As the third post in the Scientist’s Guide to R series (click here for the 1st post), we advance to the brink of the next major stage of the data analysis with R process: cleaning and transforming data. However, before we can clean or transform anything we will need to know how to do a few basic things and familiarize ourselves with some common data structures in R, which are the topics of this post.
The second post in the Scientist’s Guide to R series covers everything you need to know to get started with importing data from a variety of sources (.csv, .txt, .xlsx, etc) into R. Click here to read the full post.
In 2016, the Observatory for Population & Public Health of British Columbia launched the Chronic Disease Dashboard, an online reporting tool designed to address the gap in surveillance of chronic diseases. To protect against re-identification risks, the Ministry of Health required redacting small counts prior to releasing disease rates into public domain. These preparations, when conducted manually, have proven to be arduous, time consuming, and prone to human error.
This tutorial will be the first of many blog posts for new researchers and science program students/trainees on how to use R as an analytical and productivity tool in the process of conducting scientific research. Click here for the full post.
In the 5th post of the Scientist’s Guide to R series we explore using the tidyr package to reshape data. You’ll learn all about splitting and combining columns and how to do wide to long or long to wide transformations. Click here for the full post.