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.
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.
The workshop introduces R and RStudio and makes the case for project-oriented workflows for applied data analysis. Using logistic regression on Titanic data as an example, the participants will learn to communicate statistical findings more effectively, and will evaluate the advantages of using computational notebooks in RStudio to disseminate the results