Visualisasi Data
Assignment Week-4
Foto Kelompok 3
1 Pendahuluan
2 Persiapan Data
data_mahasiswa <- data.frame(
ID_Mahasiswa = c("M001", "M002", "M003", "M004", "M005",
"M006", "M007", "M008", "M009", "M010"),
Fakultas = c("Ekonomi", "Teknik", "Hukum", "Psikologi", "Teknik",
"Ekonomi", "Kedokteran", "Hukum", "Teknik", "Psikologi"),
Nilai_UAS = c(85, 78, 92, 70, 88, 64, 94, 73, 81, 77),
Waktu_Belajar = c(10, 8, 15, 6, 12, 4, 16, 7, 10, 9),
Kehadiran = c("95%", "88%", "98%", "82%", "97%", "75%", "99%", "85%", "91%", "89%"),
IPK = c(3.7, 3.4, 3.9, 2.9, 3.8, 2.6, 4.0, 3.3, 3.5, 3.2),
Gender = c("L", "P", "L", "P", "L", "P", "L", "L", "L", "P"),
Semester = c(5, 4, 6, 3, 5, 2, 6, 3, 4, 4)
)
print (data_mahasiswa)## ID_Mahasiswa Fakultas Nilai_UAS Waktu_Belajar Kehadiran IPK Gender
## 1 M001 Ekonomi 85 10 95% 3.7 L
## 2 M002 Teknik 78 8 88% 3.4 P
## 3 M003 Hukum 92 15 98% 3.9 L
## 4 M004 Psikologi 70 6 82% 2.9 P
## 5 M005 Teknik 88 12 97% 3.8 L
## 6 M006 Ekonomi 64 4 75% 2.6 P
## 7 M007 Kedokteran 94 16 99% 4.0 L
## 8 M008 Hukum 73 7 85% 3.3 L
## 9 M009 Teknik 81 10 91% 3.5 L
## 10 M010 Psikologi 77 9 89% 3.2 P
## Semester
## 1 5
## 2 4
## 3 6
## 4 3
## 5 5
## 6 2
## 7 6
## 8 3
## 9 4
## 10 43 Visualisasi Data
3.1 Bar Chat
Definisi
Bar chart digunakan untuk menampilkan dan membandingkan data kategori (kategorikal) dalam bentuk batang tegak atau mendatar. Setiap batang mewakili satu kategori (misalnya Fakultas, Jenis Kelamin, atau Mata Kuliah), dan panjang batang menggambarkan jumlah, rata-rata, atau nilai tertentu dari kategori tersebut.
Aturan Penggunaan
-Gunakan jika data bersifat kategorik atau diskrit. -Sumbu X (horizontal) → kategori. -Sumbu Y (vertikal) → nilai (jumlah, frekuensi, rata-rata, dll). -Hindari terlalu banyak kategori karena grafik bisa jadi sulit dibaca.
kelebihan
-Sangat mudah dipahami oleh semua orang. -Cocok untuk membandingkan nilai antar kategori. -Dapat digunakan dengan berbagai variasi (stacked bar, grouped bar, horizontal bar).
kekurangan
-Tidak cocok untuk data kontinu (angka yang bersambung). -Sulit dibaca jika jumlah kategori terlalu banyak atau label panjang.
# Memuat paket
library(ggplot2)
# Menghitung rata-rata nilai UAS per fakultas
rata_rata_uas <- aggregate(Nilai_UAS ~ Fakultas, data = data_mahasiswa, mean)
print(rata_rata_uas)## Fakultas Nilai_UAS
## 1 Ekonomi 74.50000
## 2 Hukum 82.50000
## 3 Kedokteran 94.00000
## 4 Psikologi 73.50000
## 5 Teknik 82.33333# Membuat grafik bar rata-rata nilai UAS per fakultas
ggplot(rata_rata_uas, aes(x = reorder(Fakultas, -Nilai_UAS), y = Nilai_UAS, fill = Fakultas)) +
geom_bar(stat = "identity") +
labs(
title = "Rata-rata Nilai UAS per Fakultas",
x = "Fakultas",
y = "Rata-rata Nilai UAS"
) +
theme_minimal()
3.2 Histogram
# Histogram distribusi Nilai UAS
ggplot(data_mahasiswa, aes(x = Nilai_UAS)) +
geom_histogram(
aes(y = after_stat(density), fill = after_stat(count)),
bins = 6,
color = "white",
show.legend = FALSE
) +
scale_fill_gradient(low = "#56B1F7", high = "#132B43") +
geom_density(alpha = 0.2, fill = "darkblue") +
labs(
title = "Distribusi Nilai UAS Mahasiswa",
subtitle = "Menunjukkan penyebaran nilai ujian akhir per mahasiswa",
x = "Nilai UAS",
y = "Kepadatan (Density)"
)
4
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