Bob Rudis is one creative guy, and his latest htmlwidget is for streamgraph charts, which can be useful for visualizing trends over time. He has some examples, with code, on RPubs

To check it out, I have used the US registration of baby names between 1880 and 2013.

Taking the most popular five male and female names at each end of the spectrum, the charts shows their percent of all names for each year. Roll over chart for more detail

library(streamgraph)
library(babynames)
library(dplyr)

babynames %>%
  filter(year==1880&sex=="M") %>%
  top_n(5, n) -> m1880

babynames %>%
  filter(year==2013&sex=="M") %>%
top_n(5, n) -> m2013

babynames %>% 
  filter(name %in% union(m1880$name,m2013$name)&sex=="M") %>% 
  mutate(pc=round(prop*100,2)) %>% 
streamgraph( "name", "pc", "year", interactive=TRUE) %>%
sg_axis_x(15, "year", "%Y") %>%
sg_colors("Set3")

For males, the big 5 accounted for around 30% of all names back in the day and William still ranks near the top. Noah and Jacob pretty much went out of favour only to return with a bang from the mid-60s with Mason and Liam following a couple of decades later

Female names were much more diverse than males in Victorian days and the graph also differs in shape over time. The number of Heathers registered, for example has declined from 24,304 to 312 between 1975 and 2013

However, again there has been a return in popularity of a couple of names that had had a spell of very limited appeal, in this case Emma and Sophia. Minnie may be on the margins forever with less than 100 per annum over the past forty years