Skip to contents


Survey data often encounters the challenge of dropout – instances where participants fail to complete sections of the survey due to interruptions or omissions. Handling dropouts effectively is crucial for accurate data analysis and interpretation. The dropout package provides a solution by offering insights into participant behavior during the survey process.

Understanding the Dropout Package

The dropout package empowers you with the capability to extract valuable insights from your dataset, such as:

  • Identifying the specific survey points where participants tend to stop completing the survey.
  • Detecting sections that are frequently skipped by respondents.
  • Quantifying the extent and locations of dropouts within the survey.
  • Estimating the proportion of missing values attributed to dropouts in each column.
  • Profiling respondents who discontinued the survey and pinpointing their dropout points.
  • And much more…

By leveraging these insights, you can:

  • Enhance data cleaning procedures by distinguishing dropouts and tailoring your approach accordingly.
  • Adjust your analytical strategies to accommodate dropout-related biases in the dataset.
  • Analyze omitted sections in conjunction with the collected data, yielding a comprehensive understanding.

In this vignette, we will provide an in-depth overview of the dropout package’s features and their practical utilization. We will use a sample dataset named “flying” to illustrate these concepts. This is a modified version of the Flying Etiquette Survey data behind the story: 41 percent of flyers say it’s rude to recline your seat on an airplane. You can load this preinstalled dataset into your environment using the command data(flying).


While the dropout package can function independently, integrating it with the tidyverse ecosystem (especially using dplyr) can significantly enhance your workflow. However all of the methods used in this Vignette can be transferred to using Base R code exclusively.


Exploring Dropout Insights with drop_summary

Let’s embark on a deeper exploration of the dropout package by delving into the drop_summary function. This function serves as a pivotal tool for gaining in-depth insights into dropout patterns within your dataset. To effectively utilize the drop_summary function, you should specify the last column in your dataset that corresponds to the survey items. If you encounter a warning message while using this function, it could be attributed to either of the following reasons:

  1. Your dataset contains no instances of dropout.
  2. Additional columns beyond the survey items exist, but they aren’t correctly identified as such.

For example, in the “flying” dataset, the final survey-related item is stored in the “location_census_region” column. Following this, the “survey_type” column contains supplementary survey information. Many datasets incorporate similar non-survey-related data, and it’s crucial to consider such cases. If the last column is left unspecified, the drop_summary function will assume that only survey-related items are present.

To gain a comprehensive overview of the dropout patterns within your dataset, consider the following code snippet:

drop_summary(flying, "location_census_region")
#> # A tibble: 27 × 8
#>    column_name           dropout drop_rate drop_na section_na single_na missing
#>    <chr>                   <int>     <dbl>   <dbl>      <dbl>     <dbl>   <dbl>
#>  1 respondent_id               0      0          0          0         0       0
#>  2 travel_frequency            0      0          0          0         0       0
#>  3 seat_recline               18      0.02      18        164         0     182
#>  4 height                      0      0.02      18        164        12     194
#>  5 children_under_18           1      0.02      19        164         6     189
#>  6 two_armrests                1      0.02      20        164         0     184
#>  7 middle_armrest              0      0.02      20        164         0     184
#>  8 window_shade                0      0.02      20        164         0     184
#>  9 moving_to_unsold_seat       1      0.02      21        164         0     185
#> 10 talking_to_seatmate         0      0.02      21        164         0     185
#> # ℹ 17 more rows
#> # ℹ 1 more variable: completion_rate <dbl>

Now, let’s delve into the intricacies of the drop_summary function and the valuable insights it provides in a structured format.

Understanding the Output of drop_summary

When you use the drop_summary function, the output you receive is a compact yet informative summary, packaged as either a dataframe or a tibble. This summary consists of multiple columns, each of which provides insights into different dimensions of dropout analysis within your dataset.

Output Columns:

  • column_name: Lists the names of the columns from your dataset that have been analyzed for dropouts.

  • dropout: Contains the frequency of dropouts within each listed column, allowing you to see where dropout rates might be the most significant.

  • drop_rate: Shows the overall percentage of dropout incidents in each column. This is useful for understanding the relative impact of dropouts in various parts of your dataset.

  • drop_na: Provides the percentage of missing values in each column that can be attributed specifically to dropouts. This offers insights into the nature of missing data.

  • section_na: Indicates occurrences of missing values that span at least n consecutive columns (n defaults to 3). You can adjust this parameter using section_min as shown below:

    drop_summary(data, last_col, section_min = n)

    This column is particularly useful for identifying participants who might skip entire sections of a survey without dropping out completely.

  • single_na: Reveals the percentage of single-instance missing values in each column, which are not associated with systematic dropouts or section skips.

  • completion_rate: Denotes the overall data completion rate for each analyzed column, enabling you to gauge the integrity and reliability of your dataset.

Using drop_detect to Identify Dropouts

One of the core tools in the dropout package is the drop_detect function. This function serves as a comprehensive tool for isolating and understanding individual participant dropout behaviors. Specifically, it reveals whether a participant has left the survey prematurely and pinpoints the exact juncture at which the dropout took place.

The structure and usage of drop_detect are intentionally made to resonate with the drop_summary function, ensuring consistency and ease of adoption.

Output Columns:

  • dropout: A Boolean column (TRUE or FALSE). A TRUE value signifies that the respective participant exited the survey prematurely.

  • dropout_column: For those marked as TRUE in the dropout column, this field specifies the exact column or question that triggered the dropout.

  • dropout_index: Offers a direct reference to the row number where the dropout incident occurred, facilitating easier traceability.

Example Usage:

For practical insights, consider applying drop_detect on the ‘flying’ dataset. Here’s how you can achieve this:

drop_detect(flying, "location_census_region")

Moreover, if you wish to append the extracted dropout details back to the original dataset, you can employ the bind_cols function from the dplyr package:

drop_detect(flying, "location_census_region") %>% 
  bind_cols(flying, .)

Such integration of dropout specifics into the primary dataset can act as a preliminary step for more nuanced analyses, like zoning into specific dropout triggers, assessing commonalities among dropouts, or any other relevant exploratory exercises.

Subsequent sections will delve into exemplified applications of this integrated approach.

Practical Workflow Examples

Cleaning Early Dropouts

The drop_detect function can be useful for identifying and filtering out early dropouts, i.e., participants who stopped answering the survey at a specific column. For example, you can filter for participants who did not drop out early, or had a ‘late’ dropout in the demographic part of the questions, using the dropout_index:

flying %>% 
  drop_detect("location_census_region") %>% 
  bind_cols(flying, .) %>% 
  filter(dropout == FALSE | dropout_index > 22)

Analyzing Specific Sections

If you’re interested in a specific section of questions and want to filter for dropouts and section_na, you have two approaches:

Option 1: Creating a Subset

In this approach, we create a subset of the data containing only the first 22 columns. We then apply the drop_detect function to this subset to identify dropouts and other relevant indicators:

flying %>% 
  select(1:22) %>%
  drop_detect() %>% 
  bind_cols(flying, .) %>% 
  filter(dropout == FALSE)

Option 2: Setting the last_col Parameter

You can specify the last_col parameter to the last question in the section, to identify all dropouts up to that point:

flying %>% 
  drop_detect("smoking_violation") %>% 
  bind_cols(flying, .) %>% 
  filter(dropout == FALSE)

Comparative Analysis: Age and Gender

One practical application is to compare the demographics (e.g., age and gender) between those who left out a section and those who did not. The following code generates a bar graph that breaks down dropout rates by age group and gender.


flying %>%
  drop_detect("smoking_violation") %>% 
  bind_cols(flying, .) %>% 
  filter(! %>% 
  mutate(age = factor(age, levels = c("18-29", "30-44", "45-60", "> 60"))) %>% 
  ggplot(aes(x=age, fill=dropout)) +
  geom_bar(position="dodge") +
  facet_grid(gender ~ .) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
Comparative Analysis of Age and Gender against Dropout Rates

Comparative Analysis of Age and Gender against Dropout Rates

By visualizing the data, you can more easily discern patterns and disparities among different demographic groups with respect to dropout rates.

Exploring Relationships: Dropout, Age and Gender

Another interesting avenue is to explore whether there’s a relationship between dropout behavior (or in this case leaving out a section) and demographic variables like age and gender:

test <- flying %>%
  drop_detect("smoking_violation") %>% 
  bind_cols(flying, .) %>% 
  filter(! %>%
  select(dropout, age, gender) %>% 
  mutate(dropout = as.numeric(ifelse(dropout == TRUE, 1, 0)))

glm_model <- glm(dropout ~ gender + age, data = test, family = binomial)

This can be particularly useful for hypothesis testing and can aid in uncovering patterns in your data.