It's been quite a year for R for the Rest of Us. With two new courses, updates to three other courses, training work with several organizations, and multiple consulting projects, we've been keeping ourselves busy! Let's share some highlights. New Courses and Updates to Existing Courses 2023 saw the release of two new courses: Making Beautiful Tables with R and Reproducibility for the Rest of Us . Created by Albert Rapp and Jenine Harris , respectively, these courses bring the total number of...
In our consulting work , we often think about what could be a better visualization for our clients. And sometimes it’s really small tweaks. One example of this is adding a gray background behind bar charts to show percentages as a part of the whole. We did this, for example, in reports about Connecticut housing . To show how to make this in R, let’s start with a bar chart that represents the results of some absolutely fictitious survey. It would be neat if we had another bar on top of each...
RStudio is the premier tool to write R code. And just like any tool, the better you know RStudio’s features the faster you can write code. That’s why we decided show you the best hotkeys we use all the time. Don’t type the pipe Whether it is %>% or |> , it is tempting to write out the pipe letter by letter. But the pipe is such an integral part of the tidy workflow that it makes sense to give yourself a tiny speed boost whenever you use the pipe. Just hit ctrl + shift + M to get the job done....
Last week, we started to learn about pivot_longer() and pivot_wider() . These are two essential functions to speed up you data wrangling process. Check out the video and blog post from last week if you haven’t seen that yet. As promised, this week we’ll continue on this path and learn some of the advanced tricks that these two functions have to offer. This should help you clean your data even faster. Let’s begin by revisiting what we did last week. Taylor Swift again Last week, we worked with...
Conventional wisdom tells us that data wrangling is 90% of all data work. And it’s true. Often, you will have to get your data into the right format before you can get any “real” work done. In the tidyverse, there are two powerful functions to help you with one of the most common data wrangling tasks: going from wide to long and lnog to wide. These functions are pivot_longer() and pivot_wider() . Here, we will cover the standard use case of pivot_*() (we’ll cover more of what they can do next...
Data visualization can be hard. From our experience, it can take hours and hours to perfect a visualization. Thankfully, if you’re stressed for time there are a couple of changes that always work. So let’s check out these evergreen steps we can use to make your chart instantly better. A chart to play with First off, let us create a dummy dataviz with our favorite penguins data set. Once we have that, we can apply little tweaks to make the overall look of our chart better. Use an active title...
Have you ever created a table with gt and thought to yourself “Well, maybe just showing the numbers doesn’t cut it? I need to add some visual spice to this table.” If so, then you’re in great company. Because spicing up tables with visuals is one of my favorite tricks. I like to add visual elements like small lines or bars to tables. Apart from making your table pretty, visuals help to convey an overall impression of the data to your reader. So, let me show you how you can add any chart to...
With visualizations, it’s always better if there is some visual order in your charts. For example, take a look at these two bar charts. Which one do you find easier to read? And which chart gives you a better overview over the data? If you are like most people, then chances are that you will find that the sorted bar chart is easier to read. This simple action of sorting the bars can give a lot of structure to your data visualization. In turn, your chart is easier to understand for your...
Annotations are a neat way to draw your readers attention to specific parts of your data visualization. For example, you could use, say, an arrow to point to a specific data point, a rectangle to draw a border around specific points in a scatter plot that you want to highlight or a text label to clarify something. The possibilities for annotations are endless. Here’s a plot that combines these ideas into a ggplot. You can create such annotations with the annotate() layer. Who would have...
In our consulting work at R for the Rest of Us, we create tables all the time. We don’t just make plain tables, we make tables that look good and communicate well. This is important because: A clean design is more convincing A powerful design stands out so that people actually look at our tables We can use colors to reflect our clients’ branding We recently helped the Oregon Voices project to create customized data reports for every county in Oregon. Among other things, this required creating...
