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R 使用 case_when 按组跟踪列中的更改

[英]R Using case_when to track changes in a column by group

 原文  2020-09-25 16:51:00  4   1    r/ dataframe/ if-statement/ dplyr/ case-when

问题描述

I have a dataset of course enrollment where I am trying to track whether students dropped, added, or retained a course throughout the semester and identify their enrollment 'path'.我有一个课程注册数据集，我试图在其中跟踪学生在整个学期中是否放弃、添加或保留一门课程，并确定他们的注册“路径”。 Ie I want to record if they were enrolled in BIOL101 and dropped it to take BIOL202.即我想记录他们是否注册了 BIOL101 并放弃了它以参加 BIOL202。 My dataframe looks like this:我的数据框如下所示：

YRTR    TECH_ID COU_ID  SUBJ    COU_NBR GENDER  RACE    sub_cou     status     path
20173   108      217    MUSC    2231    Male    White   MUSC 2231   retained
20173   108      218    MUSC    2281    Male    White   MUSC 2281   retained
20173   8429     574    ECON    2201    Male    White   ECON 2201   retained
20173   8429     720    BUSN    2120    Male    White   BUSN 2120   retained
20173   9883     60     ECON    2202    Male    White   ECON 2202   added
20173   15515    95     PHIL    1102    Female  White   PHIL 1102   retained
20183   8207     478    ART     1102    Female  White   ART 1102    retained
20183   8207     1306   ART     1130    Female  White   ART 1130    added
20183   8207     403    ART     1125    Female  White   ART 1125    dropped

I am trying to fill in the column on the far right, "path".我正在尝试填写最右侧的“路径”列。 The idea is that if a student is retained in a course like in the first row, the path would read 2231->2231 .这个想法是，如果学生被保留在第一行的课程中，则路径将显示为2231->2231 。 Specifically I am looking at course transfers WITHIN subjects.具体来说，我期待在WITHIN学科课程转移。 So, at the end of the data set, ID 8207 would have one path that looked like 1102->1102 and another path that looked like 1125->1130因此，在数据集的末尾，ID 8207 将有一条看起来像1102->1102路径和另一条看起来像1125->1130路径

I initially tried splitting the dataframe into two dataframes (one before, and one after the drop period) and then rejoining them like so:我最初尝试将数据帧拆分为两个数据帧（一个在丢弃期之前，一个在丢弃期之后），然后像这样重新加入它们：

data5 <- merge(x=post_drop, y=pre_drop, by=c("TECH_ID", "YRTR", "SUBJ"), all=TRUE)

And then using case_when to assign the path:然后使用 case_when 分配路径：

data5$status.x=="retained" ~ paste0(data5$COU_NBR.x, "->", data5$COU_NBR.x),
((data5$status.x=="added") & (data5$status.y=="dropped")) ~ paste0(data5$COU_NBR.y, "->", data5$COU_NBR.x),
((data5$status.x=="dropped") & (data5$status.y=="added")) ~ paste0(data5$COU_NBR.x, "->", data5$COU_NBR.y)                
)

But this doesn't get me where I want - it leaves a lot of NAs in paths and also doesn't tell me if a student dropped a course within a subject and didn't register for another (ie dropping BIOL101 and not taking another BIOL class) in which case I would want something like 101->NA or when a class is simply added (ie they weren't registered in a BIOL class initially but decided to register for BIOL101) which would be formatted like so NA->101但这并没有让我到达我想要的地方 - 它在路径中留下了很多 NA，并且也没有告诉我学生是否放弃了一个学科的课程并且没有注册另一个（即放弃 BIOL101 而不是参加另一个BIOL 类）在这种情况下，我想要像101->NA这样的东西，或者当一个类被简单地添加时（即他们最初没有在 BIOL 类中注册，但决定注册 BIOL101），其格式将像这样NA->101

1 个解决方案

解决方案1
 1 已采纳  2020-09-25 19:43:24

EDITED SOLUTION Spetember 27th已编辑解决方案 2 月 27 日

Hello again @alexvc Here's a start.你好@alexvc 这是一个开始。 Knowing a little bit about your data.对你的数据有一点了解。 You forget the case where a student dropped 1 and added 2 in which case the "path" becomes muddled.您忘记了学生删除 1 并添加 2 的情况，在这种情况下，“路径”变得混乱。 I've given you a solution that shows path clearly.我给了你一个清晰显示path的解决方案。

library(dplyr)
library(tidyr)

data5 %>%
   group_by(YRTR, TECH_ID, SUBJ, status) %>%
   mutate(numbadd =
             case_when(
                status == "added" ~ 1,
                TRUE ~ 0
             ),
          numbdrop =
             case_when(
                status == "dropped" ~ 1,
                       TRUE ~ 0
            ),
          rightside =
             case_when(
                numbadd == 1 ~ paste(COU_NBR, collapse = " and ")
                ),
          leftside =
             case_when(
                numbdrop == 1 ~ paste(COU_NBR, collapse = " and ")
             )
   ) %>%
   group_by(YRTR, TECH_ID, SUBJ) %>%
   mutate(total_add_drop = ifelse(status == "retained", 
                                  0, 
                                  sum(numbadd) + sum(numbdrop))) %>%
   tidyr::fill(leftside, rightside, .direction = "downup") %>%
   group_by(YRTR, TECH_ID, SUBJ, status) %>%
   mutate(PATH =
             case_when(
                status == "retained" ~ paste(COU_NBR, 
                                             COU_NBR, 
                                             sep = " -> "),
                status == "added" & total_add_drop == 1 ~ paste("NA", 
                                                                COU_NBR, 
                                                                sep = " -> "),
                status == "dropped" & total_add_drop == 1 ~ paste(COU_NBR, 
                                                                  "NA", 
                                                                  sep = " -> "),
                total_add_drop >= 2 ~ paste(leftside, 
                                            rightside, 
                                            sep = " -> "),
                TRUE ~ "Theres a problem"
             )) %>%
   arrange(YRTR, TECH_ID) %>%
   select(-COU_ID, -GENDER, -RACE, -rightside, -leftside, -numbadd, -numbdrop, -total_add_drop)

#> # A tibble: 17 x 7
#> # Groups:   YRTR, TECH_ID, SUBJ, status [13]
#>     YRTR TECH_ID SUBJ  COU_NBR sub_cou   status   PATH                          
#>    <dbl>   <dbl> <chr>   <dbl> <chr>     <chr>    <chr>                         
#>  1 20173     108 MUSC     2231 MUSC 2231 retained 2231 -> 2231                  
#>  2 20173     108 MUSC     2281 MUSC 2281 retained 2281 -> 2281                  
#>  3 20173    3889 ECON     2202 ECON 2202 dropped  2202 -> NA                    
#>  4 20173    8429 ECON     2201 ECON 2201 retained 2201 -> 2201                  
#>  5 20173    8429 BUSN     2120 BUSN 2120 retained 2120 -> 2120                  
#>  6 20173    9883 ECON     2202 ECON 2202 added    NA -> 2202                    
#>  7 20173   15515 PHIL     1102 PHIL 1102 retained 1102 -> 1102                  
#>  8 20183    8207 ART      1102 ART 1102  retained 1102 -> 1102                  
#>  9 20183    8207 ART      1130 ART 1130  added    1125 -> 1130 and 2345         
#> 10 20183    8207 ART      2345 ART 2345  added    1125 -> 1130 and 2345         
#> 11 20183    8207 ART      1125 ART 1125  dropped  1125 -> 1130 and 2345         
#> 12 20183    8209 ART      2345 ART 2345  added    1125 -> 2345                  
#> 13 20183    8209 ART      1125 ART 1125  dropped  1125 -> 2345                  
#> 14 20183    8270 PSYC     1001 PSYC 1001 dropped  1001 and 1002 -> 1003 and 1004
#> 15 20183    8270 PSYC     1003 PSYC 1003 added    1001 and 1002 -> 1003 and 1004
#> 16 20183    8270 PSYC     1002 PSYC 1002 dropped  1001 and 1002 -> 1003 and 1004
#> 17 20183    8270 PSYC     1004 PSYC 1004 added    1001 and 1002 -> 1003 and 1004

Your data with additional test cases您的数据与其他测试用例

data5 <- readr::read_table(
   "   YRTR    TECH_ID COU_ID  SUBJ    COU_NBR GENDER  RACE    sub_cou     status
   20173   108      217    MUSC    2231    Male    White   MUSC 2231   retained
   20173   108      218    MUSC    2281    Male    White   MUSC 2281   retained
   20173   8429     574    ECON    2201    Male    White   ECON 2201   retained
   20173   8429     720    BUSN    2120    Male    White   BUSN 2120   retained
   20173   9883     60     ECON    2202    Male    White   ECON 2202   added
   20173   3889     60     ECON    2202    Male    White   ECON 2202   dropped
   20173   15515    95     PHIL    1102    Female  White   PHIL 1102   retained
   20183   8207     478    ART     1102    Female  White   ART 1102    retained
   20183   8207     1306   ART     1130    Female  White   ART 1130    added
   20183   8207     1307   ART     2345    Female  White   ART 2345    added
   20183   8207     403    ART     1125    Female  White   ART 1125    dropped
   20183   8270     1306   PSYC    1001    Female  Black   PSYC 1001    dropped
   20183   8270     1307   PSYC    1003    Female  Black   PSYC 1003    added
   20183   8270     403    PSYC    1002    Female  Black   PSYC 1002    dropped
   20183   8209     1307   ART     2345    Female  White   ART 2345    added
   20183   8270     1306   PSYC    1004    Female  Black   PSYC 1004    added
   20183   8209     403    ART     1125    Female  White   ART 1125    dropped")

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