Tag: packages

Feed: R-bloggers. Author: Maëlle Salmon. At my day job I’m a data manager and statistician for an epidemiology project called CHAI lead by Cathryn Tonne. CHAI means “Cardio-vascular health effects of air pollution in Telangana, India” and you can find more about it in our recently published protocol paper . At my institute you could also find the PASTA and TAPAS projects so apparently epidemiologists are good at naming things, or obsessed with food… But back to CHAI! This week Sean Lopp from RStudio wrote an interesting blog post about internal packages. I liked reading it and feeling good because ... Read More

Feed: Hadoop – Cloudera Engineering Blog. Author: Sandy Ryza. Cloudera Manager lets you add a YARN service in the same way you would add any other Cloudera Manager-managed service. In Apache Hadoop 2, YARN and MapReduce 2 (MR2) are long-needed upgrades for scheduling, resource management, and execution in Hadoop. At their core, the improvements separate cluster resource management capabilities from MapReduce-specific logic. They enable Hadoop to share resources dynamically between MapReduce and other parallel processing frameworks, such as Cloudera Impala; allow more sensible and finer-grained resource configuration for better cluster utilization; and permit Hadoop to scale to accommodate more and ... Read More

Feed: R-bloggers. Author: Bob Carpenter. It was another productive week in Stan land. The big news is that Jonathan Auerbach reports that A team of Columbia students (mostly Andrew’s, including myself) recently won first place in a competition predicting elementary school enrollment. I heard 192 entered, and there were 5 finalists….Of course, we used Stan (RStan specifically). … Thought it might be Stan news worthy. I’d say that’s newsworthy. Jon also provided a link to the “challenge” page, a New York City government sponsored “call for innovations”: Enhancing School Zoning Efforts by Predicting Population Change. They took home a US$20K ... Read More

Feed: R-bloggers. Author: Euthymios Kasvikis. Introduction R Markdown is one of the most popular data science tools and is used to save and execute code to create exceptional reports whice are easily shareable. The documents that R Markdown provides are fully reproducible and support a wide variety of static and dynamic output formats. R Markdown uses markdown syntax, which provides an easy way of creating documents that can be converted to many other file types, while embeding R code in the report, so it is not necessary to keep the report and R script separately. Furthermore The report is written ... Read More

Feed: R-bloggers. Author: Fabio Veronesi. Power analysis is extremely important in statistics since it allows us to calculate how many chances we have of obtaining realistic results. Sometimes researchers tend to underestimate this aspect and they are just interested in obtaining significant p-values. The problem with this is that a significance level of 0.05 does not necessarily mean that what you are observing is real.In the book “Statistics Done Wrong” by Alex Reinhart (which you can read for free here: https://www.statisticsdonewrong.com/) this problem is discussed with an example where we can clearly see that a significance of 0.05 does not mean that ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Amelia Matteson. This article was written by Michael Rundell. A Curated List of Data Science Interview Questions Preparing for an interview is not easy – naturally there is a large amount of uncertainty regarding the data science interview questions you will be asked. No matter how much work experience or technical skill you have, an interviewer can throw you off with a set of questions that you didn’t expect. For a data science interview, an interviewer will ask questions spanning a wide range of topics, requiring strong technical knowledge and communication ... Read More

Feed: R-bloggers. Author: Florian Privé. In this post, I will talk about my package bigstatsr, which I’ve just presented in a lightning talk of 5 minutes at useR!2017. You can listen to me in action there. I should have chosen a longer talk to explain more about this package, maybe next time. I will use this post to give you a more detailed version of the talk I gave in Brussels. Motivation behind bigstatsr I’m a PhD student in predictive human genetics. I’m basically trying to predict someone’s risk of disease based on their DNA mutations. These DNA mutations are ... Read More

Feed: R-bloggers. Author: John Johnson. US Census Data The US Census collects a number of demographic measures and publishes aggregate data through its website. There are several ways to use Census data in R, from the Census API to the USCensus2010 package. If you are interested in geopolitical data in the US, I recommend exploring both these options – the Census API requires a key for each person who uses it, and the package requires downloading a very large dataset. The setups for both require some effort, but once that effort is done you don’t have to do it again ... Read More

Feed: R-bloggers. Author: Abhijit. This is a quick note about some tribulations I had running Rcpp (v. 0.12.12) code through RStudio (v. 1.0.143) on a Windows 7 box running R (v. 3.3.2). I also have RTools v. 3.4 installed. I fully admit that this may very well be specific to my box, but I suspect not. I kept running into problems with Rcpp complaining that (a) RTools wasn’t installed, and (b) the C++ compiler couldn’t find Rcpp.h. First, devtools::find_rtools was giving a positive result, so (a) was not true. Second, I noticed that the wrong C++ compiler was being called ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Shreya Gupta. 1. Objective Through this blog you will know about the Big data  applications and Big data uses in retail sector with examples along with use of RFID in retail. You will learn concepts related to Big data in retail industry like retail asset management, production tracking in retails industry, inventory control, Shipment, Regulatory Compliance, and Service and Warranty Authorizations and many other retail Big data use cases with examples. 2. Use of Big Data in Retail Industry With the growth in retail industry with millions of transactions spread across multiple ... Read More