Visualisasi Data
Exercises ~ Week 3
1 Pendahuluan
Tujuan dari laporan ini adalah untuk memvisualisasikan data penjualan sepatu yang sederhana guna mendapatkan wawasan awal mengenai performa produk. Dataset ini mencakup sepuluh jenis sapatu dengan informasi mengenai rating, jumlah terjual (sold), dan harga (price). Kontes kasusnya adalah analisis dasar katalog produk untuk mengidentifikasi produk yang populer (berdarsarkan rating tertingi dan jumlah terjual) serta hubungan dengan harga jual.
2 Persiapan Data
# Load package
library(ggplot2)
# 1. Create a Shoe Sales Table
library(knitr)
shoes <- data.frame(
Type = c(
"Champion Low Grey x Pokemon", "Rubick Low Black Natural",
"Victory Low All Black", "Reborn Low Reflective White",
"Olympic Low Black White", "Alpha Low Black Natural",
"Ethnic High Black Natural", "Evo Low Black Natural",
"J23 Low Black Brown", "Rubick Fat Low Black White"
),
Rating = c(3.8, 4.9, 4.6, 4.8, 5.0, 4.9, 4.9, 5.0, 4.7, 4.8),
Sold = c(6, 8000, 46, 697, 62, 2000, 10000, 29, 46, 80),
Price = c(310.303, 230.940, 248.940, 302.940, 302.940, 190.440, 190.440, 190.440, 275.940, 284.940))
knitr::kable(shoes, caption = "table:ventela shoe sales")| Type | Rating | Sold | Price |
|---|---|---|---|
| Champion Low Grey x Pokemon | 3.8 | 6 | 310.303 |
| Rubick Low Black Natural | 4.9 | 8000 | 230.940 |
| Victory Low All Black | 4.6 | 46 | 248.940 |
| Reborn Low Reflective White | 4.8 | 697 | 302.940 |
| Olympic Low Black White | 5.0 | 62 | 302.940 |
| Alpha Low Black Natural | 4.9 | 2000 | 190.440 |
| Ethnic High Black Natural | 4.9 | 10000 | 190.440 |
| Evo Low Black Natural | 5.0 | 29 | 190.440 |
| J23 Low Black Brown | 4.7 | 46 | 275.940 |
| Rubick Fat Low Black White | 4.8 | 80 | 284.940 |
| ### MEAN (Rata-rata) |
| #### Definisi: |
| Mean adalah nilai rata-rata yang diperoleh dengan menjumlahkan semua nilai data kemudian dibagi dengan banyaknya data. |
| Rumus: |
| Mean = (Jumlah semua nilai) / (Banyaknya data) |
| Aturan & Karakteristik: |
| 1. Dipengaruhi oleh semua nilai dalam dataset 2. Sensitif terhadap outlier (nilai ekstrem) 3. Cocok untuk data yang berdistribusi normal 4. Dapat berupa bilangan desimal meskipun data berupa bilangan bulat |
| Langkah Perhitungan: |
| 1. Jumlahkan semua nilai data 2. Hitung banyaknya data (n) 3. Bagi total jumlah dengan n |
| #### Contoh: |
| Data: 5, 7, 8, 10, 15 |
| Mean = (5 + 7 + 8 + 10 + 15) / 5 = 45 / 5 = 9 |
2.1 MEDIAN (Nilai Tengah)
2.1.1 Definisi:
Median adalah nilai yang membagi data menjadi dua bagian sama besar setelah data diurutkan.
Aturan & Karakteristik:
- Tidak dipengaruhi oleh outlier (robust)
- Cocok untuk data yang tidak berdistribusi normal
- Lebih representatif ketika ada nilai ekstrem
2.1.2 Langkah Perhitungan:
Untuk data ganjil:
- Urutkan data dari terkecil ke terbesar
- Median = nilai tepat di tengah
Untuk data genap:
- Urutkan data dari terkecil ke terbesar
- Median = rata-rata dari dua nilai tengah
2.1.3 Contoh:
Data ganjil: 3, 5, 7, 9, 11
Median = 7 (nilai ke-3)
Data genap: 3, 5, 7, 9, 11, 13
Median = (7 + 9) / 2 = 8
2.2 MODUS (Nilai Paling Sering Muncul)
2.2.1 Definisi:
Modus adalah nilai yang paling sering muncul dalam suatu dataset.
Aturan & Karakteristik:
- Dapat digunakan untuk data kategorikal dan numerik
- Satu dataset bisa memiliki: · Unimodal: satu modus · Bimodal: dua modus · Multimodal: lebih dari dua modus Tidak ada modus: semua nilai frekuensi sama
- Tidak dipengaruhi oleh nilai ekstrem
Langkah Perhitungan:
- Hitung frekuensi kemunculan setiap nilai
- Cari nilai dengan frekuensi tertinggi
- Jika ada beberapa nilai dengan frekuensi sama tertinggi, semuanya adalah modus
2.2.2 Contoh:
Data: 5, 7, 7, 8, 8, 8, 10
Modus = 8 (muncul 3 kali)
Data: 5, 5, 7, 7, 8, 8
Bimodal = 5, 7, 8 (semua muncul 2 kali)
2.3 PERBANDINGAN SINGKAT
Aspek Mean Median Modus Definisi Rata-rata Nilai tengah Nilai paling sering Penggunaan Data normal Data skewed Data kategorikal Outlier Sensitif Robust Robust Rumus ∑x/n Nilai tengah Frekuensi tertinggi Data Numerik Numerik Semua jenis
2.4 PEMILIHAN BERDASARKAN JENIS DATA
Gunakan MEAN ketika:
· Data berdistribusi normal · Tidak ada outlier yang signifikan · Membutuhkan semua nilai diperhitungkan
Gunakan MEDIAN ketika:
· Ada outlier (nilai ekstrem) · Data skewed (miring) · Data ordinal (peringkat)
Gunakan MODUS ketika:
· Data kategorikal (jenis kelamin, kategori produk) · Mengetahui nilai paling populer · Data nominal (nama, label)
###CONTOH APLIKASI DARI DATA KITA:
Usia: Mean = 40.75, Median = 41, Modus = 18
· Interpretasi: Ada banyak pelanggan muda (modus 18) meskipun rata-rata usia 41 tahun
Total Pembelian: Mean = 396.18, Median = 384.5
· Interpretasi: Mean > Median → distribusi miring ke kanan (ada beberapa pembelian besar)
Jumlah Kunjungan: Mean = 5.05, Median = 5, Modus = 5
· Interpretasi: Distribusi simetris (mean ≈ median ≈ modus)
2.5 TIPS
- Selalu hitung ketiganya untuk memahami karakteristik data
- Visualisasikan dengan histogram untuk melihat distribusi
- Pertimbangkan konteks bisnis saat memilih mana yang digunakan
- Waspada outlier - bisa mempengaruhi mean secara signifikan
““,”CustomerID”,“Age”,“Gender”,“StoreLocation”,“ProductCategory”,“TotalPurchase”,“NumberOfVisits”,“FeedbackScore” “1”,“1”,“32”,“M”,“West”,“Electronics”,“528”,“4”,“1” “2”,“2”,“37”,“F”,“South”,“Books”,“72”,“4”,“5” “3”,“3”,“63”,“M”,“West”,“Electronics”,“327”,“4”,“2” “4”,“4”,“41”,“M”,“North”,“Sports”,“391”,“7”,“1” “5”,“5”,“42”,“F”,“East”,“Electronics”,“514”,“7”,“5” “6”,“6”,“66”,“F”,“East”,“Sports”,“381”,“6”,“3” “7”,“7”,“47”,“M”,“East”,“Sports”,“510”,“5”,“1” “8”,“8”,“21”,“F”,“South”,“Clothing”,“102”,“4”,“2” “9”,“9”,“30”,“F”,“North”,“Sports”,“559”,“2”,“2” “10”,“10”,“33”,“M”,“South”,“Books”,“27”,“5”,“2” “11”,“11”,“58”,“F”,“East”,“Clothing”,“40”,“3”,“5” “12”,“12”,“45”,“M”,“North”,“Electronics”,“217”,“6”,“5” “13”,“13”,“46”,“F”,“South”,“Home”,“118”,“4”,“4” “14”,“14”,“42”,“F”,“North”,“Sports”,“532”,“6”,“3” “15”,“15”,“32”,“F”,“South”,“Books”,“25”,“3”,“3” “16”,“16”,“67”,“F”,“South”,“Home”,“87”,“4”,“1” “17”,“17”,“47”,“M”,“West”,“Home”,“77”,“7”,“3” “18”,“18”,“18”,“F”,“East”,“Books”,“80”,“7”,“3” 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