Prak. Pemrograman
Uswatun Hasanah (M0501261021)
2026-09-11
1 SOAL 1
1.1 Buat data frame bernama nilai_raw secara manual di R berdasarkan Tabel 1.
nilai_raw <- data.frame(Nama = c("Andi", "Budi", "Citra", "Dewi", "Andi"),
Matematika = c(80, 90, NA, 75, 80),
Statistika = c(85, 88, 78,"-", 85),
Umur = c(21, 20, 22, 21, 21),
stringsAsFactors = FALSE)
nilai_raw## Nama Matematika Statistika Umur
## 1 Andi 80 85 21
## 2 Budi 90 88 20
## 3 Citra NA 78 22
## 4 Dewi 75 - 21
## 5 Andi 80 85 211.2 Identifikasi nilai yang hilang pada dataset secara sistematis. Ubah tanda hubung (“-”) pada nilai Statistika milik Dewi menjadi nilai hilang standar R, yaitu NA.
1.3 Lakukan imputasi terhadap nilai Statistika yang kosong menggunakan rata-rata nilai Statistika mahasiswa lainnya yang tersedia.
1.4 Buat kolom baru bernama Status_Umur menggunakan fungsi ifelse() dengan ketentuan: Dewasa jika umur ≥ 21 tahun; Muda jika umur < 21 tahun.
## Nama Matematika Statistika Umur Status_Umur
## 1 Andi 80 85 21 Dewasa
## 2 Budi 90 88 20 Muda
## 3 Citra NA 78 22 Dewasa
## 4 Dewi 75 84 21 Dewasa
## 5 Andi 80 85 21 Dewasa1.5 Identifikasi dan hapus baris pengamatan yang duplikat.
## Nama Matematika Statistika Umur Status_Umur
## 1 Andi 80 85 21 Dewasa
## 2 Budi 90 88 20 Muda
## 3 Citra NA 78 22 Dewasa
## 4 Dewi 75 84 21 Dewasa2 SOAL 2
2.1 Buat data frame bernama profil secara manual di R berdasarkan Tabel 2.
profil = data.frame(No = 1:10,
Gender = c("f","f","f","m","m","f","m","m","f","m"),
v1= c(8,15,8,14,8,10,9,9,10,12),
v2= c(9,8,13,9,2,6,9,10,13,10),
v3= c(5,9,12,8,9,10,7,8,6,9),
v4= c(9,5,6,"NA",16,9,13,10,7,8),
v5= c(9,10,7,11,8,10,9,5,10,17),
v6= c(9,8,9,10,10,10,12,10,12,7),
v7= c(5,10,14,6,8,9,10,6,13,9),
v8= c(9,12,12,11,9,7,9,12,9,10),
v9= c(11,9,12,11,8,7,11,6,6,7),
v10= c(8,15,9,8,9,11,7,8,9,7),
stringsAsFactors = FALSE)
profil## No Gender v1 v2 v3 v4 v5 v6 v7 v8 v9 v10
## 1 1 f 8 9 5 9 9 9 5 9 11 8
## 2 2 f 15 8 9 5 10 8 10 12 9 15
## 3 3 f 8 13 12 6 7 9 14 12 12 9
## 4 4 m 14 9 8 NA 11 10 6 11 11 8
## 5 5 m 8 2 9 16 8 10 8 9 8 9
## 6 6 f 10 6 10 9 10 10 9 7 7 11
## 7 7 m 9 9 7 13 9 12 10 9 11 7
## 8 8 m 9 10 8 10 5 10 6 12 6 8
## 9 9 f 10 13 6 7 10 12 13 9 6 9
## 10 10 m 12 10 9 8 17 7 9 10 7 72.3 Urutkan data hasil penyaringan secara multikriteria, dengan ketentuan: gender diurutkan secara descending; untuk pengamatan dengan gender yang sama, v5 diurutkan secara ascending.
##
## 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## No Gender v1 v2 v3 v4 v5 v6 v7 v8 v9 v10
## 1 8 m 9 10 8 10 5 10 6 12 6 8
## 2 5 m 8 2 9 16 8 10 8 9 8 9
## 3 7 m 9 9 7 13 9 12 10 9 11 7
## 4 4 m 14 9 8 NA 11 10 6 11 11 8
## 5 10 m 12 10 9 8 17 7 9 10 7 7
## 6 3 f 8 13 12 6 7 9 14 12 12 9
## 7 1 f 8 9 5 9 9 9 5 9 11 8
## 8 2 f 15 8 9 5 10 8 10 12 9 15
## 9 6 f 10 6 10 9 10 10 9 7 7 11
## 10 9 f 10 13 6 7 10 12 13 9 6 9## [1] "C:/PRAKTIKUM PEMROGRAMAN"3 SOAL 3
3.1 Gabungkan df_sales dan df_info menggunakan fungsi merge() dengan metode Full Outer Join, sehingga seluruh toko dari kedua data frame tetap disertakan. Perhatikan bahwa nama variabel kunci pada kedua data frame berbeda, yaitu id_toko pada df_sales dan id pada df_info. Gunakan argumen penggabungan yang sesuai.
df_sales <- data.frame(
id_toko = c("T1", "T2", "T3"),
Minggu1 = c(120, 150, 95),
Minggu2 = c(135, 140, 110)
)
df_info <- data.frame(
id = c("T1", "T2", "T4"),
Kota = c("Jakarta", "Bandung", "Surabaya")
)
df_merged <- merge(
df_sales,
df_info,
by.x = "id_toko",
by.y = "id",
all = TRUE
)
df_merged## id_toko Minggu1 Minggu2 Kota
## 1 T1 120 135 Jakarta
## 2 T2 150 140 Bandung
## 3 T3 95 110 <NA>
## 4 T4 NA NA Surabaya3.2 Ubah struktur data hasil penggabungan dari format Wide menjadi Long, sehingga: Minggu1 dan Minggu2 digabung menjadi satu kolom bernama Waktu; nilai penjualan disimpan dalam satu kolom bernama Penjualan.
df_long <- reshape(
df_merged,
varying = c("Minggu1", "Minggu2"),
v.names = "Penjualan",
timevar = "Waktu",
times = c("Minggu1", "Minggu2"),
direction = "long")
df_long## id_toko Kota Waktu Penjualan id
## 1.Minggu1 T1 Jakarta Minggu1 120 1
## 2.Minggu1 T2 Bandung Minggu1 150 2
## 3.Minggu1 T3 <NA> Minggu1 95 3
## 4.Minggu1 T4 Surabaya Minggu1 NA 4
## 1.Minggu2 T1 Jakarta Minggu2 135 1
## 2.Minggu2 T2 Bandung Minggu2 140 2
## 3.Minggu2 T3 <NA> Minggu2 110 3
## 4.Minggu2 T4 Surabaya Minggu2 NA 4## [1] "C:/PRAKTIKUM PEMROGRAMAN"4 SOAL 4
4.1 Kenali struktur dataset CO2 dan identifikasi peran peubah Plant, Type, Treatment, conc, dan uptake, kemudian buat grafik menggunakan base R untuk menunjukkan hubungan conc dan uptake dengan mengatur bentuk dan ukuran titik, warna, judul, label sumbu, serta rentang sumbu, lalu tambahkan minimal dua elemen grafik menggunakan points(), lines(), abline(), atau text().
## Plant Type Treatment conc uptake
## 1 Qn1 Quebec nonchilled 95 16.0
## 2 Qn1 Quebec nonchilled 175 30.4
## 3 Qn1 Quebec nonchilled 250 34.8
## 4 Qn1 Quebec nonchilled 350 37.2
## 5 Qn1 Quebec nonchilled 500 35.3
## 6 Qn1 Quebec nonchilled 675 39.2
## 7 Qn1 Quebec nonchilled 1000 39.7
## 8 Qn2 Quebec nonchilled 95 13.6
## 9 Qn2 Quebec nonchilled 175 27.3
## 10 Qn2 Quebec nonchilled 250 37.1
## 11 Qn2 Quebec nonchilled 350 41.8
## 12 Qn2 Quebec nonchilled 500 40.6
## 13 Qn2 Quebec nonchilled 675 41.4
## 14 Qn2 Quebec nonchilled 1000 44.3
## 15 Qn3 Quebec nonchilled 95 16.2
## 16 Qn3 Quebec nonchilled 175 32.4
## 17 Qn3 Quebec nonchilled 250 40.3
## 18 Qn3 Quebec nonchilled 350 42.1
## 19 Qn3 Quebec nonchilled 500 42.9
## 20 Qn3 Quebec nonchilled 675 43.9
## 21 Qn3 Quebec nonchilled 1000 45.5
## 22 Qc1 Quebec chilled 95 14.2
## 23 Qc1 Quebec chilled 175 24.1
## 24 Qc1 Quebec chilled 250 30.3
## 25 Qc1 Quebec chilled 350 34.6
## 26 Qc1 Quebec chilled 500 32.5
## 27 Qc1 Quebec chilled 675 35.4
## 28 Qc1 Quebec chilled 1000 38.7
## 29 Qc2 Quebec chilled 95 9.3
## 30 Qc2 Quebec chilled 175 27.3
## 31 Qc2 Quebec chilled 250 35.0
## 32 Qc2 Quebec chilled 350 38.8
## 33 Qc2 Quebec chilled 500 38.6
## 34 Qc2 Quebec chilled 675 37.5
## 35 Qc2 Quebec chilled 1000 42.4
## 36 Qc3 Quebec chilled 95 15.1
## 37 Qc3 Quebec chilled 175 21.0
## 38 Qc3 Quebec chilled 250 38.1
## 39 Qc3 Quebec chilled 350 34.0
## 40 Qc3 Quebec chilled 500 38.9
## 41 Qc3 Quebec chilled 675 39.6
## 42 Qc3 Quebec chilled 1000 41.4
## 43 Mn1 Mississippi nonchilled 95 10.6
## 44 Mn1 Mississippi nonchilled 175 19.2
## 45 Mn1 Mississippi nonchilled 250 26.2
## 46 Mn1 Mississippi nonchilled 350 30.0
## 47 Mn1 Mississippi nonchilled 500 30.9
## 48 Mn1 Mississippi nonchilled 675 32.4
## 49 Mn1 Mississippi nonchilled 1000 35.5
## 50 Mn2 Mississippi nonchilled 95 12.0
## 51 Mn2 Mississippi nonchilled 175 22.0
## 52 Mn2 Mississippi nonchilled 250 30.6
## 53 Mn2 Mississippi nonchilled 350 31.8
## 54 Mn2 Mississippi nonchilled 500 32.4
## 55 Mn2 Mississippi nonchilled 675 31.1
## 56 Mn2 Mississippi nonchilled 1000 31.5
## 57 Mn3 Mississippi nonchilled 95 11.3
## 58 Mn3 Mississippi nonchilled 175 19.4
## 59 Mn3 Mississippi nonchilled 250 25.8
## 60 Mn3 Mississippi nonchilled 350 27.9
## 61 Mn3 Mississippi nonchilled 500 28.5
## 62 Mn3 Mississippi nonchilled 675 28.1
## 63 Mn3 Mississippi nonchilled 1000 27.8
## 64 Mc1 Mississippi chilled 95 10.5
## 65 Mc1 Mississippi chilled 175 14.9
## 66 Mc1 Mississippi chilled 250 18.1
## 67 Mc1 Mississippi chilled 350 18.9
## 68 Mc1 Mississippi chilled 500 19.5
## 69 Mc1 Mississippi chilled 675 22.2
## 70 Mc1 Mississippi chilled 1000 21.9
## 71 Mc2 Mississippi chilled 95 7.7
## 72 Mc2 Mississippi chilled 175 11.4
## 73 Mc2 Mississippi chilled 250 12.3
## 74 Mc2 Mississippi chilled 350 13.0
## 75 Mc2 Mississippi chilled 500 12.5
## 76 Mc2 Mississippi chilled 675 13.7
## 77 Mc2 Mississippi chilled 1000 14.4
## 78 Mc3 Mississippi chilled 95 10.6
## 79 Mc3 Mississippi chilled 175 18.0
## 80 Mc3 Mississippi chilled 250 17.9
## 81 Mc3 Mississippi chilled 350 17.9
## 82 Mc3 Mississippi chilled 500 17.9
## 83 Mc3 Mississippi chilled 675 18.9
## 84 Mc3 Mississippi chilled 1000 19.9## Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame': 84 obs. of 5 variables:
## $ Plant : Ord.factor w/ 12 levels "Qn1"<"Qn2"<"Qn3"<..: 1 1 1 1 1 1 1 2 2 2 ...
## $ Type : Factor w/ 2 levels "Quebec","Mississippi": 1 1 1 1 1 1 1 1 1 1 ...
## $ Treatment: Factor w/ 2 levels "nonchilled","chilled": 1 1 1 1 1 1 1 1 1 1 ...
## $ conc : num 95 175 250 350 500 675 1000 95 175 250 ...
## $ uptake : num 16 30.4 34.8 37.2 35.3 39.2 39.7 13.6 27.3 37.1 ...
## - attr(*, "formula")=Class 'formula' language uptake ~ conc | Plant
## .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## - attr(*, "outer")=Class 'formula' language ~Treatment * Type
## .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv>
## - attr(*, "labels")=List of 2
## ..$ x: chr "Ambient carbon dioxide concentration"
## ..$ y: chr "CO2 uptake rate"
## - attr(*, "units")=List of 2
## ..$ x: chr "(uL/L)"
## ..$ y: chr "(umol/m^2 s)"## Plant Type Treatment conc uptake
## Qn1 : 7 Quebec :42 nonchilled:42 Min. : 95 Min. : 7.70
## Qn2 : 7 Mississippi:42 chilled :42 1st Qu.: 175 1st Qu.:17.90
## Qn3 : 7 Median : 350 Median :28.30
## Qc1 : 7 Mean : 435 Mean :27.21
## Qc3 : 7 3rd Qu.: 675 3rd Qu.:37.12
## Qc2 : 7 Max. :1000 Max. :45.50
## (Other):42#Melihat hubungan conc dengan uptake
plot(
CO2$conc, CO2$uptake,
pch = 19,
cex = 1.2,
col = ifelse(CO2$Treatment == "chilled", "blue", "red"),
main = "Hubungan Konsentrasi dan Penyerapan CO2",
xlab = "Konsentrasi CO2 (conc)",
ylab = "Penyerapan CO2 (uptake)",
xlim = c(0, 1000),
ylim = c(0, 50)
)
# Elemen Tambahan 1: Garis tren rata-rata (abline)
abline(h = mean(CO2$uptake), col = "darkgreen", lty = 2, lwd = 2)
# Elemen Tambahan 2: Teks penjelas (text)
text(x = 800, y = mean(CO2$uptake) + 2, labels = "Rata-rata Uptake", col = "darkgreen", cex = 0.9)
# Elemen Tambahan 3: Legenda
legend("bottomright", legend = c("Nonchilled", "Chilled"), col = c("red", "blue"), pch = 19)
4.2 Buat satu tampilan grafik berukuran 2 × 2 yang terdiri atas empat jenis visualisasi berbeda untuk mengeksplorasi conc, uptake, Type, dan Treatment; tentukan sendiri jenis grafik yang paling sesuai dan berikan judul yang jelas pada setiap grafik.
par(mfrow = c(2, 2))
# Grafik 1: Scatter Plot conc vs uptake
plot(CO2$conc, CO2$uptake, main = "Scatter Plot: conc vs uptake",
xlab = "Konsentrasi CO2", ylab = "Uptake", col = "darkblue", pch = 16)
# Grafik 2: Boxplot uptake berdasarkan Type
boxplot(uptake ~ Type, data = CO2, main = "Boxplot: uptake berdasarkan Type",
xlab = "Tipe Tanaman", ylab = "Uptake", col = c("orange", "lightgreen"))
# Grafik 3: Boxplot uptake berdasarkan Treatment
boxplot(uptake ~ Treatment, data = CO2, main = "Boxplot: uptake berdasarkan Treatment",
xlab = "Perlakuan Suhu", ylab = "Uptake", col = c("lightblue", "pink"))
# Grafik 4: Histogram Distribusi uptake
hist(CO2$uptake, main = "Histogram: Distribusi Uptake",
xlab = "Uptake", col = "purple", border = "white")
4.3 Gunakan qplot() untuk membuat visualisasi cepat hubungan conc dan uptake berdasarkan Treatment, kemudian buat kembali visualisasi tersebut menggunakan ggplot2 dengan memetakan Treatment dan Type ke atribut visual yang berbeda di dalam aes() serta menggunakan geom_* yang sesuai.
library(ggplot2)
qplot(x = conc, y = uptake, data = CO2, color = Treatment,
geom = c("point", "smooth"),
main = "Visualisasi Cepat dengan qplot()")## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Visualisasi Ulang dengan ggplot2 (Pemetaan Treatment dan Type)
p_base <- ggplot(CO2, aes(x = conc, y = uptake, color = Treatment, shape = Type)) +
geom_point(size = 3, alpha = 0.8) +
geom_smooth(method = "loess", se = FALSE)
p_base## `geom_smooth()` using formula = 'y ~ x'
4.4 Kembangkan grafik ggplot2 tersebut dengan menambahkan minimal satu stat_, satu scale_, serta salah satu coord_*, kemudian lengkapi grafik menggunakan labs() dan theme() atau salah satu tema siap pakai agar grafik lebih informatif dan mudah dibaca.
library(ggplot2)
# Visualisasi Cepat dengan qplot()
qplot(x = conc, y = uptake, data = CO2, color = Treatment,
geom = c("point", "smooth"),
main = "Visualisasi Cepat dengan qplot()")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Visualisasi Ulang dengan ggplot2 (Pemetaan Treatment dan Type)
p_base <- ggplot(CO2, aes(x = conc, y = uptake, color = Treatment, shape = Type)) +
geom_point(size = 3, alpha = 0.8) +
geom_smooth(method = "loess", se = FALSE)
p_base## `geom_smooth()` using formula = 'y ~ x'
4.5 Simpan grafik akhir menggunakan ggsave() dalam format PNG dengan nama co2_uptake_visualisasi.png, kemudian tuliskan interpretasi mengenai hubungan antara konsentrasi CO₂ dan uptake serta perbedaan pola yang terlihat berdasarkan Type dan Treatment.
# Pengembangan grafik lengkap
p_final <- ggplot(CO2, aes(x = conc, y = uptake, color = Treatment, shape = Type)) +
geom_point(size = 2.5, alpha = 0.7) +
# 1. stat_* : Menambahkan garis tren rata-rata smoothing
stat_smooth(method = "loess", se = TRUE, size = 1) +
# 2. scale_* : Menyesuaikan skala warna dan simbol
scale_color_manual(values = c("nonchilled" = "#D95F02", "chilled" = "#7570B3")) +
scale_shape_manual(values = c("Quebec" = 16, "Mississippi" = 17)) +
scale_x_continuous(breaks = seq(0, 1000, by = 200)) +
# 3. coord_* : Membatasi rentang koordinat sumbu
coord_cartesian(xlim = c(80, 1000), ylim = c(5, 50)) +
# 4. labs() : Menambahkan judul, subtitle, dan label
labs(
title = "Eksplorasi Penyerapan CO2 Pada Tanaman Gras",
subtitle = "Dampak Konsentrasi CO2, Asal Tipe Tanaman, dan Perlakuan Suhu",
x = expression(paste("Konsentrasi ", CO[2], " (", mu, "L/L)")),
y = expression(paste("Tingkat Penyerapan/Uptake (", mu, "mol/", m^2, " sec)")),
color = "Perlakuan",
shape = "Asal Tipe",
caption = "Sumber Data: R Built-in Dataset CO2"
) +
# 5. theme() : Menggunakan tema siap pakai + kustomisasi tampilan
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold", size = 14, hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5, color = "gray30"),
legend.position = "top",
panel.grid.minor = element_blank()
)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using formula = 'y ~ x'
# Menyimpan grafik akhir menggunakan ggsave()
ggsave(
filename = "co2_uptake_visualisasi.png",
plot = p_final,
width = 8,
height = 6,
dpi = 300
)## `geom_smooth()` using formula = 'y ~ x'