WHO

Celebrating £1.4bn for the Global Fund - Trying out gganimate on Tuberculosis data from getTBinR

Last week the UK pledged to contribute £467m a year for three years to the Global Fund. The money will be spent on: providing tuberculosis (TB) treatment for more than two million people; 90 million mosquito nets to protect people from malaria; and treatment for more than three million people living with HIV. This funding will drastically improve many peoples lives and needs to be celebrated even if it comes from a broadly unpopular source.

getTBinR 0.6.0 now on CRAN - from 80 variables to more than 450!

getTBinR 0.6.0 is now on CRAN and should be available on a mirror near you shortly! This update includes multiple new Tuberculosis datasets - increasing the available number of variables through getTBinR from 80 to over 450. To help support these new datasets the package now contains a dataframe listing the available datasets and search_data_dict can now also be used to search the data dictionary for variables by dataset. On top of this, this update contains suggested changes by reviewers (@rrrlw and @strengejacke) from JOSS (see here for the review thread).

getTBinR 0.5.7 now on CRAN - Tuberculosis reports and summary plots

getTBinR 0.5.7 is now on CRAN and should be available on a mirror near you shortly! This update mainly focussed on building out new country level Tuberculosis (TB) report functionality but along the way this led to a new summary plotting function that quickly and easily shows TB trends across regions and globally. I also had some fun developing a hexsticker (Tweet at me with something you made using the package to get a physical version - whilst my postage money lasts…), reducing the dependencies with itdepends and pkgnet and dealing with some breaking changes from an uncoming dplyr update (my own fault for missing a function import).

getTBinR 0.5.5 now on CRAN - 2017 data.

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 now on CRAN - new data, map updates and a new summary function.

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.

Exploring Estimates of the Tuberculosis Case Fatality Ratio - with getTBinR

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.

Exploring Global Trends in Tuberculosis Incidence Rates - with GetTBinR

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.

getTBinR

Quickly and easily import analysis ready TB burden data, from the World Health Orgnaisation (WHO), into R. The aim of the package is to speed up access to high quality TB burden data, using a simple R interface. Generic plotting functions are provided to allow for rapid graphical exploration of the WHO TB data. This package is inspired by a blog post, which looked at WHO TB incidence rates. See here for the WHO data permissions.