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",
"M011", "M012"),
Fakultas = c("Teknik", "Teknik", "Kedokteran", "Hukum", "Kedokteran",
"Hukum", "Teknik", "Hukum", "Kedokteran", "Hukum",
"Teknik", "Kedokteran"),
Nilai_UAS = c(85, 78, 92, 70, 88, 64, 94, 73, 81, 77, 90, 86),
Waktu_belajar = c(10, 8, 15, 6, 12, 4, 16, 7, 10, 9, 13, 11),
Kehadiran = c(95, 88, 98, 82, 97, 75, 99, 85, 91, 89, 96, 93),
IPK = c(3.7, 3.4, 3.9, 2.9, 3.8, 2.6, 4.0, 3.3, 3.5, 3.2, 3.8, 3.6),
Gender = c("L", "P", "L", "P", "L", "P", "L", "L", "L", "P", "P", "L"),
Semester = c(5, 4, 3, 3, 5, 6, 6, 5, 4, 4, 3, 6)
)
print (data_mahasiswa)## ID_Mahasiswa Fakultas Nilai_UAS Waktu_belajar Kehadiran IPK Gender
## 1 M001 Teknik 85 10 95 3.7 L
## 2 M002 Teknik 78 8 88 3.4 P
## 3 M003 Kedokteran 92 15 98 3.9 L
## 4 M004 Hukum 70 6 82 2.9 P
## 5 M005 Kedokteran 88 12 97 3.8 L
## 6 M006 Hukum 64 4 75 2.6 P
## 7 M007 Teknik 94 16 99 4.0 L
## 8 M008 Hukum 73 7 85 3.3 L
## 9 M009 Kedokteran 81 10 91 3.5 L
## 10 M010 Hukum 77 9 89 3.2 P
## 11 M011 Teknik 90 13 96 3.8 P
## 12 M012 Kedokteran 86 11 93 3.6 L
## Semester
## 1 5
## 2 4
## 3 3
## 4 3
## 5 5
## 6 6
## 7 6
## 8 5
## 9 4
## 10 4
## 11 3
## 12 63 Visualisasi data
3.0.1 Bar Chart: Rata-rata IPK per Fakultas
library(ggplot2)
ggplot(data_mahasiswa, aes(x = Fakultas, y = IPK, fill = Fakultas)) +
stat_summary(fun = mean, geom = "bar") +
labs(title = "Rata-rata IPK per Fakultas", y = "IPK Rata-rata") +
theme_minimal()
3.0.2 Histogram: Distribusi Nilai UAS
ggplot(data_mahasiswa, aes(x = Nilai_UAS)) +
geom_histogram(binwidth = 5, fill = "skyblue", color = "black") +
labs(title = "Distribusi Nilai UAS", x = "Nilai UAS", y = "Frekuensi") +
theme_minimal()
3.0.3 Boxplot: Distribusi Kehadiran per Fakultas
ggplot(data_mahasiswa, aes(x = Fakultas, y = Kehadiran, fill = Fakultas)) +
geom_boxplot() +
labs(title = "Distribusi Kehadiran per Fakultas", y = "Kehadiran (%)") +
theme_minimal()
3.0.4 Pie Chart: Proporsi Mahasiswa per Fakultas
pie_data <- as.data.frame(table(data_mahasiswa$Fakultas))
colnames(pie_data) <- c("Fakultas", "Jumlah")
ggplot(pie_data, aes(x = "", y = Jumlah, fill = Fakultas)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0) +
labs(title = "Proporsi Mahasiswa per Fakultas") +
theme_void()
3.0.5 Scatter Plot: Waktu Belajar vs Nilai UAS
ggplot(data_mahasiswa, aes(x = Waktu_belajar, y = Nilai_UAS, color = Fakultas)) +
geom_point(size = 3) +
geom_smooth(method = "lm", se = FALSE, linetype = "dashed") +
labs(title = "Hubungan Waktu Belajar vs Nilai UAS",
x = "Waktu Belajar (jam)",
y = "Nilai UAS") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
3.0.6 Line Plot: Nilai UAS per Mahasiswa (tren)
ggplot(data_mahasiswa, aes(x = ID_Mahasiswa, y = Nilai_UAS, group = 1)) +
geom_line(color = "blue") +
geom_point(size = 3, color = "red") +
labs(title = "Nilai UAS per Mahasiswa", x = "ID Mahasiswa", y = "Nilai UAS") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
3.0.7 Density Plot: Estimasi distribusi IPK
ggplot(data_mahasiswa, aes(x = IPK, fill = Fakultas)) +
geom_density(alpha = 0.5) +
labs(title = "Density Plot IPK per Fakultas", x = "IPK", y = "Density") +
theme_minimal()
3.0.8 Ridgeline Plot: Distribusi Nilai UAS per Fakultas
library(ggridges)
ggplot(data_mahasiswa, aes(x = Nilai_UAS, y = Fakultas, fill = Fakultas)) +
geom_density_ridges(alpha = 0.7) +
labs(title = "Ridgeline Plot Nilai UAS per Fakultas", x = "Nilai UAS", y = "Fakultas") +
theme_ridges() +
theme(legend.position = "none")## Picking joint bandwidth of 2.97
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