Easily create interactive ggplot graphs in R with ggiraph

Produce a tooltip column in R

ggiraph only lets me use 1 column for the tooltip display screen, but I want both point out and amount in my tooltip. There is an easy option: Insert a tooltip column to the info frame with both point out and amount in 1 text string:

bar_graph_info_the latest <- bar_graph_data_recent %>%
mutate(
tooltip_text = paste0(toupper(State), "n", 
PctFullyVaccinated, "%")
)

Make the bar chart interactive with ggiraph

To build a ggiraph interactive bar chart, I changed geom_col() to geom_col_interactive() and added tooltip and info_id to the aes() mapping. I also reduced the dimension of the axis text, mainly because the ggplot dimension ended up remaining too massive.

Then I shown the interactive graph object with the girafe() function. You can set the graph width and top with width_svg and top_svg arguments within girafe(). 

hottest_vax_graph <- ggplot(bar_graph_data_recent, 
aes(x = reorder(State, PctFullyVaccinated), 
y = PctFullyVaccinated,
tooltip = tooltip_text, info_id = State #<<
)) +
geom_col_interactive(shade = "black", fill="#0072B2", dimension = .5) +  #<<
concept_negligible() +
concept(axis.text=aspect_text(dimension = six)) +  #<<
labs(title = "Percent Fully Vaccinated July 2021",
subtitle = "Information from Our Globe in Information GitHub repo"
) +
ylab("") +
xlab("") +
coord_flip()
girafe(ggobj = hottest_vax_graph, width_svg = 5, top_svg = four)

The graph will glance rather identical to the ggplot model — but if you operate the code oneself or enjoy the movie embedded over, you are going to see that you can now hover in excess of the bars and see fundamental info. 

A person point that really helps make ggiraph shine is how easy it is to url up many graphs. To demo that, of course, I’ll need to have a next visualization to url to my bar chart.

The code beneath results in a info frame with vaccination info from February 14, 2021, and a ggiraph bar chart with that info.

bar_graph_info_early <- all_data %>%
filter(date == "2021-02-14", !(location %in% not_states_or_dc)) %>%
organize(people today_fully_vaccinated_for every_hundred) %>%
mutate(
PctFullyVaccinated = spherical(people today_fully_vaccinated_for every_hundred, 1),
tooltip_text = paste0(toupper(location), "n", PctFullyVaccinated, "%")
) %>%
find(State = location, PctFullyVaccinated, tooltip_text)
early_vax_graph <- ggplot(bar_graph_data_early, aes(x = reorder(State, PctFullyVaccinated), y = PctFullyVaccinated, tooltip = tooltip_text, data_id = State)) +
geom_col_interactive(shade = "black", fill="#0072B2", dimension = .5) +
concept_negligible() +
concept(axis.text=aspect_text(dimension = six)) +
labs(title = "Fully Vaccinated as of February 14, 2021",
subtitle = "Information from Our Globe in Information"
) +
ylab("") +
xlab("") +
coord_flip()

Url interactive graphs with ggiraph

The code to url the two graphs is incredibly straightforward. Beneath I use the girafe() function to say I want to print the early_vax_graph moreover the latest_vax_graph and set the canvas width and top. I also insert an choice so when the consumer hovers, the bars transform cyan.

girafe(code = print(early_vax_graph + hottest_vax_graph), 
width_svg = 8, top_svg = four) %>% 
girafe_choices(opts_hover(css = "fill:cyan"))

Linking the two graphs helps make it easy for buyers to see what transpired to point out rankings between February and July. For case in point, by hovering in excess of Alaska in the February graph, the bar for Alaska in the July graph also turns cyan. (Without the need of that choice, the bars would transform the default yellow shade.)

Url a map and bar chart with ggiraph

This concept arrives from Kyle E. Walker, who coded a demo working with his tidycensus package deal to build a map joined with a chart. We can do the very same with this info and a map from scratch working with the albersusa package deal (though I very endorse tidycensus if you are performing with U.S. Census info).

Beneath is the code for the map. us_sf is an R straightforward characteristics geospatial object developed with the albersusa::united states of america_sf() function. point out_map creates a ggiraph map object from that us_sf object. The map code utilizes normal ggplot() syntax, but instead of geom_sf() it utilizes geom_sf_interactive(). There are also tooltip and info_id arguments in the aes() mapping. Lastly, the code eradicates any track record or axes with concept_void().

library(albersusa)
us_sf <- usa_sf("lcc") %>%
mutate(State = as.character(title))
point out_map <- ggplot() +
geom_sf_interactive(info = us_sf, dimension = .125, 
aes(info_id = State, tooltip = State)) +
concept_void()

The upcoming code block utilizes girafe() and its ggobj argument to display screen both the map and the vax graph, joined interactively.

girafe(ggobj = point out_map + hottest_vax_graph, 
width_svg = ten, top_svg = 5) %>%
girafe_choices(opts_hover(css = "fill:cyan"))

Now if I hover in excess of a point out on the map, its bar “lights up” on the bar chart.

Sharon Machlis, IDG

It requires quite minimal R code to make a static graphic interactive and to url two graphs collectively.

How to use your ggiraph info visualizations

You can insert ggiraph visualizations to an R Markdown doc and make an HTML file that functions in any web browser.

You can also help save output from the girafe() function as an HTML widget and then help save the widget to an HTML file working with the htmlwidgets package deal. For case in point:  

my_widget <- girafe(ggobj = state_map + latest_vax_graph, 
width_svg = ten, top_svg = 5) %>%
girafe_choices(opts_hover(css = "fill:cyan"))
htmlwidgets::saveWidget(my_widget, "my_widget_page.html", 
selfcontained = Legitimate)

For extra on ggiraph, look at out the ggiraph package deal site.

And for extra R tips, head to the InfoWorld Do Much more With R page.