Comprehensive Test Datasets for Tidyplots Function

These datasets provide comprehensive examples for testing and demonstrating the tidyplots function capabilities across different domains and use cases. Each dataset is designed to highlight specific features and best practices for scientific visualization.

Format

Each dataset is a data frame with the following characteristics:

tidyplots_medical:

patient_id: Unique patient identifier
treatment: Treatment group: Control, Drug_A, Drug_B
gender: Patient gender: Male, Female
age_group: Age category: Young, Middle, Elderly
age: Continuous age variable
bp_reduction: Primary outcome: Blood pressure reduction
biomarker_1, biomarker_2: Secondary biomarker measurements
qol_score: Quality of life score (0-100)
timepoint: Study timepoint: Baseline, Week_4, Week_8, Week_12
hospital: Study site: Hospital_A, Hospital_B, Hospital_C
response: Response category: High_Response, Moderate_Response, Low_Response

tidyplots_education:

student_id: Unique student identifier
teaching_method: Teaching approach: Traditional, Interactive, Online, Hybrid
subject: Academic subject: Math, Science, Literature
school_type: School type: Public, Private
grade_level: Student grade level (9-12)
pre_test_score, post_test_score: Test scores before and after intervention
improvement: Score improvement (post - pre)
engagement_score: Student engagement measure (0-100)
study_hours: Weekly study hours
performance_level: Performance category: Excellent, Good, Satisfactory, Needs_Improvement

tidyplots_business:

salesperson_id: Unique salesperson identifier
marketing_strategy: Strategy: Email, Social_Media, Direct_Mail, Phone, Mixed
product_category: Product type: Electronics, Clothing, Home_Garden, Sports
region: Geographic region: North, South, East, West
quarter: Business quarter: Q1, Q2, Q3, Q4
experience_years: Years of sales experience
sales_amount: Total sales revenue
leads_generated: Number of sales leads
conversion_rate: Lead conversion percentage
customer_satisfaction: Customer satisfaction score (1-10)
performance_tier: Performance category: Top_Performer, High_Performer, Average_Performer, Needs_Improvement

tidyplots_environmental:

station_id: Monitoring station identifier
month: Calendar month
measurement_type: Type: Temperature, Humidity, Air_Quality, Noise_Level
location_type: Location: Urban, Suburban, Rural
value: Measurement value (units vary by type)
data_quality: Quality indicator: High, Medium, Low
alert_level: Alert status: Normal, Warning, Critical
season: Season grouping: Winter, Spring, Summer, Fall

tidyplots_demo:

group: Group identifier: Group_A, Group_B, Group_C, Group_D
category: Category: Category_1, Category_2
treatment: Treatment: Control, Treatment
time_point: Time: Baseline, Follow_up
score: Main outcome variable
measurement_1, measurement_2: Additional continuous variables
success: Binary outcome variable
count_var: Count/discrete variable

Source

These datasets were created using simulated data with realistic patterns and relationships designed to showcase tidyplots functionality across different research domains.

Details

A collection of datasets designed to showcase the comprehensive features of the tidyplots function, including various plot types, statistical elements, and customization options.

The collection includes five specialized datasets:

1. Medical Research Dataset (tidyplots_medical):

Clinical trial data with treatment effects
Variables: patient characteristics, treatments, biomarkers, outcomes
Use cases: Treatment comparisons, biomarker analysis, clinical outcomes
Sample size: 2,160 observations

2. Educational Research Dataset (tidyplots_education):

Student performance across teaching methods
Variables: student demographics, teaching methods, test scores
Use cases: Educational effectiveness, performance analysis
Sample size: 4,800 observations

3. Business Analytics Dataset (tidyplots_business):

Sales performance and marketing strategy analysis
Variables: sales metrics, marketing strategies, regional data
Use cases: Business intelligence, sales analysis, performance tracking
Sample size: 48,000 observations

4. Environmental Monitoring Dataset (tidyplots_environmental):

Environmental measurements across monitoring stations
Variables: temperature, humidity, air quality, location types
Use cases: Environmental monitoring, seasonal analysis, pollution tracking
Sample size: 1,728 observations

5. Simple Demo Dataset (tidyplots_demo):

Basic dataset for tutorials and quick demonstrations
Variables: groups, categories, treatments, scores
Use cases: Learning tidyplots, quick examples, method demonstrations
Sample size: 100 observations

References

Bengler, J. (2024). tidyplots: Streamlined plotting with tidy data. https://jbengler.github.io/tidyplots/

Examples

if (FALSE) { # \dontrun{
# Load all datasets
data(tidyplots_datasets)

# Load individual datasets
data(tidyplots_medical)
data(tidyplots_education)
data(tidyplots_business)
data(tidyplots_environmental) 
data(tidyplots_demo)

# Basic scatter plot with medical data
tidyplots(data = tidyplots_medical,
          xvar = "age",
          yvar = "bp_reduction",
          color = "treatment")

# Box plot with statistical elements
tidyplots(data = tidyplots_medical,
          xvar = "treatment",
          yvar = "bp_reduction",
          plotType = "boxplot",
          color = "treatment",
          showMean = TRUE,
          showCI = TRUE,
          colorScheme = "friendly")

# Violin plot with education data
tidyplots(data = tidyplots_education,
          xvar = "teaching_method",
          yvar = "improvement",
          plotType = "violin",
          color = "school_type",
          violinPoints = TRUE,
          showMedian = TRUE,
          colorScheme = "seaside")

# Line plot with environmental data
tidyplots(data = tidyplots_environmental[tidyplots_environmental$measurement_type == "Temperature", ],
          xvar = "month",
          yvar = "value",
          plotType = "line",
          color = "location_type",
          lineType = "mean",
          showCI = TRUE,
          ciType = "ribbon")

# Histogram with demo data
tidyplots(data = tidyplots_demo,
          xvar = "score",
          plotType = "histogram",
          color = "group",
          histogramBins = 20,
          showDistribution = TRUE,
          distributionType = "density")

# Faceted plot with business data
tidyplots(data = tidyplots_business,
          xvar = "experience_years",
          yvar = "sales_amount",
          color = "marketing_strategy",
          facet = "region",
          plotType = "points",
          pointType = "jitter",
          showMean = TRUE,
          colorScheme = "viridis")

# Bar plot with statistical testing
tidyplots(data = tidyplots_medical,
          xvar = "treatment",
          yvar = "qol_score",
          plotType = "bar",
          color = "treatment",
          barType = "mean",
          showSEM = TRUE,
          showPValue = TRUE,
          showSignificance = TRUE)
} # }

Format

Source

Details

References

See also

Examples