Data Preparation

d = read_csv("books.csv", col_type=cols(date=col_date(format="%Y-%m-%d")))

# sum counts for each date
d = d %>%
  group_by(date) %>%
  summarize(count=n())

# add "zero events" on January 1st and December 31st
zeroes.jan1  = tibble(date=floor_date(d$date, 'year') %>% unique(), count=0)
zeroes.dec31 = tibble(date=ceiling_date(d$date, 'year') %>% unique() - 1, count=0)
d = rbind(zeroes.jan1, d, zeroes.dec31)

# cumulative count for each year
d = d %>%
  mutate(year=year(date)) %>%
  group_by(year) %>%
  mutate(cumcount=cumsum(count)) %>%
  arrange(date)

# all date on the same year, for comparison
d$ndate = as.Date(format(d$date,"2016-%m-%d"))
# years aren't numbers... for the sake of these graphs
d$year  = factor(d$year)
d$month = factor(month(d$date), 1:12)

cols = brewer.pal(length(levels(d$year)),"Paired")

Each Event as a Dot

ggplot(d %>% filter(count > 0), aes(ndate, y=factor(year, levels=rev(levels(year))), color=year)) +
  geom_point(alpha=0.5, size=2) +
  scale_x_date(labels=date_format("%b"), date_breaks="1 month") +
  scale_color_manual(values=cols) +
  ylab("")

Cumulative Plots

# as geom_step
ggplot(d, aes(ndate, cumcount, color=year)) +
  geom_step(alpha=0.5, size=1) +
  ylab("books read") +
  scale_x_date(labels=date_format("%b"), date_breaks="1 month") +
  scale_y_continuous(breaks=seq(0, 100, by=5)) +
  scale_color_manual(values=cols)

# as geom_line
ggplot(d, aes(ndate, cumcount, color=year)) +
  geom_line(alpha=0.5, size=1) +
  ylab("books read") +
  scale_x_date(labels=date_format("%b"), date_breaks="1 month") +
  scale_y_continuous(breaks=seq(0, 100, by=5)) +
  scale_color_manual(values=cols)

# facet by year
ggplot(d, aes(ndate, cumcount, color=year)) +
  geom_line(alpha=0.5, size=1) +
  ylab("books read") +
  scale_x_date(labels=date_format("%b"), date_breaks="1 month") +
  scale_y_continuous(breaks=seq(0, 100, by=5)) +
  scale_color_manual(values=cols, guide=FALSE) +
  facet_wrap(~ year, ncol=4) +
  theme(axis.text.x=element_text(angle=90))