MarioKart

The Data

The data this week comes from Mario Kart World Records and contains world records for the classic (if you’re a 90’s kid) racing game on the Nintendo 64.

mariokart <- read_csv("records.csv")

Change in records

By looking at the world record vs time (for the Three Lap shortcut races), it seems there are some pretty easily identifiable jumps in time.

mariokart %>%
  filter(type == "Three Lap")  %>%
  filter(shortcut == "Yes") %>%
  ggplot(aes(x = date, y = time)) + 
  geom_point() + 
  geom_step() + 
  facet_wrap(~track)

But what if we wanted to measure the percent decrease in time? The new variable of interest will be:

\[\% \mbox{ decrease} = \Bigg(\frac{\mbox{time}_i - \mbox{time}_{i+1}}{\mbox{time}_i}\Bigg)\cdot100 = \Bigg(1 - \frac{\mbox{time}_{i+1}}{\mbox{time}_i}\Bigg)\cdot100\] It is interesting to note that the biggest percent reductions in time happen at the very beginning, except for Choco Mountain.

mariokart %>%
  filter(type == "Three Lap", shortcut == "Yes") %>%
  group_by(track) %>%
  mutate(time_lag = dplyr::lag(time)) %>%
  mutate(perc_decr = (1 - time / time_lag)*100) %>%
  ggplot(aes(x = date)) +
  geom_point(aes( y = perc_decr)) +
  geom_step(aes( y = perc_decr, color = "Percent decrease")) +
  geom_step(aes( y = time/3, color = "Record time")) +
  scale_y_continuous(sec.axis = sec_axis(~.*3, name = "Time (sec)")) +
  scale_color_manual(values = c("#D89A9E", "#114B5F")) +
  facet_wrap(~track) +
  labs(
    y = "Decrease [%]",
    x = "Date",
    color = "Trendline:",
#    strip.text.y = element_text(angle = 0, size = rel(4)),
    caption = "Tidy Tuesday Plot: @hardin47 | Data: MarioKart") +
    ggtitle("MarioKart record times") +
  theme(legend.position = "top",
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 10),
        plot.title = element_text(size = 18))

praise()

## [1] "You are priceless!"