getTBinR 0.5.5 is now on CRAN and should be available on a mirror near you shortly! This update is mainly about highlighting the availability of TB data for 2017, although some small behind the scenes changes were required to get the code set up going forward for yearly updates. A few more plotting options have been added, along with the corresponding tests (definitely the most exciting news). The full changelog is below along with a short example highlighting some of the changes in the 2017 data.
getTBinR 0.5.4 is now on CRAN and should be available on a mirror near you shortly! This update includes an additional data set for 2016 containing variables related to drug resistant Tuberculosis, some aesthetic updates to mapping functionality and a new summarise_tb_burden function for summarising TB metrics. Behind the scenes there has been an extensive test overhaul, with vdiffr being used to test images, and several bugs fixes. See below for a full list of changes and some example code exploring the new functionality.
Introduction I recently attended the Public Health Research and Science Conference, run by Public Health England (PHE), at the University of Warwick. I was mainly there to present some work that I have been doing (along with my co-authors) estimating the direct effects of the 2005 change in BCG vaccination policy on Tuberculosis (TB) incidence rates (slides) but it was also a great opportunity to see what research is being done within, and partnered with, PHE.
This is a quick post exploring estimates of the case fatality ratio for Tuberculosis (TB) from data published by the World Health Organisation (WHO). It makes use of getTBinR (which is now on CRAN), pacman for package management, hrbrthemes for plot themes, and pathwork for combining multiple plots into a storyboard. For an introduction to using getTBinR to explore the WHO TB data see this post.
It is estimated that in 2016 there was more than 10 million cases of active TB, with 1.
In November I attended Epidemics, which is a conference focused on modelling infectious diseases. There was a lot of great work and perhaps most excitingly a lot of work being offered as R packages.
I’ve recently begun wrapping all my analytical work in R packages, as it makes producing reproducible research a breeze! Unfortunately all of this work is still making it’s way towards publication and for a variety of reasons can’t be shared until it has passed this hurdle.
This interactive dashboard uses data on Tuberculosis incidence from 1913-1916 released by Public Health England and combines it with data on the interventions against Tuberculosis that have been discovered/implemented over the last century. The data was cleaned and imported into R using the tbinenglanddataclean R package, which also contains information on how to apply for additional data, scripts to clean data extracts and graphing functions to visualise them. The dashboard is a work in progress and additional interventions, new figures and increased interactivity will be added over time.
Previously I have looked at visualising the Property Partner portfolio using tableau, and explored their resale date from July 2017. In this post I will be exploring the August Open House resale data focussing on property premiums over both initial and latest valuation. The code for this post is available here.
Property Partner advertises returns by combining both projected capital and rental returns. Properties are held for 5 years at which point any capital gain can be realised.
As peer to peer lending matures platforms have begun to have increasingly divergent stances on sharing their data, making it increasingly important that their is external pressure on them to improve their data sharing. This blog series will focus on platforms sharing their data, exploring what their data is saying and suggesting possible changes to their releases that would make it easier for investors to gain insights on their own.
We are again looking at the peer to peer (P2P) loan book for funding circle (FC), with the focus being the variation in return based on portfolio composition and diversification. FC states that the average return on investment is 6.6%, with 93% of investors that invested in more than 100 companies, with a maximum exposure of 1% earning 5% or more. The original purpose of this blog was to look at various portfolio’s and estimate the risk that they carry for the investor.
This is the first in a series of blog posts looking at P2P lenders, which will be aimed at surfacing more details about the structures of their loan books. I hope to expand this series by looking at optimal strategies, predictive modelling, and interactive data science.
Since 2005, with the launch of Zopa P2P companies have provided a middle path between cash holdings, and investing in stocks and shares. Funding circle, which was launched in 2010, has currently facilitated over £1 Billion in loans to British businesses.