Locations = list ( cells_column_labels ( vars ( Karla ) ),Ĭells_body ( vars ( Karla ) ) ) ) %>% tab_style ( Style = list ( cell_text (font = "Karla", decorate = "underline" ) ), Locations = list ( cells_column_labels ( vars ( Default ) ),Ĭells_body ( vars ( Default ) ) ) ) %>% tab_style ( Style = list ( cell_text (font = "Default", decorate = "underline" ) ), Importantly, gt already automatically follows best practices for the most part so we have to change some of the defaults to get bad examples. In this case, you want to right align numbers and ideally choose mono-spaced or numerically-aligned fonts, while avoiding “oldstyle” fonts which have numbers with varying vertical placement. Inspiration: 3: Right-Align Numbers and Heads Note that I’ve also manually calculated a summary row at the bottom just as an alternative example, although you could again create a grand_summary_row() with gt and probably should since you could add as many arbitrary summary rows as you’d like (by group even!). In this improved example we’ve clearly indicated the Avg. The “grammar” of tables can be seen below. Note that while it uses gt, it also further supports kable, kableExtra, flextable (for Word docs), and tibble methods. flextable - A very useful package for Word-based tablesĪdditionally - if your primary output is model summaries rather than data tables, make sure to check out the wonderful gtsummary R package which extends the gt package for statistical model summaries. DT or reactable - great for reactive tables formattable - great for custom fill of cells and HTML While gt is fantastic, I also greatly enjoy other table packages in R: It provides a Grammar of Tables to turn tabular data into a proper table! Gt is again, an R package to create tables in R. Can I guide the reader to use the table in a better way?.How can I best present the data relationships to accomplish the primary function?.What is the Primary Function of this table?.All too often we do all the heavy lifting to clean, summarize, and model the data only to let our readers down by not preparing a useful final data product. Again, it’s important to not just throw data into a tabular format and call it a day, but rather make specific design decisions to help guide the reader towards the purpose of the table. With this taxonomy you can specifically choose what you purpose/function you are giving the table. If there is any specific feedback or input that you would find helpful, include that detail in your commentary. Share your creation in the SWD community by September 30th at 5PM PDT. Check out the resources below and try your hand to create some beautiful tables!įind some data of interest that will lend itself well and create and share a table. The Storytelling with Data community ( #SWDchallenge) is hosting a “Build a Table” challenge for September 2020. 10 Guidelines in the Journal of Benefit Cost Analysis - In this article, Jon dives even deeper into the WHY and longer explanations of the guidelines, along with some best practice examples Cole Nussbaumer Knaflic Twitter Thread - In this thread, Jon covers the highpoints of each of the 10 guidelines Please note that the 10 Guidelines section headers are quoted verbatim from Jon’s article (after asking permission), while the tables themselves and text descriptions are original content inspired from his work. Rule 10: Add visualizations When Appropriate.Rule 9: Group Similar Data and Increase White Space.Rule 6: Guide Your Reader with Space between Rows and Columns.Rule 5: Select the Appropriate Level of Precision.Rule 2: Use Subtle Dividers Rather Than Heavy Gridlines.
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