Category: R statistical package

2020 has been a tough year, and I’ve been doing my best to keep busy (and distracted from all the insanity – both at the personal and worldwide levels). Earlier...continue reading.

UFO Sightings by Shape and YearEarlier last week, I taught part 2 of a course on using R and tidyverse for my work colleagues. I wanted a fun dataset to...continue reading.

I’ve spent the month of April blogging my way through the tidyverse, while using my reading dataset from 2019 as the example. Today, I thought I’d bring many of those...continue reading.

Here we are at the last post in Blogging A to Z! Today, I want to talk about adding additional axes to your ggplot, using the options for fill or...continue reading.

Yesterday, I talked about scale_x. Today, I’ll continue on that topic, focusing on the y-axis.The key to using any of the scale_ functions is to know what sort of data...continue reading.

These next two posts will deal with formatting scales in ggplot2 – x-axis, y-axis – so I’ll try to limit the amount of overlap and repetition.Let’s say I wanted to...continue reading.

Once again, I’m dipping outside of the tidyverse, but this package and its functions have been really useful in getting data quickly in (and out) of R.For work, I have...continue reading.

In this series, I’ve covered five terms for data manipulation:arrangefiltermutateselectsummariseThese are the verbs that make up the grammar of data manipulation. They all work with group_by to perform these functions...continue reading.

This will be a very short post for a line of code I’ve found unbelievably useful as I analyze data for work. I’m working with datasets containing millions of rows...continue reading.

One of the easiest ways to make a beautiful ggplot is by using a theme. ggplot2 comes with a variety of pre-existing themes. I’ll use the genre statistics summary table...continue reading.

Today, we’ll finally talk about summarise! It’s very similar to mutate, but instead of adding or altering a variable in a dataset, it aggregates your data, creating a new tibble...continue reading.

The tidyverse is full of functions for reading data, beginning with “read_”. The read_csv I’ve used to access my reads2019 data is one example, falling under the read_delim functions. read_tsv...continue reading.

Two years ago, when I did Blogging A to Z of R, I talked about qplots. qplots are great for quick plots – which is why they’re named as such...continue reading.

We’ve used ggplots throughout this blog series, but today, I want to introduce another package that helps you customize scales on your ggplots – the scales package. I use this...continue reading.

This will be a quick post on another tidyverse function, order_by. I’ll admit, I don’t use this one as often as arrange. It can be useful, though, if you don’t...continue reading.

Today, we’ll start digging into some of the functions used to summarise data. The full summarise function will be covered for the letter S. For now, let’s look at one...continue reading.

Today, we finally talk about the mutate function! I’ve used it a lot throughout the series so far, so it’s nice to get to discuss what it is and how...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.