README.md
In UBC-MDS/strapvizr: Bootstrapped Confidence Intervals, Plots, and More

StrapvizR

Performs bootstrapping of a sample to produce plots and statistics for use in final reports and documents.

The purpose of this package is to simplify and automate the process of creating simple bootstrap distributions of numerical samples. The package has a module which intakes a sample and relevant parameters such as the desired confidence bounds and number of simulations. The module will perform the simulation statistics to generate the bootstrap distribution and relevant statistics such as the sample mean and bootstrapped confidence interval. The package also has a module for visualization of the bootstrapped confidence interval, and for creating a professional publication-ready table of the relevant statistics.

The package builds on the dplyr package and the ggplot2 package to achieve the above mentioned functionalities. The BootCI package seems to streamline the confidence interval process but does not include plotting support. StrapvizR streamlines the process of bootstrapping from data to visualization and embedding in documents which is not available as a single bundle. Some tutorials on bootstrap confidence intervals at sources (1), (2) and (3) encourage the reader to plot the results manually.

You can install the latest version of strapvizr from GitHub

# install.packages("devtools")
devtools::install_github("UBC-MDS/strapvizr")

To import the strapvizr package

library(strapvizr)

Please find the detailed documentation here.

bootstrap: Returns a sampling distribution of specified replicates is generated for a specified estimator with replacement for a given bootstrap sample size.
calculate_boot_stats: Calculates a confidence interval for a given sampling distribution as well as other bootstrapped statistics.
plot_ci: Creates a histogram of a bootstrapped sampling distribution with its confidence interval and observed sample statistic.
tabulate_stats: Generates a table that contains a given sampling distribution’s mean and standard deviation along with relevant statistics as well as a summary table of the bootstrap distributions parameters. The code automatically saves the tables as html documents.