L is for Log Transformation
When visualizing data, outliers and skewed data can have a huge impact, potentially making your visualization difficult to understand. We can use many of the tricks covered so far to...continue reading.
When visualizing data, outliers and skewed data can have a huge impact, potentially making your visualization difficult to understand. We can use many of the tricks covered so far to...continue reading.
A few times in this series, I’ve wanted to display part of a dataset, such as key variables, like Title, Rating, and Pages. The tidyverse allows you to easily keep...continue reading.
Today, we’ll start digging into the wonderful world of joins! The tidyverse offers several different types of joins between two datasets, X and Y:left_join – keeps all rows from X...continue reading.
The tidyverse includes many packages meant to make importing, wrangling, analyzing, and visualizing data easier. The haven package allows you to important files from other statistical software, such as SPSS,...continue reading.
For the letter G, I’d like to introduce a very useful function: group_by. This function lets you group data by one or more variables. By itself, it may not seem...continue reading.
For the letter F – filters! Filters are incredibly useful, especially when combined with the main pipe %>%. I frequently use filters along with ggplot functions, to chart a specific...continue reading.
For the letter E, I want to talk about a set of operators provided by tidyverse (specifically the magrittr package) that makes for much prettier, easier-to-read code: pipes. The main...continue reading.
For the letter D, I’m going to talk about the dummy_cols functions, which isn’t actually part of the tidyverse, but hey: my posts, my rules. This function is incredibly useful...continue reading.
For the letter C, we’ll talk about the coalesce function. If you’re familiar with SQL, you may have seen this function before. It combines two or more variables into a...continue reading.
Moving on to the letter B, today we’ll talk about merging datasets that contain the same variables but add new cases. This is easily done with bind_rows. Let’s say I...continue reading.
The arrange function allows you to sort a dataset by one or more variable, either ascending or descending. This function is especially helpful if you plan on aggregating your data...continue reading.
As I was preparing some graphics for a presentation recently, I started digging into some of the different color palette options. My motivation was entirely about creating graphics that weren’t...continue reading.
As I conduct some analysis for a content validation study, I wanted to quickly blog about a fun plot I discovered today: ggpairs, which displays scatterplots and correlations in a...continue reading.
Stacked Bar Chart for Rank Data At work on Friday, I was trying to figure out the best way to display some rank data. What I had were rankings from...continue reading.
In my last Statistics Sunday post, I briefly mentioned the concept of regular expressions, also known as regex (though note that in some contexts, these refer to different things -...continue reading.
First Statistics Sunday in far too long! It’s going to be a short one, but it describes a great trick I learned recently while completing a time study for our...continue reading.
Statistics Sunday: Some Psychometrics Tricks in R It’s been a long time since I’ve posted a Statistics Sunday post! Now that I’m moved out of my apartment and into my...continue reading.
When You Need a New Book to ReadI log all of my books on Goodreads. On top of that, whenever I hear about a new book I have to read,...continue reading.
Statistics Sunday: Visualizing RegressionI had some much needed downtime this weekend, after an exhausting week, along with some self-care – Saturday I had a one-hour deep tissue massage, which left...continue reading.
Using Text Analysis to Become a Better WriterWe all have words we love to use, and that we perhaps use too much. As an example: I have a tendency to...continue reading.