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.
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.
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.
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.
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.