Lazy Bayesian

One skill that everyone in R should have is how to read in external data files. Many people who have some exposure to R will have some familiarity with this skill, but little knowledge of the many formats R can handle. This is often because many people's exposure is from a singular class or a project they did once. My hope is to provide the reader with a broader understanding of R's ability to handle a number of data formats. In this post, I will cover, How to read in .csv, Stata, SPSS, SAS, and Excel spreadsheet files. Some formatting options and different abilities you ought to know. Some explanations regarding help documentation and using function arguments/options. Saving and loading Rdata files for minimal hassle once the data is just the way you want it. Reading in Text Files and Function Options The basic function for reading in data is read.table() . I mention this one first because the other functions for reading in external data are based off of this one. In ...

Continuing on with the discussion of for loops and apply functions bring us to another set of apply functions used to, well, apply a function to data in different ways. In this post, I will be: Discussing the arrays or data arrangements for which the different apply functions are designed. That is, when to use each one. Comparing for loops to tapply, lapply, and sapply. I will write for loops for each so you can better familiarize yourself with for loops and situations where you can use the apply functions, instead. The data I will be using for this is the same data set that I used for the apply function post . This is some code I used to prepare the data to get it to its current state. Some of which I will be discussing later. I mostly provide this for the sake of disclosure and clarity. lapply and sapply: Apply a function over a Vector or List This is the most apparent and obvious replacement to a for loop. You give lapply the information set that you wish to iterat...

There are many options when it comes to statistical computing, but R is freely available, powerful, robust, and always getting better. Most statistical software packages have exorbitant costs associated with obtaining personal or group licenses. But with R, you get an extremely powerful software package that is just as good, if not better, for no cost! This software is ever-improving and growing thanks to the many people who contribute to this project and make this all possible. This post is designed to be a first time exposure to R for those with no experience and want to start learning how to code. Whether you are a student in a stats course trying to learn or are trying to acquire a little R know-how in order to expand you business intelligence skills, this post is designed to help people get started. In this post, I will be giving you a basic knowledge of R skills so you can start doing simple analyses quickly. Specifically, I will be covering How to acquire R and Rstudio. Rs...

When analyzing data you often have to iterate through a set of values, or apply the same function to to that set. While R does not have a great reputation for iterative processes, the apply functions are a way around writing a slow for loop. Mastering the use of the apply functions will make your coding much more efficient and versatile. In this post, I will discuss the following: for loops. When and how to use them. I'll also briefly mention while loops. How to use apply . I will address  tapply ,  lapply , and  sapply in a subsequent post. To help demonstrate how the apply function can be used instead of a for loop, I will carry out the same task using both methods. Before I do that, I am going to go over some looping basics for those who may be unfamiliar or may need a review. For Loop Basics A for loop iterates through the elements of a vector (a set of values), where at each iteration will be represented by the object provided in the for statement. ...

Data analysts don't always have the luxury of having numerical data to analyze. Many times data comes in the form of open text. For example, consumer product reviews or feedback, and comment threads through online merchants or CRM (customer relationship management, e.g. salesforce) portals can all be open text. It's no simple task turning open text into usable information. Word clouds are one way of approaching this task by highlighting superlative terms. There are a number of word cloud libraries in R, my favorite being "wordcloud2". It outputs an html document that allows you to hover over cloud terms to see its frequency. I mention this because word clouds are so common, however, I won't be spending any more time on this post about them. In this post I'll be discussing the following: A very brief discussion about extracting online data using 'rvest'. Basic options for cleaning text data. The polarity function from the qdap package. We live...

Network analysis is just a moniker for graphically describing network relationships. Whether you are a health official trying to describe the spread of communicable diseases or a business analyst describing the progress of a sales campaign or incentive, network analysis helps others view and better understand a network dynamic. You will need to download the 'network' package for this. In this post I will be doing the following: Provide a simple made up example to understand what network analysis is. Expand upon the simple example by adding hyper edges, different shapes and colors, and changing labels for vertices and edges to convey additional information. Provide R code with explanations of how to generate these graphics. Let's begin with a quick example so it is clear what network analysis is. At its simplest, a network analysis is a graphical depiction of the movement of some unit among various entities. In the above graphic, I have nine entities with the arr...

This post is intended for those with some exposure to R and shiny. If you are brand new to Shiny or Rmarkdown, then you may want to review this post before proceeding onward. I'll address the following: Loading and using data in your document Adjusting margins in your shiny document. Margins are by default set at a specific width for all shiny documents. Provide example code for R, Rshiny, Rmarkdown dashboard. Includes two selector inputs, one to choose which column of the daily trading data to use and the other to select which cryptocurrencies to plot. date range input render table with correlation matrix render line graph with options to select which cryptocurrencies to graph. On my last post I gave an explanation of the tutorial code that appears when you open a new Rmarkdown document. This time I built a small dashboard with online cryptocurrency trading data. I pulled this data from this webpage which has all sorts of cyrpto trading data. I used the three daily...

Creating dashboards is an excellent way to present dynamic and actionable analytic output. There is a plethora of proprietary dashboard software packages but they cost exorbitant amounts to do something that isn't as powerful or flexible as R shiny, which is freely available. Admittedly, many of these software packages provide data / database integration and other bells and whistles, but you can accomplish the same things with a little know how. My hope is to enable people to produce their desired and perhaps needed dashboard free of cost (other than man hours) without having to commit to a third party vendor. I'll be covering basics here, but if people are also interested in more advanced features such as interactive plots, I can write another tutorial going over that. I use R Markdown because I find using two R files, one for the UI and the other for the R code (server file) is terribly obnoxious and a bit cumbersome. R Markdown allows you to do it all in one .rmd file. R...

WinBUGs and JAGS (Just Another Gibbs Sampler) are convenient and effective tools for estimating bayesian models for your data. Both use an openBUGs style syntax, although they do have their differences (e.g. censored data, or matrix operations). Even more convenient is that there are packages that allow you to pass BUGs syntax, call the Gibbs sampler, and pull the MCMC chain(s) back all within R. For me, the choice of whether to use WinBUGS, or JAGS (or openBUGs for that matter) is more a question of what OS you are running. WinBUGs is a Windows application and JAGS runs on OSX. I've never attempted to run these on Linux, so if anyone knows, feel free to provide that in the comments. If you need to obtain WinBUGS or JAGS, click on the hyperlinks to download them. The data I'm using comes from the 2010 US Census Data. It contains the median age for men and women per "place". In census language, "place" typically refers to cities or towns. Some cleaning ...