Author Archives: Monika Wahi

Dataset Source Documentation: Necessary for Data Science Projects with Multiple Data Sources

If you work on a big data project with multiple source datasets, you run the risk of forgetting exactly how you blended them together.

Dataset source documentation is good to keep when you are doing an analysis with data from multiple datasets. Read my blog to learn how easy it is to throw together some quick dataset source documentation in PowerPoint so that you don’t forget what you did.

Joins in Base R: Alternative to SQL-like dplyr

In base R, you can execute SQL-like joins, as long as you use the correct code syntax.

Joins in base R must be executed properly or you will lose data. Read my tutorial on how to correctly execute left joins in base R.

NHANES Data: Pitfalls, Pranks, Possibilities, and Practical Advice

If you are interested in population-level surveillance data, you might have thought about using NHANES data in portfolio projects.

NHANES data piqued your interest? It’s not all sunshine and roses. Read my blog post to see the pitfalls of NHANES data, and get practical advice about using them in a project.

Color in Visualizations: Using it to its Full Communicative Advantage

When using big data, you will want to make visualizations. How do you use color to the greatest communicative advantage?

Color in visualizations of data curation and other data science documentation can be used to enhance communication – I show you how!

Defaults in PowerPoint: Setting Them Up for Data Visualizations

The defaults in PowerPoint are really set up for making presentations, not data visualizations.

Defaults in PowerPoint are set up for slides – not data visualizations. Read my blog post for tips on reconfiguring PowerPoint to make it easy for dataviz!

Text and Arrows in Dataviz Can Greatly Improve Understanding

Adding text and arrows to diagrams can help your audience navigate the image, and understand what you are trying to communicate.

Text and arrows in dataviz, if used wisely, can help your audience understand something very abstract, like a data pipeline. Read my blog post for tips in choosing images for your data visualizations!

Shapes and Images in Dataviz: Making Choices for Optimal Communication

If you use good judgment in choosing chapes and images to add to your data visualizations, your audience will be enlightened.

Shapes and images in dataviz, if chosen wisely, can greatly enhance the communicative value of the visualization. Read my blog post for tips in selecting shapes for data visualizations!

Ask Me Anything About Data Science or Public Health!

If you want expert consultation every month from a professor, leader, and data scientist, then we have the perfect service for you.

Ask me anything about data science or public health every month! Subscribe to my “Ask Me Anything” membership, and get all your questions answered in real time!

Table Editing in R is Easy! Here Are a Few Tricks…

When you use a data analysis program like R or SAS, you often have to do some data editing. It can be difficult because the software was intended for calculations, not transformation.

Table editing in R is easier than in SAS, because you can refer to columns, rows, and individual cells in the same way you do in MS Excel. Read my blog post for example R table editing code.

R for Logistic Regression: Example from Epidemiology and Biostatistics

Logistic regression calculate the log odds of the probability of the outcome. Many people are used to using SAS for logistic regression, but you can also use R.

R for logistic regression in health data analytics is a reasonable choice, if you know what packages to use. You don’t have to use SAS! My blog post provides you example R code and a tutorial!

Verified by MonsterInsights