_4
#> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr>
#> 1 1 2001~ aaa 123 456 789 NA 12:12 13:12 14:12 <NA>
#> 2 2 2001~ ddd 113 145 NA NA 15:12 16:12 <NA> <NA>
#> 3 3 2001~ bbb 789 145 113 145 17:12 18:12 19:12 20:12
数据:
pivot_wider