The required packages and data preparation scripts are available in Part I, and Part II of this series. In Part III Jonathan noticed that MACK and FWP paid some huge dividends. Let’s investigate.
These were most likely special dividends of some sort and that might be worth investigating but it’s not our project today so let’s filter those two out of the data and then recreate the plot. Let’s remove the geom_label so we see just the dots again, this time by date instead of year.
nasdaq_dividends %>%
filter(symbol != "MACK" & symbol != "FWP") %>%
group_by(symbol) %>%
slice(n()) %>%
ggplot(aes(x = date, y = dividends, color = symbol)) +
geom_point() +
# geom_label(aes(label = symbol)) +
scale_y_continuous(labels = scales::dollar) +
scale_x_date(breaks = scales::pretty_breaks(n = 10)) +
labs(x = "", y = "div/share", title = "Nasdaq dividends") +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5)) 
That’s a snapshot of the last dividend paid by each company and we can see the clustering a bit better. Quite a few companies paid their last dividend before 2019, which might indicate it’s not a regular dividend. Let’s check out the entire history of each company by removing slice(n()).
nasdaq_dividends %>%
filter(symbol != "MACK" & symbol != "FWP") %>%
group_by(symbol) %>%
ggplot(aes(x = date, y = dividends, color = symbol)) +
geom_point() +
# geom_label(aes(label = symbol)) +
scale_y_continuous(labels = scales::dollar) +
scale_x_date(breaks = scales::pretty_breaks(n = 10)) +
labs(x = "", y = "div/share", title = "Nasdaq dividends") +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5)) 
In the next installment of this series, Jonathan will show us how to wrap that chart in a call to ggplotly() and get part of that functionality out of the box.
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