graphR.

The purpose of graphR. is to automatize and simplify the analysis of RVTools exports and to give a visual presentation of the information contained within one Excel export. RVTools is a VMware utility that connects to a vCenter and gathers information with an impressive level of detail on the VMware environment (e. g. on virtual machines, on ESX hosts, on the network configuration). The data collection is fast and easy. The result can be stored in a Microsoft Excel file. RVTools exports are a great way to collect data on VMware environments. However, analyzing RVTool exports, especially of complex environments can be time-consuming, error-prone, and cumbersome.
That's where graphR. steps in. GraphR. processes RVTool exports which are saved as Microsoft Excel or as comma-separated files. It performs some statistical analysis on the data contained within the Microsoft Excel file. The dataset is visualized through some beautiful-looking diagrams. Finally, all tables and charts are assembled in one downloadable PDF report. Hence graphR. enables you to generate a concise report with some great graphics in order to derive meaningful insights on the analyzed VMware environment.

If you are interested, find out more at the graphR. website. There you can also try the app online and get started right away.

To provide feedback or to point out bugs please reach out via mail: graphr.feedback@gmail.com.

If you find the project helpful please consider supporting graphR. as a sponsor - Buy Me a Coffee.

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Table of Contents

• Prerequisites
• Getting Started
• Customize
• How to use graphR.
• Built With
• Author
• Support

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Prerequisites

To run graphR. you just need an environment that supports Docker containers. To customize graphR. according to your needs the installation of the open-source programming language R is recommended.

Getting Started

The easiest way to use graphR. is to pull the latest pre-build Docker container from Dockerhub and run it within your environment. The following commands will download graphR. from Dockerhub and make it available on your environment using it's ip-address

docker pull smichard/graphr
docker run -d -p 80:3838 smichard/graphr

Customize

To customize graphR. according to your needs, e. g. by adding new ways to plot the data, altering threshold values, or adding a custom design just clone this repository:

git clone https://github.com/smichard/graphR.git

Since the core of graphR. is written in R the installation of R is recommended to see the changes taking effect. If you are using R-Studio as a code editor the graphr_dashboard.Rproj file contains all necessary files to adjust graphR.

Following is a short description of the most important files:

• app.R - the main file, which is needed by the Shiny web framework to display the web app. Here the GUI of the web app is described, also the libraries.R and the server_rv.R files are sourced
• server_rv.R - contains all necessary functions to ingest the raw data, perform some basic analysis, generate diagrams, and to finally generate the pdf report
• plottingFunctions.R - a set of functions to display text, data frames and diagrams on slides
• libraries.R - contains a list of all required R packages, and also sources the plottingFunctions.R file

In case you want to use custom backgrounds according to your corporate identity just replace the image files within the /graphr/backgrounds folder and make sure to use the .png file format. The recommended image dimensions are 960 px times 540 px.

Once all changes are done you can build your own custom graphR. container using the following commands:

docker build -t <project name> .
docker run -d -p 80:3838 <project name>

How to use graphR.

The use of graphR. is designed to be simple:

1. Collect the data with the RVTools and save the export as .xls, .xlsx or as .csv file
2. Upload the .xls / .xlsx file (recommended) or the tabvInfo.csv to graphR. and hit Generate Report
3. Enjoy your report

get a glimpse through this YouTube video:

Built With

• R - The open source programming language for statistical computing
• R-Studio - Used as code editor for R and for debugging and visualization
• Shiny - Used as web application framework for R
• Docker - Used to package all dependencies into one container

Author

• Stephan Michard - reach out on Twitter or via mail.

Support

Please file bugs and issues on the GitHub issues page. The code and documentation are released with no warranties or SLAs and are intended to be supported through a community-driven process. If you find the project helpful and it adds value to your work, it would be nice if you would support the project as a sponsor, to ensure the long-term existence of the project - Buy Me a Coffee.