Tag: visualization

Feed: R-bloggers. Author: R on The CuRious Financial Risk ManageR. In this post we will close the trilogy on (old style) swap pricing. In particular, we will look at how downloading the data for the variable rate needed to calculate the variable leg accrual. Part 1gave the general idea behind tidy pricing interest rate swaps using a 7 linespipe Part 2went much more into detail and priced some real world contract comparing the resultsobtained vs Bloomberg and showing significantly good results. The only part missing was calculating the accrual for the floating leg. To do thiswe need the information about ... Read More

Feed: R-bloggers. Author: rOpenSci - open tools for open science. The grainchanger package provides functionality for data aggregation to a coarser resolution via moving-window or direct methods. Why do we need new methods for data aggregation? As landscape ecologists and macroecologists, we often need to aggregate data in order to harmonise datasets. In doing so, we often lose a lot of information about the spatial structure and environmental heterogeneity of data measured at finer resolution. The issues around scale disconnects are both conceptual and practical: Conceptual issues Technical issues The scale we measure this stuff at varies too Ecological response ... Read More

Feed: R-bloggers. Author: Keith Goldfeld. In the last two posts, I introduced the notion of time-varying intra-cluster correlations in the context of stepped-wedge study designs. (See here and here). Though I generated lots of data for those posts, I didn’t fit any models to see if I could recover the estimates and any underlying assumptions. That’s what I am doing now. My focus here is on the simplest case, where the ICC’s are constant over time and between time. Typically, I would just use a mixed-effects model to estimate the treatment effect and account for variability across clusters, which is ... Read More

Feed: R-bloggers. Author: hrbrmstr. Despite being a full-on denizen of all things digital I receive a fair number of dead-tree print magazines as there’s nothing quite like seeing an amazing, large, full-color print data-driven visualization up close and personal. I also like supporting data journalism through the subscriptions since without cash we will only have insane, extreme left/right-wing perspectives out there. One of these publications is The Economist (I’d subscribe to the Financial Times as well for the non-liberal perspective but I don’t need another mortgage payment right now). The graphics folks at The Economist are Top Notch and a ... Read More

Feed: Cloud – Cloudera Engineering Blog. Author: Shelby Khan. Fig 1 – Architecture Introduction Many of Cloudera’s customers set up Cloudera Manager to collect their clusters’ diagnostic data on a regular schedule and automatically send that data to Cloudera. Cloudera analyzes this data, identifies potential problems in the customer’s environment, and alerts customers, requiring fewer back-and-forths with our customers when they file a support case and provides Cloudera with critical information to improve future versions of all of Cloudera’s software. If Cloudera discovers a serious issue, Cloudera searches this diagnostic data and proactively notifies Cloudera customers who might encounter problems ... Read More

Feed: R-bloggers. Author: Nathan Piccini. Think of this as the one-stop-shop/dictionary/directory for your machine learning algorithms. The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you’ll find 101 machine learning algorithms, including useful infographics to help you know when to use each one (if available). 101 Machine Learning Algorithms Each of the accordian drop downs are embeddable if you want to take them with you. All you have to do is click the little ’embed’ button in the lower left hand corner and copy/paste ... Read More

Feed: R-bloggers. Author: Sebastian Wolf. Related R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more... If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook ... Read More

Feed: R-bloggers. Author: Mirai Solutions. The European R conference will visit Milan in 2020! Mirai Solutions is delighted to actively support and participate in the organization of the event. The European R Users Meeting (eRum) is a biennial conference, taking place in Europe during those years when the useR is held overseas. It is a unique chance for R practitioners to get together, exchange experiences, broaden their knowledge around R and initiate collaborations with like-minded people. After Poznan in 2016, and Budapest in 2018, the 2020 edition will take place in Milan, Italy, through May 27-30, as announced officially by ... Read More

Feed: R-bloggers. Author: Learning Machines. It can be argued that most important decisions in life are some variant of an exploitation-exploration problem. Shall I stick with my current job or look for a new one? Shall I stay with my partner or seek a new love? Shall I continue reading the book or watch the movie instead? In all of those cases the question is always whether I should “exploit” the thing I have or whether I should “explore” new things. If you want to learn how to tackle this most basic trade off read on… At the core this ... Read More

Feed: R-bloggers. Author: R on datawookie. For some time I’ve wanted to recreate the cover art from Joy Division’s Unknown Pleasures album. The visualisation depicts successive pulses from the pulsar PSR B1919+21, discovered by Jocelyn Bell in 1967. Album art.Data The first obstacle was acquiring the data. I found a D3 visualisation by Mike Bostock. This in turn pointed me to a CSV file in a gist belonging to @borgar. After reading the CSV data into pulsar I applied some light wrangling (the raw data is a matrix). library(dplyr) library(tidyr) pulsar read.csv(CSV_URL, header = FALSE) %>% mutate(row = row_number()) %>% ... Read More