Central Tendency

Assignment ~ Week 6

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1 Tabel dataset

library(ggplot2)
library(DT)
library(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

library(reshape2)
library(corrplot)

## corrplot 0.95 loaded

# --- Right-skewed data (with extreme values) ---
set.seed(123)

data_skew <- read.csv("4 Central Tendency – Introduction to Statistics.csv")
numeric_data <- data_skew %>% select(where(is.numeric))
subset_data <- numeric_data[, 1:4]

 DT::datatable(
   data_skew,
   caption = "Central Tendency",
   rownames = F,
   options = list(pageLenght = 10)
 )

2 Distribusi data variabel numerik

subset_data_long <- melt(subset_data)

## No id variables; using all as measure variables

ggplot(subset_data_long, aes(x = value, fill = variable)) +
geom_histogram(color = "white", bins = 30, alpha = 0.7) +
facet_wrap(~ variable, scales = "free", ncol = 2) +
labs(title = "Distribusi Data untuk Setiap Variabel Numerik",
x = "Nilai", y = "Frekuensi") +
theme_minimal()

Distribusi data untuk setiap variabel numerik menggambarkan nilai nilai tersebar dalam variabel berikut membantu kita memahami pola untuk menganalisis Central Tendencym.

3 Boxplot

ggplot(subset_data_long, aes(x = variable, y = value, fill = variable)) +
geom_boxplot(alpha = 0.8) +
labs(title = "Boxplot Perbandingan Sebaran Empat Variabel Numerik",
x = "Variabel", y = "Nilai")

Dari boxplot terlihat bahwa TotalPurchase memiliki sebaran nilai yang paling besar dan terdapat outlier. Variabel Age memiliki sebaran paling kecil, artinya datanya lebih seragam. Sementara X dan CustomerID berada di rentang menengah dengan variasi sedang. Ini menunjukkan bahwa TotalPurchase paling bervariasi antar pelanggan, sedangkan Age paling stabil.


# Distribusi Total Purchase

``` r
# Pastikan paket yang dibutuhkan
library(ggplot2)
library(dplyr)

# Hitung statistik ringkas untuk kolom TotalPurchase
stats_summary <- data_skew %>%
  summarise(
    TotalPurchase_Mean = mean(TotalPurchase, na. = TRUE),
    TotalPurchase_Median = median(TotalPurchase, na.rm = TRUE),
    TotalPurchase_Mode = as.numeric(names(sort(table(TotalPurchase), decreasing = TRUE)[1]))
  )

# Buat histogram dengan mean, median, dan mode
tp_hist <- ggplot(data_skew, aes(x = TotalPurchase)) +
  geom_histogram(aes(y = after_stat(density)), 
                 binwidth =50, fill = "#5ab4ac", color = "white", alpha = 0.8) +
  geom_density(color = "#2b8cbe", linewidth = 1.3, alpha = 0.9) +
  geom_vline(aes(xintercept = stats_summary$TotalPurchase_Mean, color = "Mean"), 
             linewidth = 1.2) +
  geom_vline(aes(xintercept = stats_summary$TotalPurchase_Median, color = "Median"), 
             linewidth = 1.2, linetype = "dashed") +
  geom_vline(aes(xintercept = stats_summary$TotalPurchase_Mode, color = "Mode"), 
             linewidth = 1.2, linetype = "dotdash") +
  scale_color_manual(values = c("Mean" = "red", "Median" = "blue", "Mode" = "green")) +
  labs(
    title = "Distribusi TotalPurchase",
    subtitle = "Menunjukkan Nilai Mean, Median, dan Mode",
    x = "TotalPurchase",
    y = "Density",
    color = "Statistik"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    legend.position = "bottom"
  )

# Tampilkan plot
print(tp_hist)

Bisa di lihat dari visualisasi diatas bahwa distribusi totalpurchase terlihat miring ke ke kanan, jika dilihat dari nilai mean,media,modus menunjukan sebagian besar data totalpurchase bernilai kecil akan tetapi ada beberapa nilai outlier sehingga menaikan nilai rata-rata

4 Kesimpulan

Dari hasil analisis kami dapat disimpulkan bahwa sebagian besar pelanggan berbelanja dalam jumlah kecil, sementara hanya sedikit pelanggan dengan pembelian yang sangat tinggi. Hal ini menunjukkan adanya perbedaan signifikan antar pelanggan, sehingga perusahaan perlu berfokus untuk meningkatkan nilai pembelian pelanggan umum sekaligus mempertahankan pelanggan dengan pembelian besar.

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