Visualisasi Hubungan antar Peubah Numerik

temperature <- seq(15, 40, by = 1)
ice_cream_sales <- c(100, 110, 120, 130, 145, 160, 175, 190, 210, 230, 
                     250, 270, 300, 330, 360, 390, 420, 450, 480, 510, 
                     540, 580, 620, 660, 700, 740)

data <- data.frame(temperature, ice_cream_sales)

# Menyimpan dataset ke file CSV
write.csv(data, "C:/Users/keyzh/Downloads/correlated_data.csv", row.names = FALSE)
library(ggplot2)
data <- read.csv("C:/Users/keyzh/Downloads/correlated_data.csv")

ggplot(data, aes(x = temperature, y = ice_cream_sales)) +
  geom_point(color = 'blue') +
  labs(title = "Hubungan antara Suhu vs Tingkat Penjualan Es Krim ",
       x = "Suhu (°C)",
       y = "Tingkat Penjualan Es Krim") +
  theme_minimal()

Dari data tersebut, tingkat kenaikan suhu memiliki hubungan yang linier positif dengan tingkat penjualan es krim. Dapat dilihat bahwa semakin tinggi suhu maka tingkat penjualan juga semakin tinggi.

Visualisasi dengan banyak peubah

library(ggplot2)
library(reshape2)
## Warning: package 'reshape2' was built under R version 4.3.3
# Contoh data: menggunakan dataset mtcars
data <- mtcars

# Hitung korelasi
cor_matrix <- cor(data)

# Reshape data untuk ggplot
melted_cor <- melt(cor_matrix)

# Buat heatmap
ggplot(data = melted_cor, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile() +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1,1), space = "Lab", 
                       name="Correlation") +
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 12, hjust = 1)) +
  coord_fixed()

library(pheatmap)
## Warning: package 'pheatmap' was built under R version 4.3.3
# Contoh data: menggunakan dataset mtcars
data <- mtcars

# Hitung korelasi
cor_matrix <- cor(data)

# Buat heatmap
pheatmap(cor_matrix, 
         color = colorRampPalette(c("blue", "white", "red"))(50), 
         display_numbers = TRUE, 
         cluster_rows = FALSE, 
         cluster_cols = FALSE)

Dari data tersebut, warna merah menunjukkan nilai korelasi positif yang sangat kuat., warna biru menunjukkan sebaliknya, yaitu nilai korelasi negatif yang kuat. Sedangkan warna putih menunjukkan tidak ada korelasi.

Visualisasi Time Series

library(readxl)
## Warning: package 'readxl' was built under R version 4.3.2
data <- read_excel("C:/Users/keyzh/Downloads/argentina.xlsx")
head(data)
## # A tibble: 6 × 2
##   time    death
##   <chr>   <dbl>
## 1 1/22/20     0
## 2 1/23/20     0
## 3 1/24/20     0
## 4 1/25/20     0
## 5 1/26/20     0
## 6 1/27/20     0
data$time <- as.Date(data$time, format = "%m/%d/%y")
ggplot(data, aes(x = time, y = death)) +
  geom_point() +
  labs(title = "Scatter Plot of Time vs Death",
       x = "Time",
       y = "Death") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
## Warning: Removed 48 rows containing missing values (`geom_point()`).

Dari data tingkat kematian yang disebabkan oleh virus Covid-19 di Argentina, dari bulan januari hingga pertengahan april jumlah kematian masih berada di angka 0. Namun seiring berjalannya waktu, semakin hari jumlah kematian juga semakin banyak dan tertinggi di bulan Juni mencapai angka 500.

Visualisasi Spasial

library("ggplot2")
library("sf")
## Warning: package 'sf' was built under R version 4.3.3
## Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE
library("rnaturalearth")
## Warning: package 'rnaturalearth' was built under R version 4.3.3
library("rnaturalearthdata")
## Warning: package 'rnaturalearthdata' was built under R version 4.3.3
## 
## Attaching package: 'rnaturalearthdata'
## The following object is masked from 'package:rnaturalearth':
## 
##     countries110
library(sf)
library(ggspatial)
## Warning: package 'ggspatial' was built under R version 4.3.3
data <- read.csv("C:/Users/keyzh/OneDrive/Desktop/OneDrive/Documents/keke/SEMESTER 4/Visualisasi Data/EUvaccineP10.csv") 
data <- data.frame(
  region = c("UK", "Serbia", "Malta", "Hungary", "Denmark", "Estonia", "Iceland", "Lithuania", "Norway", "Slovenia", "Finland", "Greece", "Switzerland", "Poland", "Ireland", "Spain", "Portugal", "Austria", "Slovakia", "Cyprus", "Italy", "Sweden", "Romania", "France", "Germany", "Czech Republic", "Belgium", "Netherlands", "Luxembourg", "Croatia", "Latvia", "Bulgaria"),
  Perc_vaccinated = c(37.98, 29.5, 27.19, 17.95, 14.4, 14.01, 13.73, 12.95, 12.74, 12.41, 12.33, 12.31, 11.96, 11.94, 11.94, 11.45, 11.41, 11.39, 11.37, 11.33, 11.11, 10.83, 10.76, 10.75, 10.58, 10.1, 9.89, 9.45, 8.76, 7.33, 5.15, 4.88)
)
world <- ne_countries(scale = "medium", returnclass = "sf")

europe <- world[world$continent == "Europe",]

europe <- merge(europe, data, by.x = "name", by.y = "region", all.x = TRUE)
ggplot(data = europe) +
  geom_sf(aes(fill = Perc_vaccinated)) +
  scale_fill_viridis_c(option = "plasma", na.value = "grey50") +
  theme_minimal() +
  labs(
    title = "Vaccination Rates in Europe",
    fill = "% Vaccinated"
  ) +
  theme(
    plot.title = element_text(hjust = 0.5)
  )

Tingkat vaksinasi tertinggi ditandai dengan warna yang paling terang yaitu warna kuning pada negara United Kingdom (UK). Sedangkan semakin gelap warnanya, berarti jumlah vaksinasi semakin rendah. Negara yang menduduki jumlah vaksinasi terendah adalah Bulgaria.