问题描述
我有一个数据框结构，该结构可以计算每月使用此mutate函数找到的 Response.Status 的总和：
Private Declare PtrSafe Function KillTimer Lib "user32" (ByVal hwnd As LongPtr,_
ByVal nIDEvent As LongPtr) As Long
Private Declare PtrSafe Function SetTimer Lib "user32" (ByVal hwnd As LongPtr,_
ByVal nIDEvent As LongPtr,_
ByVal uElapse As Long,_
ByVal lpTimerFunc As LongPtr) As LongPtr
Private Declare PtrSafe Sub Sleep Lib "kernel32" (ByVal dwMilliseconds As Long)
' Delete the specified sub folder from a Zip file.
' Example:
' Call DeleteZipSubFolder("E:\first.zip\first\second")
Sub DeleteZipSubFolder(PathToZipFolder)
Dim oShell As Object ' Reference: Microsoft Shell Controls And Automation
On Error Resume Next
Set oShell = CreateObject("Shell.Application")
TimerID = SetTimer(0,100,AddressOf TimerProc)
oShell.NameSpace(PathToZipFolder).Self.InvokeVerb "Delete"
KillTimer 0,TimerID
Set oShell = nothing
On Error Goto 0
End Sub
Public Sub TimerProc(ByVal hwnd As LongPtr,_
ByVal wMsg As Long,_
ByVal idEvent As LongPtr,_
ByVal dwTime As Long)
' Wait for the Shell confirmation dialog to appear
Sleep 100
' Use the Alt+Y shortcut to click the Yes button
SendKeys "%Y"
End Sub
我想做的就是使用表中创建的那些值，根据每个Response.Status组中的人数计算平均值。
DF1 <- complete_df %>%
mutate(Month = format(as.Date(date,format = "%Y/%m/%d"),"%m/%Y"),UNSUBSCRIBE = if_else(UNSUBSCRIBE == "TRUE","UNSUBSCRIBE",NA_character_)) %>%
pivot_longer(c(Response.Status,UNSUBSCRIBE),values_to = "Response.Status") %>%
drop_na() %>%
count(Month,Response.Status) %>%
pivot_wider(names_from = Month,names_sep = "/",values_from = n)
# A tibble: 7 x 16
Response.Status `01/2020` `02/2020` `03/2020` `04/2020` `05/2020` `06/2020` `07/2020` `08/2020` `09/2019` `09/2020` `10/2019` `10/2020` `11/2019` `11/2020` `12/2019`
<chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 EMAIL_OPENED 1068 3105 4063 4976 2079 1856 4249 3638 882 4140 865 2573 1167 684 862
2 NOT_RESPONDED 3187 9715 13164 15239 5458 4773 12679 10709 2798 15066 2814 8068 3641 1931 2647
3 PARTIALLY_SAVED 5 34 56 8 28 22 73 86 11 14 7 23 8 8 2
4 SUBMITTED 216 557 838 828 357 310 654 621 214 1001 233 497 264 122 194
5 SURVEY_OPENED 164 395 597 1016 245 212 513 625 110 588 123 349 202 94 120
6 UNDELIVERED_OR_BOUNCED 92 280 318 260 109 127 319 321 63 445 69 192 93 39 74
7 UNSUBSCRIBE 397 1011 1472 1568 727 737 1745 2189 372 1451 378 941 429 254 355
我制作了一个单独的表，其中包含基于这些组名的总和值：
structure(list(Response.Status = c("EMAIL_OPENED","NOT_RESPONDED","PARTIALLY_SAVED","SUBMITTED","SURVEY_OPENED","UNDELIVERED_OR_BOUNCED"
),`01/2020` = c(1068L,3187L,5L,216L,164L,92L),`02/2020` = c(3105L,9715L,34L,557L,395L,280L),`03/2020` = c(4063L,13164L,56L,838L,597L,318L),`04/2020` = c(4976L,15239L,8L,828L,1016L,260L),`05/2020` = c(2079L,5458L,28L,357L,245L,109L),`06/2020` = c(1856L,4773L,22L,310L,212L,127L),`07/2020` = c(4249L,12679L,73L,654L,513L,319L),`08/2020` = c(3638L,10709L,86L,621L,625L,321L),`09/2019` = c(882L,2798L,11L,214L,110L,63L),`09/2020` = c(4140L,15066L,14L,1001L,588L,445L),`10/2019` = c(865L,2814L,7L,233L,123L,69L),`10/2020` = c(2573L,8068L,23L,497L,349L,192L),`11/2019` = c(1167L,3641L,264L,202L,93L),`11/2020` = c(684L,1931L,122L,94L,39L),`12/2019` = c(862L,2647L,2L,194L,120L,74L)),row.names = c(NA,-6L),class = c("tbl_df","tbl","data.frame"))
解决方法
如果我正确理解了您的问题，则您有2个data.frame / tibble。在“结构”部分中显示的一个通知每个响应状态的人数。现在，您想获得每人的价值。如果是这样，这是可能的解决方案：
# people/users data set
df2 <- data.frame(Response.Status = c("EMAIL_OPENED","NOT_RESPONDED","PARTIALLY_SAVED","SUBMITTED","SURVEY_OPENED","UNDELIVERED_OR_BOUNCED","UNSUBSCRIBE"),PEOPLE = c(451,1563,4,71,53,47,135))
df %>% # this is your "structure"
tidyr::pivot_longer(-Response.Status,names_to = "DATE",values_to = "nmbr") %>%
dplyr::group_by(Response.Status) %>%
dplyr::summarise(SUM = sum(nmbr)) %>%
dplyr::inner_join(df2) %>%
dplyr::mutate(MEAN_PP = SUM / PEOPLE)
Response.Status SUM PEOPLE MEAN_PP
<chr> <int> <dbl> <dbl>
1 EMAIL_OPENED 36207 451 80.3
2 NOT_RESPONDED 111889 1563 71.6
3 PARTIALLY_SAVED 385 4 96.2
4 SUBMITTED 6906 71 97.3
5 SURVEY_OPENED 5353 53 101
6 UNDELIVERED_OR_BOUNCED 2801 47 59.6