在 R 中重塑长到宽的数据 [英] Reshape Long to Wide Data in R

问题描述

我正在尝试在 R 中重塑一些用户数据.我有一个包含会话 ID 的 data.frame.每个会话都有一个 User_ID 和日期.我想使用User_ID"变量作为我的密钥"，但仅用于具有新访问者"的userType"的观察.因此，每个新访问者"将有一行.然后将每个后续会话 ID 作为单独的变量传递.例如，如果一个用户 ID 总共有 3 个会话 ID，那么总共有 6 个变量:

I am trying to reshape some user data in R. I have a data.frame of session IDs. Each session has a User_ID and date. I would like to use the "User_ID" variable as my "Key" but only for the observations that have "userType" of "New Visitor". Therefore, there will be a single row for each "New Visitor". Then pass each subsequent Session ID as separate variable. For instance, if a User ID has 3 Session IDs in total, there would be a total of 6 variables:

例如，如果这是用户的数据框:

For instance, if this is the data frame for a user:

    date <- c('2015-01-01','2015-01-02','2015-01-02','2015-01-10')
    userID <- c('100105276','100105276','100105276','100105276')
    sessionID <- c('1452632119','1452634303','1452637067','1453600979')
    userType <- c('New Visitor','Returning Visitor','Returning Visitor','Returning Visitor')
    df <- cbind(date,userID,sessionID,userType)

相反，我想返回这个:

    userID      sessionID1  date1      SessionID2  date2      SesionID3 date3
    100105276   1452632119  2015-01-01 1452634303  2015-01-02 100105276 2015-01-02

如果存在没有后续 sessionID 的任何用户 ID，则将传递na"值，其中变量缺少值.我已经阅读了使用 tidyr 或 reshape2 来执行此操作的详细信息，但我一直无法让它们完全按照我的要求进行操作.

If there are any userIDs that did not have subsequent sessionIDs, a "na" value would be passed where variables are missing values. I've read up on using tidyr or reshape2 to do this, but I haven't been able to get them to do exactly what I am looking for.

推荐答案

鉴于你的数据是按 userID 和 sessionID 排序的，并且每一行都是一个唯一的会话，你可以这样做:

Given your data is ordered by userID and sessionID, and each row is a unique session, you could do:

library(data.table)

# Transform data into data.frame
df <- data.table(df)
df[, id := sequence(.N), by = c("userID")] # session sequence number per user

# Spread columns
reshape(df, timevar = "id", idvar = "userID", direction = "wide")
#     userID     date.1 sessionID.1  userType.1     date.2 sessionID.2        userType.2     date.3 sessionID.3        userType.3     
#1 100105276 2015-01-01  1452632119 New Visitor 2015-01-02  1452634303 Returning Visitor 2015-01-02  1452637067 Returning Visitor

在此输出中，userType 也作为变量包含在内，但之后您可以随时删除它们.

In this output userType is also included as a variable, but you can always drop them afterwards.

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