examplecode.R

library("dplyr")
library("tidyr")
library("ggplot2")

# Create data frame with columns: country, variable, value
long_df_by_country <-
  df %>%
    select(country, var1, var2, var3) %>%
    gather(variable, value, - country)
    
# For each country and variable, run a t-test
# do() is used to run an arbitrary command for each group
# the broom function tidy() converts the results of t.test to a data frame
# With %>% you can use . to refer to the result of the previous command
# This creates a data frame with columns: country, variable, and various columns with results from t.tests
t_test_results <- 
  long_df_by_country %>%
  group_by(country, variable) %>%
  do(tidy(t.test(.$variable)))
  
# To plot the countries and variables in the same plot
ggplot(t_test_results, aes(x = country, y = estimate, ymin = conf.low, ymax = conf.high, color = variable)) +
  geom_pointrange(position = "dodge")
  
# To plot with countries and variables facetted
ggplot(t_test_results, aes(x = country, y = estimate, ymin = conf.low, ymax = conf.high)) +
  geom_pointrange(position = "dodge") + 
  facet_wrap(~ variable)
  
# Both of these plots would be better if country was sorted by the average of one of these variables
# using reorder()