Introduccion a gráficos dinámicos con Highcharter

Introducción
Gráficos dinámicos

Introducción

Highcharter

Highcharter es un paquete de R para crear gráficos dinámicos de Highcharts que utilizan internamente javascript.

Highcharts es una biblioteca de gráficos javascript muy madura y flexible y tiene una gran y poderosa API.

Las principales características de este paquete son:

Permite generar varios tipos de gráficos con el mismo estilo: dispersión, burbuja, línea, serie de tiempo, mapas de calor, treemap, gráficos de barras, redes, etc.
Permite generar varios objetos R con una sola función. Con la función hchart() se puede usar: data.frames, numéricos, histogramas, caracteres, densidad, factores, ts, mts, xts, stl, ohlc, acf, pronósticos, mforecast, ets, igraph, dist, dendrogram, phylo, survfit clases.
Soporta gráficos Highstock, gráficos de velas.
Soporta temas como economist, financial times, google, 538 entre otros.
Soporta interesante complementos de movimiento, puntos de arrastre, fuente, anotaciones entre otros.

La función hchart permite visualizar los siguientes tipos de gráficos:

    Data Frames   ·   Numeric & Histograms   ·   Densities   ·   Character & Factor   ·   Time Series   ·   Seasonal Decomposition of Time Series by Loess   ·   Forecast package   ·   igraph   ·   xts from quantmod package   ·   xts ohlc objects   ·   Autocovariance & Autocorrelation   ·   Multivariate Time series   ·   Survival Models   ·   Principal Components   ·   Matrix   ·   Distance matrix   ·   Correlation matrix

Todo esto lo puedes revisar en la web oficial de highcharter.

Gráficos dinámicos

Función hchart - “Dispersión”

easypackages::libraries(c("dplyr", "highcharter"))

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

## Loading required package: highcharter

## Highcharts (www.highcharts.com) is a Highsoft software product which is

## not free for commercial and Governmental use

## All packages loaded successfully

data(diamonds, mpg, package = "ggplot2")

hchart(mpg, "scatter", hcaes(x = displ, y = hwy, group = class)) %>%
  hc_title(text = "Dispersión diamonds") %>%
  hc_add_theme(hc_theme_economist())

Función hchart - “columnas”

mpgman2 <- mpg %>%
  count(class, year) %>%
  glimpse()

## Observations: 14
## Variables: 3
## $ class <chr> "2seater", "2seater", "compact", "compact", "midsize", "...
## $ year  <int> 1999, 2008, 1999, 2008, 1999, 2008, 1999, 2008, 1999, 20...
## $ n     <int> 2, 3, 25, 22, 20, 21, 6, 5, 16, 17, 19, 16, 29, 33

hchart(mpgman2, "column", hcaes(x = class, y = n, group = year)) %>% hc_add_theme(hc_theme_darkunica())

Función hchart - “barras”

hchart(mpgman2, "bar", hcaes(x = class, y = n, group = year))  %>% hc_add_theme(hc_theme_gridlight())

Función hchart - “treemap”

mpgman3 <- mpg %>% 
  group_by(manufacturer) %>% 
  summarise(n = n(), unique = length(unique(model))) %>% 
  arrange(-n, -unique) %>% 
  glimpse()

## Observations: 15
## Variables: 3
## $ manufacturer <chr> "dodge", "toyota", "volkswagen", "ford", "chevrol...
## $ n            <int> 37, 34, 27, 25, 19, 18, 14, 14, 13, 9, 8, 5, 4, 4, 3
## $ unique       <int> 4, 6, 4, 4, 4, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1

hchart(mpgman3, "treemap", hcaes(x = manufacturer, value = n, color = unique))

Función hchart - “lineal”

library(ggplot2)
economics_long2 <- economics_long %>% 
  filter(variable %in% c("pop", "uempmed", "unemploy")) %>% 
  print()

## Source: local data frame [1,722 x 4]
## Groups: variable [3]
## 
##          date variable  value     value01
##        <date>   <fctr>  <dbl>       <dbl>
## 1  1967-07-01      pop 198712 0.000000000
## 2  1967-08-01      pop 198911 0.001628811
## 3  1967-09-01      pop 199113 0.003282177
## 4  1967-10-01      pop 199311 0.004902803
## 5  1967-11-01      pop 199498 0.006433395
## 6  1967-12-01      pop 199657 0.007734807
## 7  1968-01-01      pop 199808 0.008970739
## 8  1968-02-01      pop 199920 0.009887457
## 9  1968-03-01      pop 200056 0.011000614
## 10 1968-04-01      pop 200208 0.012244731
## # ... with 1,712 more rows

hchart(economics_long2, "line", hcaes(x = date, y = value01, group = variable)) %>% hc_add_theme(hc_theme_google())

Función hchart - “numérico o histograma”

hchart(diamonds$price) %>% hc_add_theme(hc_theme_elementary())

Función hchart - “sectores”

hchart(mpgman2 %>% filter(year==2008) , "pie", hcaes(x = class, y = n)) %>% hc_add_theme(hc_theme_538())

Función hchart - “densidad”

hchart(density(diamonds$price), type = "area", color = "#B71C1C", name = "Price") %>% hc_add_theme(hc_theme_elementary())

Función hchart - “quantmod”

library(quantmod)

## Loading required package: xts

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## 
## Attaching package: 'xts'

## The following objects are masked from 'package:dplyr':
## 
##     first, last

## Loading required package: TTR

## Version 0.4-0 included new data defaults. See ?getSymbols.

x <- getSymbols("USD/JPY", src = "oanda", auto.assign = FALSE)

## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
## 
## This message is shown once per session and may be disabled by setting 
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.

hchart(x)

Función hchart - “forecast”

library("forecast")
airforecast <- forecast(auto.arima(AirPassengers), level = 95)
hchart(airforecast) %>% hc_add_theme(hc_theme_sandsignika())

Función hchart - “Componentes Principales”

hchart(princomp(USArrests, cor = TRUE)) %>% hc_add_theme(hc_theme_538())

Boxplot

data(diamonds, package = "ggplot2")

hcboxplot(x = diamonds$x, var = diamonds$color,
          name = "Length", color = "#2980b9") %>%
  hc_add_theme(hc_theme_economist())

Gráfico de barras y de sectores incrustado

data("favorite_bars")
data("favorite_pies")

highchart() %>%
  # Data
  hc_add_series(favorite_pies, "column", hcaes(x = pie, y = percent), name = "Pie") %>%
  hc_add_series(favorite_bars, "pie", hcaes(name = bar, y = percent), name = "Bars") %>% 
        hc_add_theme(hc_theme_ffx()) %>%
  # Optiosn for each type of series
  hc_plotOptions(
    series = list(
      showInLegend = FALSE,
      pointFormat = "{point.y}%"
    ),
    column = list(
      colorByPoint = TRUE
    ),
    pie = list(
      colorByPoint = TRUE, center = c('30%', '10%'),
      size = 120, dataLabels = list(enabled = FALSE)
    )) %>%
  # Axis
  hc_yAxis(
    title = list(text = "percentage of tastiness"),
    labels = list(format = "{value}%"), max = 100
  ) %>%
  hc_xAxis(categories = favorite_pies$pie) %>%
  # Titles and credits
  hc_title(
    text = "This is a bar graph describing my favorite pies
    including a pie chart describing my favorite bars"
  ) %>%
  hc_subtitle(text = "In percentage of tastiness and awesomeness") %>%
  hc_credits(
    enabled = TRUE, text = "Source: HIMYM",
    style = list(fontSize = "12px")
  )

Mapas dinámicos

mapdata <- get_data_from_map(download_map_data("countries/us/us-all"))
glimpse(mapdata)

## Observations: 52
## Variables: 19
## $ hc-group    <chr> "admin1", "admin1", "admin1", "admin1", "admin1", ...
## $ hc-middle-x <dbl> 0.36, 0.56, 0.51, 0.47, 0.41, 0.43, 0.71, 0.46, 0....
## $ hc-middle-y <dbl> 0.47, 0.52, 0.67, 0.52, 0.38, 0.40, 0.67, 0.38, 0....
## $ hc-key      <chr> "us-ma", "us-wa", "us-ca", "us-or", "us-wi", "us-m...
## $ hc-a2       <chr> "MA", "WA", "CA", "OR", "WI", "ME", "MI", "NV", "N...
## $ labelrank   <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", ...
## $ hasc        <chr> "US.MA", "US.WA", "US.CA", "US.OR", "US.WI", "US.M...
## $ woe-id      <chr> "2347580", "2347606", "2347563", "2347596", "23476...
## $ state-fips  <chr> "25", "53", "6", "41", "55", "23", "26", "32", "35...
## $ fips        <chr> "US25", "US53", "US06", "US41", "US55", "US23", "U...
## $ postal-code <chr> "MA", "WA", "CA", "OR", "WI", "ME", "MI", "NV", "N...
## $ name        <chr> "Massachusetts", "Washington", "California", "Oreg...
## $ country     <chr> "United States of America", "United States of Amer...
## $ region      <chr> "Northeast", "West", "West", "West", "Midwest", "N...
## $ longitude   <chr> "-71.99930000000001", "-120.361", "-119.591", "-12...
## $ woe-name    <chr> "Massachusetts", "Washington", "California", "Oreg...
## $ latitude    <chr> "42.3739", "47.4865", "36.7496", "43.8333", "44.37...
## $ woe-label   <chr> "Massachusetts, US, United States", "Washington, U...
## $ type        <chr> "State", "State", "State", "State", "State", "Stat...

set.seed(1234)

data_fake <- mapdata %>% 
  select(code = `hc-a2`) %>% 
  mutate(value = 1e5 * abs(rt(nrow(.), df = 10)))

glimpse(data_fake)

## Observations: 52
## Variables: 2
## $ code  <chr> "MA", "WA", "CA", "OR", "WI", "ME", "MI", "NV", "NM", "C...
## $ value <dbl> 119426.518, 255662.317, 3668.575, 122246.930, 79283.064,...

hcmap("countries/us/us-all", data = data_fake, value = "value",
      joinBy = c("hc-a2", "code"), name = "Fake data",
      dataLabels = list(enabled = TRUE, format = '{point.name}'),
      borderColor = "#0EAD82", borderWidth = 0.1,
      tooltip = list(valueDecimals = 2, valuePrefix = "$", valueSuffix = " USD"))