In Part IV and Part V Jonathan created a chart and showed how to wrap that chart in a call to
ggplotly() . The required packages and data preparation scripts are available in Part I, and Part II of this series. In Part III Jonathan worked with data for MACK and FWP dividends.
Let’s add back the name and sector data by
left_join()ing our wrangled data object.
nasdaq_dividends %>% left_join(nasdaq_wrangled, by = "symbol") %>% head()
# A tibble: 6 x 9 symbol date dividends company last_sale_price market_cap ipo_year <chr> <date> <dbl> <chr> <dbl> <dbl> <dbl> 1 AVGO 2010-12-13 0.07 Broadc… 272. 108330 2009 2 AVGO 2011-03-16 0.08 Broadc… 272. 108330 2009 3 AVGO 2011-06-15 0.09 Broadc… 272. 108330 2009 4 AVGO 2011-09-15 0.11 Broadc… 272. 108330 2009 5 AVGO 2011-12-15 0.12 Broadc… 272. 108330 2009 6 AVGO 2012-03-15 0.13 Broadc… 272. 108330 2009 # … with 2 more variables: sector <chr>, industry <chr>
Once the company name data is joined, we can incorporate into the plot by adding
label_tooltip = company to
ggplot(aes(...)) and then setting
tooltip = "label_tooltip", to alert
plotly about the new tooltip data.
Visit the blog to download the R code or to explore the interactive version of the above chart: http://www.reproduciblefinance.com/2019/08/25/tech-dividends/
ggplotly( nasdaq_dividends %>% filter(symbol != "MACK" & symbol != "FWP") %>% left_join(nasdaq_wrangled, by = "symbol") %>% group_by(symbol) %>% ggplot(aes(x = date, y = dividends, color = symbol, label_tooltip = company)) + geom_point() + scale_y_continuous(labels = scales::dollar) + scale_x_date(breaks = scales::pretty_breaks(n = 10)) + labs(x = "", y = "div/share", title = "Nasdaq dividends") + theme(plot.title = element_text(hjust = 0.5)), tooltip = "label_tooltip" )
In the next installment of this series, Jonathan use
mutate to create a new column that holds our label information.
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