Category: Visualization

I have wrapped up my ggplot2 series, with the last post being on radar charts:

First, we need to install ggradar and load our relevant libraries. Then, I create a quick standardization function which divides our variable by the max value of that variable in the vector. It doesn’t handle niceties like divide by 0, but we won’t have any zero values in our data frames.

The radar_data data frame starts out simple: build up some stats by continent. Then I call the mutate_each_ function to call standardize for each variable in the vars set. mutate_each_is deprecated and I should use something different like mutate_at, but this does work in the current version of ggplot2 at least.

Finally, I call the ggradar() function. This function has a large number of parameters, but the only one you absolutely need is plot.data. I decided to change the sizes because by default, it doesn’t display well at all on Windows.

It was a lot of fun putting this series together. I think the most important part of the series was learning just how easy ggplot2 is once you sit down and think about it in a systemic manner.

Dean Attali announces a new shiny package:

shinyalert uses the sweetalert JavaScript library to create simple and elegant modals in Shiny. Modals can contain text, images, OK/Cancel buttons, an input to get a response from the user, and many more customizable options. A modal can also have a timer to close automatically.

Simply call shinyalert() with the desired arguments, such as a title and text, and a modal will show up. In order to be able to call shinyalert() in a Shiny app, you must first call useShinyalert() anywhere in the app’s UI.

It does look nice.  Check out Dean’s GitHub repo for more information. H/T R-Bloggers

Anisa Dhana uses the National Health and Nutrition Examination Survey and visualizes results with ggplot2:

From the plots above I find that regardless the different levels of diastolic and systolic blood pressure there is no substantial correlation between cholesterol and blood pressure. However, it is better to build the correlation line with geom_smooth or to calculate the Spearman correlation, although in this post we focus only on the visualization.

Lets build the correlation line.

Click through for several examples of visuals.

I have a post on extending ggplot2’s functionality with cowplot:

Notice that I used geom_path().  This is a geom I did not cover earlier in the series.  It’s not a common geom, though it does show up in charts like this where we want to display data for three variables.  The geom_line() geom follows the basic rules for a line:  that the variable on the y axis is a function of the variable on the x axis, which means that for each element of the domain, there is one and only one corresponding element of the range (and I have a middle school algebra teacher who would be very happy right now that I still remember the definition she drilled into our heads all those years ago).

But when you have two variables which change over time, there’s no guarantee that this will be the case, and that’s where geom_path() comes in.  The geom_path() geom does not plot y based on sequential x values, but instead plots values according to a third variable.  The trick is, though, that we don’t define this third variable—it’s implicit in the data set order.  In our case, our data frame comes in ordered by year, but we could decide to order by, for example, life expectancy by setting data = arrange(global_avg, m_lifeExp).  Note that in a scenario like these global numbers, geom_line() and geom_path() produce the same output because we’ve seen consistent improvements in both GDP per capita and life expectancy over the 55-year data set.  So let’s look at a place where that’s not true.

The cowplot library gives you an easier way of linking together different plots of different sizes in a couple lines of code, which is much easier than using ggplot2 by itself.

I have another post in my ggplot2 series, this time covering facets:

Notice that we create a graph per continent by setting facets = ~continent.  The tilde there is important—it’s a one-sided formula.  You could also write c("continent") if that’s clearer to you.

I also set the number of columns, guaranteeing that we see no more than 3 columns of grids. I could alternatively set nrow, which would guarantee we see no more than a certain number of rows.

There are a couple other interesting features in facet_wrap. First, we can set scales = "free" if we want to draw each grid as if the others did not exist. By default, we use a scale of “fixed” to ensure that everything plots on the same scale. I prefer that for this exercise because it lets us more easily see those continental clusters.

Facets let you compare multiple graphs quickly.  They’re great for fast comparison, but as I show in the post, you can distort the way the data looks by lining it up horizontally or vertically.

I have another part of my ggplot2 series up, this time on themes and legends:

You are not limited to using defaults in your graphs.  Let’s go back to the minimal theme but change the fonts a bit.  I want to make the following changes:

1. Use Gill Sans fonts instead of the default

2. Increase the title font size a little bit

3. Decrease the X axis font size a little bit

4. Remove the Y axis; the subtitle makes it clear what the Y axis contains

By the time we’re through this, we have publication-quality visuals in a few dozen lines of code.  I also have provided a bonus rant on Windows and R and fonts because that’s a nasty experience.

I have another post in my ggplot2 series:

Annotations are useful for marking out important comments in your visual.  For example, going back to our wealth and longevity chart, there was a group of Asian countries with extremely high GDP but relatively low average life expectancy.  I’d like to call out that section of the visual and will use an annotation to do so.  To do this, I use the annotate() function.  In this case, I’m going to create a text annotation as well as a rectangle annotation so you can see exactly the points I mean.

By this point, we’re getting closer and closer to high-quality graphics.

I continue my ggplot2 series:

We have used a new geom here, geom_smooth.  The geom_smooth function creates a smoothed conditional mean.  Basically, we’re drawing some line as a result of a function based on this input data.  Notice that there are two parameters that I set:  method and se.  The method parameter tells the function which method to use.  There are five methods available, including using a Generalized Additive Model (gam), Locally Weighted Scatterplot Smoothing (loess), and three varieties of Linear Models (lm, glm, and rlm).  The se parameter controls whether we want to see the standard error bar.

I don’t cover all of the mapping options and all of the geoms, but I think it’s enough to get a grip on the concept.