Author: Sara

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.

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.