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R融化重塑数据

[英]R Melt reshape data

 原文  2018-08-07 22:33:52  2   4    r/ reshape/ reshape2

问题描述

这是我的数据： 

Day        Morning_1_id     Var1        Morning_2_id     Var2      Afternoon_1_id     Var3      Afternoon_2_id     Var4
1     20180501-033-000001 3.156667 20180501-033-000002 2.866667 20180501-033-000008 2.946667 20180501-033-000009 3.133333
2     20180502-033-000001 2.986667 20180502-033-000002 2.930000 20180502-033-000020 3.076667 20180502-033-000021 3.013333
3     20180503-033-000001 3.073333 20180503-033-000002 3.070000 20180503-033-000011 3.106667 20180503-033-000012 2.900000
4     20180507-033-000001 3.236667 20180507-033-000002 2.990000 20180507-033-000015 3.043333 20180507-033-000016 3.116667
5     20180508-033-000001 3.030000 20180508-033-000002 3.150000 20180508-033-000015 3.156667 20180508-033-000017 3.343333
6     20180509-033-000001 3.010000 20180509-033-000002 3.020000 20180509-033-000007 3.000000 20180509-033-000008 3.156667
7     20180510-033-000001 2.916667 20180510-033-000002 3.103333 20180510-033-000007 3.336667 20180510-033-000008 3.066667
8     20180511-033-000001 3.293333 20180511-033-000002 3.163333 20180511-033-000013 2.980000 20180511-033-000014 2.940000
9     20180514-033-000001 3.136667 20180514-033-000002 3.186667 20180514-033-000007 2.766667 20180514-033-000008 3.100000
10    20180516-033-000001 3.116667 20180516-033-000002 3.283333 20180516-033-000008 3.133333 20180516-033-000009 3.040000
11    20180517-033-000003 2.843333 20180517-033-000004 3.120000 20180517-033-000008 3.060000 20180517-033-000009 3.033333
12    20180518-033-000001 3.033333 20180518-033-000002 3.290000 20180518-033-000007 3.006667 20180518-033-000008 2.973333
13    20180521-033-000002 3.173333 20180521-033-000003 2.993333 20180521-033-000008 2.983333 20180521-033-000009 3.020000
14    20180523-033-000001 3.336667 20180523-033-000002 3.026667 20180523-033-000007 3.300000 20180523-033-000008 3.210000

可复制形式： 

structure(list(Day = 1:14, Morning_1_id = structure(1:14, .Label = c("20180501-033-000001", 
"20180502-033-000001", "20180503-033-000001", "20180507-033-000001", 
"20180508-033-000001", "20180509-033-000001", "20180510-033-000001", 
"20180511-033-000001", "20180514-033-000001", "20180516-033-000001", 
"20180517-033-000003", "20180518-033-000001", "20180521-033-000002", 
"20180523-033-000001"), class = "factor"), Var1 = c(3.156666667, 
2.986666667, 3.073333333, 3.236666667, 3.03, 3.01, 2.916666667, 
3.293333333, 3.136666667, 3.116666667, 2.843333333, 3.033333333, 
3.173333333, 3.336666667), Morning_2_id = structure(1:14, .Label = c("20180501-033-000002", 
"20180502-033-000002", "20180503-033-000002", "20180507-033-000002", 
"20180508-033-000002", "20180509-033-000002", "20180510-033-000002", 
"20180511-033-000002", "20180514-033-000002", "20180516-033-000002", 
"20180517-033-000004", "20180518-033-000002", "20180521-033-000003", 
"20180523-033-000002"), class = "factor"), Var2 = c(2.866666667, 
2.93, 3.07, 2.99, 3.15, 3.02, 3.103333333, 3.163333333, 3.186666667, 
3.283333333, 3.12, 3.29, 2.993333333, 3.026666667), Afternoon_1_id = structure(1:14, .Label = c("20180501-033-000008", 
"20180502-033-000020", "20180503-033-000011", "20180507-033-000015", 
"20180508-033-000015", "20180509-033-000007", "20180510-033-000007", 
"20180511-033-000013", "20180514-033-000007", "20180516-033-000008", 
"20180517-033-000008", "20180518-033-000007", "20180521-033-000008", 
"20180523-033-000007"), class = "factor"), Var3 = c(2.946666667, 
3.076666667, 3.106666667, 3.043333333, 3.156666667, 3, 3.336666667, 
2.98, 2.766666667, 3.133333333, 3.06, 3.006666667, 2.983333333, 
3.3), Afternoon_2_id = structure(1:14, .Label = c("20180501-033-000009", 
"20180502-033-000021", "20180503-033-000012", "20180507-033-000016", 
"20180508-033-000017", "20180509-033-000008", "20180510-033-000008", 
"20180511-033-000014", "20180514-033-000008", "20180516-033-000009", 
"20180517-033-000009", "20180518-033-000008", "20180521-033-000009", 
"20180523-033-000008"), class = "factor"), Var4 = c(3.133333333, 
3.013333333, 2.9, 3.116666667, 3.343333333, 3.156666667, 3.066666667, 
2.94, 3.1, 3.04, 3.033333333, 2.973333333, 3.02, 3.21)), class = "data.frame", row.names = c(NA, 
-14L))

这是我想要的： 

Day Id                  Var         Time
1   20180501-033-000001 3.156666667 Morning1
2   20180502-033-000001 2.986666667 Morning1
3   20180503-033-000001 3.073333333 Morning1
4   20180507-033-000001 3.236666667 Morning1
5   20180508-033-000001 3.03        Morning1
6   20180509-033-000001 3.01        Morning1
7   20180510-033-000001 2.916666667 Morning1
8   20180511-033-000001 3.293333333 Morning1
9   20180514-033-000001 3.136666667 Morning1
10  20180516-033-000001 3.116666667 Morning1
11  20180517-033-000003 2.843333333 Morning1
12  20180518-033-000001 3.033333333 Morning1
13  20180521-033-000002 3.173333333 Morning1
14  20180523-033-000001 3.336666667 Morning1
1   20180501-033-000002 2.866666667 Morning2
2   20180502-033-000002 2.93        Morning2
3   20180503-033-000002 3.07        Morning2
4   20180507-033-000002 2.99        Morning2
5   20180508-033-000002 3.15        Morning2
6   20180509-033-000002 3.02        Morning2
7   20180510-033-000002 3.103333333 Morning2
8   20180511-033-000002 3.163333333 Morning2
9   20180514-033-000002 3.186666667 Morning2
10  20180516-033-000002 3.283333333 Morning2
11  20180517-033-000004 3.12        Morning2
12  20180518-033-000002 3.29        Morning2
13  20180521-033-000003 2.993333333 Morning2
14  20180523-033-000002 3.026666667 Morning2
1   20180501-033-000008 2.946666667 Afternoon1
2   20180502-033-000020 3.076666667 Afternoon1
3   20180503-033-000011 3.106666667 Afternoon1
4   20180507-033-000015 3.043333333 Afternoon1
5   20180508-033-000015 3.156666667 Afternoon1
6   20180509-033-000007 3           Afternoon1
7   20180510-033-000007 3.336666667 Afternoon1
8   20180511-033-000013 2.98        Afternoon1
9   20180514-033-000007 2.766666667 Afternoon1
10  20180516-033-000008 3.133333333 Afternoon1
11  20180517-033-000008 3.06        Afternoon1
12  20180518-033-000007 3.006666667 Afternoon1
13  20180521-033-000008 2.983333333 Afternoon1
14  20180523-033-000007 3.3         Afternoon1
1   20180501-033-000009 3.133333333 Afternoon2
2   20180502-033-000021 3.013333333 Afternoon2
3   20180503-033-000012 2.9         Afternoon2
4   20180507-033-000016 3.116666667 Afternoon2
5   20180508-033-000017 3.343333333 Afternoon2
6   20180509-033-000008 3.156666667 Afternoon2
7   20180510-033-000008 3.066666667 Afternoon2
8   20180511-033-000014 2.94        Afternoon2
9   20180514-033-000008 3.1         Afternoon2
10  20180516-033-000009 3.04        Afternoon2
11  20180517-033-000009 3.033333333 Afternoon2
12  20180518-033-000008 2.973333333 Afternoon2
13  20180521-033-000009 3.02        Afternoon2
14  20180523-033-000008 3.21        Afternoon2

我想进行从宽到长的转换，以使Ids和'Var'的值逐日堆积。 我还想要一个名为“时间”的附加列，该列将取决于初始ID，即“ Morning_1_id”，“ Morning_2_id”，“ Afternoon_1_id”和“ Afternoon_2_id”。 这个怎么做？ 我尝试使用来自reshape2的融合，但无法完成。

4 个解决方案

解决方案1
 2 已采纳  2018-08-08 03:16:59

这是使用dplyr将表转换为所需格式的解决方案： 

library(dplyr)

mydata<- reshape(mydata, direction='long', 
                varying=c('Morning_1_id', 'Var1', 'Morning_2_id', 'Var2', 'Afternoon_1_id', 'Var3', 'Afternoon_2_id', 'Var4'), 
                timevar='Var',
                times=c('Morning1', 'Morning2', 'Afternoon1', 'Afternoon2'),
                v.names=c('Id', 'Var'),
                idvar='Day')

mydata<- tibble::rownames_to_column(mydata)
mydata$rowname<- gsub("^.*\\.","", mydata$rowname)
names(mydata)<- c("Time", "Day", "Var", "Id")
mydata<- mydata[,c(2,4,3,1)]

解决方案2
 1  2018-08-07 23:02:12

这是一个tidyverse选项

已根据@Calum You的评论更正 

df %>%
  gather(Time, Var, -Day, -c(Var1, Var2, Var3, Var4)) %>%
  mutate(Time = gsub('.{3}$', '',Time),
         start = substr(Time, 1, 1),
         end = substr(Time, nchar(Time), nchar(Time)),
         id = paste0(start,end),
         Val = case_when(id=='M1' ~ Var1,
                         id=='M2' ~ Var2,
                         id=='A1' ~ Var3,
                         id=='A2' ~ Var4)) %>% 
  dplyr::select(Day, Id=Var, Val, Time)

原始不正确的代码 

df %>%
   gather(Time, Var, -Day, -c(Var1, Var2, Var3, Var4)) %>%
   gather( key, value, -Day, -Time, -Var) %>% 
   mutate(Time = gsub('.{3}$', '',Time)) %>% 
   dplyr::select(Day, Id=Var, Var=value, Time)

解决方案3
 1  2018-08-08 02:03:45

通过建立序列的第二列的列表，然后行绑定所有df元素，来考虑基数R： 

df_list <- lapply(seq(3, length(df), 2), function(i) {
  sub <- df[c(1, (i-1):i)]                                      # SUBSET BY COLS
  sub <- transform(sub, Time = sub("_id", "", names(df)[i-1]))  # ADD TIME VAR
  setNames(sub, c("Day", "Id", "Var", "Time"))                  # RENAME COLS  
})

long_df <- do.call(rbind, df_list)

head(long_df, 20)    
#    Day                  Id      Var      Time
# 1    1 20180501-033-000001 3.156667 Morning_1
# 2    2 20180502-033-000001 2.986667 Morning_1
# 3    3 20180503-033-000001 3.073333 Morning_1
# 4    4 20180507-033-000001 3.236667 Morning_1
# 5    5 20180508-033-000001 3.030000 Morning_1
# 6    6 20180509-033-000001 3.010000 Morning_1
# 7    7 20180510-033-000001 2.916667 Morning_1
# 8    8 20180511-033-000001 3.293333 Morning_1
# 9    9 20180514-033-000001 3.136667 Morning_1
# 10  10 20180516-033-000001 3.116667 Morning_1
# 11  11 20180517-033-000003 2.843333 Morning_1
# 12  12 20180518-033-000001 3.033333 Morning_1
# 13  13 20180521-033-000002 3.173333 Morning_1
# 14  14 20180523-033-000001 3.336667 Morning_1
# 15   1 20180501-033-000002 2.866667 Morning_2
# 16   2 20180502-033-000002 2.930000 Morning_2
# 17   3 20180503-033-000002 3.070000 Morning_2
# 18   4 20180507-033-000002 2.990000 Morning_2
# 19   5 20180508-033-000002 3.150000 Morning_2
# 20   6 20180509-033-000002 3.020000 Morning_2

解决方案4
 0  2018-08-07 23:12:20

这是另一种tidyverse方法。 不同的Var列对应于特定时间这一事实使情况变得复杂，但是时间的指示与id列中表示时间的方式不同。 因此，您需要某种匹配两者的方式。 在这里，我使用var_renamer内的命名列表来var_renamer 。 一旦一致地命名了列，就可以使用gather和separate来生成正确的变量，以将它们重新spread为所需的格式。 请注意，我mutate Time mutate为有序因子，因此可以按时间对它进行排序，而不必按字母顺序进行arrange 。 

df <- structure(list(Day = 1:14, Morning_1_id = structure(1:14, .Label = c("20180501-033-000001", "20180502-033-000001", "20180503-033-000001", "20180507-033-000001", "20180508-033-000001", "20180509-033-000001", "20180510-033-000001", "20180511-033-000001", "20180514-033-000001", "20180516-033-000001", "20180517-033-000003", "20180518-033-000001", "20180521-033-000002", "20180523-033-000001"), class = "factor"), Var1 = c(3.156666667, 2.986666667, 3.073333333, 3.236666667, 3.03, 3.01, 2.916666667, 3.293333333, 3.136666667, 3.116666667, 2.843333333, 3.033333333, 3.173333333, 3.336666667), Morning_2_id = structure(1:14, .Label = c("20180501-033-000002", "20180502-033-000002", "20180503-033-000002", "20180507-033-000002", "20180508-033-000002", "20180509-033-000002", "20180510-033-000002", "20180511-033-000002", "20180514-033-000002", "20180516-033-000002", "20180517-033-000004", "20180518-033-000002", "20180521-033-000003", "20180523-033-000002"), class = "factor"), Var2 = c(2.866666667, 2.93, 3.07, 2.99, 3.15, 3.02, 3.103333333, 3.163333333, 3.186666667, 3.283333333, 3.12, 3.29, 2.993333333, 3.026666667), Afternoon_1_id = structure(1:14, .Label = c("20180501-033-000008", "20180502-033-000020", "20180503-033-000011", "20180507-033-000015", "20180508-033-000015", "20180509-033-000007", "20180510-033-000007", "20180511-033-000013", "20180514-033-000007", "20180516-033-000008", "20180517-033-000008", "20180518-033-000007", "20180521-033-000008", "20180523-033-000007"), class = "factor"), Var3 = c(2.946666667, 3.076666667, 3.106666667, 3.043333333, 3.156666667, 3, 3.336666667, 2.98, 2.766666667, 3.133333333, 3.06, 3.006666667, 2.983333333, 3.3), Afternoon_2_id = structure(1:14, .Label = c("20180501-033-000009", "20180502-033-000021", "20180503-033-000012", "20180507-033-000016", "20180508-033-000017", "20180509-033-000008", "20180510-033-000008", "20180511-033-000014", "20180514-033-000008", "20180516-033-000009", "20180517-033-000009", "20180518-033-000008", "20180521-033-000009", "20180523-033-000008"), class = "factor"), Var4 = c(3.133333333, 3.013333333, 2.9, 3.116666667, 3.343333333, 3.156666667, 3.066666667, 2.94, 3.1, 3.04, 3.033333333, 2.973333333, 3.02, 3.21)), class = "data.frame", row.names = c(NA, -14L))

library(tidyverse)
var_renamer <- function(name) {
  time_list <- list(
    "1" = "Morning_1", "2" = "Morning_2", "3" = "Afternoon_1", "4" = "Afternoon_2"
  )
  timenum = str_remove(name, "Var")
  timestr = map_chr(timenum, ~ time_list[[.x]])
  str_c(timestr, "-Var")
}

df %>%
  rename_at(vars(starts_with("Var")), var_renamer) %>%
  rename_all(funs(str_replace(., "_id", "-Id"))) %>%
  gather(colname, val, -Day) %>%
  separate(colname, c("Time", "id_var"), sep = "-") %>%
  mutate(Time = factor(
    x = Time,
    levels = c("Morning_1", "Morning_2", "Afternoon_1", "Afternoon_2"),
    ordered = TRUE
  )) %>%
  spread(id_var, val) %>%
  arrange(Time, Day)
#> Warning: attributes are not identical across measure variables;
#> they will be dropped
#>    Day        Time                  Id         Var
#> 1    1   Morning_1 20180501-033-000001 3.156666667
#> 2    2   Morning_1 20180502-033-000001 2.986666667
#> 3    3   Morning_1 20180503-033-000001 3.073333333
#> 4    4   Morning_1 20180507-033-000001 3.236666667
#> 5    5   Morning_1 20180508-033-000001        3.03
#> 6    6   Morning_1 20180509-033-000001        3.01
#> 7    7   Morning_1 20180510-033-000001 2.916666667
#> 8    8   Morning_1 20180511-033-000001 3.293333333
#> 9    9   Morning_1 20180514-033-000001 3.136666667
#> 10  10   Morning_1 20180516-033-000001 3.116666667
#> 11  11   Morning_1 20180517-033-000003 2.843333333
#> 12  12   Morning_1 20180518-033-000001 3.033333333
#> 13  13   Morning_1 20180521-033-000002 3.173333333
#> 14  14   Morning_1 20180523-033-000001 3.336666667
#> 15   1   Morning_2 20180501-033-000002 2.866666667
#> 16   2   Morning_2 20180502-033-000002        2.93
#> 17   3   Morning_2 20180503-033-000002        3.07
#> 18   4   Morning_2 20180507-033-000002        2.99
#> 19   5   Morning_2 20180508-033-000002        3.15
#> 20   6   Morning_2 20180509-033-000002        3.02
#> 21   7   Morning_2 20180510-033-000002 3.103333333
#> 22   8   Morning_2 20180511-033-000002 3.163333333
#> 23   9   Morning_2 20180514-033-000002 3.186666667
#> 24  10   Morning_2 20180516-033-000002 3.283333333
#> 25  11   Morning_2 20180517-033-000004        3.12
#> 26  12   Morning_2 20180518-033-000002        3.29
#> 27  13   Morning_2 20180521-033-000003 2.993333333
#> 28  14   Morning_2 20180523-033-000002 3.026666667
#> 29   1 Afternoon_1 20180501-033-000008 2.946666667
#> 30   2 Afternoon_1 20180502-033-000020 3.076666667
#> 31   3 Afternoon_1 20180503-033-000011 3.106666667
#> 32   4 Afternoon_1 20180507-033-000015 3.043333333
#> 33   5 Afternoon_1 20180508-033-000015 3.156666667
#> 34   6 Afternoon_1 20180509-033-000007           3
#> 35   7 Afternoon_1 20180510-033-000007 3.336666667
#> 36   8 Afternoon_1 20180511-033-000013        2.98
#> 37   9 Afternoon_1 20180514-033-000007 2.766666667
#> 38  10 Afternoon_1 20180516-033-000008 3.133333333
#> 39  11 Afternoon_1 20180517-033-000008        3.06
#> 40  12 Afternoon_1 20180518-033-000007 3.006666667
#> 41  13 Afternoon_1 20180521-033-000008 2.983333333
#> 42  14 Afternoon_1 20180523-033-000007         3.3
#> 43   1 Afternoon_2 20180501-033-000009 3.133333333
#> 44   2 Afternoon_2 20180502-033-000021 3.013333333
#> 45   3 Afternoon_2 20180503-033-000012         2.9
#> 46   4 Afternoon_2 20180507-033-000016 3.116666667
#> 47   5 Afternoon_2 20180508-033-000017 3.343333333
#> 48   6 Afternoon_2 20180509-033-000008 3.156666667
#> 49   7 Afternoon_2 20180510-033-000008 3.066666667
#> 50   8 Afternoon_2 20180511-033-000014        2.94
#> 51   9 Afternoon_2 20180514-033-000008         3.1
#> 52  10 Afternoon_2 20180516-033-000009        3.04
#> 53  11 Afternoon_2 20180517-033-000009 3.033333333
#> 54  12 Afternoon_2 20180518-033-000008 2.973333333
#> 55  13 Afternoon_2 20180521-033-000009        3.02
#> 56  14 Afternoon_2 20180523-033-000008        3.21

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