R-Analytics

Note. This article was left as reference, for an improved version, go to:  http://laranikalranalytics.blogspot.com/2021/03/updated-using-r-and-h2o-to-identify.html Introduction: We will identify anomalous products on the production line by using measurements from testing stations and deep learning models. Anomalous products are not failures, these anomalies are products close to the measurement limits, so we can display warnings before the process starts to make failed products and in this way the stations get maintenance.  Before starting we need the next software installed and working: - R language installed. - All the R packages mentioned in the R sources.( On my GitHub  ) - Testing stations data, I suggest to go station by station. - H2O open source framework. - Java 8 ( For H2O ). Open JDK:  https://github.com/ojdkbuild/contrib_jdk8u-ci/releases - R studio. Get your data. About the data: Since I cannot use my real data, for this article I am usi...

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...

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...

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...