In this example, I am using a geographical data frame containing country codes

geonames.cities %>%
  select(countrycode) %>%
  head()
##   countrycode
## 1          AD
## 2          AD
## 3          AE
## 4          AE
## 5          AE
## 6          AE

Here we are going to find the top 10 countries having the highest number of rows

.var <- .(countrycode)

len <- nrow(geonames.cities) # total rows in data frame

result <- 
geonames.cities %>%
  ddply(.var, summarize, freq = length(eval(as.name(as.character(.var))))) %>% # our var is country code
  mutate(prop = freq / len * 100) %>% # calculate proportion of each city
  arrange(-freq) %>% # sort by frequency
  head(10) # top 10

result %>%
  knitr::kable()
countrycode freq prop
US 2973 12.643531
IN 2446 10.402313
BR 1201 5.107596
RU 1089 4.631284
DE 1057 4.495194
CN 800 3.402229
JP 737 3.134303
GB 711 3.023731
FR 633 2.692013
IT 575 2.445352
Let’s plot the result as a graph. 
result %>%
  ggplot() +
  geom_bar(aes(x = countrycode, y = prop), fill = "royalblue", stat = "identity")

As you can note, that the x axis takes the alphabetical order by default. Let’s change this to sort based on the freq.

result %>%
  mutate(countrycode = factor(countrycode, countrycode)) %>% #retain the sort order
  ggplot() +
  geom_bar(aes(x = countrycode, y = prop), fill = "royalblue", stat = "identity") +
  geom_text(aes(
    x = countrycode,
    y = prop,
    label = paste0(round(prop, 2), "%")
  ), hjust = 0, size = 2) +
  scale_y_continuous(
    label = function(x)
      paste0(round(x), "%"),
    expand = c(0.1, 0.1)
  ) +
  coord_flip()

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