Draw Bars and Emoji Faces in ggplot2 to Visualize Population Distribution in Happiness & Wealth in the U.S.

library(ggplot2)library(dplyr)
# use the "happy" dataset from this package# install.packages("ggmosaic")library(ggmosaic) 
# draw Emoji / Chernoff faces# devtools::install_github('Selbosh/ggChernoff')library(ggChernoff)
theme_set(theme_classic())

# Calculate the number of people in each combined level of happiness and financial status. h <- happy %>% as_tibble() %>%   # use only year 2018  filter(year == 2018 ) %>%  # remove rows with NA values in the "happy" or "finrela" columns  filter(! (is.na(happy) | is.na(finrela))) %>%     # summarize the number of people in each happiness-wealth condition  group_by(finrela, happy) %>%   summarise(N = n()) %>%     # turn 'happy' into a factor variable with specified level order  # so as to visualize happy people at the top of the bar plot  mutate(happy = factor(    happy,levels = c("very happy", "pretty happy", "not too happy")))
head(h, n = 3)

# Load font from [Google font repository](https://fonts.google.com/). library(showtext)font_add_google(name = "Gochi Hand", family = "gochi")font_add_google(name = "Schoolbell", family = "bell")showtext_auto()

# Create a bar plot showing happiness distribution at different financial conditionp1 <- h %>%    ggplot(aes(x = finrela, y = N, fill = happy)) +   geom_col(position = "fill", alpha = .4, width = 1, color = "white")p1

# Add lines to highlight the relation between happiness and financial conditionp2 <- p1 +   geom_line(position = position_fill(vjust = .5),             aes(group = happy, color = happy),             linewidth = 1)p2

# Draw Emoji / Chernoff faces. p3 <- p2 +   geom_chernoff(    # turn the factor variable into integer     aes(smile = 3 - unclass(happy),        brow =  3 - unclass(happy)),     position = position_fill(vjust = .5),    show.legend = F, # no show the associated legend    size = 10) p3

#Revise the axial labels and titles, plot title, and theme.p4 <- p3 +   scale_x_discrete(labels = c("$", "$$", "$$$", "$$$$", "$$$$$"),                   name = "Financial condition") +  scale_y_continuous(breaks = seq(0, 1, .2),                      name = "Proportion",                      expand = expansion(mult = 0)) +   ggtitle("happiness possitively related with money") +    theme(    axis.line = element_blank(),    legend.position = "none",    text = element_text(size = 18, family = "bell"),        # increase margin above x-axis title, and on the right side of y-axis title    # use t-r-b-l for top, right, bottom, and left, respectively    axis.title.x = element_text(margin = margin(t = 10)),     axis.title.y = element_text(margin = margin(r = 10)),        plot.title = element_text(hjust = .5, family = "gochi",                              margin = margin(b = 10, unit = "pt"))) p4

# Label the happiness level at the right edge of the plot.p5 <- p4 +   annotate(geom = "text",            x = 5.6, # treat x-axis as a numerical scale             y = c(.8, .3, .03),           label = c("- Very\n  happy!", "- Good", "- Unhappy"),           hjust = 0,  # justify to the left            fontface = "bold", size = 5, family = "bell",           color = c("firebrick4", "green4", "steelblue4")) +    # do NOT clip elements that overflow beyond the plot boundary  coord_cartesian(clip = "off") +  # make space for the text annotation on the right side of plot   theme(plot.margin = margin(r = 80, t = 10))
p5