Flex Dashboard

In plotly: Create Interactive Web Graphics via 'plotly.js'

library(plotly)
library(maps)
knitr::opts_chunk$set(message = FALSE)

Rows {data-height:600}

Chart A

# This example modifies code from Hadley Wickham -- https://gist.github.com/hadley/233134
# It also uses data from Nathan Yau's flowingdata site -- http://flowingdata.com/
unemp <- read.csv("http://datasets.flowingdata.com/unemployment09.csv")
names(unemp) <- c("id", "state_fips", "county_fips", "name", "year", 
                  "?", "?", "?", "rate")
unemp$county <- tolower(gsub(" County, [A-Z]{2}", "", unemp$name))
unemp$state <- gsub("^.*([A-Z]{2}).*$", "\\1", unemp$name)
county_df <- map_data("county")
names(county_df) <- c("long", "lat", "group", "order", "state_name", "county")
county_df$state <- state.abb[match(county_df$state_name, tolower(state.name))]
county_df$state_name <- NULL
state_df <- map_data("state")
choropleth <- merge(county_df, unemp, by = c("state", "county"))
choropleth <- choropleth[order(choropleth$order), ]
choropleth$rate_d <- cut(choropleth$rate, breaks = c(seq(0, 10, by = 2), 35))

# provide a custom tooltip to plotly with the county name and actual rate
choropleth$text <- with(choropleth, paste0("County: ", name, "<br>Rate: ", rate))
p <- ggplot(choropleth, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = rate_d, text = text), 
               colour = alpha("white", 1/2), size = 0.2) + 
  geom_polygon(data = state_df, colour = "white", fill = NA) +
  scale_fill_brewer(palette = "PuRd") + theme_void()
# just show the text aesthetic in the tooltip
ggplotly(p, tooltip = "text")

Chart B

crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)
crimesm <- tidyr::gather(crimes, variable, value, -state)
states_map <- map_data("state")
g <- ggplot(crimesm, aes(map_id = state)) +
  geom_map(aes(fill = value), map = states_map) +
  expand_limits(x = states_map$long, y = states_map$lat) +
  facet_wrap( ~ variable) + theme_void()
ggplotly(g)

Rows {data-height:400}

Chart C

m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
  stat_density_2d() + xlim(0.5, 6) + ylim(40, 110)
ggplotly(m)

Chart D

m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
  stat_density_2d(aes(fill = ..level..), geom = "polygon") + 
  xlim(0.5, 6) + ylim(40, 110)
ggplotly(m)

Chart E

m <- ggplot(faithful, aes(x = eruptions, y = waiting)) + geom_hex() 
ggplotly(m)

plotly documentation built on May 29, 2024, 2:23 a.m.