Data Time Series

installed.packages("quantmod")
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library(quantmod)
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Data Saham: Close

  1. Saham The Walt Disney Company (DIS)
  2. Saham Warner Bros Discovery Inc (WBD)
  3. Saham Comcast Corp (CMCSA)
symbols <- c("DIS","WBD","CMCSA")

data <- do.call(merge, lapply(symbols, function(s){
  Cl(getSymbols(s, src="yahoo",
                from="2023-01-01",
                to="2025-12-31",
                auto.assign=FALSE))
}))

colnames(data) <- symbols
head(data)
##              DIS   WBD    CMCSA
## 2023-01-03 88.97  9.54 33.33646
## 2023-01-04 91.98 10.38 34.29241
## 2023-01-05 91.92 10.86 34.65792
## 2023-01-06 93.92 11.32 35.51078
## 2023-01-09 94.77 11.61 35.19213
## 2023-01-10 95.56 12.56 35.50141

Load Package

library(ggplot2)
library(tidyr)
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library(dplyr)
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Plot Single Data Series

Data 1: DIS

dis <- data.frame(
  Time = as.Date(index(data)),
  Price = coredata(data)[,"DIS"])

head(dis)
##         Time Price
## 1 2023-01-03 88.97
## 2 2023-01-04 91.98
## 3 2023-01-05 91.92
## 4 2023-01-06 93.92
## 5 2023-01-09 94.77
## 6 2023-01-10 95.56
str(dis)
## 'data.frame':    751 obs. of  2 variables:
##  $ Time : Date, format: "2023-01-03" "2023-01-04" ...
##  $ Price: num  89 92 91.9 93.9 94.8 ...
# plot time series
d1 <- ggplot(dis, aes(x=Time, y=Price))+
  geom_line()+
  xlab("Date")
d1

Data 2: WBD

wbd <- data.frame(
  Time = as.Date(index(data)),
  Price = coredata(data)[,"WBD"])

head(wbd)
##         Time Price
## 1 2023-01-03  9.54
## 2 2023-01-04 10.38
## 3 2023-01-05 10.86
## 4 2023-01-06 11.32
## 5 2023-01-09 11.61
## 6 2023-01-10 12.56
str(wbd)
## 'data.frame':    751 obs. of  2 variables:
##  $ Time : Date, format: "2023-01-03" "2023-01-04" ...
##  $ Price: num  9.54 10.38 10.86 11.32 11.61 ...
# plot time series
d2 <- ggplot(wbd, aes(x=Time, y=Price))+
  geom_line()+
  xlab("Date")
d2

Data 3: CMCSA

cmcsa <- data.frame(
  Time = as.Date(index(data)),
  Price = coredata(data)[,"CMCSA"])

head(cmcsa)
##         Time    Price
## 1 2023-01-03 33.33646
## 2 2023-01-04 34.29241
## 3 2023-01-05 34.65792
## 4 2023-01-06 35.51078
## 5 2023-01-09 35.19213
## 6 2023-01-10 35.50141
str(cmcsa)
## 'data.frame':    751 obs. of  2 variables:
##  $ Time : Date, format: "2023-01-03" "2023-01-04" ...
##  $ Price: num  33.3 34.3 34.7 35.5 35.2 ...
# plot time series
d3 <- ggplot(cmcsa, aes(x=Time, y=Price))+
  geom_line()+
  xlab("Date")
d3

Plot Multiple Data Series

multi <- data.frame(
  Time = as.Date(index(data)),
  coredata(data)
)

d4 <- pivot_longer(
  multi,
  cols = -Time,
  names_to = "Stock",
  values_to = "Price"
)

head(d4)
## # A tibble: 6 × 3
##   Time       Stock Price
##   <date>     <chr> <dbl>
## 1 2023-01-03 DIS   89.0 
## 2 2023-01-03 WBD    9.54
## 3 2023-01-03 CMCSA 33.3 
## 4 2023-01-04 DIS   92.0 
## 5 2023-01-04 WBD   10.4 
## 6 2023-01-04 CMCSA 34.3
ggplot(d4, aes(x = Time, y = Price, color = Stock)) +
  geom_line(size = 1) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  labs(
    title = "Stock Price Comparison",
    x = "Date",
    y = "Close Price"
  )
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
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Tambah Pemisah Tahunan

Single 1: DIS

d1 <- ggplot(dis, aes(x=Time, y=Price))+
  geom_line()+
  geom_vline(
    xintercept = as.Date(c("2024-01-01","2025-01-01")),
    linetype = "dashed"
  )+
  xlab("Date")+
  ylab("Close Price")+
  ggtitle("DIS Stock Price")
d1

Single 2: WBD

d2 <- ggplot(wbd, aes(x=Time, y=Price))+
  geom_line()+
  geom_vline(
    xintercept = as.Date(c("2024-01-01","2025-01-01")),
    linetype = "dashed"
  )+
  xlab("Date")+
  ylab("Close Price")+
  ggtitle("WBD Stock Price")
d2

Single 3: CMCSA

d3 <- ggplot(cmcsa, aes(x=Time, y=Price))+
  geom_line()+
  geom_vline(
    xintercept = as.Date(c("2024-01-01","2025-01-01")),
    linetype = "dashed"
  )+
  xlab("Date")+
  ylab("Close Price")+
  ggtitle("CMCSA Stock Price")
d3

Multi

ggplot(d4, aes(x = Time, y = Price, color = Stock)) +
  geom_line(size = 1) +
  geom_vline(
    xintercept = as.Date(c("2024-01-01","2025-01-01")),
    linetype = "dashed"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  labs(
    title = "Stock Price with Year Separation",
    x = "Date",
    y = "Close Price"
  )

## Eksplorasi Data - Boxplot

ggplot(d4, aes(x = Stock, y = Price, fill = Stock)) +
  geom_boxplot() +
  theme_minimal() +
  labs(
    title = "Distribution of Stock Prices",
    x = "Stock",
    y = "Close Price"
  )

## Kesimpulan Berdasarkan visualisasi data time series harga saham selama periode 2023-2025, terlihat bahwa ketiga perusahaan memiliki pola pergerakan yang berbeda. Saham The Walt Disney Company (DIS) memiliki harga yang paling tinggi dibandingkan dua perusahaan lainnya dan menunjukkan fluktuasi yang cukup besar sepanjang periode pengamatan. Hal ini terlihat dari rentang harga yang lebih lebar pada grafik time series maupun boxplot.

Saham Comcast Corporation (CMCSA) berada pada tingkat harga menengah dengan pergerakan yang relatif lebih stabil dibandingkan DIS. Variasi harga yang tidak terlalu besar pada boxplot menunjukkan bahwa perubahan harga saham CMCSA cenderung lebih konsisten selama tiga tahun tersebut.

Sementara itu, saham Warner Bros. Discovery (WBD) memiliki harga paling rendah dibandingkan kedua perusahaan lainnya. Namun pada bagian akhir periode terlihat adanya kenaikan harga yang cukup signifikan. Hal ini juga terlihat pada boxplot dengan munculnya beberapa nilai yang lebih tinggi dari mayoritas data, yang menunjukkan adanya peningkatan harga pada akhir periode pengamatan.

Secara keseluruhan, visualisasi ini menunjukkan bahwa DIS memiliki volatilitas harga yang lebih tinggi, CMCSA relatif lebih stabil, dan WBD menunjukkan peningkatan harga pada akhir periode.