Read and Wrangle Your Data

🚀 Read Workflow

1. Export your survey responses from Qualtrics

When exporting from Qualtrics:

Click “Download Data”.
Choose CSV format.
Critically, select “Use choice text” rather than coded values.

⚡ If you skip selecting “Use choice text,” your conjoint data may fail to load properly!

2. Load essential packages

library(tidyverse)
library(projoint)

3. Read your CSV file into R using read_Qualtrics()

# Example: If your file is located in a "data" folder
data <- read_Qualtrics("data/your_file.csv")

Or, if using an example bundled with projoint:

# Inspect the imported data:
data

## # A tibble: 518 × 218
##    StartDate           EndDate             Status     Progress
##    <dttm>              <dttm>              <chr>         <dbl>
##  1 2022-03-01 10:44:18 2022-03-01 10:44:43 IP Address      100
##  2 2022-03-01 10:44:06 2022-03-01 10:47:59 IP Address      100
##  3 2022-03-01 10:45:30 2022-03-01 10:49:03 IP Address      100
##  4 2022-03-01 10:52:18 2022-03-01 10:56:29 IP Address      100
##  5 2022-03-01 10:54:34 2022-03-01 10:57:30 IP Address      100
##  6 2022-03-01 10:56:51 2022-03-01 10:58:06 IP Address      100
##  7 2022-03-01 10:58:09 2022-03-01 11:00:45 IP Address      100
##  8 2022-03-01 11:01:43 2022-03-01 11:01:51 IP Address      100
##  9 2022-03-01 10:58:35 2022-03-01 11:03:44 IP Address      100
## 10 2022-03-01 11:00:14 2022-03-01 11:04:37 IP Address      100
## # ℹ 508 more rows
## # ℹ 214 more variables: `Duration (in seconds)` <dbl>, Finished <lgl>,
## #   RecordedDate <dttm>, ResponseId <chr>, DistributionChannel <chr>,
## #   UserLanguage <chr>, Q_RecaptchaScore <dbl>, Q1.2 <chr>, Q2.2 <chr>,
## #   Q2.3 <chr>, Q2.4 <chr>, Q2.5 <chr>, Q2.6 <chr>, Q2.7 <chr>, Q2.8 <chr>,
## #   Q2.9 <chr>, Q3.1 <chr>, Q4.2 <chr>, Q4.3 <chr>, Q4.4 <chr>, Q4.5 <chr>,
## #   Q4.6 <chr>, Q4.7 <chr>, Q4.8 <chr>, Q4.9 <chr>, Q5.1 <chr>, Q6.1 <chr>, …

🚀 Wrangle Workflow

1. Reshape Your Data

Outcome naming & order (important)

List .outcomes in 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_labels accordingly.

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

outcomes <- paste0("choice", 1:8)
out3 <- reshape_projoint(
  .dataframe = exampleData3,
  .outcomes = outcomes,
  .repeated = FALSE
)

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 × 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
## # ℹ 6,390 more rows

fill_TRUE$data[selected_vars]

## # A tibble: 6,400 × 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
## # ℹ 6,390 more rows

Tip:
- 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:

out4

## <projoint_data>
## - data:     6400 rows, 13 columns
## - labels:   24 levels, 4 columns

5. Arranging Attribute and Level Labels

To reorder or relabel attributes:

Save labels:

save_labels(out1, "temp/labels_original.csv")

Edit the CSV (change order, label columns; leave level_id untouched)
Save it as “labels_arranged.csv” or something else.
Reload labels:

data(out1_arranged, package = "projoint")

Compare using our example:

mm <- projoint(out1, .structure = "profile_level", .estimand = "mm")
plot(mm)

mm <- projoint(out1_arranged, .structure = "profile_level", .estimand = "mm")
plot(mm)

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Read and Wrangle Your Data

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🚀 Read Workflow

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🚀 Wrangle Workflow

Example (Flipped Repeated Task)