This also applies to human communication in the sense that the lower the entropy rate, which in this case means the uncertainty on the recipient side on the message that will be sent, the easier it is to compress the message. This could be interpreted in human communication to mean that the certainty from the message could come from the abundance or lack thereof of context, signals that could provide clues as to what the message might be. Considering this context, the message might be compressed into very small messages. A good example of this is air traffic controllers and the military.

Sensemaking...

Good game design

Could not generate the same stat_summary( ) plot with that code, did a little research and stack overflow suggested two solutions: use geom_line( )

ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) + geom_line() + stat_summary(fun.y = "median", geom = "point", size = 3)

or, reduce the amount of data by grouping it

data = diamonds %>% group_by(cut) %>% summarise(min = min(depth), max = max(depth), median = median(depth))

ggplot(data, aes(x = cut, y = median, ymin = min, ymax = max)) + geom_linerange() + geom_pointrange()

Source: https://stackoverflow.com/questions/41850568/r-ggplot2-pointrange-example