Part of the Process

Photo by  Štefan Štefančík  on  Unsplash Since my previous blog post I have made some progress on being able to replace most of what I currently use Excel for with R scripts. I have also launchjed a project to collect together some useful recipes for other Product Managers on GitHub called " R for Product Management " So far I have samples that cover the following data sources: Google Analytics - to my previous learning have added analysis of browsers used by site visitors and an example Shniy app for data exploration. InfluxDB - Working with time series data generated by the product, the two examples here are API response times and feature usage. This includes an example of manipulating time series data to set missing values to 0 for plotting. UptimeRobot - Simple example of taking error data and using a pivot table to explore the data, after some cleaning and filtering. This kind of workflow can be useful with large data sets. One thing that I really wa

Like a lot of Product Managers I use Excel with tools like Google Analytics a lot. Probably like many people I find Excel very frustrating. So having been technical in a previous life , I decided to give R a try . What is R?   R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. After taking a simple intro course, I started to look around at examples of doing more interesting things. From a work point of view Topic modelling seemed really interesting. Unfortunately a lot of the example code missed a large step (or two) in being useful!  So I forked the most complete example that I could find in GitHub called " Text-Mining ". This did lead me down a bit of a rabbit hole but eventually I had an R script that collected data, cleaned