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Category: R

Themes And Legends In ggplot2

Published 2018-02-08 by Kevin Feasel

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.

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Labels And Annotations In ggplot2

Published 2018-02-07 by Kevin Feasel

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.

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Library Paths In R

Published 2018-02-05 by Kevin Feasel

Stacia Varga troubleshoots an issue integrating Power BI with R:

As I was putting together an example of using an R script as a Power BI data source, I ran into some issues on my development machine that was frankly driving me crazy. When I tried to run the query in Power BI with my R script (that ran successfully in the IDE, by the way), I kept getting this message:

DataSource.Error: ADO.NET: R script error.
Error in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]) :

  namespace 'scales' 0.3.0 is being loaded, but >= 0.4.1 is required

Error: package or namespace load failed for 'rnoaa'

Execution halted

Stacia’s answer works as long as the .libPaths() results match expectations.  Another idea would be to set the R_LIBS_USER user-level environment variable to the desired starting directory and that should force the directory in the environment variable to be first when calling .libPaths().

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Dealing With Dates In R

Published 2018-02-02 by Kevin Feasel

Mathew McLean shows how to convert strings to dates using a couple well-known packages and introduces flipTime:

The package flipTime provides utilities for working with time series and date-time data. The package can be installed from GitHub using

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require(devtools)
install_github("Displayr/flipTime")

I will discuss only two functions from the package in this post, AsDate() and AsDateTime(). These are used for the conversion of date and date-time strings, respectively. These functions build on the convenience and speed of the lubridate function. Furthermore, the flipTime functions provide additional functionality (making them easier to use). The functions are smart about identifying the proper format to use. So the user doesn’t need to specify the format(s) as inputs. At the same time, both AsDate() and AsDateTime() are careful to not convert any strings to dates when they are not formatted as dates. Additionally, it will also warn the user when the dates are not in an unambiguous format.

Check it out.

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ARIMA In R

Published 2018-02-02 by Kevin Feasel

Subhasree Chatterjee shows us how to use R to implement an ARIMA model:

Once the data is ready and satisfies all the assumptions of modeling, to determine the order of the model to be fitted to the data, we need three variables: p, d, and q which are non-negative integers that refer to the order of the autoregressive, integrated, and moving average parts of the model respectively.

To examine which p and q values will be appropriate we need to run acf() and pacf() function.

pacf() at lag k is autocorrelation function which describes the correlation between all data points that are exactly k steps apart- after accounting for their correlation with the data between those k steps. It helps to identify the number of autoregression (AR) coefficients(p-value) in an ARIMA model.

ARIMA feels like it should be too simple to work, but it does.

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ggplot2 Scales And Coordinates

Published 2018-02-02 by Kevin Feasel

I continue my series on ggplot2:

The other thing I want to cover today is coordinate systems.  The ggplot2 documentation shows seven coordinate functions.  There are good reasons to use each, but I’m only going to demonstrate one.  By default, we use the Cartesian coordinate system and ggplot2 sets the viewing space.  This viewing space covers the fullness of your data set and generally is reasonable, though you can change the viewing area using the xlim and ylim parameters.

The special coordinate system I want to point out is coord_flip, which flips the X and Y axes.  This allows us, for example, to turn a column chart into a bar chart.  Taking our life expectancy by continent, data I can create a bar chart whereas before, we’ve been looking at column charts.

There are a lot of pictures and more step-by-step work.  Most of these are still 3-4 lines of code, so again, pretty simple.

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ggplot2 Mappings And Geoms

Published 2018-02-01 by Kevin Feasel

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.

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Using FreeTDS To Connect To SQL Server

Published 2018-02-01 by Kevin Feasel

Steph Locke embraces the pain of FreeTDS:

If you use SQL Server (or Azure SQL DB) as your data store and you need to connect to the databasse from shinyapps.io, you’re presently stuck with FreeTDS. If you have any control over infrastructure I cannot recommend highly enough the actual ODBC Driver on Linux for ease. Alas, shinyapps.io does not let you control the infrastructure. We have to make do with with FreeTDS and it can be pretty painful to get right.

Due to how obtuse the error messages you end up getting back from FreeTDS in your shiny app and the time to deploy an app, you might just want to cry a little. I know I did. Determined to succeed, here is my solution to getting a working database connection that you can also use to test you’re doing it right. If you’re on a particularly old version of SQL Server though, I can’t guarantee this will work for you.

Read on for more.  I also have an older post on working with FreeTDS, though I ended up using TDS_Version = 8.0 instead of 7.4.

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Structural Topic Models In R

Published 2018-01-31 by Kevin Feasel

Julia Silge has a great post on building Structural Topic Models in R using stm and tidytext:

The stm package has a summary() method for trained topic models like these that will print out some details to your screen, but I want to get back to a tidy data frame so I can use dplyr and ggplot2 for data manipulation and data visualization. I can use tidy() on the output of an stm model, and then I will get the probabilities that each word is generated from each topic.

I haven’t watched the video yet, but that’s on my to-do list for today.

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The Grammar of Graphics

Published 2018-01-31 by Kevin Feasel

I’ve started a new series:

Instead, we will start with Wickham’s paper on ggplot2.  This gives us the basic motivation behind the grammar of graphics by covering what a grammar does for us:  “A grammar provides a strong foundation for understanding a diverse range of graphics. A grammar may also help guide us on what a well-formed or correct graphic looks like, but there will still be many grammatically correct but nonsensical graphics. This is easy to see by analogy to the English language: good grammar is just the first step in creating a good sentence” (3).

With a language, we have different language components like nouns (which can be subjects, direct objects, or indirect objects), verbs, adjectives, adverbs, etc.  We put together combinations of those individual components to form complete sentences and transmit ideas.  Our particular word choice and language component usage will affect the likelihood of success in idea transmission, but to an extent, we can work iteratively on a sentence, switching words or adding phrases to get the point across the way we desire.

With graphics, we can do the same thing.  Instead of thinking of “a graph” as something which exists in and of itself, we should think of different objects that we combine into its final product:  a graph.

I call this first post the poor man’s literature review.  The rest of the series is code- and visual-heavy.

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