R两个数据表上的变异和子集数据

Question

我想获得ECDF的所有项目类似（在整个数据表中）到每行中的项目编号，并将ECDF列添加到数据表的末尾（EstimatePrediction）。

这适用于单个项目，因此它们可以逐个检查。

#Set Current ItemNumber 
    currentItemNumber = “XXXXX” 
    #Set Estimate Days 
    currentEstimate = 5 
    #Gets the index of the ItemNumber from the Matches table 
    itemNoIndex = ((matches%>%subset(Item_No ==itemNumber))$ItemIndex[1]) 
    #Gets all the matching indexs that equal the index and select data 
    matchingItems = matches%>%filter(ItemIndex == itemNoIndex) %>% 
          filter(MatchItemIndex != ItemIndex) %>% 
          merge(data.filter %>% 
          select(ITEM_NO,ACTUAL_DAYS),by = 'ITEM_NO') 
    #Get the ECDF of all matching items at the estimate 
    ecdf(matchingItems $ACTUAL_DAYS)(currentEstimate) 

我想采取上面的R代码和修改为整个data.filter数据表工作。问题是它只适用于data.filter数据的第一行。第一行之后的行是基于第一行的数据，而不是它们自己的。

EstimatePrediction = data.filter %>% mutate(PROBABILITY_PREDICTION = ecdf((matches%>%subset(ItemIndex == ((matches%>%subset(Item_No== ITEM_NO))$ItemIndex[1])) %>% 
subset(MatchItemIndex != ItemIndex) %>% 
merge(data.filter, by = 'ITEM_NO'))$ACTUAL_DAYS)(ESTIMATE_DAYS)) 

我对R很新，所以我很乐意提供任何建议。我可以迭代data.filter来获得正确的输出，但速度非常慢。

样本数据

Matches 

MatchItemIndex ItemIndex MatchItemOrder Item_No Count Cumulative 
      <int>  <int>   <int> <chr> <int>  <int> 
1    1   1    1 CBL233J 14   14 
2    2   2    1 CGW112N  4   4 
3    3   3    1 CAT418D  5   5 
4    4   4    1 BRH131T 29   29 
5    5   5    1 CQD390A 17   17 
6    6   6    1 CEE533J 11   11 

    data.filter 

    ITEM_NO ESTIMATE_DAYS ACTUAL_DAYS 
1: CBL233J   10   6 
2: CGW112N   22   12 
3: CAT418D   22   18 
4: BRH131T   33   16 
5: CQD390A   21   15 
6: CEE533J    7   2 

编辑**** 我现在能得到的输出，我需要它只是很慢：

data.filter = data.filter%>%mutate(Index = 1:n()) 
loopData = data.filter%>%select(ITEM_NO, ACTUAL_DAYS, ESTIMATE_DAYS, Index) 
simpleV = unlist(loopData) 
outputTest = 1:nrow(loopData) 
ptm <- proc.time() 
for(i in 1:nrow(loopData)){ 

    #Get Index for Item Number 
    itemNoIndex = (matches%>%subset(ITEM_NO == simpleV[paste('ITEM_NO',i,sep="")]))$ItemIndex[1] 
    #Find all the matches that have the same index 
    allNNItemData = matches%>%subset(ItemIndex == itemNoIndex) %>% 
    subset(MatchItemIndex != ItemIndex) %>% 
    merge(data.filter, by = 'ITEM_NO') 

    outputTest[i] = ecdf(allNNItemData$ACTUAL_DAYS)(simpleV[paste('ESTIMATE_DAYS',i,sep="")]) 
} 
proc.time() - ptm 

Answer 1

看是否有此解决它：

library(tidyverse) 

#-- Declare objects 
Matches <- tibble(MatchItemIndex = 1:6, ItemIndex = 1:6, 
        MatchItemOrder = rep(1,6), Item_No = c("CBL233J", "CGW112N", 
                 "CAT418D", "BRH131T", 
                 "CQD390A", "CEE533J"), 
        Count = c(14,4,5,29,17,11), Cumulative = c(14,4,5,29,17,11)) 

data.filter <- tibble(ITEM_NO = c("CBL233J", "CGW112N", 
            "CAT418D", "BRH131T", 
            "CQD390A", "CEE533J"), 
         ESTIMATE_DAYS = c(10, 22, 22, 33, 21, 7), 
         ACTUAL_DAYS = c(6, 12, 18, 16, 15, 2)) 

#-- Get matching items by item no 
matchingItems <- intersect(Matches$Item_No, data.filter$ITEM_NO) 

#-- Filter data.filter to matching items 
df <- filter(data.filter, ITEM_NO == matchingItems) 

#-- Do analysis 
ecdf(df$ACTUAL_DAYS)(currentEstimate)