RClean

Nicholas Uhorchak 2018-03-05

Section 1 Basic Information

1.1 Name

RClean

1.2 Title

RCleaner, an interactive data cleaning tool provides users the dynamic ability to import and clean data. At its core, it provides R users functionality similar to that of Microsoft Excel with regards to preparation of a dataset for analysis.

1.3 Description

1.3A Features

Utilizing R to import and clean data is often a time consuming task. Without preparation of the dataset in excel or other software, R users must use scripts or command line R code for this task. The Interactive Data Cleaning tool will afford users the ability to do the following:

• Initialize RCleaner gadget with a dataset
• Visually inspect the dataset called into the gadget
• Select data columns to remove
• Select data rows to remove
• Provide the ability to rename columns in the dataframe
• Provide the option to scale the data.
• Provide the option to mean center the data
• Provide the ability to encode nominal data to numerical data

1.3B End users

This analytic is being developed for those users in need of hasty data cleaning or those who would otherwise not wish to spend a large amount of time writing code to prepare data for analysis. Typical users will have working knowledge of R, however prefer the point and click abilities of Microsoft Excel or other similar software.

1.3C Required knowledge/skills

Users must be able to navigate R studio and understand how to use an R Gadget. In addition, they should be aware of the types of data contained in the dataset to be analyzed, whether numerical or categorical, such that they are aware of the application of some functions of this analytic tool.

1.3D Statistical methods utilized

• Mean center data
• Scale Data
• Generate indicator variables

1.3E R Packages utilized

This analytic will utilize the following existing R packages:

• shiny
• DT
• shinythemes
• markdown
• dummies

1.4 End user access

End users will call this gadget from the associated R package

1.5 Security concerns

None

1.6 Design constraints

Currently, the gadget only handles DF, matrix or tibble like objects with 2 or more columns. Single vectors are not handled.

Section 2 Delivery and Schedule Information

2.1 Feature Review

Feature	Description	Rank	Status	Value to user	Inputs	Outputs	Use?	Time?	Current or future version
Visual inspection of data	This feature will open the newly imported DF so the user can look at the data	1	COMPLETE	Quick and easy visual exploration of the dataset imported	Some dataset	Dataset output onto screen	Visual exporation of data	Yes	Current
Select releveant data columns to retain/remove	Allow the user to select what columns to either retain or remove from the current data	2	COMPLETE	Easily remove unwanted variables from the dataset	button click	Modified DF	Data cleaning	Yes	Current
Select releveant data rows to retain/remove	Allow the user to select what rows to either retain or remove from the current data	3	COMPLETE	Easily remove unwanted rows from the dataset	button click	Modified DF	Data cleaning	Yes	Current
Save clean data	User can save the "clean" data to a new dataframe in R	4	COMPLETE	Cleaned data saved for analysis	new name for clean DF	Clean DF	Save cleaned DF for future use	Yes	Current
Scale Data	Allow the user to scale the data	5	COMPLETE	Scale the data for future use	button click	Modified DF	Data prep	No	Current
Mean center data	Allow the user to center the data	6	COMPLETE	Mean center the data for future use	button click	Modified DF	Data prep	No	Current
Rename columns	Allow the user to rename columns in the DF	7	COMPLETE	Rename columns if necessary	Column names if necessary	Modified DF	Data cleaning	No	Future
Create indicator variables	Allow the user to create "dummy" variables to represent nominal data	8	COMPLETE	Create indicator variables	Variables to encode	Modified DF	Data prep	No	Future
Write "clean" data to excel	Allow user to write the clean data to new excel file	9	COMPLETE	Clean data is saved into external file for future use	file location	excel document	save file as excel doc for future use	No	Future
Modify DF cells	Allow users to click on a cell and change data values	10	not started	single cell value modification	N/A	modified DF	change cells	No	Future
Impute missing values	Allow the user to impute missing values	11	not started	NA	Method of imputation	Modified DF	Data prep	No	Future