Question

I have a dataframe filled with course IDs, student IDs, week numbers (1 for the first week, 2 for the second, …), and some information about what each user did in each course on each week. The final two columns of the df are non-NA if an instructor ‘intervened’ with the student in that course in that week, and NA otherwise. I want to compare each student’s behavior before to after the week of their first intervention.

So what I’d like to make is a column, ‘HasIntervened’, which is FALSE for weeks less than that of the student’s first intervention and TRUE for weeks greater than or equal, but I’m having a hell of a time creating that simple column. I’m fairly certain that aggregate is going to be the way to go, but I’m just not thinking about the problem in the right way.

Here is the dput of the first 60 rows (5 students’ worth) of the dataframe:

structure(list(UserID = c(4188948L, 4188948L, 4188948L, 4188948L, 
4188948L, 4188948L, 4735684L, 4735684L, 4735684L, 4735684L, 4735684L, 
4735684L, 6292486L, 6292486L, 6292486L, 6292486L, 6292486L, 6292486L, 
6469671L, 6469671L, 6469671L, 6469671L, 6469671L, 6469671L, 6538263L, 
6538263L, 6538263L, 6538263L, 6538263L, 6538263L, 6621258L, 6621258L, 
6621258L, 6621258L, 6621258L, 6621258L, 6891869L, 6891869L, 6891869L, 
6891869L, 6891869L, 6891869L, 6891869L, 6891869L, 6891869L, 6891869L, 
6891869L, 6891869L, 6978155L, 6978155L, 6978155L, 6978155L, 6978155L, 
6978155L, 7195846L, 7195846L, 7195846L, 7195846L, 7195846L, 7195846L
), CourseID = c(6567871L, 6567871L, 6567871L, 6567871L, 6567871L, 
6567871L, 6567168L, 6567168L, 6567168L, 6567168L, 6567168L, 6567168L, 
6567864L, 6567864L, 6567864L, 6567864L, 6567864L, 6567864L, 6567159L, 
6567159L, 6567159L, 6567159L, 6567159L, 6567159L, 6567162L, 6567162L, 
6567162L, 6567162L, 6567162L, 6567162L, 6567853L, 6567853L, 6567853L, 
6567853L, 6567853L, 6567853L, 6567159L, 6567159L, 6567159L, 6567159L, 
6567159L, 6567159L, 6567864L, 6567864L, 6567864L, 6567864L, 6567864L, 
6567864L, 6567873L, 6567873L, 6567873L, 6567873L, 6567873L, 6567873L, 
6567859L, 6567859L, 6567859L, 6567859L, 6567859L, 6567859L), 
WeekInCourse = c(1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 
3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 
4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 
5, 6, 1, 2, 3, 4, 5, 6), WeekPostCount = c(1L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5L, 3L, 4L, 3L, 3L, 0L, 4L, 
0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 2L, 
2L, 0L, 0L, 4L, 0L, 3L, 0L, 3L, 0L, 0L, 0L), WeekLoginCount = c(2L, 
1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 4L, 4L, 1L, 0L, 
0L, 0L, 3L, 3L, 1L, 0L, 0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 1L, 
1L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 4L, 1L, 0L, 0L, 
0L, 0L, 3L, 3L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L), 
WeekPointsPercent = c(0, 0, 0, 0, 0, 0, 0, 0.185714285714286, 
0.375, 0.2, 0, 0, 0, 0.85, 0.7, 0.4, 0.7, 0.7, 0, 0.857142857142857, 
0.35, 0, 0, 0.712765957446808, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0, 0, 0.5, 0.5, 
0, 0, 0.7, 1, 1, 0.375, 0.723076923076923, 0, 0.738636363636364
), CumulativePointsPercent = c(0, 0, 0, 0, 0, 0, 0, 0.185714285714286, 
0.254545454545455, 0.235294117647059, 0.235294117647059, 
0.10958904109589, 0, 0.85, 0.8, 0.533333333333333, 0.55, 
0.563636363636364, 0, 0.857142857142857, 0.623076923076923, 
0.476470588235294, 0.476470588235294, 0.600558659217877, 
0, 1, 0.0666666666666667, 0.0666666666666667, 0.0461538461538462, 
0.0461538461538462, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0.25, 0.166666666666667, 0.0555555555555556, 0.05, 0.0454545454545455, 
0, 0.5, 0.5, 0.166666666666667, 0.15, 0.2, 1, 1, 0.615384615384615, 
0.669230769230769, 0.621428571428571, 0.666666666666667), 
RiskEstimate = c(0.627717786405816, 0.986868933315635, 0.986687587608184, 
0.993909863003438, 0.997123961252086, 0.995862152216296, 
0.914011371723269, 0.925359536086114, 0.902625588346349, 
0.956922151061089, 0.977244888475535, 0.975006380719003, 
0.215420992232115, 0.174623555825523, 0.241380495376484, 
0.699712463799006, 0.692014530298594, 0.697966901130338, 
0.765071150059092, 0.763071307309743, 0.767261726128078, 
0.835918063362269, 0.854949153314029, 0.805318343915736, 
0.792873572656207, 0.790581615380765, 0.82622599277251, 0.9330287497742, 
0.965763061363497, 0.951226314109191, 0.851355921713566, 
0.991081300877175, 0.989671569185701, 0.995402298000919, 
0.997671718747865, 0.996593366142757, 0.738690043138604, 
0.865412845144037, 0.831369850200541, 0.93845410260835, 0.968400480533385, 
0.9533338828382, 0.624930735381371, 0.981915016747928, 0.985037736895337, 
0.994680902796769, 0.996907588471311, 0.995388109404559, 
0.887995464972052, 0.970620002831325, 0.97136665697772, 0.992618626388727, 
0.99543249839328, 0.992149889176406, 0.923802324633255, 0.984464950934932, 
0.978726967214146, 0.971473084822075, 0.97886220009245, 0.979311013989987
), RiskBin = c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L), InterventionID = c(NA, 26L, NA, NA, NA, 
NA, NA, NA, NA, NA, 50L, NA, NA, NA, NA, NA, 73L, NA, NA, 
NA, NA, NA, 56L, NA, NA, NA, NA, 46L, NA, NA, NA, 33L, NA, 
NA, NA, NA, 15L, NA, NA, 43L, 53L, NA, NA, NA, NA, NA, 71L, 
NA, NA, NA, NA, NA, 78L, NA, NA, 36L, NA, NA, 80L, NA), InterventionType = structure(c(NA, 
2L, NA, NA, NA, NA, NA, NA, NA, NA, 3L, NA, NA, NA, NA, NA, 
2L, NA, NA, NA, NA, NA, 3L, NA, NA, NA, NA, 3L, NA, NA, NA, 
2L, NA, NA, NA, NA, 3L, NA, NA, 3L, 2L, NA, NA, NA, NA, NA, 
2L, NA, NA, NA, NA, NA, 2L, NA, NA, 3L, NA, NA, 3L, NA), .Label = c("", 
"At-Risk Form", "Email", "Other", "Phone"), class = "factor")), .Names = c("UserID", 
"CourseID", "WeekInCourse", "WeekPostCount", "WeekLoginCount", 
"WeekPointsPercent", "CumulativePointsPercent", "RiskEstimate", 
"RiskBin", "InterventionID", "InterventionType"), row.names = c(NA, 
60L), class = "data.frame")

Answer 1

courses$HasIntervened <- as.logical( with(courses, ave(InterventionID,
                                                 UserID, CourseID,  # grouping factors
                                           FUN=function(x) cumsum( !is.na(x) ) ) ) )