R/shiny_onesampt.R

In ABACUS: Apps Based Activities for Communicating and Understanding Statistics

#' Shiny App to Demonstrate One-Sample Student's t-Test
#'
#' @name shiny_onesampt
#' @aliases shiny_onesampt
#' @description An interactive Shiny app to demonstrate one-sample Student's t-test.
#' @usage shiny_onesampt()
#'
#' @details The interactive Shiny app demonstrates the principles of the hypothesis testing of means
#'     in a one-sample design where the population variance is unknown.
#'     The true population parameters are provided by the user.
#'     The user changes the hypothesised population mean and other features and explores
#'     how Student's t-test compares the hypothesised mean
#'     with the mean of the sample randomly drawn from the population.
#'
#'     The left panel includes the user inputs for \strong{Simulation Features}, \strong{Population Parameters},
#'     \strong{Sample Characteristics}, and \strong{Distribution Function}.
#'     To use the app at first instance, just click the \code{Update} button.
#'     To alter the input values, edit the text box or move the point on the slider and
#'     explore the changes in different tabs (see below).
#'
#'     To obtain identical outcomes in a separate run of the app,
#'     set a common seed value at the bottom of the left panel and click \code{Update}.
#'     All subsequent updates will produce identical results provided other inputs are identical.
#'     The seed value is ignored when the option \code{check the box to update instantly} is selected.
#'
#' @return The outcomes are presented in several tabs.
#'     \item{Population}{contains the density plots of the population and
#'     rug plots of the sample units randomly drawn from the population.
#'     It also includes the population parameter values chosen by the user.}
#'     \item{Sample}{contains the dot plot and box plot of the sample drawn
#'     randomly from the population and rug plot of the sample units.
#'     It also includes the mean and standard deviation of the random sample.}
#'     \item{Test Statistic}{presents the plot showing the mean difference
#'     between the sample mean and hypothesised mean and corresponding 95\% confidence intervals (CI).
#'     The tab also contains the distribution of the test statistic \code{t}
#'     with the observed value of the test statistic and probabilities under the given value of the Type 1 error}
#'     \item{Summary}{includes the summary of the sampled data and outcomes
#'     from the one-sample Student's t-test. Different sections are:
#'     (1) Hypothesis, highlighting the null and alternative hypothesis;
#'     (2) Sample, tabulating the full sampled data;
#'     (3) Summary Statistics, summarising the summary information of the sample;
#'     (4) Test Statistic, presenting the outputs from the one-sample Student's t-test.
#'     (5) Confidence Interval, highlighting the mean difference and corresponding 95\% confidence intervals (CI).}
#'
#' @note \url{https://shiny.abdn.ac.uk/Stats/apps/}
#'
#' @author Mintu Nath
#'
#' @seealso Function in base R for normal distribution and t distribution including
#'          \code{\link{dnorm}}, \code{\link{pnorm}}, \code{\link{qnorm}}, \code{\link{rnorm}},
#'          \code{\link{dt}}, \code{\link{pt}}, \code{\link{qt}}, \code{\link{rt}}
#'          The app \code{\link{shiny_onesampz}} performs the hypothesis testing of mean
#'          when the population variance is unknown.
#'
#' @examples
#' if(interactive()){
#'     library(ggplot2)
#'     library(shiny)
#'     library(ABACUS)
#'     # Run shiny app
#'     shiny_onesampt()
#' }
#'
#' @import shiny
#' @import ggplot2
#' @export

# Function

shiny_onesampt <- function() {

  shiny::runApp(appDir = system.file("app_onesampt", package = "ABACUS"), launch.browser = TRUE)
  Sys.setenv("shiny_onesampt" = "")

}