Our first post about Health_Stack!

This post will provide an overview of the main data science cycle and how to interact with health_stack.

This post will provide a quick overview of R, some common terms and the data objects within R.

This post will provide a brief overview of the Rstudio environment that you will be using going forward.

To make data crunching a bit easier you can install packages that already have predefined functions within them. By the end of this post you will know how to install basic packages.

The pipe operator is useful to simplify both the writing and reading of code. In this post, we will cover the basic of how to use it.

In order to analyse data within RStudio you have to be able to import the data. In this post, we will cover the basics of how to read in data from a few common file formats.

Data cleaning is the process of detecting and correcting potentially inaccurate records in your dataset. This post will walk through some common techniques identify, and handle such records.

One of the most commonly performed actions in any data analysis is the creation or mutation of our data. This post will walk through how to generate new columns as well mutate existing columns using both base R functionality as well as the dplyr package.

In this post, we will cover the basics on how to manipulate data by subsetting data frames, filtering by a set variable and how to merge datasets together.