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Creating Bar Charts with create_bar()

bar_vignette.Rmd

Introduction

The create_bar() function creates grouped/clustered bar charts, perfect for comparing categories across different groups or segments. Unlike histograms (which show distributions) or stacked bars (which show composition), bar charts excel at side-by-side comparisons.

Basic Bar Charts

Simple Category Counts 

# Sample data
data <- data.frame(
  category = c("A", "A", "B", "B", "B", "C", "C", "C", "C")
)

# Create bar chart
plot <- create_bar(
  data = data,
  x_var = "category"
)

plot

With Custom Labels 

plot <- create_bar(
  data = data,
  x_var = "category",
  title = "Category Distribution",
  x_label = "Categories",
  y_label = "Count"
)

Grouped Bar Charts

Basic Grouping 

# Survey data
survey_data <- data.frame(
  question = rep(c("Q1", "Q2", "Q3"), each = 50),
  score_range = sample(c("Low", "Medium", "High"), 150, replace = TRUE)
)

# Grouped bar chart
plot <- create_bar(
  data = survey_data,
  x_var = "question",
  group_var = "score_range",
  horizontal = TRUE,
  bar_type = "percent"
)

plot

With Custom Colors 

plot <- create_bar(
  data = survey_data,
  x_var = "question",
  group_var = "score_range",
  horizontal = TRUE,
  bar_type = "percent",
  color_palette = c(
    "#E74C3C",  # Red for Low
    "#F39C12",  # Orange for Medium
    "#27AE60"   # Green for High
  ),
  group_order = c("Low", "Medium", "High")
)

Horizontal vs. Vertical

Vertical Bars 

plot <- create_bar(
  data = data,
  x_var = "category",
  group_var = "segment",
  horizontal = FALSE  # Vertical
)

Horizontal Bars (Better for Long Labels) 

data <- data.frame(
  question = rep(c(
    "I know how to search effectively",
    "I can evaluate information quality",
    "I understand data privacy"
  ), each = 40),
  response = sample(c("Agree", "Disagree"), 120, replace = TRUE)
)

plot <- create_bar(
  data = data,
  x_var = "question",
  group_var = "response",
  horizontal = TRUE,  # Much better for long labels!
  bar_type = "percent"
)

Count vs. Percent

Count 

plot <- create_bar(
  data = data,
  x_var = "category",
  group_var = "segment",
  bar_type = "count",  # Show raw counts
  y_label = "Number of Responses"
)

Percent 

plot <- create_bar(
  data = data,
  x_var = "category",
  group_var = "segment",
  bar_type = "percent",  # Show percentages
  y_label = "Percentage"
)

Working with Numeric Variables

Automatic Binning 

# Age data
age_data <- data.frame(
  age = sample(18:65, 200, replace = TRUE)
)

# Automatically bins numeric values
plot <- create_bar(
  data = age_data,
  x_var = "age"
)

Custom Binning 

plot <- create_bar(
  data = age_data,
  x_var = "age",
  x_breaks = c(18, 25, 35, 50, 65),
  x_bin_labels = c("18-24", "25-34", "35-49", "50-64")
)

Advanced Styling

Custom Ordering 

data <- data.frame(
  satisfaction = sample(c("Very Satisfied", "Satisfied", "Neutral", 
                         "Dissatisfied", "Very Dissatisfied"), 
                       100, replace = TRUE)
)

plot <- create_bar(
  data = data,
  x_var = "satisfaction",
  x_order = c("Very Dissatisfied", "Dissatisfied", "Neutral", 
              "Satisfied", "Very Satisfied")
)

Colorful Individual Bars 

# When no group_var, can color each bar differently
data <- data.frame(
  category = c("A", "B", "C", "D")
)

plot <- create_bar(
  data = data,
  x_var = "category",
  color_palette = c("#3498DB", "#E74C3C", "#F39C12", "#27AE60")
)

Real-World Examples

Survey Response Comparison 

# Knowledge assessment across topics
knowledge_data <- data.frame(
  topic = rep(c("Search Skills", "Critical Thinking", 
                "Data Privacy", "Source Evaluation"), each = 100),
  proficiency = sample(c("Beginner", "Intermediate", "Advanced"), 
                      400, replace = TRUE)
)

plot <- create_bar(
  data = knowledge_data,
  x_var = "topic",
  group_var = "proficiency",
  horizontal = TRUE,
  bar_type = "percent",
  title = "Self-Reported Proficiency by Topic",
  x_label = "",
  y_label = "Percentage of Respondents",
  color_palette = c("#E74C3C", "#F39C12", "#27AE60"),
  group_order = c("Beginner", "Intermediate", "Advanced")
)

plot

Demographic Breakdown 

demo_data <- data.frame(
  age_group = rep(c("18-24", "25-34", "35-44", "45-54", "55+"), each = 80),
  device_type = sample(c("Mobile", "Desktop", "Tablet"), 400, replace = TRUE)
)

plot <- create_bar(
  data = demo_data,
  x_var = "age_group",
  group_var = "device_type",
  horizontal = FALSE,
  bar_type = "percent",
  title = "Device Usage by Age Group",
  color_palette = c("#3498DB", "#95A5A6", "#F39C12")
)

Using with create_viz()

Integrate with the dashboard workflow:

viz <- create_viz(
  type = "bar",
  horizontal = TRUE,
  bar_type = "percent",
  color_palette = c("#E74C3C", "#F39C12", "#27AE60")
) %>%
  add_viz(
    x_var = "question1",
    group_var = "response_category",
    title = "Question 1 Results"
  ) %>%
  add_viz(
    x_var = "question2",
    group_var = "response_category",
    title = "Question 2 Results"
  ) %>%
  add_viz(
    x_var = "question3",
    group_var = "response_category",
    title = "Question 3 Results"
  )

# All inherit the defaults!

With Filters 

viz <- create_viz(
  type = "bar",
  x_var = "satisfaction",
  group_var = "score_range",
  horizontal = TRUE,
  bar_type = "percent"
) %>%
  add_viz(title = "Wave 1", filter = ~ wave == 1) %>%
  add_viz(title = "Wave 2", filter = ~ wave == 2) %>%
  add_viz(title = "Wave 3", filter = ~ wave == 3)

With Tabgroups 

viz <- create_viz(
  type = "bar",
  horizontal = TRUE,
  bar_type = "percent"
) %>%
  add_viz(
    x_var = "satisfaction",
    group_var = "score_range",
    title = "By Age",
    tabgroup = "demographics/age"
  ) %>%
  add_viz(
    x_var = "satisfaction",
    group_var = "score_range",
    title = "By Gender",
    tabgroup = "demographics/gender"
  ) %>%
  add_viz(
    x_var = "satisfaction",
    group_var = "score_range",
    title = "By Education",
    tabgroup = "demographics/education"
  )

Comparison with Other Chart Types

When to Use Bar Charts

Use create_bar() when: - Comparing categories across groups - Showing side-by-side comparisons - Displaying survey responses by demographics - You want grouped/clustered bars

Use create_stackedbar() when: - Showing composition (parts of a whole) - Displaying Likert scale responses - Emphasizing proportions within categories

Use create_histogram() when: - Showing distributions of continuous variables - Displaying frequency distributions - Analyzing data spread and shape

Use create_timeline() when: - Showing changes over time - Displaying trends - Comparing time series

Tips and Best Practices

  1. Use horizontal bars for long labels - Much more readable
  2. Choose percent for comparisons - Easier to interpret than counts
  3. Order categories meaningfully - Use x_order or group_order
  4. Limit colors - 3-5 colors maximum for clarity
  5. Use consistent colors - Same meaning = same color across charts

See Also