In SValv/displayR: Easy and quick data exploration

Info

Dear User

This is a package for easy and quick exploration and visualisation of data made by Simon Valverde(@SValv), if you encounter any bugs or problems please feel free to contact me on github or via email to s.valverde146@gmail.com. Since I am farely new to coding and have no experience with development of anything this is actually very likely. I'd also welcome feedback of any kind to improve this little baby-project.

This project is generally directed to people with little experience in R who are interested in easy and quick visualisation and exploration.

Usage

displayR has only one function called "displayR" it has five arguments:

DF(default="None"): a dataframe or object convertable to a dataframe that will be displayed. If you dont specify a dataframe, a menu to select one will open. It works with most formats like .txt, .csv etc (using fread), it also works with .xls/.xlsx data using readxl. If you encounter any problems with loading the data, try loading it manually.

displayR()
displayR(data)

factordeclare(default= TRUE): if your dataframe isnt already structured (the factors are not declared as factors) this argument will do it for you by using the limit argument as margin.

displayR(data,factordeclare = FALSE)

limit(default=8): describes the maximum number of instances where the variable is converted into a factor, if the instances(determined by the length of the unique-vector) are higher, the variable wont be converted.

displayR(data,factordeclare = TRUE,limit = 8)

colorPalette(default="None"): this argument gives you more control about the coloring of your plots, if you specify colors you will see them as factor levels in the plots. Note that it is important that your color vector contains as many colors as the levels of your highest-level factor.

displayR(data,colorPalette = c("green","yellow","black",....,"other custom colors"))

if nothing is given the default palette is:

c("blue","red","green","orange","violet","black","cyan","yellow4","brown","grey60","pink")

dependencies and note of thanks

The dependencies of this package are as follows:

data.table

dplyr

DT

ggplot2

plotly

readxl

shiny

shinydashboard

shinythemes

Thanks a lot to the people providing these packages, to Julia Himmelsbach who helped me to come up with a few functionalities and to my colleage and friend Lena Pfeiffer who helped a lot with creative input.

Citations of packages

To cite package ‘data.table’ in publications use:

Matt Dowle and Arun Srinivasan (2019). data.table: Extension of data.frame. R package version 1.12.8. https://CRAN.R-project.org/package=data.table

To cite package ‘dplyr’ in publications use:

Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2020). dplyr: A Grammar of Data Manipulation. R package version 0.8.5. https://CRAN.R-project.org/package=dplyr

To cite package ‘DT’ in publications use:

Yihui Xie, Joe Cheng and Xianying Tan (2020). DT: A Wrapper of the JavaScript Library 'DataTables'. R package version 0.12. https://CRAN.R-project.org/package=DT

To cite ggplot2 in publications, please use:

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

To cite plotly in publications use:

Carson Sievert (2018) plotly for R. https://plotly-r.com

To cite readxl in publications use:

Hadley Wickham and Jennifer Bryan (2019). readxl: Read Excel Files. R package version 1.3.1. https://CRAN.R-project.org/package=readxl

To cite package ‘shiny’ in publications use:

Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2020). shiny: Web Application Framework for R. R package version 1.4.0.2. https://CRAN.R-project.org/package=shiny

To cite package ‘shinydashboard’ in publications use:

Winston Chang and Barbara Borges Ribeiro (2018). shinydashboard: Create Dashboards with 'Shiny'. R package version 0.7.1. https://CRAN.R-project.org/package=shinydashboard

To cite package ‘shinythemes’ in publications use: