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.
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.
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!
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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!
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Out in the real world you may often find yourself working with data from multiple sources. It will probably be stored in separate files and you’ll need to combine them before you can attempt to answer any of your research questions. Click here to learn how you can combine data frames using a set of dplyr functions called joins.
The 4th post in the Scientist’s Guide to R series introduces data transformation techniques useful for wrangling/tidying/cleaning data. Specifically, we will be learning how to use the 6 core functions (and a few others) from the popular dplyr package, perform similar operations in base R, and chain operations with the pipe operator (%>%) to streamline the process. Click here for the full post.
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.