Data Visualization With Highcharter: Solutions

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#    Exercise 1    #
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library("highcharter")
data(mpg, package = "ggplot2")
hchart(mpg, "scatter", hcaes(x = displ, y = hwy))

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#    Exercise 2    #
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library("highcharter")
data(mpg, package = "ggplot2")
hchart(mpg, "scatter", hcaes(x = displ, y = hwy, group = class))


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#    Exercise 3    #
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highchart() %>%
  hc_chart(type = "column") %>%
  hc_add_series(data = c(5000, 6000, 7000, 8000, 9900),
                name = "Downloads")

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#    Exercise 4    #
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highchart() %>%
  hc_chart(type = "column") %>%
  hc_title(text = "A highcharter chart") %>%
  hc_add_series(data = c(5000, 6000, 7000, 8000, 9900)
                )

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#    Exercise 5    #
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highchart() %>%
  hc_chart(type = "column") %>%
  hc_title(text = "A highcharter chart") %>%
  hc_xAxis(categories = 2012:2016) %>%
  hc_add_series(data = c(5000, 6000, 7000, 8000, 9900),
                name = "Sales")

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#    Exercise 6    #
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data(diamonds, package = "ggplot2")
hchart(diamonds$cut, colorByPoint = TRUE, name = "Cut")

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#    Exercise 7    #
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hchart(diamonds$price, color = "#B71C1C", name = "Price")

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#    Exercise 8    #
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install.packages("forecast")
library("forecast")
airforecast <- forecast(auto.arima(AirPassengers), level = 95)
hchart(airforecast)

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#                  #
#    Exercise 9    #
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####################

data(unemployment)
hcmap("countries/us/us-all-all", data = unemployment,
      name = "Unemployment", value = "value", joinBy = c("hc-key", "code"),
      borderColor = "transparent")%>%
  hc_colorAxis(dataClasses = color_classes(c(seq(0, 10, by = 2), 50)))

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#    Exercise 10   #
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data(unemployment)
hcmap("countries/us/us-all-all", data = unemployment,
      name = "Unemployment", value = "value", joinBy = c("hc-key", "code"),
      borderColor = "transparent") %>%
  hc_colorAxis(dataClasses = color_classes(c(seq(0, 10, by = 2), 50))) %>%
  hc_legend(layout = "vertical", align = "right",
            floating = TRUE, valueDecimals = 0, valueSuffix = "%")