合併數據

Question

我有以下數據：

Data <- data.frame(Project=c(123,123,123,123,123,123,124,124,124,124,124,125,125,125,126,126), 
        Value=c(1,4,7,3,8,9,8,3,2,5,6,2,2,1,8,3), 
        OldValue=c("","Open","In Progress","Complete","Open","In Progress","Complete","Open","In Progress","System Declined","In Progress","","Open","In Progress","In Progress",""), 
        NewValue=c("Open","In Progress","Complete","Open","In Progress","Complete","Open","In Progress","System Declined","In Progress","Complete","Open","In Progress","Complete","","In Progress")) 

Data$First <- ifelse(((Data$OldValue==""|Data$OldValue=="Complete"|Data$OldValue=="System Declined")&Data$NewValue=="Open"),Data$Value,NA) 
Data$Second <- ifelse(((Data$OldValue=="Open"|Data$OldValue=="Complete"|Data$OldValue=="System Declined")&Data$NewValue=="In Progress"),Data$Value,NA) 
Data$Third <- ifelse(((Data$NewValue=="Complete"|Data$NewValue=="System Declined")&Data$OldValue=="In Progress"),Data$Value,NA) 

對於每個唯一的項目ID，我想將第一，第二&第三值組合成一排。我只是想這樣做，如果在的NewValue列中的值遵循兩種下面的序列中的：

公開賽正在進行，完成 或 打開，在進步，系統拒絕

所以項目123將有兩個數據行以及項目125將會有一個。第10行和第11行將被排除，因爲它不符合上述順序

對此進行編碼的最簡單方法是什麼？

Answer 1

使用dplyr一個解決方案：

library(dplyr) 
Data %>% group_by(Project) %>% 
    mutate(
     fl = as.numeric(NewValue), 
     flag = paste(lag(fl, 2, default = 0), 
        lag(fl, 1, default = 0), 
        fl, sep = ''), 
     merge = paste(lag(Value, 2, default = 0), 
         lag(Value, 1, default = 0), 
         Value, sep = ',') 
    ) %>% 
    filter(flag == '321' | flag == '324') %>% 
    select(Project, merge) 

# Project merge 
#  <dbl> <chr> 
# 1  123 1,4,7 
# 2  123 3,8,9 
# 3  124 8,3,2 
# 4  125 2,2,1 

Answer 2

這將是實現目標的一種方式。我想創建一個數字序列，它可以表示您指定的兩種模式（即，開放式進度完成和開放式進度系統已拒絕）。出於這個原因，我使用fct_collapse()將因子水平摺疊爲三。然後，我將新的因子水平轉換爲數字。然後，我想在每個Project內創建一個子組，我在第二個mutate()中完成了子組。接下來的任務是更改First,Second和Third中元素的順序。你想在一行中有數字。所以我用sort()。有一個條件適用於此操作，即identical(check[1:3], as.numeric(1:3))。如果您有兩種模式中的任何一種，則應該預期在check中會有一個1，2，3的序列。您對每個組使用此邏輯檢查。只要符合該邏輯條件，sort()就應用於由Project和group定義的每個組中的三列。最後，我刪除了check和group，我用這個整個操作。

library(dplyr) 
library(forcats) 

Data %>% 
mutate(check = as.numeric(
        as.character(fct_collapse(NewValue, 
              `1` = "Open", 
              `2` = "In Progress", 
              `3` = c("Complete", "System Declined"))))) %>% 
group_by(Project) %>% 
mutate(group = cumsum(c(TRUE, diff(check) != 1))) %>% 
group_by(Project, group) %>% 
mutate_at(vars(First:Third), 
      funs(if(identical(check[1:3], as.numeric(1:3))){ 
       sort(., na.last = TRUE)} else{.} 
     )) %>% 
select(-check, -group) 

# group Project Value  OldValue  NewValue First Second Third 
# <int> <dbl> <dbl>   <fctr>   <fctr> <dbl> <dbl> <dbl> 
#1  1  123  1       Open  1  4  7 
#2  1  123  4   Open  In Progress NA  NA NA 
#3  1  123  7  In Progress  Complete NA  NA NA 
#4  2  123  3  Complete   Open  3  8  9 
#5  2  123  8   Open  In Progress NA  NA NA 
#6  2  123  9  In Progress  Complete NA  NA NA 
#7  1  124  8  Complete   Open  8  3  2 
#8  1  124  3   Open  In Progress NA  NA NA 
#9  1  124  2  In Progress System Declined NA  NA NA 
#10  2  124  5 System Declined  In Progress NA  5 NA 
#11  2  124  6  In Progress  Complete NA  NA  6 
#12  1  125  2       Open  2  2  1 
#13  1  125  2   Open  In Progress NA  NA NA 
#14  1  125  1  In Progress  Complete NA  NA NA