sexDisaggregate

To Obtain and Analyze Sex-disaggregated Datasets

Description

sexDisaggregate is an R package that separates CSV datasets by sex, performs comparison visualization and helps users to examine their datasets from a sex-disaggregated perspective.

Installation

To install the latest version of the package:

require("devtools")
devtools::install_github("lifangy6/sexDisaggregate", build_vignettes = TRUE)
library("sexDisaggregate")

To run the shinyApp:

sexDisaggregate::runSexDisaggregate()

Overview

To have a glance of the package:

ls("package:sexDisaggregate")
data(package = "sexDisaggregate")
browseVignettes("sexDisaggregate")

sexDisaggregate contains 2 functions:

Function separateCSV separates provided CSV file into 3 new versions: cleaned both-sex dataframe, female-only dataframe and male-only dataframe.

Function visualizeDiff visualize 4 graphs that show sex percentage and specific category vs. sex in the dataset.

Contributions

The author of the package is Fangyi Li.

• Package ggplot2 is used to create plots in function visualizeDiff.

• Package cowplot is used to comnine plots in function visualizeDiff.

• Package shiny is used to develop shinyApp of the package.

References

• Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, ipert A, Borges B (2022). shiny: Web Application Framework for R. R package version 1.7.3, https://CRAN.R-project.org/package=shiny.

• Claus O. Wilke (2017). cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. https://github.com/wilkelab/cowplot.

• Heinemann, A., Martinez, V. M., & Alas, C. R. (n.d.). Why we need more sex-disaggregated Social Protection Data and what we’re doing to get there. World Bank Blogs. Retrieved November 14, 2022, from https://blogs.worldbank.org/opendata/why-we-need-more-sex-disaggregated-social-protection-data-and-what-were-doing-get-there

• R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

• Wickham, H. and Bryan, J. (2019). R Packages (2nd edition). Newton, Massachusetts: O’Reilly Media. https://r-pkgs.org/

• Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.

• Silva, Anjali. “Anjalisilva/TestingPackage: A Simple R Package Illustrating Components of an R Package: 2019-2022 BCB410H - Applied Bioinformatics, University of Toronto, Canada.” GitHub, https://github.com/anjalisilva/TestingPackage.

Acknowledgements

This package was developed as part of an assessment for 2022 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. sexDisaggregate welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues. Many thanks to those who provided feedback to improve this package.

Tree Structure

- sexDisaggregate
  |- sexDisaggregate.Rproj
  |- DESCRIPTION
  |- NAMESPACE
  |- LICENSE
  |- README
  |- inst
    CITATION
    |- extdata
      |- example_data.csv
      |- example_barplot.png
      |- example_densityplot.png
      |- Overview.jpg
    |- shiny-scripts
      |- app.R
  |- man
    |- separateCSV.Rd
    |- visualizeDiff.Rd
    |- runSexDisaggregate.Rd
  |- R
    |- separateCSV.R
    |- visualizeDiff.R
    |- runSexDisaggregate.R
  |- vignettes
    |- Introduction_sexDisaggregate.Rmd
    |- example_barplot.png
    |- example_densityplot.png
    |- shiny_example.png
  |- tests
    |- testthat.R
    |- testthat
      |- test-separateCSV.R
      |- test-visualizeDiff.R

Quick Tutorial

Step 1. Separate example dataset

exampleDataPath <- system.file("extdata", "example_data.csv", package = "sexDisaggregate")
dataList <- sexDisaggregate::separateCSV(filepath = exampleDataPath, sexTag = "Sex", femaleTag = "F", maleTag = "M")
dataList

Step 2.1: Bar plot approach

plots1 <- visualizeDiff(dataList = dataList, yTag = "Asthma", visMethod = "Bar")
plots1

Step 2.2: Density plot approach

plots2 <- visualizeDiff(dataList = dataList, yTag = "Sodium Intake (mg)", visMethod = "Density")
plots2