🚀 Wrangle Workflow
1. Reshape Your Data
Outcome naming & order (important)
- List
.outcomesin the order questions were asked.
- If you have a repeated task, its outcome must be the last element.
- For base tasks (all but last), the function reads the digits in each name as the task id (e.g.,
"choice4","Q4","task04"→ task 4).
- The repeated base task is inferred from the first base outcome’s digits. The repeated outcome itself need not contain digits—only its position (last) matters.
- Outcome strings should end with your choice labels; by default we parse the last character and expect
"A"/"B". If your survey uses"1"/"2"(or other endings), set.choice_labelsaccordingly.
Example (Flipped Repeated Task)
outcomes <- paste0("choice", 1:8)
outcomes1 <- c(outcomes, "choice1_repeated_flipped")
out1 <- reshape_projoint(
.dataframe = exampleData1,
.outcomes = outcomes1,
.choice_labels = c("A", "B"),
.alphabet = "K",
.idvar = "ResponseId",
.repeated = TRUE,
.flipped = TRUE
)Key Arguments:
.outcomes: Outcome columns (include repeated task last).choice_labels: Profile labels (e.g., “A”, “B”).idvar: Respondent ID variable.alphabet: Variable prefix (“K”).repeated,.flipped: If repeated task exists and is flipped
2. Variations: Repeated vs. Non-Repeated
Not-Flipped Repeated Task
outcomes <- paste0("choice", 1:8)
outcomes2 <- c(outcomes, "choice1_repeated_notflipped")
out2 <- reshape_projoint(
.dataframe = exampleData2,
.outcomes = outcomes2,
.repeated = TRUE,
.flipped = FALSE
)No Repeated Task
3. The .fill Argument: Should You Use It?
Use .fill = TRUE to “fill” missing values based on IRR
agreement.
fill_FALSE <- reshape_projoint(
.dataframe = exampleData1,
.outcomes = outcomes1,
.fill = FALSE
)
fill_TRUE <- reshape_projoint(
.dataframe = exampleData1,
.outcomes = outcomes1,
.fill = TRUE
)Compare:
selected_vars <- c("id", "task", "profile", "selected", "selected_repeated", "agree")
fill_FALSE$data[selected_vars]## # A tibble: 6,400 Ă— 6
## id task profile selected selected_repeated agree
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 R_00zYHdY1te1Qlrz 1 1 1 1 1
## 2 R_00zYHdY1te1Qlrz 1 2 0 0 1
## 3 R_00zYHdY1te1Qlrz 2 1 1 NA NA
## 4 R_00zYHdY1te1Qlrz 2 2 0 NA NA
## 5 R_00zYHdY1te1Qlrz 3 1 1 NA NA
## 6 R_00zYHdY1te1Qlrz 3 2 0 NA NA
## 7 R_00zYHdY1te1Qlrz 4 1 0 NA NA
## 8 R_00zYHdY1te1Qlrz 4 2 1 NA NA
## 9 R_00zYHdY1te1Qlrz 5 1 1 NA NA
## 10 R_00zYHdY1te1Qlrz 5 2 0 NA NA
## # â„ą 6,390 more rows## # A tibble: 6,400 Ă— 6
## id task profile selected selected_repeated agree
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 R_00zYHdY1te1Qlrz 1 1 1 1 1
## 2 R_00zYHdY1te1Qlrz 1 2 0 0 1
## 3 R_00zYHdY1te1Qlrz 2 1 1 NA 1
## 4 R_00zYHdY1te1Qlrz 2 2 0 NA 1
## 5 R_00zYHdY1te1Qlrz 3 1 1 NA 1
## 6 R_00zYHdY1te1Qlrz 3 2 0 NA 1
## 7 R_00zYHdY1te1Qlrz 4 1 0 NA 1
## 8 R_00zYHdY1te1Qlrz 4 2 1 NA 1
## 9 R_00zYHdY1te1Qlrz 5 1 1 NA 1
## 10 R_00zYHdY1te1Qlrz 5 2 0 NA 1
## # â„ą 6,390 more rowsTip:
- Use .fill = TRUE for small-sample or subgroup analysis
(helps increase power).
- Use .fill = FALSE (default) when in doubt for safer
estimates.
4. What If Your Data Is Already Clean?
If you already have a clean dataset, use
make_projoint_data():
out4 <- make_projoint_data(
.dataframe = exampleData1_labelled_tibble,
.attribute_vars = c(
"School Quality", "Violent Crime Rate (Vs National Rate)",
"Racial Composition", "Housing Cost",
"Presidential Vote (2020)", "Total Daily Driving Time for Commuting and Errands",
"Type of Place"
),
.id_var = "id",
.task_var = "task",
.profile_var = "profile",
.selected_var = "selected",
.selected_repeated_var = "selected_repeated",
.fill = TRUE
)Preview:
## <projoint_data>
## - data: 6400 rows, 13 columns
## - labels: 24 levels, 4 columns5. Arranging Attribute and Level Labels
To reorder or relabel attributes:
- Save labels:
Edit the CSV (change
order, label columns; leavelevel_iduntouched)Save it as “labels_arranged.csv” or something else.
Reload labels:
Compare using our example:
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