Tag: Hadoop

Feed: R-bloggers. Author: xi'an. I came across this question on X validated that wondered about one of our examples in Monte Carlo Statistical Methods. We have included a section on Laplace approximations in the Monte Carlo integration chapter, with a bit of reluctance on my side as this type of integral approximation does not directly connect to Monte Carlo methods. Even less in the case of the example as we aimed at replacing a coverage probability for a Gamma distribution with a formal Laplace approximation. Formal due to the lack of asymptotics, besides the length of the interval (a,b) which ... Read More

Feed: R-bloggers. Author: Anisa Dhana. In comparison with other statistical software (e.g., SAS, STATA, and SPSS), R is the best for data visualization. Therefore, in all posts I have written for DataScience+ I take advantage of R and make plots using ggplot2 to visualize all the findings. For example, previously I plotted the percentiles of body mass index in the NHANES 2005-2014 and got exactly same results as the paper published in JAMA. In this post, I will make a map of the prevalence of Alzheimer disease mortality by the state in the USA. The Centers for Disease Control and ... Read More

Feed: R-bloggers. Author: Jozef's Rblog. In this summertime post in the case4base series, we will look at useful tools in base R, which let us profile our code without any extra packages needed to be installed. We will cover simple and easy to use speed profiling, more complex profiling of performance and memory and, as always, look at alternatives to base R as well, with a special shout out to profiling integration in RStudio. Base function system.time returns the difference between two proc.time calls within which it evaluates an expression provided as argument. The simplest usage can be seen below: ... Read More

Feed: R-bloggers. Author: Thinking inside the box. A new RcppArmadillo release 0.9.100.5.0, based on the new Armadillo release 9.100.5 from earlier today, is now on CRAN and in Debian. It once again follows our (and Conrad’s) bi-monthly release schedule. Conrad started with a new 9.100.* series a few days ago. I ran reverse-depends checks and found an issue which he promptly addressed; CRAN found another which he also very promptly addressed. It remains a true pleasure to work with such experienced professionals as Conrad (with whom I finally had a beer around the recent useR! in his home town) and ... Read More

Feed: IBM Big Data & Analytics Hub - All Content; Author: danny-arnold; Oracle generated a lot of buzz prior to Oracle OpenWorld 2017 last September with their announcement of the world’s first self-driving database - Oracle Autonomous Database. However, not many details were released at announcement time. Now that the first Oracle Autonomous Database service, Autonomous Data Warehouse Cloud, was finally released in March 2018 let’s take a look at what Oracle provides.What is the Autonomous Database? Oracle Autonomous Database is comprised of Oracle Database 18c Enterprise Edition (with all database options and management packs included from a software packaging ... Read More

Feed: R-bloggers. Author: . Topics and Categories in the Russian Troll Tweets I decided to return to the analysis I conducted for the IRA tweets dataset. (You can read up on that analysis and R code here.) Specifically, I returned to the LDA results, which looked like they lined up pretty well with the account categories identified by Darren Linvill and Patrick Warren. But with slightly altered code, we can confirm that or see if there’s more to the topics data than meets the eye. (Spoiler alert: There is more than meets the eye.) I reran much of the original ... Read More

Feed: R-bloggers. Author: Ilya Kipnis. This is a review of James Picerno’s Quantitative Investment Portfolio Analytics in R. Overall, it’s about as fantastic a book as you can get on portfolio optimization until you start getting into corner cases stemming from large amounts of assets. Here’s a quick summary of what the book covers: 1) How to install R. 2) How to create some rudimentary backtests. 3) Momentum. 4) Mean-Variance Optimization. 5) Factor Analysis 6) Bootstrapping/Monte-Carlo simulations. 7) Modeling Tail Risk 8) Risk Parity/Vol Targeting 9) Index replication 10) Estimating impacts of shocks 11) Plotting in ggplot 12) Downloading/saving data ... Read More

Feed: R-bloggers. Author: Tim Bock. Step 1: Create a Tidy data frame The very first step in creating visualizations is to get the data in a useful format. In the case of Sankey diagrams, the trick is to get the data into the tidy data format. This post uses a simple example to make it clear how everything fits together. Below, you can see the R code to create a small data frame. I’ve shown this as a table, followed by the resulting Sankey diagram. my.data = data.frame(Married = c("Yes","Yes", "Yes", "No", "No"), Pet = c("Yes", "Yes", "No", "Yes", "No"), ... Read More

Feed: R-bloggers. Author: Jakob Gepp. A while back, I started to create an R package that would help me and my collegues at STATWORX with our daily work. After writing the DESCRIPTION file, I did not want to do this ever again. I found the desc package which let's you parse, manipulate and reformat this file via some functions and ergo a script. If you want more info on that, check out their github. However, I was too lazy still, because I had to manually write the NEWS file. I longed for the functionality of editing the NEWS file in ... Read More

Feed: R-bloggers. Author: mikerspencer. I was at the EdinbR talk this week by the RStudio community lead – Curtis Kephart. It was really interesting, but I disagree with his suggestion to point and click different parameters when you want to generate multiple reports from the same RMarkdown file. This might be acceptable if you have one or two, but any more and the chance for error and tedium is greatly increased. This blog post shows you how to loop (yes – an actual for loop!) through a variable to generate different reports for each of its unique values. First, we ... Read More