In dimitriskokoretsis/datavis: Functions for data visualization

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

datavis

This is a short summary of datavis' functionality. For a comprehensive guide aimed towards programming beginners, check the handbook Data visualization with the 'datavis' R package. For a full description of the datavis functions, check their help documentation in pdf form or by browsing the package after installation.

R package for easy data visualization

The goal of datavis is to simplify the creation, annotation and export of bar plots and box plots of publication-grade quality. It is meant for academic researchers of any programming experience - newcomers and programming experts alike.

To a large extent, datavis wraps around the well-known ggplot2 package, but specializes in very specific plots (dodged bar and box plots based on sample distributions), aiming to increase efficiency in handling these plots.

Why to use datavis

Using the datavis functions, you can easily perform the following tasks with very few lines of code:

Create bar/box plots with summary statistics:
- arithmetic or geometric mean
- plus/minus corresponding standard deviation, or custom, user-calculated error measure
- optionally, individual data points
Customize plots aesthetically.
Annotate plots with custom text above bars/boxes.
Export plots in various formats for any purpose.

Installation

You can install datavis from GitHub by executing the following lines in the R console:

install.packages("devtools")
devtools::install_github("dimitriskokoretsis/datavis")

Example

The following example demonstrates the use of datavis functions to create, annotate and export a bar plot.

Data import and plot creation

# Data import using the fread function of the data.table package
demo.data.1 <- data.table::fread("guide/demo_data/demo_data_1.csv")
knitr::kable(demo.data.1)

# Loading of datavis package
library(datavis)

# Creation of bar plot with the bar_point_plot function of datavis
# See function's help documentation for more information
plot.1 <- demo.data.1 |>
  bar_point_plot(x="factor.1", # X axis grouping based on "factor.1" field
                 y="value", # Y axis value is "value" field
                 color.group="factor.2", # Color grouping based on "factor.2" field
                 x.axis="Factor 1", # Give a better title to x axis
                 y.axis="Value", # Give a better title to y axis
                 legend.title="Factor 2", # Give a better title to the legend
                 jitterwidth=0.7) # Adjust horizontal jitter of individual data points

plot.1

Import of statistics data and plot annotation

# Import of statistics data from Tukey's honest significant difference (HSD) test
demo.data.1.TukeyHSD <- data.table::fread("guide/demo_data/demo_data_1_TukeyHSD.csv")
knitr::kable(demo.data.1.TukeyHSD)

# Annotation of original plot with the plot_stats function of datavis
# See function's help documentation for more information
plot.1.TukeyHSD <- plot.1 |>
  plot_stats(d=demo.data.1.TukeyHSD, # The data.frame containing the labels to be plotted.
             labels="HSDgroups", # The name of the labels column in the supplied data.frame.
             position="dodge") # Positioning of labels in the X dimension.

plot.1.TukeyHSD

Plot export

# Export of plot in PDF, SVG, PNG and Rds formats with the plot_save function of datavis
# See function's help documentation for more information
plot.1.TukeyHSD |>
  plot_save(filepath="guide/demo_plots/plot_1_TukeyHSD", # Path to export files
            height=4,width=5) # Dimensions in inches