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如何使用基于本地日期设置的RGA包从Google Analytics帐户获取每周数据

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尝试使用下面的（示例）代码从Google Analytics帐户中获取数据 . 我希望结果按周分组;因此将 ga:week 添加为最后一个维度 .

ga_data <- get_ga(id, start.date = "2017-02-27", end.date = "2017-03-05",
                  metrics = "ga:bounceRate, ga:sessions,
                  dimensions = "ga:Medium, ga:week",
                  segment = "gaid::xxxxxxxxxxxxxx",
                  include.empty.rows = "TRUE")

通过这种方式，它接受星期日作为一周的第一天;但是我希望它从周一开始（就像在我的本地设置中一样） .

我有3个问题：

1）是否可以使用包中的参数（“RGA”）对其进行编辑？

2）如果不可能，我如何每周手动分组？

3） get_ga() 函数中的参数 fetch.by 究竟是什么？ （在文档中说"character. Split the query by date range. Allowed values: "日", "周", "月", "季", "年“ . ）

如果需要任何其他信息，请与我们联系 . 提前致谢 .

EDIT ：关于@ArtemKlevtsov的评论

当我将日期添加为新维度时，它会变为低于每日格式;然后我需要使用"weekDesired"列和其他编码手动聚合我添加的所有指标（原始代码中有更多指标） . 我对吗？我可以做这个;但只是想确保没有更简单的方法来减少编码 .

> ga_data  <- get_ga(id, start.date = "2017-02-26", end.date = "2017-03-06",
+                                metrics = "ga:bounceRate, ga:sessions",
+                                dimensions = "ga:Medium, ga:week, ga:date",
+                                segment = "gaid::xxxxxxxxxxxxxx",
+                                include.empty.rows = "TRUE")

> library(lubridate)
> ga_data$weekDay <- wday(ga_data$date, label = T)
> ga_data$weekDesired <- format(ga_data$date, "%W")
> head(ga_data,16)

   Medium  week       date bounceRate sessions weekDay weekDesired
    <chr> <chr>     <dttm>      <dbl>    <int>   <ord>       <chr>
1  (none)    09 2017-02-26   66.66667        3     Sun          08
2  (none)    09 2017-02-27   50.00000        6     Mon          09
3  (none)    09 2017-02-28   80.00000        5    Tues          09
4  (none)    09 2017-03-01   20.00000        5     Wed          09
5  (none)    09 2017-03-02   57.14286       14   Thurs          09
6  (none)    09 2017-03-03   75.00000        8     Fri          09
7  (none)    09 2017-03-04  100.00000        4     Sat          09
8  (none)    10 2017-03-05  100.00000        4     Sun          09
9  (none)    10 2017-03-06   38.46154       13     Mon          10
10 banner    09 2017-02-26   22.22222        9     Sun          08
11 banner    09 2017-02-27   36.84211       19     Mon          09
12 banner    09 2017-02-28   58.33333       12    Tues          09
13 banner    09 2017-03-01   53.33333       15     Wed          09
14 banner    09 2017-03-02   50.00000       12   Thurs          09
15 banner    09 2017-03-03   54.54545       11     Fri          09
16 banner    09 2017-03-04   25.00000       12     Sat          09
r google-analytics

1 回答

  • 1

    使用 ga:isoYearIsoWeek 而不是 ga:week 作为维度解决了问题 . 现在，当我删除 ga:date 时，它将每周一次（从星期一开始） .

    对于下面有类似问题的人，链接可能有所帮助

    https://developers.google.com/analytics/devguides/reporting/core/dimsmets#view=detail&group=time&jump=ga_isoyearisoweek

    > ga_data  <- get_ga(id, start.date = "2017-02-26", end.date = "2017-03-06",
+                    metrics = "ga:bounceRate, ga:sessions",
+                    dimensions = "ga:Medium, ga:isoYearIsoWeek, ga:date",
+                    segment = "gaid::4SZBNy34Taypmuk_Mczdow",
+                    include.empty.rows = "TRUE")

> ga_data$weekDay <- wday(ga_data$date, label = T)
> ga_data$weekDesired <- format(ga_data$date, "%W")
> head(ga_data,20)

   Medium **isoYearIsoWeek**       date bounceRate sessions weekDay **weekDesired**
    <chr>          <chr>     <dttm>      <dbl>    <int>   <ord>       <chr>
1  (none)         201708 2017-02-26   66.66667        3     Sun          08
2  (none)         201709 2017-02-27   50.00000        6     Mon          09
3  (none)         201709 2017-02-28   80.00000        5    Tues          09
4  (none)         201709 2017-03-01   20.00000        5     Wed          09
5  (none)         201709 2017-03-02   57.14286       14   Thurs          09
6  (none)         201709 2017-03-03   75.00000        8     Fri          09
7  (none)         201709 2017-03-04  100.00000        4     Sat          09
8  (none)         201709 2017-03-05  100.00000        4     Sun          09
9  (none)         201710 2017-03-06   38.46154       13     Mon          10
10 banner         201708 2017-02-26   22.22222        9     Sun          08
11 banner         201709 2017-02-27   36.84211       19     Mon          09
12 banner         201709 2017-02-28   58.33333       12    Tues          09
13 banner         201709 2017-03-01   53.33333       15     Wed          09
14 banner         201709 2017-03-02   50.00000       12   Thurs          09
15 banner         201709 2017-03-03   54.54545       11     Fri          09
16 banner         201709 2017-03-04   25.00000       12     Sat          09
17 banner         201709 2017-03-05   27.27273       11     Sun          09
18 banner         201710 2017-03-06   44.44444       18     Mon          10
19    cpc         201708 2017-02-26   52.15239     4646     Sun          08
20    cpc         201709 2017-02-27   52.73286     4885     Mon          09
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