Functional data can come from many different areas of study. Some of the most common examples come from finance (for example stock prices over time), or from health research (such as fMRI time series). Analyzing data of this form has been done traditionally using time series analysis techniques. However, viewing the data as functional, rather than individual observed points, can lead to more natural interpretations and analysis. Here we will be looking at a single example data set, and learning how to represent discrete data as functional data objects.

This post dives into the elucidate package and how it can help you explore data using sets of functions for interrogating, describing, visualising, interacting with, and correcting data. This is a key stage of any analysis where errors are found and interesting relationships begin to appear. Click here for the full post.

In this issue: Capturing Screen into GIFs with open source software High precision summations Placing axis labels on each facet with lemon pacakge Tidy evaluation in functions with {{}} from rlang package Referencing current data set when building sublayers in ggplot2 Screen Recording by Craig Hutton Demonstrating the functionality of a given function or technique is often more effective and efficient when using animated GIFs. The following two software will help you create GIFs with ease and for free!

Dates/times are the last type of data you’ll probably work with on a fairly regular basis. This post will show you how to use the lubridate tidyverse package in R so you’ll know how to handle dates & times when you encounter them. Click here to read the full post.

Graphing the trends of suicides in Florida from 2006 to 2017 among youth between 10 and 24 years of age.

Rcpp is an R library allowing for easy integration of C++ code in your R workflow. It allows you to create optimized functions for when R just isn’t fast enough. It can also be used as a bridge between R and C++ giving you the ability to access the existing C++ libraries. Continue reading to learn how you can use Rcpp in your projects!

Read full post

Factors are one of the two remaining types of data you’ll encounter on a fairly regular basis. This post will show you how to use the forcats tidyverse package in R so you’ll know how to handle factors when you encounter them. Click here to read the full post.

Being able to work with character strings is an essential skill in data analysis and science. In this post we’ll learn a few of the ways in which the stringr package and regular expressions (AKA “regex” or “regexps”) makes working with strings in R considerably easier. Click here to read the full post.

Bootstrapping is a statistical technique for analyzing the distributional properties of sample data (such as variability and bias). It has many uses, and is generally quite easy to implement. Continue reading to learn how you can perform a bootstrap procedure in R!

Read full post