The purpose of this post is to show an easy way to find data frame columns which have all missing data. We live in a world of big data. Often our data frames have several hundred if not thousands of variables. Not all variables are useful in that they may not contain any actual values. This is often the case with data exported from various business software. I like to get a list of these columns as part of the data processing step in a research project.
In this post I will show you how to count the number of items a user has selected (or bought, or triggered) within a data feed. Transaction data can be a treasure trove of user behavior if you can get it into the right format for counting. I have put together a simple example below on how you can use R to get counts of user click behaviors at various levels of aggregation.
The purpose of this post is to give an example of how to visualize a quadratic linear regression and also how to find the values of the predictor variable which give you the min and max fitted values of your dependent variable. Given the curvilinear relationship between the independent and and dependent variable, it is not always obvious which values of the independent variable will produce the highest and lowest predicted values in the dependent variable.
R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. You can embed an R code chunk like this: summary(cars) ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.
Hugo uses the excellent go html/template library for its template engine. It is an extremely lightweight engine that provides a very small amount of logic. In our experience that it is just the right amount of logic to be able to create a good static website. If you have used other template systems from different languages or frameworks you will find a lot of similarities in go templates. This document is a brief primer on using go templates.
Step 1. Install Hugo Goto hugo releases and download the appropriate version for your os and architecture. Save it somewhere specific as we will be using it in the next step. More complete instructions are available at installing hugo Step 2. Build the Docs Hugo has its own example site which happens to also be the documentation site you are reading right now. Follow the following steps: Clone the hugo repository Go into the repo Run hugo in server mode and build the docs Open your browser to http://localhost:1313 Corresponding pseudo commands: