Analisis Gaji di Bidang Teknologi Informasi

library(gridExtra)

# Membuat data frame
data <- data.frame(
  Negara = c("Indonesia", "Filipina", "Thailand", "Singapore"),
  Bidang = rep("Teknologi Informasi", 4),
  Rentang_Umur = rep("20-30", 4),
  Rata_Rata_Gaji = c(12.5, 6.8, 11, 86) # Dalam juta Rupiah
)

# Menampilkan data
print(data)

##      Negara              Bidang Rentang_Umur Rata_Rata_Gaji
## 1 Indonesia Teknologi Informasi        20-30           12.5
## 2  Filipina Teknologi Informasi        20-30            6.8
## 3  Thailand Teknologi Informasi        20-30           11.0
## 4 Singapore Teknologi Informasi        20-30           86.0

Visulisasi Pie Chart

pie_chart <- ggplot(data, aes(x = "", y = Rata_Rata_Gaji, fill = Negara)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0) +
  theme_void() +
  labs(title = "Proporsi Gaji Rata-Rata per Negara") +
  scale_fill_brewer(palette = "Set3")

pie_chart

Visulisasi Bar Chart

bar_chart <- ggplot(data, aes(x = Negara, y = Rata_Rata_Gaji, fill = Negara)) +
  geom_bar(stat = "identity") +
  theme_economist() +
  labs(title = "Gaji Rata-Rata per Negara", x = "Negara", y = "Gaji (Juta Rupiah)") +
  scale_fill_brewer(palette = "Set2")

bar_chart

Visulisasi Histogram

histogram <- ggplot(data, aes(x = Rata_Rata_Gaji)) +
  geom_histogram(binwidth = 10, fill = "lightgreen", color = "black") +
  theme_fivethirtyeight() +
  labs(title = "Distribusi Gaji", x = "Gaji (Juta Rupiah)", y = "Frekuensi")

histogram

Visulisasi Density Plot

density_plot <- ggplot(data, aes(x = Rata_Rata_Gaji)) +
  geom_density(fill = "lightblue", alpha = 0.5) +
  theme_clean() +
  labs(title = "Density Plot Gaji", x = "Gaji (Juta Rupiah)", y = "Density")

density_plot

Visulisasi Box Plot

box_plot <- ggplot(data, aes(y = Rata_Rata_Gaji)) +
  geom_boxplot(fill = "orange", color = "black") +
  theme_tufte() +
  labs(title = "Box Plot Gaji", y = "Gaji (Juta Rupiah)")

box_plot

Semua Ditampilkan

grid.arrange(pie_chart, bar_chart, histogram, density_plot, box_plot, ncol = 2)

# Menghitung statistik deskriptif
mean_gaji <- mean(data$Rata_Rata_Gaji)
median_gaji <- median(data$Rata_Rata_Gaji)
q1_gaji <- quantile(data$Rata_Rata_Gaji, 0.25)
q3_gaji <- quantile(data$Rata_Rata_Gaji, 0.75)
range_gaji <- range(data$Rata_Rata_Gaji)
var_gaji <- var(data$Rata_Rata_Gaji)
sd_gaji <- sd(data$Rata_Rata_Gaji)

# Menampilkan hasil
cat("Mean:", mean_gaji, "\n")

## Mean: 29.075

cat("Median:", median_gaji, "\n")

## Median: 11.75

cat("Q1:", q1_gaji, "\n")

## Q1: 9.95

cat("Q3:", q3_gaji, "\n")

## Q3: 30.875

cat("Range:", range_gaji, "\n")

## Range: 6.8 86

cat("Varians:", var_gaji, "\n")

## Varians: 1446.023

cat("Standar Deviasi:", sd_gaji, "\n")

## Standar Deviasi: 38.0266