将多行合并为单行 [英] Merging multiple rows into single row

问题描述

我在 R 中的数据框有一些问题.我的数据框看起来像这样:

I've some problems with my data frame in R. My data frame looks something like this:

ID  TIME    DAY        URL_NAME      VALUE  TIME_SPEND
1    12:15  Monday      HOME         4        30
1    13:15  Tuesday     CUSTOMERS    5        21  
1    15:00  Thursday    PLANTS       8        8    
1    16:21  Friday      MANAGEMENT   1        6
....

所以，我想将包含相同ID"的行写入一行.看起来像这样:

So, I want to write the rows, containing the same "ID" into one single row. Looking something like this:

ID  TIME    DAY         URL_NAME     VALUE    TIME_SPEND  TIME1  DAY1        URL_NAME1      VALUE1  TIME_SPEND1  TIME2    DAY2        URL_NAME2      VALUE2  TIME_SPEND2  TIME3    DAY3        URL_NAME3      VALUE3  TIME_SPEND3
1    12:15  Monday      HOME         4        30          13:15  Tuesday     CUSTOMERS      5       21           15:00    Thursday    PLANTS         8       8            16:21    Friday      MANAGEMENT     1       6

我的第二个问题是，大约有 1.500.00 个唯一 ID，我想为整个数据框执行此操作.

My second problem is, that there are about 1.500.00 unique IDs and i would like to do this for the whole data frame.

我没有找到适合我的问题的任何解决方案.我很乐意提供任何解决方案或链接来处理我的问题.

I did not find any solution fitting to my problem. I would be happy about any solutions or links to handle my problem.

推荐答案

我建议使用data.table"包中的 dcast，它允许您一次重塑多个度量变量.

I'd recommend using dcast from the "data.table" package, which would allow you to reshape multiple measure variables at once.

示例:

library(data.table)
as.data.table(mydf)[, dcast(.SD, ID ~ rowid(ID), value.var = names(mydf)[-1])]
#    ID TIME_1 TIME_2 TIME_3   DAY_1   DAY_2    DAY_3 URL_NAME_1 URL_NAME_2 URL_NAME_3 VALUE_1 VALUE_2
# 1:  1  12:15  13:15  15:00  Monday Tuesday Thursday       HOME  CUSTOMERS     PLANTS       4       5
# 2:  2  14:15  10:19     NA Tuesday  Monday       NA  CUSTOMERS  CUSTOMERS         NA       2       9
#    VALUE_3 TIME_SPEND_1 TIME_SPEND_2 TIME_SPEND_3
# 1:       8           30           19           40
# 2:      NA           21            8           NA

<小时>

以下是使用的示例数据:

---

Here's the sample data used:

mydf <- data.frame(
  ID = c(1, 1, 1, 2, 2),
  TIME = c("12:15", "13:15", "15:00", "14:15", "10:19"),
  DAY = c("Monday", "Tuesday", "Thursday", "Tuesday", "Monday"),
  URL_NAME = c("HOME", "CUSTOMERS", "PLANTS", "CUSTOMERS", "CUSTOMERS"),
  VALUE = c(4, 5, 8, 2, 9),
  TIME_SPEND = c(30, 19, 40, 21, 8)
)
mydf
#   ID  TIME      DAY  URL_NAME VALUE TIME_SPEND
# 1  1 12:15   Monday      HOME     4         30
# 2  1 13:15  Tuesday CUSTOMERS     5         19
# 3  1 15:00 Thursday    PLANTS     8         40
# 4  2 14:15  Tuesday CUSTOMERS     2         21
# 5  2 10:19   Monday CUSTOMERS     9          8

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