Data Context

The visualization uses TidyTuesday’s The Great American Coffee Taste Test (May 2024), whose data context and information can be accessed at this link

coffee_survey <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-05-14/coffee_survey.csv')

Research Question

What is the connection between monthly spending in coffee and coffee preferences among the survey respondents?

Data Transformation

Code

df <- coffee_survey %>%
  select(style, roast_level, total_spend) %>%
  filter(roast_level %in% c("Light", "Medium", "Dark")) %>%
  filter(!is.na(style) & !is.na(total_spend)) %>%
  mutate(spending = ifelse(total_spend %in% c("$60-$80", "$80-$100", ">$100"), ">$60", total_spend)) %>%
  mutate(spending = fct_relevel(spending, c("<$20", "$20-$40","$40-$60", ">$60"))) %>%
  mutate(roast_level = fct_relevel(roast_level, c("Light", "Medium", "Dark"))) %>%
  group_by(spending, roast_level, style) %>%
  mutate(count = n())

Data Visualization

Code

showtext_auto()

ggplot(data = df,
       aes(axis1 = spending,   
           axis2 = roast_level,
           axis3 = style,
           y = count)) +
  geom_alluvium(aes(fill = spending), curve_type = "quintic") +
  geom_stratum(fill = "#f3e9dc") +
  geom_text(stat = "stratum", size = 3,
            aes(label = after_stat(stratum))) +
 scale_x_discrete(limits = c("Spending", "Roast Level", "Style"), labels = c("Spending", "Roast Level", "Style"),expand = c(.05, .05)) +
  #scale_x_continuous(breaks = 1:3, labels = c("Spending", "Roast Level", "Style")) +
  labs(title = "Coffee Roast Level and Style Preferences, by Monthly Spending on Coffee", fill = "Monthly Spending") +
  scale_fill_viridis_d() +
  theme_classic() +
  theme(legend.position = "bottom", 
        axis.line =  element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(), 
        axis.text.y = element_blank(), 
        text = element_text(family = "Inter", size = 10),
        legend.title = element_text(face = "bold"),
        axis.text.x = element_text(face = "bold"),
        plot.title = element_text(family = "lobstertwo", size = 20, face = "bold", 
                                color = "#37251b", hjust = 0.5),
        panel.background  = element_rect(fill = "#f3e9dc", color = "#f3e9dc"),
        plot.background   = element_rect(fill = "#f3e9dc", color = "#f3e9dc"), 
        legend.background = element_rect(fill = "#f3e9dc", color = "#f3e9dc"))

Survey respondents with high monthly coffee spending (>$60) tends to prefer light roast beans over medium or dark roast, and they seems to have a penchant for fruity, bright, and juicy coffee notes.