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分组后所有RAW的值是否相同?

Omar Gonzales · 技术社区 · 2 年前

在做了一些转换之后,我得到了重复的数据。

分组:


paid <- df%>%
                 group_by(date) %>%
                 summarise(sessions = sum(sessions),
                           revenue_usd = sum(transaction_revenue)/3.8,
                           bounceRate = bounces/sessions,
                           transactions = sum(transactions))

df <- structure(list(date = structure(c(19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 19186, 
19186, 19186, 19186, 19186, 19186, 19186, 19186), class = "Date"), 
    source = c("(direct)", "(not set)", "127.0.0.1:20489", "13d612549ae98efa4eebca49fbda5aa9.safeframe.googlesyndication.com", 
    "214791a1533c2a50e91a0172b59da402.safeframe.googlesyndication.com", 
    "408b8fdfaa1d06bb140607437bd90f10.safeframe.googlesyndication.com", 
    "5e1452d421694493662f87729e4e130d.safeframe.googlesyndication.com", 
    "8042dc59e10779732adc12d6359c3ab9.safeframe.googlesyndication.com", 
    "84c4aa5f9449946d1cb5812ca3ea633a.safeframe.googlesyndication.com", 
    "89a3e733827acf8133fab396ec550646.safeframe.googlesyndication.com", 
    "9ad91f3bc7d10dce0ba6fb1c18d32c0e.safeframe.googlesyndication.com", 
    "9d81febecab8a704ce297781effecc07.safeframe.googlesyndication.com", 
    "accounts.google.com", "actitudfem.com", "admin.pagoefectivo.pe", 
    "ads.us.criteo.com", "adsintegrity.net", "anmosugoi.com", 
    "beneficios.bbva.pe", "biggestchef.com", "bing", "content.jwplatform.com", 
    "criteo", "criteo", "Criteo", "Criteo,criteo", "cse.google.com", 
    "cuotealo.viabcp.com", "cyberwow.pe", "diariocorreo.pe", 
    "dinersclub.pe", "docs.google.com", "duckduckgo", "ecosia.org", 
    "edmmkt", "elcomercio", "elcomercio", "estudiantes.samsung.com.pe", 
    "facebook", "facebook", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network", "facebook-network", 
    "facebook-network", "facebook-network"), medium = c("(none)", 
    "(not set)", "referral", "referral", "referral", "referral", 
    "referral", "referral", "referral", "referral", "referral", 
    "referral", "referral", "referral", "referral", "referral", 
    "referral", "referral", "referral", "referral", "organic", 
    "referral", "consideration", "retargeting", "(not set)", 
    "consideration", "referral", "referral", "referral", "referral", 
    "referral", "referral", "organic", "organic", "image", "cpm", 
    "cpm", "referral", "cpm", "social", "(not set)", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", "cpa", 
    "cpa", "cpa", "cpa", "cpa"), sessions = c(1746, 4, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 40, 1, 1, 13, 1, 40, 1, 
    3636, 2921, 4, 1, 4, 54, 20, 1, 66, 1, 1, 3, 1, 1, 29, 9, 
    1, 86, 75, 1, 1, 3, 1, 1, 7, 1, 3, 1, 1, 3, 1, 2, 3, 1, 1, 
    2, 1, 4, 6, 1, 2, 6, 12, 5, 73, 2, 7, 1, 131, 22, 5, 14, 
    69, 8, 1, 4, 72, 2, 3, 1, 6, 21, 20, 72, 17, 7, 1, 15, 218, 
    148, 278, 159, 30, 2, 373, 1, 1, 3, 5, 1, 3, 1, 1, 1, 1, 
    1, 5, 1, 1, 2, 2, 2, 2, 1, 2, 12, 1, 1, 2, 1, 1, 1, 2, 1, 
    144, 14, 1, 4, 54, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 5, 1, 
    1, 5, 1, 10, 156, 2, 9, 2, 19, 34, 179, 1, 2, 4, 447, 1, 
    4, 2, 321, 1, 1, 5, 163, 3, 4, 318, 6, 2, 9, 162, 2, 59, 
    1, 5, 414, 1, 4, 3, 312, 7, 701, 2, 2, 2, 147, 1, 4, 30, 
    137, 28, 1, 14, 1, 1, 1, 451), transactions = c(17, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 
    0, 2, 0, 0, 0, 29, 0, 0, 3, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 3), transaction_revenue = c(44183, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1115.52, 0, 0, 1375.67, 
    0, 0, 0, 0, 4198, 0, 0, 0, 59269, 0, 0, 6147, 0, 0, 0, 0, 
    0, 0, 1999, 0, 599, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 2599, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 4199, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2599, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7397), bounces = c(1278, 
    4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 1, 6, 1, 
    23, 1, 3294, 2333, 2, 1, 3, 4, 12, 0, 32, 1, 1, 3, 1, 1, 
    29, 7, 1, 66, 49, 0, 1, 3, 1, 1, 5, 1, 0, 0, 1, 2, 1, 2, 
    3, 0, 1, 2, 1, 4, 6, 0, 1, 4, 9, 5, 61, 2, 6, 1, 115, 20, 
    4, 12, 63, 7, 1, 4, 62, 2, 2, 1, 5, 17, 16, 59, 17, 6, 1, 
    10, 194, 132, 235, 136, 27, 2, 274, 1, 1, 3, 4, 1, 3, 1, 
    1, 0, 1, 1, 3, 1, 1, 1, 2, 1, 2, 0, 2, 7, 0, 1, 1, 1, 1, 
    1, 1, 1, 129, 12, 1, 4, 49, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 
    0, 5, 1, 1, 2, 1, 3, 133, 2, 5, 2, 16, 27, 150, 0, 1, 4, 
    377, 1, 4, 1, 254, 1, 1, 3, 120, 3, 3, 248, 6, 1, 9, 123, 
    2, 47, 1, 4, 340, 1, 4, 3, 244, 7, 557, 2, 2, 2, 117, 1, 
    4, 23, 100, 22, 1, 12, 0, 1, 0, 325)), totals = list(list(
    sessions = "89600", transactions = "247", transactionRevenue = "547126.09", 
    bounces = "73354")), minimums = list(list(sessions = "1", 
    transactions = "0", transactionRevenue = "0.0", bounces = "0")), maximums = list(
    list(sessions = "8355", transactions = "65", transactionRevenue = "145010.41", 
        bounces = "7614")), isDataGolden = TRUE, rowCount = 737L, row.names = c(NA, 
200L), class = "data.frame")

会话信息:

> sessionInfo()
R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)

Matrix products: default

locale:
[1] LC_COLLATE=Spanish_Peru.utf8  LC_CTYPE=Spanish_Peru.utf8    LC_MONETARY=Spanish_Peru.utf8
[4] LC_NUMERIC=C                  LC_TIME=Spanish_Peru.utf8    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] openxlsx_4.2.5         janitor_2.1.0          forcats_0.5.1          stringr_1.4.0          dplyr_1.0.9           
 [6] purrr_0.3.4            readr_2.1.2            tidyr_1.2.0            tibble_3.1.7           ggplot2_3.3.6         
[11] tidyverse_1.3.1        googleAnalyticsR_1.0.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3              lubridate_1.8.0           assertthat_0.2.1          digest_0.6.29            
 [5] utf8_1.2.2                R6_2.5.1                  cellranger_1.1.0          backports_1.4.1          
 [9] reprex_2.0.1              httr_1.4.3                pillar_1.7.0              rlang_1.0.3              
[13] curl_4.3.2                readxl_1.4.0              rstudioapi_0.13           whisker_0.4              
[17] googleAuthR_2.0.0         munsell_0.5.0             broom_1.0.0               compiler_4.2.1           
[21] modelr_0.1.8              askpass_1.1               pkgconfig_2.0.3           openssl_2.0.2            
[25] tidyselect_1.1.2          fansi_1.0.3               crayon_1.5.1              tzdb_0.3.0               
[29] dbplyr_2.2.1              withr_2.5.0               rappdirs_0.3.3            grid_4.2.1               
[33] jsonlite_1.8.0            gtable_0.3.0              lifecycle_1.0.1           DBI_1.1.3                
[37] magrittr_2.0.3            scales_1.2.0              zip_2.2.0                 cli_3.3.0                
[41] stringi_1.7.6             cachem_1.0.6              fs_1.5.2                  snakecase_0.11.0         
[45] xml2_1.3.3                ellipsis_0.3.2            generics_0.1.3            vctrs_0.4.1              
[49] tools_4.2.1               measurementProtocol_0.1.0 glue_1.6.2                hms_1.1.1                
[53] fastmap_1.1.0             colorspace_2.0-3          gargle_1.2.0              rvest_1.0.2              
[57] memoise_2.0.1             haven_2.5.0               usethis_2.1.6

2 回复 | 直到 2 年前

duncanw 2 年前

正如里奇的评论所表明的那样,这是因为 bounces/sessions 返回向量。

bounceRate 总结之后,我想你会得到你想要的。

paid <- df%>%
  group_by(date) %>%
  summarise(sessions = sum(sessions),
            revenue_usd = sum(transaction_revenue)/3.8,
            bounces = sum(bounces),
            transactions = sum(transactions)) %>% 
  mutate(bounceRate = bounces/sessions)

Soccerama 2 年前

与duncanw几乎相同,但有一个更简单的解决方案,它提供了与原始列相同的输出列:

    paid <- df%>%
      group_by(transaction_date) %>% 
      summarise(sessions=sum(sessions),
          revenue_usd= sum(transaction_revenue)/3.8,
          bounceRate=sum(bounces)/sum(sessions),
          transactions = sum(transactions))

Anup Tirpude 2 年前

我从我这边测试了一下,结果和你的一样。我很惊讶,并开始研究它。这就是我发现的,

没有任何聚合功能,它只是一个简单的划分,应该在行级别发生/执行,并且由于没有提供用于汇总的函数,因此结果保留在行级别。

paid <- df%>%
group_by(date) %>%
summarise(sessions = sum(sessions),
        revenue_usd = sum(transaction_revenue)/3.8,
        bounceRate = bounces/sessions,
        transactions = sum(transactions))

此代码的结果是所有行保持不变,如下所示:

paid <- df%>%
group_by(date) %>%
summarise(sessions = sum(sessions),
        revenue_usd = sum(transaction_revenue)/3.8,
        # bounceRate = bounces/sessions,
        transactions = sum(transactions))

所以在我看来,你可以在这个比率上使用任何一个和,如果你觉得它不正确,你可以先总结,然后取这个比率。我的建议是在总结后使用第二种方法。我把这两个的代码都放在下面,

   paid <- df%>%
   group_by(date) %>%
   summarise(sessions = sum(sessions),
        revenue_usd = sum(transaction_revenue)/3.8,
        bounceRate = sum(bounces/sessions),
        transactions = sum(transactions))

这会给你结果

方法02:

paid <- df%>%
group_by(date) %>%
summarise(sessions = sum(sessions),
        revenue_usd = sum(transaction_revenue)/3.8,
        transactions = sum(transactions),
        total_bounces = sum(bounces),
        total_sessions = sum(sessions)) %>%
mutate(bounceRate = total_bounces/total_sessions)

这将导致以下结果,我认为这更合适。

如果这能解决你的问题。快乐的编码!