r - 数据框子集内的计算 [R]

Question

在子集计算方面遇到困难。我能够使用 ave、tapply、ddply 获得客户平均购买量(因素)等总体统计数据，但我无法计算 <每个客户的 strong>逐次访问 统计数据。下面一些简化的数据来说明我的数据和理想的结果。

当前数据框:(请注意，访问 #1 是最近的访问)

  customer  visit      date    purchase_amt
    sarah          2    2013-08-09      5
    sarah          3    2013-07-21      8
    sarah          4    2013-06-23      9
    sarah          5    2013-06-02      1
    sarah          1    2013-08-20      8
    henry          1    2013-07-04      4
    che            1    2013-08-27      2
    che            2    2013-07-27      1
    che            3    2013-07-05      8
    che            4    2013-06-14      3
    dt             3    2013-04-05      9
    dt             2    2013-06-07      1
    dt             1    2013-07-11      6

这些是我正在寻找的结果:

customer  visit    date purchase_amt    days since  amt_diff
sarah       2   2013-08-09  5               19        -3
sarah       3   2013-07-21  8               28        -1
sarah       4   2013-06-23  9               21         8
sarah       5   2013-06-02  1               NA        NA
sarah       1   2013-08-20  8               11         3
henry       1   2013-07-04  4               NA        NA
che         1   2013-08-27  2               31         1
che         2   2013-07-27  1               22        -7
che         3   2013-07-05  8               21         5
che         4   2013-06-14  3               NA        NA
dt          3   2013-04-05  9               NA        NA
dt          2   2013-06-07  1               63        -8
dt          1       2013-07-11    6         34         5

总而言之，我想找到客户的最近访问及其属性，然后找到下一次访问属性并计算两者的各种统计数据。当没有更多以前的访问时返回“NA”。

Answer 1

是这样的吗？假设您的数据名为 df:

library(plyr)

# convert dates to class 'Date'
df$date <- as.Date(df$date)

# order by customer and date
df <- df[order(df$customer, df$date), ]
# or since plyr is loaded anyway:
df <- arrange(df, customer, date) 

# per customer, calculate differences in date and purchase, between consecutive visits
# pad differences with a leading NA
df2 <- ddply(.data = df, .variables = .(customer), mutate,
      days_since = c(NA, diff(date)),
      amt_diff = c(NA, diff(purchase_amt)))

df2
# customer visit       date purchase_amt days_since amt_diff
# 1       che     4 2013-06-14            3         NA       NA
# 2       che     3 2013-07-05            8         21        5
# 3       che     2 2013-07-27            1         22       -7
# 4       che     1 2013-08-27            2         31        1
# 5        dt     3 2013-04-05            9         NA       NA
# 6        dt     2 2013-06-07            1         63       -8
# 7        dt     1 2013-07-11            6         34        5
# 8     henry     1 2013-07-04            4         NA       NA
# 9     sarah     5 2013-06-02            1         NA       NA
# 10    sarah     4 2013-06-23            9         21        8
# 11    sarah     3 2013-07-21            8         28       -1
# 12    sarah     2 2013-08-09            5         19       -3
# 13    sarah     1 2013-08-20            8         11        3