Read and Wrangle Your Data

In projoint: Conjoint Analysis with Reliability Correction and Visualization

library(projoint)

📥 Read Your Data

Before you can reshape or analyze your conjoint survey data, you first need to import it into R. In projoint, use the read_Qualtrics() function to quickly read properly formatted Qualtrics files.

---

🚀 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:

data <- read_Qualtrics(
  system.file("extdata", "mummolo_nall_replication.csv", package = "projoint")
)

# Inspect the imported data:
data

---

# Global default settings for all figures
knitr::opts_chunk$set(
  fig.width = 7,
  fig.height = 5,
  fig.align = "center",
  dpi = 300  # Optional: high-resolution plots
)

# Helper functions for special figure sizes
narrow_fig <- function() list(fig.width = 5, fig.height = 4)
wide_fig <- function() list(fig.width = 8, fig.height = 5)
tall_fig <- function() list(fig.width = 6, fig.height = 7)

# Load libraries
library(projoint)
data(exampleData1, package = "projoint")
data(exampleData2, package = "projoint")
data(exampleData3, package = "projoint")
data(exampleData1_labelled_tibble, package = "projoint")
data(out1_arranged, package = "projoint")

🛠️ Wrangle Your Data

Preparing your data correctly is one of the most important steps in conjoint analysis. Fortunately, the reshape_projoint() function in projoint makes this easy.

---

🚀 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$data[selected_vars]
fill_TRUE$data[selected_vars]

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

5. Arranging Attribute and Level Labels

To reorder or relabel attributes:

1. Save labels:

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

1. Edit the CSV (change order, label columns; leave level_id untouched)

2. Save it as "labels_arranged.csv" or something else.

3. 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)

---

🏠 Home: Home

projoint documentation built on Feb. 16, 2026, 5:10 p.m.