Bölüm 3 Highcharter Kütüphanesi

highcharter kütüphanesi, JavaScript dilinde bulunan highcharts paketinin R diline uyarlanmış halidir.

3.1 Neden highcharter?

• Fonksiyonellik
• İnteraktif veri görselleştirme
• Karşılaştırma grafikleri
• JS gücü

# install.packages("highcharter")
library(highcharter)
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo

3.1.1 Serpilme Diyagramı

# install.packages("palmerpenguins")
library(palmerpenguins)
data(penguins, package = "palmerpenguins") 
hchart(
  penguins,
  "scatter",
  hcaes(x = flipper_length_mm, y = bill_length_mm, group = species)
)

3.1.2 Histogram

set.seed(1)
data = data.frame(
        cinsiyet = factor(rep(c("Kadin", "Erkek"), each=100)),
        kilo = c(rnorm(100, 55), rnorm(100, 58))
        )
data %>% head() %>% kable()

cinsiyet	kilo
Kadin	54.37355
Kadin	55.18364
Kadin	54.16437
Kadin	56.59528
Kadin	55.32951
Kadin	54.17953

hchart(
  data$kilo, 
  color = "red", name = "Kilo"
  )

3.1.3 Bar grafiği

library(gapminder)
gapminder%>%
       dplyr::filter(year == 2007)%>%
        dplyr::arrange(-pop) %>%
hchart( type = "column", #type = "bar" olursa yatay olur
        hcaes(x = country, y = pop),
        color ='red')

3.1.4 Çizgi grafiği

data(economics_long, package = "ggplot2")

economics_long2 <- dplyr::filter(economics_long, variable %in% c("pop", "uempmed", "unemploy"))

economics_long2 %>% head() %>% kable()

date	variable	value	value01
1967-07-01	pop	198712	0.0000000
1967-08-01	pop	198911	0.0016353
1967-09-01	pop	199113	0.0032953
1967-10-01	pop	199311	0.0049223
1967-11-01	pop	199498	0.0064590
1967-12-01	pop	199657	0.0077656

hchart(economics_long2, "line", hcaes(x = date, y = value01, group = variable))

highchart() |>
  hc_add_series(data = sample(1:12)) |> 
  hc_add_series(data = sample(1:12) + 10) |> 
  hc_tooltip(
    # tooltip üzerine çizgi ekler (daha iyi takip edebilmek için)
    crosshairs = TRUE,
    # aynı anda tüm değerleri görebilmek için
    sort = TRUE
    ) 

** Farklı özellikler için highcharts sitesinden yararlanılabilir: ** highcharts dataLabels

3.1.5 Dağılım grafiği

density(data$kilo)
#> 
#> Call:
#>  density.default(x = data$kilo)
#> 
#> Data: data$kilo (200 obs.);  Bandwidth 'bw' = 0.5315
#> 
#>        x               y            
#>  Min.   :51.19   Min.   :5.707e-05  
#>  1st Qu.:53.87   1st Qu.:1.348e-02  
#>  Median :56.55   Median :8.380e-02  
#>  Mean   :56.55   Mean   :9.326e-02  
#>  3rd Qu.:59.22   3rd Qu.:1.697e-01  
#>  Max.   :61.90   Max.   :2.025e-01

hchart(
  density(data$kilo), 
  type = "area", name = "Kilo"
  )

kadin <- data %>% filter(cinsiyet == "Kadin")
erkek <- data %>% filter(cinsiyet == "Erkek")
hchart(
  density(erkek$kilo), type = "area", 
   name = "Erkek"
  ) %>%
  hc_add_series(
    density(kadin$kilo), type = "area",
    name = "Kadın"
    )

3.1.6 Kutu grafiği

library(readr)
# Verinin yüklenmesi ve hazırlığı
df <-
  read_csv(
    "https://raw.githubusercontent.com/mekhatria/demo_highcharts/master/Olympics2012CapitalLetter.csv"
  )
#> Rows: 10858 Columns: 8
#> ── Column specification ────────────────────────────────────
#> Delimiter: ","
#> chr (5): name, sport, nationality, sex, date_of_birth
#> dbl (3): age, height, weight
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
df = subset(df, select = -c(nationality, date_of_birth, name, age))

my_data <- df %>% filter((sport == "Gymnastics" &
                   sex == "male")  |
                  (sport == "Canoe" &
                     sex == "male") |
                  (sport == "Hockey" &
                     sex == "male") | (sport == "Modern Pentathlon" & sex == "male")
  )
my_data = subset(my_data, select = -c(sex))

my_data %>% head() %>% kable()

sport	height	weight
Canoe	2.02	115
Canoe	1.88	102
Canoe	1.88	100
Canoe	1.92	98
Canoe	1.88	98
Canoe	1.84	97

hcboxplot(
  x = my_data$height,
  var = my_data$sport,
  name = "Length",
  color = "#2980b9",
  outliers = TRUE
) %>%
  hc_chart(type = "column") %>% # type="bar" olursa yatay eksende gözükür
  # En üstte bulunan başlık
  hc_title(text = "Male height by descipline (Olympic 2012)") %>%
  # y eksenindeki değişiklikler için
  hc_yAxis(title = list(text = "Height in metre")) %>%
  # Grafiğin üstüne serpilme diyagramı eklemek için
  hc_add_series(
    data = my_data,
    type = "scatter",
    hcaes(x = "sport", y = "my_data$height", group = "sport")
  ) %>%
  # serpilme diyagramı ayarları için scatter = list(...)
  hc_plotOptions(scatter = list(
    color = "red",
    marker = list(
      radius = 2,
      symbol = "circle",
      lineWidth = 1
    )
  ))  %>%
  hc_plotOptions(scatter = list(jitter = list(x = .1, y = 0)))
#> Warning: 'hcboxplot' is deprecated.
#> Use 'data_to_boxplot' instead.
#> See help("Deprecated")

3.1.7 Örnekler

data(favorite_bars)
favorite_bars %>% head() %>% kable()

bar	percent
Mclaren’s	30
McGee’s	28
P & G	27
White Horse Tavern	12
King Cole Bar	3

data(favorite_pies)
favorite_pies %>% head() %>% kable()

pie	percent
Strawberry Rhubarb	85
Pumpkin	64
Lemon Meringue	75
Blueberry	100
Key Lime	57

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_plotOptions(
    series = list(
      showInLegend = FALSE,
      pointFormat = "{point.y}%",
      # her bir kategori için farklı renk
      colorByPoint = TRUE
      ),
    pie = list(
      # pie chart'ın konumu ve boyutu
      center = c('30%', '10%'),
      size = 120,
      dataLabels = list(enabled = FALSE)
      )
    ) |>
  # eksenler
  hc_yAxis(
    title = list(text = "percentage of tastiness"),
    labels = list(format = "{value}%"), 
    max = 100
  ) |> 
  hc_xAxis(
    categories = favorite_pies$pie
    ) |>
  # Titles, subtitle, caption and credits
  hc_title(
    text = "How I Met Your Mother: Pie Chart Bar Graph"
  ) |> 
  hc_subtitle(
    text = "This is a bar graph describing my favorite pies
    including a pie chart describing my favorite bars"
  ) |>
  hc_caption(
    text = "The values represented are in percentage of tastiness and awesomeness."
    ) |> 
  hc_credits(
    enabled = TRUE, text = "Source: HIMYM",
    href = "https://www.youtube.com/watch?v=f_J8QU1m0Ng",
    style = list(fontSize = "12px")
  ) |> 
  hc_size(
    height = 600
    )

df <- data.frame(
  stringsAsFactors = FALSE,
  name = c(
    "The Left",
    "Social Democratic Party",
    "Alliance 90/The Greens",
    "Free Democratic Party",
    "Christian Democratic Union",
    "Christian Social Union in Bavaria",
    "Alternative for Germany"
  ),
  count = c(69, 153, 67, 80, 200, 46, 94),
  col = c("#BE3075", "#EB001F", "#64A12D", "#FFED00",
          "#000000", "#008AC5", "#009EE0"
  ),
  abbrv = c("DIE LINKE", "SPD", "GRÜNE", "FDP", "CDU", "CSU", "AfD")
)

hchart(
  df,
  type="item",
  hcaes(
    name = name,
    y = count,
    label = abbrv,
    color = col
  ),
  name = "Representatives",
  # legendda gösterilmesi için
  showInLegend = TRUE,
  size = "100%",
  # pie chartın yerinin grafik alanına göre belirlenmesi (yatay, dikey)
  center = list("50%", "75%"),
  # pie chartarın başlama derecesi
  startAngle = -100,
  # pie chartın bitiş dercesi
  endAngle  = 100
) %>%
  hc_title(text = "Item chart with different layout") %>%
  hc_legend(labelFormat = '{name} <span style="opacity: 0.4">{y}</span>')

library(forcats)
library(stringr)
library(purrr)
pkmn_min <- as.data.frame(pokemon) |>
  count(type_1, color = type_1_color) |>
  mutate(type_1 = fct_reorder(type_1, .x = n)) |>
  arrange(desc(type_1))

pkmn_ddn <- pokemon |>
  count(type_1, type_2, color = type_mix_color) |>
  arrange(type_1, desc(n)) |>
  mutate(type_2 = ifelse(is.na(type_2), str_c("only ", type_1), type_2)) |>
  group_nest(type_1) |>
  mutate(
    id = type_1,
    type = "column",
    # in the drilldown we'll give the mapping via creating the columns
    data = map(data, mutate, name = type_2, y  = n),
    data = map(data, list_parse)
  )

hchart(
  pkmn_min,
  type = "column",
  hcaes(x = type_1, y = n, color = color, drilldown = type_1),
  name = "Pokémons"
  ) |>
  hc_drilldown(
    activeAxisLabelStyle = list(textDecoration = "none"),
    allowPointDrilldown = TRUE,
    series = list_parse(pkmn_ddn)
  ) |>
  hc_yAxis(
    title = list(text = ""),
    endOnTick = FALSE,
    opposite = TRUE
    ) |>
  hc_xAxis(
    title = list(text = ""),
    endOnTick = FALSE,
    gridLineWidth = 0,
    tickWidth = 0
    ) |>
  hc_chart(
    style = list(fontFamily = "Gloria Hallelujah")
  )

library(quantmod)
SPY <- getSymbols("SPY", from = Sys.Date() - lubridate::years(1), auto.assign = FALSE)
SPY <- adjustOHLC(SPY)

SPY.SMA.10 <- SMA(Cl(SPY), n = 5)
SPY.SMA.200 <- SMA(Cl(SPY), n = 100)
SPY.RSI.14 <- RSI(Cl(SPY))
SPY.RSI.SellLevel <- xts(rep(70, NROW(SPY)), index(SPY))
SPY.RSI.BuyLevel <- xts(rep(30, NROW(SPY)), index(SPY))


highchart(type = "stock") |> 
  hc_yAxis_multiples(create_yaxis(3, height = c(2, 1, 1), turnopposite = TRUE)) |> 
  hc_add_series(SPY, yAxis = 0, name = "SPY") |> 
  hc_add_series(SPY.SMA.10, yAxis = 0, name = "Fast MA") |> 
  hc_add_series(SPY.SMA.200, yAxis = 0, name = "Slow MA") |> 
  hc_add_series(SPY$SPY.Volume, color = "gray", yAxis = 1, name = "Volume", type = "column") |> 
  hc_add_series(SPY.RSI.14, yAxis = 2, name = "Osciallator", color = hex_to_rgba("green", 0.7)) |>
  hc_add_series(SPY.RSI.SellLevel, color = hex_to_rgba("red", 0.7), yAxis = 2, name = "Sell level") |> 
  hc_add_series(SPY.RSI.BuyLevel, color = hex_to_rgba("blue", 0.7), yAxis = 2, name = "Buy level") |> 
  hc_tooltip(valueDecimals = 2) |> 
  hc_size(height = 800)

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