Are you parallelizing your raster operations? You should!

If you plan to do anything with the raster package you should definitely consider parallelize all your processes, especially if you are working with very large image files. I couldn’t find any blog post describing how to parallelize with the raster package (it is well documented in the package documentation, though). So here my notes.
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Thursday, 17 January 2019

2018 Italian general election: Details on my simulation

This article describes the simulation behind the app that you find here

This simulation of the results for the 2018 general election is based on the results from the last two national elections (the Italian parliament election in 2013 and the European Parliament election 2014) and national polls conducted until 16 February 2018. The simulation is based on one assumption, which is reasonable but not necessarily realistic: the relative territorial strength of parties is stable. From this assumption derives that if the national support for a party (as measured by national voting intention polls) varies, it varies consistently and proportionally everywhere. A rising tide lifts all boats and vice versa. The assumption has some empirical justification. If we compare the difference from the national support (in percentage) for each district in 2013 and 2014 we see a significant correlation, especially in the major parties.

Votes to party in the 2018 Chamber districts

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Tuesday, 27 February 2018

NDVI, risk assessment and developing countries

The Normalized Difference Vegetation Index (NDVI) estimates the greenness of plants covering the surface of the Earth by measuring the light reflected by the vegetation into space. The main idea behind the NDVI is that visible and near-infrared light is absorbed in different proportions by healthy and unhealthy plants: a green plant will reflect 50% of the near infrared-light it receives and only 8% of the visible light while an unhealthy plant will reflect respectively 40% and 30%. NDVI can then be used to quantitatively compare vegetation conditions across time and space (and indeed is quite widely used, a Google Scholar search on NDVI produced 60,500 hits).

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Thursday, 14 February 2013

tweets


Twitter: frbailo

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blogroll


RSS r-bloggers.com

  • The Bachelorette Ep. 2 – Petal to the Metal – Data and Drama in R
    Since only one man was eliminated this week (for his failure to adequately compliment Clare), this week’s blog post focuses on the suitors’ Instagram accounts. On average, the men have roughly doubled their growth in instagram followers since the show began. Please check out our dashboard (built with shiny) ... The post The Bachelorette Ep. […]
  • Jan Vitek – R MELTS BRAINS – or How I Learned to Love Failing at Compiling R
    Few weeks ago we finished Why R? 2020 conference. We had an honour to host Jan Vitek, a Professor of Computer Science at Northeastern University. This post contains a biography of the speaker and an abstract of his talk: R MELTS BRAINS - or How I Lear... The post Jan Vitek - R MELTS BRAINS […]
  • Turn a shiny application into a tablet or desktop app
    Since we first demoed it at our really successful trip to Strata London last year, a few people have asked... The post Turn a shiny application into a tablet or desktop app appeared first on Mango Solutions. The post Turn a shiny application into a tablet or desktop app first appeared on R-bloggers.
  • Upcoming workshop: From Excel to R
    Discover how to use R to break free from Excel’s limitations at Mirai’s From Excel to R workshop. Excel’s lengthy formulas and complex macros, files that take forever to open and crash as frequently as you try to update them, major mistakes due to a... The post Upcoming workshop: From Excel to R first appeared […]
  • eXplainable AI + Shiny = xai2shiny
    xai2shiny is a new tool for lightning-quick deployment of machine learning models and their explorations using Shiny.By Anna KozakThe explainability of machine learning models has already proven to be an essential part of building a successful model. T... The post eXplainable AI + Shiny = xai2shiny first appeared on R-bloggers.

RSS Simply Statistics

  • Palantir Shows Its Cards
    File this under long-term followup, but just about four years ago I wrote about Palantir, the previously secretive but now soon to be public data science company, and how its valuation was a commentary on the value of data science more generally. Well, just recently Palantir filed to go public and therefore submitted a registration […]
  • Asymptotics of Reproducibility
    Every once in a while, I see a tweet or post that asks whether one should use tool X or software Y in order to “make their data analysis reproducible”. I think this is a reasonable question because, in part, there are so many good tools out there! This is undeniably a good thing and […]
  • Amplifying people I trust on COVID-19
    Like a lot of people, I’ve been glued to various media channels trying to learn about the latest with what is going on with COVID-19. I have also been frustrated - like a lot of people - with misinformation and the deluge of preprints and peer reviewed material. Some of this information is critically important […]

RSS Statistical Modeling, Causal Inference, and Social Science