按列分组的数据框上R中的行之间的差异 [英] Difference between rows in R on dataframe grouped by column

问题描述

我希望通过app_name获得不同版本的计数差异。我的数据集如下所示：app_name，version_id，count，[difference]

I'm looking to get the difference in counts by version by app_name. My dataset looks like this: app_name, version_id, count, [difference]

这是数据集

    data = structure(list(app_name = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 
2L, 3L, 3L), .Label = c("a", "b", "c"), class = "factor"), version_id = c(1, 
1.1, 2.3, 2, 3.1, 3.3, 4, 1.1, 2.4), count = c(600L, 620L, 620L, 
200L, 200L, 250L, 250L, 15L, 36L)), .Names = c("app_name", "version_id", 
"count"), class = "data.frame", row.names = c(NA, -9L))

给出此data.frame，如何获得app_name和amp; version_id？每个应用程序的初始（第一）版本差异为零，因为两者之间没有差异。

Given this data.frame, how can I get the lagged difference in count by both app_name & version_id? the initial (first) version diff for each app would be zero, since there would be no difference.

以下是使用最终的差异列显示最终结果的示例

Here is an example of what the final results would look like with that final 'diff' column

structure(list(app_name = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 
2L, 3L, 3L), .Label = c("a", "b", "c"), class = "factor"), version_id = c(1, 
1.1, 2.3, 2, 3.1, 3.3, 4, 1.1, 2.4), count = c(600L, 620L, 620L, 
200L, 200L, 250L, 250L, 15L, 36L), diff = c(0, 20, 0, 0, 0, 1.25, 
0, 0, 2.4)), .Names = c("app_name", "version_id", "count", "diff"
), class = "data.frame", row.names = c(NA, -9L))

推荐答案

尝试使用 dplyr 和 lag ：

library(dplyr)
data %>% group_by(app_name) %>%
         mutate(diffvers = version_id - dplyr::lag(version_id, default = version_id[1]),
                diffcount = count - dplyr::lag(count, default = count[1]))

Source: local data frame [9 x 5]
Groups: app_name [3]

  app_name version_id count diffvers diffcount
    (fctr)      (dbl) (int)    (dbl)     (int)
1        a        1.0   600      0.0         0
2        a        1.1   620      0.1        20
3        a        2.3   620      1.2         0
4        b        2.0   200      0.0         0
5        b        3.1   200      1.1         0
6        b        3.3   250      0.2        50
7        b        4.0   250      0.7         0
8        c        1.1    15      0.0         0
9        c        2.4    36      1.3        21

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