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.

Understanding binomial logistic regression using R

Logistic regression is a statistical test that uses independent variables (categorical or numerical) to predict a categorical dependent variable.  It is based on the principles of linear regression.  As the outcome (dependent) variable is categorical, though, logistic regression computes the probability of this variable.

There are many methods of creating and testing the validity of a logistic regression model.  In the link is a web page with an explanation of binomial logistic regression and how to use the R programming language to construct and understand your model.