Scientific data visualization using the grafify R package for easy and accessible plotting. Provides quick exploration graphs with few lines of code, color-blind friendly palettes, and integrated statistical analysis. Perfect for clinical research, experimental data, and scientific publications. Features scatter plots with error bars, distribution plots, before-after comparisons, and factorial designs with professional styling.
Usage
grafify(
data,
vars,
groups = NULL,
blocks = NULL,
facet_var = NULL,
plot_type = "scatterbar",
x_var,
y_var,
error_type = "sd",
summary_function = "mean",
color_palette = "default",
reverse_palette = FALSE,
use_grafify_theme = TRUE,
jitter_width = 0.3,
transparency = 0.7,
point_size = 3,
line_size = 1,
log_transform = "none",
add_statistics = FALSE,
stat_method = "anova_1way",
posthoc_comparisons = FALSE,
comparison_method = "pairwise",
befafter_shape_var = NULL,
befafter_id_var = NULL,
show_individual_points = TRUE,
show_summary_stats = TRUE,
show_model_diagnostics = FALSE,
export_data = FALSE,
plot_width = 8,
plot_height = 6,
title_text = "",
x_label = "",
y_label = "",
legend_position = "right",
experimental_design = "crd",
random_effects = NULL,
alpha_level = 0.05
)Arguments
- data
The data as a data frame containing scientific or clinical variables for professional visualization with grafify.
- vars
Continuous variables for plotting. Can be used as X, Y variables or multiple variables for distribution analysis.
- groups
Categorical variable for grouping data in plots. Creates different colors, shapes, or panels for each group.
- blocks
Variable for blocking or pairing observations. Used in randomized block designs or before-after comparisons.
- facet_var
Variable for creating multiple panels (facets). Useful for complex experimental designs with multiple factors.
- plot_type
Type of plot to create. Each type is optimized for different data structures and research questions.
- x_var
Variable for X-axis. Can be continuous or categorical depending on the plot type selected.
- y_var
Variable for Y-axis. Should be continuous for most plot types.
- error_type
Type of error bars to display. SD shows data spread, SEM shows precision of mean, CI95 shows statistical confidence.
- summary_function
Function used to summarize data for central tendency. Geometric mean is useful for log-scaled or ratio data.
- color_palette
Color palette for the plot. All palettes are color-blind friendly and designed for scientific publications.
- reverse_palette
Reverse the order of colors in the selected palette. Useful for adjusting color assignments to groups.
- use_grafify_theme
Apply the grafify theme for classic-style scientific plots. Provides clean, publication-ready styling.
- jitter_width
Amount of horizontal jittering for data points. Helps avoid overlapping points in categorical plots.
- transparency
Transparency level for data points (alpha value). Lower values make points more transparent.
- point_size
Size of data points in the plot.
- line_size
Thickness of lines (error bars, connecting lines).
- log_transform
Apply logarithmic transformation to axes. Useful for data spanning multiple orders of magnitude.
- add_statistics
Perform and display statistical analysis on the plot. Integrates with grafify's built-in statistical functions.
- stat_method
Statistical method to apply. Results will be displayed as text annotations or in separate output sections.
- posthoc_comparisons
Perform post-hoc pairwise comparisons after ANOVA. Uses emmeans for estimated marginal means and contrasts.
- comparison_method
Method for post-hoc comparisons. Pairwise compares all groups, vs reference compares to control group, trends test for patterns.
- befafter_shape_var
Variable defining before/after or timepoint for paired data. Used in before-after plot types.
- befafter_id_var
Variable identifying individual subjects or experimental units. Used to connect paired observations in before-after plots.
- show_individual_points
Display individual data points on the plot. Recommended for transparency and data exploration.
- show_summary_stats
Display summary statistics table with means, SD, SEM, and sample sizes.
- show_model_diagnostics
Display model diagnostic plots (Q-Q plots, residuals) when statistical analysis is performed.
- export_data
Export the processed data used for plotting. Includes summary statistics and model results.
- plot_width
Width of the plot in inches for optimal display and export.
- plot_height
Height of the plot in inches for optimal display and export.
- title_text
Custom title for the plot. Leave empty for automatic title based on variables and analysis type.
- x_label
Custom label for X-axis. Leave empty to use variable name.
- y_label
Custom label for Y-axis. Leave empty to use variable name.
- legend_position
Position of the plot legend.
- experimental_design
Type of experimental design for appropriate statistical analysis. Affects model specification and interpretation.
- random_effects
Variables to include as random effects in mixed models. Typically subjects, blocks, or experimental units.
- alpha_level
Significance level for statistical tests and confidence intervals.
Value
A results object containing:
results$todo | a html | ||||
results$main_plot | an image | ||||
results$summary_stats | a html | ||||
results$statistical_analysis | a html | ||||
results$posthoc_results | a html | ||||
results$diagnostic_plots | an image | ||||
results$qqplot | an image | ||||
results$palette_preview | an image | ||||
results$plot_interpretation | a html | ||||
results$export_info | a html |
Examples
# Example: Scientific scatter plot with error bars
data(clinical_data)
#> Warning: data set ‘clinical_data’ not found
grafify(
data = clinical_data,
vars = c("biomarker1", "biomarker2"),
groups = "treatment_group",
plot_type = "scatterbar",
error_type = "sd",
color_palette = "vibrant"
)
#> Error: object 'clinical_data' not found