📖 Introduction

Welcome to this comprehensive guide on using the viz_stackedbar function from the dashboardr package. This unified function creates highly customizable interactive stacked bar charts from survey data, supporting two powerful modes:

Mode 1: Grouped/Crosstab Mode (use x_var + stack_var)

Shows how one variable breaks down by another (e.g., education by gender)
Use when your data is in long/tidy format
Example: viz_stackedbar(data, x_var = "education", stack_var = "gender")

Mode 2: Multi-Variable/Battery Mode (use x_vars)

Compares response distributions across multiple survey questions
Use when you have multiple columns with the same response scale (e.g., Likert items)
Example: viz_stackedbar(data, x_vars = c("q1", "q2", "q3"))

The function handles many common data preparation tasks automatically, including:

Converting haven_labelled columns (from SPSS imports) to R factors
Mapping raw values to descriptive labels
Binning continuous variables into meaningful categories
Handling missing values explicitly or implicitly
Creating both count-based and percentage-based visualizations
Customizing colors, ordering, and interactive tooltips

This vignette demonstrates both modes using the General Social Survey (GSS) Panel 2020 dataset.

⚙️ Getting Started

First, let’s load the necessary libraries and examine our data set.

library(gssr)
library(dplyr)
library(highcharter)
library(tidyr)
library(dashboardr)

# Load GSS Panel 2020 data
data(gss_panel20)

📋 Data Preparation

Let’s prepare our working dataset using the 2020 wave variables.

# Create a working dataset with key _1a variables from 2020
gss_clean <- gss_panel20 %>%
  select(
    # Demographics
    age_1a, sex_1a, race_1a, degree_1a, region_1a,
    # Attitudes and behaviors
    happy_1a, trust_1a, fair_1a, helpful_1a,
    polviews_1a, partyid_1a, attend_1a,
    # Economic
    income_1a, class_1a
  ) %>%
  # Remove completely empty rows
  filter(if_any(everything(), ~ !is.na(.)))

# Check the data structure
glimpse(gss_clean)
#> Rows: 2,867
#> Columns: 14
#> $ age_1a      <dbl+lbl> 47, 61, 72, 43, 55, 53, 50, 23, 45, 71, 33, 86, 32, 60…
#> $ sex_1a      <dbl+lbl> 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, …
#> $ race_1a     <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 2, 2, 1, 1, 1, 3, …
#> $ degree_1a   <dbl+lbl> 3, 1, 3, 1, 4, 2, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, …
#> $ region_1a   <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, …
#> $ happy_1a    <dbl+lbl>     2,     2,     1,     2,     1,     1,     2,     1…
#> $ trust_1a    <dbl+lbl> NA(i),     3,     1, NA(i),     1,     1, NA(i),     2…
#> $ fair_1a     <dbl+lbl> NA(i),     1,     1, NA(i),     2,     2, NA(i),     1…
#> $ helpful_1a  <dbl+lbl> NA(i),     2,     2, NA(i),     3,     1, NA(i),     1…
#> $ polviews_1a <dbl+lbl>     4,     2,     6,     4,     3,     3,     3,     5…
#> $ partyid_1a  <dbl+lbl>     3,     2,     5,     5,     1,     1,     5,     2…
#> $ attend_1a   <dbl+lbl> 0, 0, 7, 6, 0, 0, 1, 5, 6, 0, 5, 3, 5, 6, 1, 8, 8, 8, …
#> $ income_1a   <dbl+lbl> NA(n),    12,    12, NA(n), NA(n),    12, NA(n),    12…
#> $ class_1a    <dbl+lbl>     3, NA(d),     3,     3,     3,     3,     3,     2…

# Examine some key variables
table(gss_clean$degree_1a, useNA = "always")
#> 
#>    0    1    2    3    4 <NA> 
#>  328 1461  216  536  318    8
table(gss_clean$happy_1a, useNA = "always")
#> 
#>    1    2    3 <NA> 
#>  806 1601  452    8

📊 Basic Stacked Bar Charts

Example 1: Education by Gender (Count-based)

Let’s start with a basic stacked bar chart showing educational attainment by gender.

# Create basic stacked bar chart
plot1 <- viz_stackedbar(
  data = gss_clean,
  x_var = "degree_1a",
  stack_var = "sex_1a",
  title = "Educational Attainment by Gender",
  subtitle = "GSS Panel 2016 - Raw counts",
  x_label = "Highest Degree Completed",
  y_label = "Number of Respondents",
  stack_label = "Gender",
  stacked_type = "counts"
)

plot1

Example 2: Happiness Distribution (Percentage-based)

Now let’s create a percentage-based stacked bar chart to show happiness distribution across education levels.

# Define education order for logical display
education_order <- c("less than high school", "high school", "associate/junior college", "bachelor's", "graduate")

# Create percentage stacked bar chart
plot2 <- viz_stackedbar(
  data = gss_clean,
  x_var = "degree_1a",
  stack_var = "happy_1a",
  title = "Happiness Distribution Across Education Levels",
  subtitle = "Percentage breakdown within each education category",
  x_label = "Education Level",
  y_label = "Percentage of Respondents",
  stack_label = "Happiness Level",
  stacked_type = "percent",
  x_order = education_order,
  stack_order = c("very happy", "pretty happy", "not too happy"),
  tooltip_suffix = "%",
  color_palette = c("#2E86AB", "#A23B72", "#F18F01")
)

plot2

⚡ Advanced Features

Example 3: Age Binning with Political Views

Let’s demonstrate binning continuous variables by creating age groups and examining political views.

# First, let's clean and prepare the age variable
gss_clean_age <- gss_clean %>%
  # Ensure age is numeric and remove missing values for this analysis
  filter(!is.na(age_1a), !is.na(polviews_1a)) %>%
  mutate(
    # Convert age to numeric if it isn't already
    age_numeric = as.numeric(age_1a)
  )

# Check the cleaned data
cat("Cleaned age summary:\n")
#> Cleaned age summary:
summary(gss_clean_age$age_numeric)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   18.00   34.00   50.00   49.26   62.00   89.00

# Define age breaks and labels (adjusted if needed based on actual data range)
age_range <- range(gss_clean_age$age_numeric, na.rm = TRUE)
cat("Age range in data:", age_range[1], "to", age_range[2], "\n")
#> Age range in data: 18 to 89

# Adjust breaks to match actual data range
age_breaks <- c(18, 30, 45, 60, 75, Inf)
age_labels <- c("18-29", "30-44", "45-59", "60-74", "75+")

# Map political views to shorter labels
polviews_map <- list(
  "extremely liberal" = "Ext. Liberal",
  "liberal" = "Liberal", 
  "slightly liberal" = "Sl. Liberal",
  "moderate, middle of the road" = "Moderate",
  "slightly conservative" = "Sl. Conservative",
  "conservative" = "Conservative",
  "extremely conservative" = "Ext. Conservative"
)

polviews_order <- list("Ext. Liberal", "Liberal", "Sl. Liberal",
                       "Moderate", "Sl. Conservative", "Conservative", 
                       "Ext. Conservative")



# Create chart with age binning and value mapping using the numeric age
plot3 <- viz_stackedbar(
  data = gss_clean_age,
  x_var = "age_numeric",  # Use the numeric version
  stack_var = "polviews_1a",
  title = "Political Views by Age Group",
  subtitle = "Distribution of political ideology across age cohorts",
  x_label = "Age Group",
  stack_label = "Political Views",
  x_breaks = age_breaks,
  x_bin_labels = age_labels,
  stack_map_values = polviews_map,
  stacked_type = "percent",
  tooltip_suffix = "%",
  x_tooltip_suffix = " years",
  color_palette = c("#d7191c", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#66c2a5", "#2b83ba"),
  stack_order = polviews_order
)

plot3

Example 4: Including Missing Values

Let’s create a chart that explicitly shows missing data patterns.

## Example 4: Including Missing Values

# Let's create a chart that explicitly shows missing data patterns.

# Create chart including NA values (using default "(Missing)" labels)
plot4 <- viz_stackedbar(
  data = gss_clean,
  x_var = "race_1a",
  stack_var = "attend_1a",
  title = "Religious Attendance by Race/Ethnicity",
  subtitle = "Including non-responses as explicit categories",
  x_label = "Race/Ethnicity",
  stack_label = "Religious Attendance Frequency",
  include_na = TRUE,
  stacked_type = "percent",
  tooltip_suffix = "%",
  color_palette = c("#8e0152", "#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", 
                   "#e6f5d0", "#b8e186", "#7fbc41", "#4d9221", "#276419")
)

plot4

Example 5: Custom Value Mapping

Let’s demonstrate comprehensive value mapping for cleaner labels.


# Create mappings for cleaner display
sex_map <- list("male" = "Men", "female" = "Women")
class_map <- list(
  "lower class" = "Lower",
  "working class" = "Working", 
  "middle class" = "Middle",
  "upper class" = "Upper"
)

# Create chart with custom mappings
plot5 <- viz_stackedbar(
  data = gss_panel20,
  x_var = "class_1a",
  stack_var = "sex_1a",
  title = "Gender Distribution Across Social Classes",
  subtitle = "With custom labels and ordering",
  x_label = "Self-Reported Social Class",
  stack_label = "Gender",
  x_map_values = class_map,
  stack_map_values = sex_map,
  x_order = c("Lower", "Working", "Middle", "Upper"),
  stack_order = c("Women", "Men"),
  stacked_type = "counts",
  tooltip_prefix = "Count: ",
  color_palette = c("#E07A5F", "#3D5A80")
)

plot5

🔬 Complex Analysis Examples

Example 6: Regional Patterns in Trust

Let’s examine how trust levels vary across regions and social classes.


# Recode labels to fix the mistake
trust_map <- list(
  "can't trust" = "Can Trust",
  "can't be too careful" = "Can't Be Too Careful",
  "depends" = "It Depends"
)

# Create regional trust analysis
plot6 <- viz_stackedbar(
  data = gss_panel20,
  x_var = "region_1a",
  stack_var = "trust_1a",
  stack_map_values = trust_map,
  title = "Do You Trust Strangers?",
  subtitle = "Regional variation in interpersonal trust",
  x_label = "US Region",
  stack_label = "Trust Level",
  stack_order = c("Can Trust", "Can't Be Too Careful", "It Depends"),
  stacked_type = "percent",
  tooltip_suffix = "%",
  color_palette = c("#2E8B57", "#CD5C5C", "#DAA520")
)

plot6

📊 Multi-Variable Mode: Comparing Survey Questions

The viz_stackedbar function also supports comparing multiple survey questions side-by-side. This is particularly useful for visualizing survey batteries (sets of questions with the same response scale).

Example 7: Basic Multi-Variable Comparison

When you have multiple columns representing different questions with the same response categories, use x_vars to compare them:

# Define the questions to compare
social_questions <- c("trust_1a", "fair_1a", "helpful_1a")
social_labels <- c(
  "Interpersonal Trust",
  "Fairness of Others",
  "Helpfulness of Others"
)

# Create multi-variable comparison chart
plot7 <- viz_stackedbar(
  data = gss_clean,
  x_vars = social_questions,
  x_var_labels = social_labels,
  title = "Social Attitudes and Trust",
  subtitle = "Distribution of responses across social attitude questions",
  x_label = "Social Attitude Dimension",
  stack_label = "Response Level",
  stacked_type = "percent",
  tooltip_suffix = "%"
)

plot7

Example 8: Multi-Variable with Response Mapping

You can standardize response labels across questions and customize the display. It’s helpful to first check what the actual response values are:

# First, examine what the actual response values are
cat("Unique trust responses:\n")
#> Unique trust responses:
print(unique(as.character(gss_clean$trust_1a)))
#> [1] NA  "3" "1" "2"

cat("\nUnique fair responses:\n")
#> 
#> Unique fair responses:
print(unique(as.character(gss_clean$fair_1a)))
#> [1] NA  "1" "2" "3"

Now create a mapping to standardize the labels:

# Create response mapping for cleaner labels
response_map <- list(
  "can't trust" = "High Trust/Positive",
  "can't be too careful" = "Low Trust/Negative",
  "depends" = "Situational/Neutral",
  "would try to be fair" = "High Trust/Positive",
  "would take advantage of you" = "Low Trust/Negative",
  "try to be helpful" = "High Trust/Positive",
  "looking out for themselves" = "Low Trust/Negative"
)

# Define response order (from negative to positive)
response_order <- c("Low Trust/Negative", "Situational/Neutral", "High Trust/Positive")

# Create chart with custom mapping and ordering
plot8 <- viz_stackedbar(
  data = gss_clean,
  x_vars = social_questions,
  x_var_labels = social_labels,
  title = "Social Trust Dimensions with Standardized Responses",
  subtitle = "Responses mapped to consistent positive/negative categories",
  x_label = "Trust Dimension",
  stack_label = "Trust Level",
  stack_map_values = response_map,
  stack_order = response_order,
  stacked_type = "percent",
  tooltip_suffix = "%",
  color_palette = c("#d62728", "#ffbb78", "#2ca02c"),
  include_na = TRUE,
  na_label_stack = "No Answer"
)

plot8

Example 9: Single Variable with x_vars (Compact Display)

The x_vars parameter also works with a single variable. This is useful when you want the compact styling of multi-variable mode for a single question:

# Single variable with x_vars - great for compact horizontal displays
plot9a <- viz_stackedbar(
  data = gss_clean,
  x_vars = "happy_1a",
  x_var_labels = "General Happiness",
  title = "Happiness Distribution",
  x_label = "Well-being Measure",
  stack_label = "Happiness Level",
  stacked_type = "percent",
  horizontal = TRUE,
  tooltip_suffix = "%",
  color_palette = c("#2E8B57", "#FFD700", "#CD5C5C", "grey")
)

plot9a

Example 9b: Horizontal Multi-Variable Chart

For better readability with long labels, use horizontal orientation:

# Horizontal chart for survey battery
plot9b <- viz_stackedbar(
  data = gss_clean,
  x_vars = c("trust_1a", "fair_1a", "helpful_1a"),
  x_var_labels = c(
    "Can people be trusted?",
    "Are people generally fair?",
    "Are people generally helpful?"
  ),
  title = "Social Capital Dimensions",
  subtitle = "GSS Panel 2016",
  stacked_type = "percent",
  horizontal = TRUE,
  tooltip_suffix = "%",
  color_palette = c("#8c510a", "#d8b365", "#f6e8c3", "grey"),
  include_na = TRUE,
  na_label_stack = "No response"
)

plot9b

Example 10: Survey Battery Analysis

Survey batteries are sets of related questions with the same response scale. Here’s how to create a comprehensive battery analysis:

# Create a social trust battery
trust_battery <- c("trust_1a", "fair_1a", "helpful_1a")
trust_battery_labels <- c(
  "Interpersonal Trust",
  "Perceived Fairness",
  "Perceived Helpfulness"
)

# Create a comprehensive battery analysis
plot10 <- viz_stackedbar(
  data = gss_clean,
  x_vars = trust_battery,
  x_var_labels = trust_battery_labels,
  title = "Social Trust Battery - Complete Analysis",
  subtitle = "Comprehensive view of social trust dimensions with enhanced tooltips",
  x_label = "Trust Dimension",
  stack_label = "Response Category",
  stacked_type = "percent",
  tooltip_prefix = "Percentage: ",
  tooltip_suffix = "% of respondents",
  show_var_tooltip = TRUE,
  include_na = TRUE,
  na_label_stack = "No answer",
  color_palette = c("#8c510a", "#d8b365", "#f6e8c3", "darkgrey")
)

plot10

Example 11: Publication-Ready Chart

Let’s create a fully customized, publication-ready chart:

# Create the most polished example
plot11 <- viz_stackedbar(
  data = gss_clean,
  x_vars = social_questions,
  x_var_labels = c(
    "Interpersonal Trust\n('Can most people be trusted?')",
    "Perceived Fairness\n('Do people try to be fair?')",
    "Perceived Helpfulness\n('Are people helpful?')"
  ),
  title = "Social Capital Dimensions in American Society",
  subtitle = "General Social Survey Panel 2016 (N = 2,867 respondents)\nPercentage distribution of responses across social trust measures",
  x_label = "Social Trust Dimension",
  stack_label = "Response Category",
  stacked_type = "percent",
  tooltip_prefix = "",
  tooltip_suffix = "% of respondents",
  x_tooltip_suffix = "",
  include_na = TRUE,
  na_label_stack = "No response",
  color_palette = c("#b2182b", "#ef8a62", "#fddbc7", "darkgrey"),
  show_var_tooltip = TRUE
)

plot11

🏷️ Labels and Tooltips Reference

Summary of Label and Tooltip Options

The viz_stackedbar() function offers extensive customization for labels and tooltips:

Parameter	Description	Example
`x_label`	X-axis title	`"Question"`
`y_label`	Y-axis title (auto-set based on stacked_type)	`"Percentage"`
`stack_label`	Legend title	`"Response Category"`
`x_var_labels`	Custom labels for each question (multi-variable mode)	`c("Trust", "Fairness")`
`tooltip_prefix`	Text before value in tooltip	`"Score: "`
`tooltip_suffix`	Text after value in tooltip	`"%"`, `" respondents"`
`x_tooltip_suffix`	Text after category name in tooltip	`" question"`
`show_var_tooltip`	Show question name in tooltip (multi-variable mode)	`TRUE`

# Example with all label/tooltip options
viz_stackedbar(
  data = gss_clean,
  x_vars = c("trust_1a", "fair_1a"),
  x_var_labels = c("Trust", "Fairness"),
  title = "Social Attitudes",
  x_label = "Attitude Measure",
  y_label = "Percent of Respondents",
  stack_label = "Response Level",
  stacked_type = "percent",
  tooltip_prefix = "",
  tooltip_suffix = "% responded",
  show_var_tooltip = TRUE
)

When to Use Each Mode

Mode	Use Case	Parameters
Grouped/Crosstab	One variable broken down by another	`x_var` + `stack_var`
Multi-Variable	Compare multiple questions side-by-side	`x_vars`

Use Grouped Mode when: - You want to show how education levels differ by gender - You’re creating a cross-tabulation visualization - Your data is already in long/tidy format

Use Multi-Variable Mode when: - You’re comparing multiple survey questions - Your questions share the same response categories - You want to visualize a Likert scale battery

💡 Summary and Best Practices

✅ Key Features Demonstrated

Two flexible modes: Grouped/crosstab (x_var + stack_var) and multi-variable (x_vars)
Basic stacked bars with both count and percentage displays
Age binning for continuous variables
Value mapping for cleaner, more descriptive labels
Custom ordering for logical presentation of categories
Missing value handling with explicit NA categories
Multi-variable comparisons for survey batteries and Likert scales
Custom color palettes for different data types and branding
Comprehensive tooltips with prefixes, suffixes, and formatting
Horizontal orientation for better readability with long labels

🎯 Best Practices for Stacked Bar Charts

General Guidelines

Choose appropriate stacking type

Use “normal” or “counts” for comparing absolute counts across groups
Use “percent” for comparing proportions within groups

Order categories logically

When remapping values, remember to use the variable names as in the DataFrame
Use natural ordering for ordinal variables (e.g., Likert scales)
Consider frequency-based ordering for nominal categories
Place “Other” or “Missing” categories at the end

Multi-Variable Mode Best Practices

Choose questions with similar response scales

Use questions that have the same or compatible response categories
Consider mapping different scales to common categories when appropriate

Order questions logically

Group related concepts together
Consider ordering by typical response patterns (most positive to least positive)
Place most important questions first

Use appropriate stacking type

Use “percent” for comparing response patterns across questions
Use “counts” when absolute counts matter more than proportions

Handle missing data thoughtfully

Decide whether to include or exclude missing categories
Use include_na = TRUE when missing patterns are meaningful
Provide clear labels for missing categories

Use appropriate colors

Use diverging palettes for scales with meaningful center points
Use qualitative palettes for nominal categories
Ensure sufficient contrast between adjacent categories
Consider colorblind accessibility

Customize tooltips for clarity

Include units and context in tooltips
Use prefixes/suffixes to clarify meaning
Format numbers appropriately for your audience

Consider your audience

Use descriptive labels rather than codes
Provide clear titles and subtitles
Include sample sizes in subtitles when relevant

🌍 Common Use Cases

The viz_stackedbar function is particularly useful for:

Survey response analysis: Displaying Likert scale responses across demographics
Demographic breakdowns: Showing composition of groups by various characteristics
Attitude research: Comparing opinions across different populations
Market research: Analyzing customer segments and preferences
Educational research: Examining outcomes across different groups
Health surveys: Displaying health behaviors or outcomes by demographics

📚 Conclusion

The viz_stackedbar() function provides a unified, comprehensive solution for creating publication-ready stacked bar charts from survey data. Its two flexible modes handle the most common visualization needs:

Grouped/Crosstab Mode (x_var + stack_var): Show how one variable breaks down by another
Multi-Variable Mode (x_vars): Compare response distributions across multiple survey questions

Key advantages include:

Unified interface - one function for both crosstabs and survey batteries
Automatic data preparation for common survey data formats
Smart mode detection based on the parameters you provide
Flexible binning and mapping for continuous and coded variables
Comprehensive missing data handling options
Interactive tooltips for enhanced data exploration
Publication-ready styling with extensive customization options

Note: If you were previously using viz_stackedbars() for multi-variable comparisons, you can now use viz_stackedbar() with the same parameters. The old function still works but viz_stackedbar() is now the recommended approach for all stacked bar chart needs.

Creating Interactive Stacked Bar Charts with viz_stackedbar