I stepped up to the Storytelling with Data Challenge for July 2019: identify data that makes sense to plot in a radial view and visualize it.
I love great visualizations, but does that mean I should exclusively commit myself to the Art and Science of Viz? Is being a friendly connoisseur good enough?
A little white space between R Markdown sections gives the reader a little pause in the story before changing the subject otherwise all the cells get smooshed together by default and it ends up looking like one long run-on sentence.
To emphasize value differences in a bar chart, using gradient colors is a nice option. ggplot makes it easy with scale_fill_gradient2().
Today I completed the Udacity Deep Learning Nanodegree (DLND) program. The program included submitting 5 projects for review, which is the best part because we get our hands dirty. AWS (Amazon Web Services) was introduced, and we got to use AWS SageMaker for free (with Education ‘credit’). All-in-all I feel like I’ve been introduced to … [Read More]
Writing data to a file using write.csv failes to retain column data types. A better way to save/restore data objects to/from files is by using RDS and RData.
Instead of a cumbersomely nested ifelse statement, use dplyr’s mutate and case_when functions instead.
Convert data.frame factor columns to character columns before trying to replace NULL values.
Find NULLs in a dataframe using is.na().
Ctrl-Z to the rescue. ‘nuf said.
Adding temporary print statements to troubleshoot Python code is quick and dirty. And messy. Incorporating logging into your code habit is a great alternative to relying on ad-hoc print statements that just need to get deleted later.
Save a Google Colab notebook to another format, such as HTML or PDF.