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Displaying our "R - Quality Control Individual Range Chart Made Nice" inside a Java web App using AJAX - How To.

Prerequisites: What you should have installed: Java, it can be OpenJDK, you can get it from here:  https://github.com/ojdkbuild/ojdkbuild Tomcat, any version from 8 up. Eclipse EE:  Eclipse IDE for Java EE . Spring Tools Suite For Eclipse: https://spring.io/tools . You can install it from Eclipse Marketplace. R: https://www.r-project.org . Inside R, at least these packages: install.packages( "tidyverse", dependencies = TRUE ) install.packages( "rmarkdown", dependencies = TRUE ) install.packages( "dygraphs", dependencies = TRUE ) install.packages( "qcc", dependencies = TRUE ) install.packages( "rattle", dependencies = TRUE ) install.packages( "Rcmdr", dependencies = TRUE ) install.packages( "stlplus", dependencies = TRUE ) Rstudio, to edit your RMarkdown files: https://www.rstudio.com . Rtools: https://cran.r-project.org/bin/windows/Rtools/index.html . Pandoc, this is to convert markdown files t...

R - Quality Control Individual Range Chart Made Nice.

In R we have the qcc package but charts are not very nice, specially if you want to put your chart in a HTML file. Here I describe the process of creating the chart starting by using the qcc package and ending by using our own calculations and a nice dygraphs chart. You might avoid all the comments if you go directly to my github.com repository: https://github.com/LaranIkal/R-ANALYTICS Note. Due to github restrictions for html files sizes, the html file needs to be downloaded before you can open it. If you want to continue here, you can see the R code and outputs I copied from the html file(  QualityControl_IndividualRangeChart.html  ) result from the R markdown file(  QualityControl_IndividualRangeChart.Rmd  ) on my github.com repository: # Loading needed libraries # R quality control library suppressWarnings( suppressMessages( library ( qcc ) ) ) # One of the R nice charts library suppressWarnings( suppressMessages( library ( dygraphs ) ) ) measurem...

Time Series Analysis With Documentation And Steps I Follow For Analytics Projects.

To do this I will create a prediction of the open values for Bitcoin in the next 3 days. The process I follow is based on CRISP-DM methodology:  https://www.datasciencecentral.com/profiles/blogs/crisp-dm-a-standard-methodology-to-ensure-a-good-outcome 1.- Planning the activities. To plan the activities I use a spread sheet document, below I show the spread sheet sample, if you would like the document, please go to the next link: https://github.com/LaranIkal/R-ANALYTICS/blob/master/BitCoinPredictionsAdmin%26FollowUp.ods Activity Activity Description DueDate Activity Owner Status Comments Functional Requirement Specification A Text Document explaining the objectives of this project. 4/19/2018 Carlos Kassab Done Get Data For Analysis Get initial data to create feasibility analysis 4/19/2018 Carlos Kassab Done ETL Development ETL to get final data for next analysis 4/20/2018 Carlo...