In LieberInstitute/shinycsv: Explore a table interactively in a shiny application

Quick start to using to `r Githubpkg('LieberInstitute/shinycsv')`

## Track time spent on making the vignette
startTime <- Sys.time()

## Bib setup
library("knitcitations")

## Load knitcitations with a clean bibliography
cleanbib()
cite_options(hyperlink = "to.doc", citation_format = "text", style = "html")
# Note links won't show for now due to the following issue
# https://github.com/cboettig/knitcitations/issues/63

## Write bibliography information
bibs <- c(
    BiocStyle = citation("BiocStyle"),
    devtools = citation("devtools"),
    DT = citation("DT"),
    knitcitations = citation("knitcitations"),
    knitr = citation("knitr")[3],
    R = citation(),
    RColorBrewer = citation("RColorBrewer"),
    readxl = citation("readxl"),
    rio = citation("rio"),
    rmarkdown = citation("rmarkdown")[1],
    shinycsv = citation("shinycsv"),
    shiny = citation("shiny"),
    testthat = citation("testthat"),
    vcd = citation("vcd")
)

write.bibtex(bibs,
    file = "quickstartRef.bib"
)
bib <- read.bibtex("quickstartRef.bib")

## Assign short names
names(bib) <- names(bibs)

This package has the code for a shiny application that allows you to interactively explore a table. The application allows you to subset or sort your data in different ways, explore one variable at a time, or make two-way plots. It also shows you the code to reproduce the figures and can handle many different types of tables thanks to r citep(bib[['rio']]). The app also lets you choose which color to use in your plots.

You can view this shiny app deployed at libd.shinyapps.io/shinycsv. If you are interested in the showcase mode, then check out libd.shinyapps.io/shinycsv-showcase.

Basics

Install `r Githubpkg('LieberInstitute/shinycsv')`

R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. r Githubpkg('LieberInstitute/shinycsv') is a R package available via GitHub. R can be installed on any operating system from CRAN after which you can install r Githubpkg('LieberInstitute/shinycsv') by using the following commands in your R session:

## If needed:
# install.packages('devtools')

library("devtools")
install_github("LieberInstitute/shinycsv")

How to use

To use this package, simply run:

shinycsv::explore()

The above command will open a shiny application where you can either upload a CSV file that has the variable names in the first column, a Rdata file created with the save() function that has a single data.frame object stored in it, an Excel file with a table in the first sheet, or many other files as described at the rio vignette. Using a Rdata file allows you to have stronger control regarding the variable types (factor, date, etc) for each column of your table.

Asking for help

As package developers, we try to explain clearly how to use our packages and in which order to use the functions. But R has a steep learning curve so it is critical to learn where to ask for help. For r Githubpkg('LieberInstitute/shinycsv') please post issues in GitHub. However, please note that if you want to receive help you should adhere to the posting guidelines. It is particularly critical that you provide a small reproducible example and your session information so package developers can track down the source of the error.

Citing `r Githubpkg('LieberInstitute/shinycsv')`

We hope that r Githubpkg('LieberInstitute/shinycsv') will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!

## Citation info
citation("shinycsv")

Reproducibility

The r Githubpkg('LieberInstitute/shinycsv') package r citep(bib[['shinycsv']]) was made possible thanks to:

R r citep(bib[['R']])
r Biocpkg('BiocStyle') r citep(bib[['BiocStyle']])
r CRANpkg('devtools') r citep(bib[['devtools']])
r CRANpkg('DT') r citep(bib[['DT']])
r CRANpkg('knitcitations') r citep(bib[['knitcitations']])
r CRANpkg('knitr') r citep(bib[['knitr']])
r CRANpkg('RColorBrewer') r citep(bib[['RColorBrewer']])
r CRANpkg('readxl') r citep(bib[['readxl']])
r CRANpkg('rio') r citep(bib[['rio']])
r CRANpkg('rmarkdown') r citep(bib[['rmarkdown']])
r CRANpkg('shiny') r citep(bib[['shiny']])
r CRANpkg('testthat') r citep(bib[['testthat']])
r CRANpkg('vcd') r citep(bib[['vcd']])

Code for creating the vignette

## Create the vignette
library("rmarkdown")
system.time(render("shinycsv-quickstart.Rmd", "BiocStyle::html_document"))

## Extract the R code
library("knitr")
knit("shinycsv-quickstart.Rmd", tangle = TRUE)

## Clean up
file.remove("quickstartRef.bib")

Date the vignette was generated.

## Date the vignette was generated
Sys.time()

Wallclock time spent generating the vignette.

## Processing time in seconds
totalTime <- diff(c(startTime, Sys.time()))
round(totalTime, digits = 3)

R session information.

## Session info
library("devtools")
options(width = 120)
session_info()

Bibliography

This vignette was generated using r Biocpkg('BiocStyle') r citep(bib[['BiocStyle']]) with r CRANpkg('knitr') r citep(bib[['knitr']]) and r CRANpkg('rmarkdown') r citep(bib[['rmarkdown']]) running behind the scenes.

Citations made with r CRANpkg('knitcitations') r citep(bib[['knitcitations']]).