Data scientists need good skills in communication, data mining, data wrangling and more. Joyce Norris-Montanari explains.
In my first two posts of this blog series, we heard why two students chose to pursue a STEM field and what appealed to them about data science. We also heard how they put their knowledge to work on a real-world data science project. Today, we'll hear their advice to future [...]
Two students’ advice on data science, SAS and more was published on SAS Voices by Georgia Mariani
We hear a lot about data science nowadays, but do you ever wonder how it’s being used to help solve real-world problems? In my first post of this blog series, we heard why two students chose to pursue a STEM field and what appealed to them about data science. Today, we'll hear [...]
Are you curious? Do you have a passion for science, technology, engineering and math (STEM)? Do you enjoy robotics or statistics? Do you like to solve hard problems? If you answered yes to any of the above, you might have what it takes to be a data scientist. Recently, I [...]
Why should you learn data science? Two students’ perspectives was published on SAS Voices by Georgia Mariani
In a previous posting, SAS Customer Intelligence 360 was highlighted in the context of delivering relevant product, service, and content recommendations using automated machine learning within digital experiences. Shifting gears, SAS recognizes there are different user segments for our platform. This post will focus on building custom analytical recommendation models [...]
I bet many of you didn’t even know the term machine learning five years ago. But Gartner did. The Gartner Magic Quadrant for Data Science and Machine Learning Platforms, 2018 was just released, and SAS has been in the leader’s quadrant for five years straight. According to Gartner, “This Magic [...]
Gartner names data science and machine learning leaders was published on SAS Voices by David Tareen
If you’ve ever used Amazon or Netflix, you’ve experienced the value of recommendation systems firsthand. These sophisticated systems identify recommendations autonomously for individual users based on past purchases and searches, as well as other behaviors. By supporting an automated cross-selling approach, they empower brands to offer additional products or services [...]
In Part 1 and Part 2 of this blog posting series, we discussed: Our current viewpoints on marketing attribution and conversion journey analysis in 2017. The selection criteria of the best measurement approach. Introduced our vision on handling marketing attribution and conversion journey analysis. We would like to conclude this [...]
In this guest blog entry, Randall Pruim offers an alternative way based on a different formula interface. Here's Randall:
For a number of years I and several of my colleagues have been teaching R to beginners using an approach that includes a combination of
latticepackage for graphics,
- several functions from the
statspackage for modeling (e.g.,
lm(), t.test()), and
mosaicpackage for numerical summaries and for smoothing over edge cases and inconsistencies in the other two components.
goal ( y ~ x , data = mydata, ... )
Many data analysis operations can be executed by filling in four pieces of information (goal, y, x, and mydata) with the appropriate information for the desired task. This allows students to become fluent quickly with a powerful, coherent toolkit for data analysis.
Trouble in paradise
As the earlier post noted, the use of
latticehas some drawbacks. While basic graphs like histograms, boxplots, scatterplots, and quantile-quantile plots are simple to make with
lattice, it is challenging to combine these simple plots into more complex plots or to plot data from multiple data sources. Splitting data into subgroups and either overlaying with multiple colors or separating into sub-plots (facets) is easy, but the labeling of such plots is not as convenient (and takes more space) than the equivalent plots made with
ggplot2. And in our experience, students generally find the look of
ggplot2graphics more appealing.
ggplot2into a first course is challenging. The syntax tends to be more verbose, so it takes up more of the limited space on projected images and course handouts. More importantly, the syntax is entirely unrelated to the syntax used for other aspects of the course. For those adopting a “Less Volume, More Creativity” approach,
ggplot2is tough to justify.
ggformula, an R package that provides a formula interface to
ggplot2graphics. Our hope is that this provides the best aspects of
lattice(the formula interface and lighter syntax) and
ggplot2(modularity, layering, and better visual aesthetics).
gf. Here are two examples, either of which could replace the side-by-side boxplots made with
latticein the previous post.
%>%, also commonly called a pipe) between the two layers and adjust the transparency so we can see both where they overlap.
ggformulapackage provides two ways to create these facets. The first uses
|very much like
latticedoes. Notice that the
gf_lm()layer inherits information from the the
gf_points()layer in these plots, saving some typing when the information is the same in multiple layers.
gf_facet_grid()and can be more convenient for complex plots or when customization of facets is desired.
ggformalaalso fits into a tidyverse-style workflow (arguably better than
ggplot2itself does). Data can be piped into the initial call to a
ggformulafunction and there is no need to switch between
+when moving from data transformations to plot operations.
ggformulastrengthens this approach by bringing a richer graphical system into reach for beginners without introducing new syntactical structures. The full range of
ggplot2features and customizations remains available, and the
ggformulapackage vignettes and tutorials describe these in more detail.