1 Packages

library(tidyverse)
library(extrafont)

hot_dogs <- read_csv("http://bit.ly/cs631-hotdog", 
    col_types = cols(
      gender = col_factor(levels = NULL)
    ))

2 Visualizing new world records (for males)

Let’s adapt Nathan Yau’s hot dog contest example.

The first thing we notice is that we don’t have data about whether each year’s winner is a record or not. Since our data is nicely tidy, we can use dplyr window functions:

• First, we use base R’s cummax to create a new variable that reflects the maximum HDB eaten cumulatively, that is, compared to all earlier years. For this reason, the arrange(year) here is critical.

• Next, we want to know if the hdb_record is actually a new record or not, compared to all previous years. We can use case_when to create a logical variable that is TRUE if the hdb_record for a given year is greater than the hdb_record from the year before (using dplyr::lag). If not, this variable is FALSE.

hot_dogs_records <- hot_dogs  %>% 
  filter(year >= 1980 & gender == 'male') %>%  
  arrange(year) %>% 
  mutate(hdb_record = cummax(num_eaten),
         new_record = case_when(
           hdb_record > lag(hdb_record) ~ TRUE,
           TRUE ~ FALSE
         )) %>% 
  filter(year >= 1981)

We’ll also make our x-axis ticks again…

years_to_label <- seq(from = 1981, to = 2017, by = 4)
years_to_label

 [1] 1981 1985 1989 1993 1997 2001 2005 2009 2013 2017

hd_years <- hot_dogs_records %>%
  distinct(year) %>% 
  mutate(year_lab = ifelse(year %in% years_to_label, year, ""))

hdb_records <- ggplot(hot_dogs_records, 
                      aes(x = year, y = num_eaten)) + 
  geom_col(aes(fill = new_record)) + 
  labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017") +
  scale_fill_manual(values = c('#284a29', '#629d62')) + 
  scale_y_continuous(expand = c(0, 0),
                     breaks = seq(0, 70, 10)) +
  scale_x_continuous(expand = c(0, 0), 
                     breaks = hd_years$year,
                     labels = hd_years$year_lab) + 
  coord_cartesian(xlim = c(1980, 2018), ylim = c(0, 80)) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5),
        axis.text = element_text(size = 12),
        panel.background = element_blank(),
        axis.line.x = element_line(color = "gray92", 
                                   size = 0.5),
        axis.ticks = element_line(color = "gray92", 
                                  size = 0.5),
        text = element_text(family = "Lato"),
        legend.position = "bottom",
        panel.grid.minor = element_blank())
hdb_records

3 Visualizing background data

We’ll do this to highlight differences in gender.

https://drsimonj.svbtle.com/plotting-background-data-for-groups-with-ggplot2

hot_dogs_both <- hot_dogs %>% 
  filter(year >= 1981) 
hot_dog_behind <- hot_dogs_both %>% 
  filter(gender == "male") %>% 
  select(-gender)

hdb_facets <- ggplot(hot_dogs_both, 
                      aes(x = year, y = num_eaten)) + 
  geom_col(data = hot_dog_behind, fill = '#4254a7', alpha = .1) +
  geom_col(aes(fill = gender), show.legend = FALSE) + 
  facet_wrap(~gender) +
  labs(x = "", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017") +
  scale_fill_manual(values = c('#4254a7', '#f4b31a')) + 
  scale_y_continuous(expand = c(0, 0),
                     breaks = seq(0, 70, 10)) +
  scale_x_continuous(expand = c(0, 0),
                     breaks = seq(1981, 2017, 6)) +
  coord_cartesian(xlim = c(1980, 2018), ylim = c(0, 80)) +
  theme(axis.text = element_text(size = 10),
        panel.background = element_blank(),
        axis.line.x = element_line(color = "grey80", 
                                   size = 0.5),
        axis.ticks = element_line(color = "grey80", 
                                  size = 0.5),
        text = element_text(family = "Lato"),
        legend.position = "bottom",
        panel.grid.minor = element_blank(),
        panel.spacing = unit(1, "lines"))
hdb_facets