Dial Gauge

Devin Knight explains the dial gauge custom visual:

  • The effectiveness of gauges on dashboards is an often debated topic.

  • The Dial Gauge is completely data driven. Which means not only must your measure (drives the needle) come from a dataset but also the different thresholds ranges must come from your dataset too.

  • There are no specific Format settings for the Dial Gauge, which does limit you a bit with what you can do with this gauge.

There are certain scenarios in which I think the dial gauge works well.  The best scenario is the the same as its analog counterpart:  when you are measuring a single continuous variable with a safe range and meaningful range differences.  This scenario occurs less often than you might think.

Related Posts

Thinking About Font Sizes

Stephanie Evergreen shares some good information on font sizes: Did you know that you regularly read type set in size 8, or even smaller? In printed materials, captions and less important information (think: photograph credits, newsletter headline subtext, magazine staff listings) are usually reduced to something between 7.5 to 9 points. We generally read that […]

Read More

Using wrapr For A Consistent Pipe With ggplot2

John Mount shows how you can use the wrapr pipe to perform data processing and building a ggplot2 visual: Now we can run a single pipeline that combines data processing steps and ggplot plot construction. data.frame(x = 1:20) %.>% mutate(., y = cos(3*x)) %.>% ggplot(., aes(x = x, y = y)) %.>% geom_point() %.>% geom_line() %.>% ggtitle("piped ggplot2") Check […]

Read More

Categories

December 2016
MTWTFSS
« Nov Jan »
 1234
567891011
12131415161718
19202122232425
262728293031