Tag: column

Feed: R-bloggers. Author: Sharp Sight. Last week’s blog post about Amazon’s search for a location for a second headquarters left me thinking about the company’s growth. After looking at the long term growth of the stock price, it occurred to me that visualizing the stock price data would be a great example of how to create a line chart in R using ggplot2. So in this blog post, I’ll show you how to make a line chart with ggplot2, step by step. Let’s jump in. First, we’ll load several packages: we’ll load readr, which we’ll use to read in the ... Read More

Feed: R-bloggers. Author: Christoph Safferling. Q: What has 8 legs, 8 arms and 8 eyes?A: 8 pirates. Avast, ye scurvy scum! Today be September 19, the International Talk Like A Pirate Day. While it’s a silly “holiday”, it’s a great chance to switch your Facebook to Pirate English (“Arr, this be pleasing to me eye!”). Also, it’s as good a reason as any to go sailing the Internet Main, scouring for booty (data)! Well, any excuse, y’know… What is today known (more or less) as “Pirate English” (the bit with all the “arrr!”, “ahoy, ye matey!”, etc) is in fact ... Read More

Feed: Planet MySQL; Author: Shahriyar Rzayev; Hi,In this post I am going to show some tricks on using BATS framework + PyTest + Python X Plugin things, to have combined, simple test solutions. Let’s describe MySQL X Plugin side, here is the full class: # Connecting to MySQL and working with a Session import mysqlx class MyXPlugin: def __init__(self, schema_name, collection_name): # Connect to a dedicated MySQL server self.session = mysqlx.get_session({ 'host': 'localhost', 'port': 33060, 'user': 'bakux', 'password': 'Baku12345', 'ssl-mode': mysqlx.SSLMode.DISABLED }) self.schema_name = schema_name self.collection_name = collection_name # Getting schema object self.schema = self.session.get_schema(self.schema_name) # Creating collection self.schema.create_collection(self.collection_name, reuse=True) ... Read More

Feed: R-bloggers. Author: Chris Baker. Why care about patents? 1. Patents play a critical role in incentivizing innovation, withoutwhich we wouldn't have much of the technology we rely on everyday What does your iPhone, Google's PageRank algorithm, and a buttersubstitute called Smart Balance all have in common? …They all probably wouldn't be here if not for patents. A patentprovides its owner with the ability to make money off of something thatthey invented, without having to worry about someone else copying theirtechnology. Think Apple would spend millions of dollars developing theiPhone if Samsung could just come along and rip itoff?Probably not ... Read More

Feed: CDH – Cloudera Engineering Blog. Author: Cy Jervis. Modeling EHR Data in Healthcare In this case study, we take a look at modeling electronic health record (EHR) data with deep learning and Deeplearning4j (DL4J). We draw inspiration from recent research showing that carefully designed neural network architectures can learn effectively from the complex, messy data collected in EHRs. Specifically, we describe how to train an  long short-term memory recurrent neural network (LSTM RNN) to predict in-hospital mortality among patients hospitalized in the intensive care unit (ICU). Of particular note, we show that even for a dataset orders of magnitude ... Read More

Feed: R-bloggers. Author: business-science.io - Articles. Employee turnvover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. In this post, we’ll use two cutting edge techniques. First, we’ll use the h2o package’s new FREE automatic machine learning algorithm, h2o.automl(), to ... Read More

Feed: R-bloggers. Author: Jakub Kwiecien. This is common case when working with data that your source is a remote database. Usual ways to cope this when using R is either to load all the data into R or to perform the heaviest joins and aggregations with SQL before loading the data. Both of them have cons: the former one is limited by the memory capacity and may be very slow and the later forces you to use two technologies thus is more complicated and prone to errors. Solution to these problems is to use dplyr with dbplyr to communicate with ... Read More

Feed: R-bloggers. Author: Thinking inside the box. A brandnew and very exciting (to us, at least) package called pinp just arrived on CRAN, following a somewhat unnecessarily long passage out of incoming. It is based on the PNAS LaTeX Style offered by the Proceeding of the National Academy of Sciences of the United States of America, or PNAS for short. And there is already a Markdown version in the wonderful rticles packages. But James Balamuta and I thought we could do one better when we were looking to typeset our recent PeerJ Prepint as an attractive looking vignette for use ... Read More

Feed: AWS Government, Education, & Nonprofits Blog. Author: publicsector. Many local administrations deal with air pollution through the collection and analysis of air quality data. With AWS, cities can now create a serverless analytics solution using AWS Glue and AWS QuickSight for processing and analyzing a high volume of sensor data. This allows you to start quickly without worrying about servers, virtual machines, or instances, so you can focus on your core business logic to help your organization meet its analytics objectives.You can pick up data from on-premises databases or Amazon Simple Storage Service (Amazon S3) buckets, transform it, and ... Read More

Feed: R-bloggers. Author: Easy Guides. The ggpubr R package facilitates the creation of beautiful ggplot2-based graphs for researcher with non-advanced programming backgrounds. The current material presents a collection of articles for simply creating and customizing publication-ready plots using ggpubr. To see some examples of plots created with ggpubr click the following link: ggpubr examples. ggpubr Key features: Wrapper around the ggplot2 package with a less opaque syntax for beginners in R programming. Helps researchers, with non-advanced R programming skills, to create easily publication-ready plots. Makes it possible to automatically add p-values and significance levels to box plots, bar plots, line ... Read More