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library(coronavirus) |
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df <- coronavirus %>% |
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# dplyr::filter(date == max(date)) %>% |
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dplyr::group_by(Country.Region, type) %>% |
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dplyr::summarise(total = sum(cases)) %>% |
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tidyr::pivot_wider(names_from = type, |
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values_from = total) %>% |
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dplyr::arrange(-confirmed) %>% |
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dplyr::ungroup() %>% |
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dplyr::mutate(country = factor(Country.Region, levels = Country.Region)) |
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df$flag <- NA |
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df$flag[1:10] <- "Top 10 Countries" |
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corona<- plotly::highlight_key(df) |
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widgets <- crosstalk::bscols( |
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widths = c(12, 12, 12), |
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crosstalk::filter_select("country", "Country", corona, ~country) |
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# crosstalk:: filter_checkbox("flag", "Top 10", corona, ~flag, inline = FALSE) |
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# filter_slider("confirmed", "Confirmed Cases", corona, ~confirmed), |
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# filter_slider("recovered", "Recovered Cases", corona, ~recovered) |
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) |
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crosstalk::bscols( |
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widths = c(4, 12), widgets, |
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plotly::plot_ly(data = corona, x = ~ country, y = ~ confirmed, type = "bar", name = "Confirmed") %>% |
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plotly::add_trace(y = ~ recovered, name = "Recovered") %>% |
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plotly::add_trace(y = ~ death, name = "Death") %>% |
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plotly::layout(barmode = 'stack', |
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yaxis = list(title = "Total Cases (log scaled)", |
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type = "log"), |
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xaxis = list(title = "Country")) |
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) |
