A quick useful aside: Using shorthand for functions The _if() scoped variant: perform an operation on variables that satisfy a logical criteria select_if() rename_if() mutate_if() summarise_if() The _at() scoped variant: perform an operation only on variables specified by name Select helpers rename_at() mutate_at() summarise_at() The _all() scoped variant: perform an operation on all variables at once rename_all() mutate_all() summarise_all() Conclusion I often find myself wishing that I could apply the same mutate function to several columns in a data frame at once, such as convert all factors to characters, or do something to all columns that have missing values, or select all variables whose names end with _important.
Here I provide the code I used to create the figures from my previous post on alternatives to grouped bar charts. You are encouraged to play with them yourself! The key to creating unique and creative visualizations using libraries such as ggplot (or even just straight SVG) is (1) to move away from thinking of data visualization only as the default plot types (bar plots, boxplots, scatterplots, etc), and (2) to realise that most visualizations are essentially lines and circles that you can arrange however you desire in space.
At some point in your life you have probably found yourself standing face-to-face with a beast known as a grouped bar chart. Perhaps it was in a research paper where the authors were trying to compare the results of several models across different datasets, or it was in a talk given by a colleague who was trying to compare the popularity of different products among distinct groups of consumers. The first time you encountered a grouped bar chart you might have thought “what a neat way to put so much information in a single plot!