R tutorial: Just getting started with R? Here is a post on inspecting univariate data

If you are new to R, then perhaps a look at simple univariate data is a good place to start.  In this RPubs post, I take a look at both categorical and numerical data.  It is quite easy to calculate descriptive statistics of univariate data and to visualize it using plots.  Click the link and have a look.

By the way, the file is also available on GitHub.

World Bank data on maternal mortality using R

The World Bank provides open data for many indicators across most countries, spanning the last few decades.

This data is available online with searches available by country codes (iso2c and iso3c), indicator names, and by dates. The indicators can be viewed here. It can also be accessed via an application programming interface (API).  The WDI library in R provides access through this API, allowing for easy search and retrieval of data.

In this post, written as an R-markdown file, and available on RPubs and GitHub, I showcase the WDI library by looking at maternal mortality rates for the United States, Brazil, and South Africa.

Follow the links and have a look.

R tutorial: Testing assumptions for parametric tests

In this post, written as an R-markdown file and posted on RPubs, I discuss the assumptions for the use of parametric tests in R.

Parametric tests such as the various t tests, analysis of variance (ANOVA), and correlations are only valid if certain assumptions are met. When these assumptions are not met, the use of these tests in your research may lead to false claims.

In the post I show you the most important assumptions and how to test for them using the R programming language.

The post is available on RPubs and the markdown file is on GitHub.

Rpubs markdown files and YouTube videos on R

R is a programming language designed by statisticians for statistical analysis. It is a free programming language and is available for download (Windows, Mac, and Linux).

Bar a few eccentricities, it is quite easy to learn R. We make extensive use of it in the Klopper Research Group, where, alongside other programming languages, I use it to teach my students how to conduct proper data analysis.

I have started to create a series of R markdown files that are published on the Rpubs website . I am also making a series of YouTube videos on the use of R. The first set is on the use of the Plotly library to create interactive HTML widget plots in R.

Course on SPSS for medical statistics

At a recent meeting of fellow surgeons in my department, an interesting difference of opinion arose.  It relates to our trainees’ knowledge of statistics.  Unfortunately, the meeting did not allow any time to properly discuss the topic.

Some background to illuminate your way.  Registration as a medical specialist in South Africa is regulated by the Health Professions Council.  In recent years, the Council has introduced the completion of a mandatory research project, culminating in a dissertation.  This accompanies the usual prescribed formal examinations.

Universities in the country manage the research projects by way of a Master’s degree, for which all trainees must register.

The difference of opinion was simple.  From the opposite corner of the ring, it was suggested that our trainees require no knowledge of statistical analysis and should hand in their data to a statistician and merely use the results in their reports.

I do not share this opinion and feel strongly that all medical professionals should have an understanding of the topic.  While not all doctors and specialists are interested in research, I do believe that an understanding of statistics empowers the individual when evaluating published research.  This in turns helps to inform and change their practice.  As a surgeon, I know it does mine.  With no formal program for statistical teaching in our department, I looked towards open education.

To this end, I was a leading proponent in getting the University of Cape Town to sign up with the Coursera and FutureLearn massive open online course platforms.  The creation of twelve courses were funded by the Vice Chancellor and my course on Understanding Medical Research was the first to launch on Coursera.  It has been a phenomenal experience and the feedback has been tremendous.

Unfortunately, austerity measures have curtailed these efforts.  I funded my second course on Coursera through an external loan.  It is on the use of Julia (mathematical biology using scientific computing) and was created in collaboration with the Applied Mathematics Department.  The honors section of the course is on data management and statistical analysis.

To further my resolve in teaching medical statistics, I have taken to the Udemy platform with a course on medical statistics using Mathematica.  In the last few days I have also launched a course on the use of SPSS in healthcare and life science statistics.  Udemy is an interesting platform and I would encourage its use.

Link to the course: SPSS for healthcare and life science statistics

My opinion, though, is clear.  Learning to analyze data, is an empowering skill for everyone in healthcare.