Exercise chapter 2
Ahmad Hikmah Ali Pahrizi
2024-09-20
Exercise 2.1 : Analisis Pengeluaran Rumah Tangga Berdasarkan Gender Menggunakan Visualisasi Data
1. Memuat library yang diperlukan
library(ggplot2)
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, union2. Membuat data frame untuk Tabel 2.3
Data ini berisi informasi pengeluaran untuk housing, food, goods, services, serta gender dari 40 individu
household_data <- data.frame(
housing = c(820, 184, 921, 488, 721, 614, 801, 396, 864, 845, 404, 781, 457, 1029, 1047, 552, 718, 495, 382, 1090,
497, 839, 798, 892, 1585, 755, 388, 617, 248, 1641, 1180, 619, 253, 661, 1981, 1746, 1865, 238, 1199, 1524),
food = c(114, 74, 66, 80, 83, 55, 56, 59, 65, 64, 97, 47, 103, 71, 90, 91, 104, 114, 77, 59,
591, 942, 1308, 842, 781, 764, 655, 879, 438, 440, 1243, 684, 422, 739, 869, 746, 915, 522, 1095, 964),
goods = c(183, 6, 1686, 103, 176, 441, 357, 61, 1618, 1935, 33, 1906, 136, 244, 653, 185, 583, 65, 230, 313,
153, 302, 668, 287, 2476, 428, 153, 757, 22, 6471, 768, 99, 15, 71, 1489, 2662, 5184, 29, 261, 1739),
services = c(154, 20, 455, 115, 104, 193, 214, 80, 352, 414, 47, 452, 108, 189, 298, 158, 304, 74, 147, 177,
291, 365, 584, 395, 1740, 438, 233, 719, 65, 2063, 813, 204, 48, 188, 1032, 1594, 1767, 75, 344, 1410),
gender = c("female", "female", "female", "female", "female", "female", "female", "female", "female", "female",
"female", "female", "female", "female", "female", "female", "female", "female", "female", "female",
"male", "male", "male", "male", "male", "male", "male", "male", "male", "male",
"male", "male", "male", "male", "male", "male", "male", "male", "male", "male")
)
print(household_data)## housing food goods services gender
## 1 820 114 183 154 female
## 2 184 74 6 20 female
## 3 921 66 1686 455 female
## 4 488 80 103 115 female
## 5 721 83 176 104 female
## 6 614 55 441 193 female
## 7 801 56 357 214 female
## 8 396 59 61 80 female
## 9 864 65 1618 352 female
## 10 845 64 1935 414 female
## 11 404 97 33 47 female
## 12 781 47 1906 452 female
## 13 457 103 136 108 female
## 14 1029 71 244 189 female
## 15 1047 90 653 298 female
## 16 552 91 185 158 female
## 17 718 104 583 304 female
## 18 495 114 65 74 female
## 19 382 77 230 147 female
## 20 1090 59 313 177 female
## 21 497 591 153 291 male
## 22 839 942 302 365 male
## 23 798 1308 668 584 male
## 24 892 842 287 395 male
## 25 1585 781 2476 1740 male
## 26 755 764 428 438 male
## 27 388 655 153 233 male
## 28 617 879 757 719 male
## 29 248 438 22 65 male
## 30 1641 440 6471 2063 male
## 31 1180 1243 768 813 male
## 32 619 684 99 204 male
## 33 253 422 15 48 male
## 34 661 739 71 188 male
## 35 1981 869 1489 1032 male
## 36 1746 746 2662 1594 male
## 37 1865 915 5184 1767 male
## 38 238 522 29 75 male
## 39 1199 1095 261 344 male
## 40 1524 964 1739 1410 male3. Menambahkan kolom total_expenditure (pengeluaran total)
Menjumlahkan pengeluaran pada setiap kategori (housing, food, goods, services) untuk setiap individu
household_data <- household_data %>%
mutate(total_expenditure = housing + food + goods + services)
print(household_data)## housing food goods services gender total_expenditure
## 1 820 114 183 154 female 1271
## 2 184 74 6 20 female 284
## 3 921 66 1686 455 female 3128
## 4 488 80 103 115 female 786
## 5 721 83 176 104 female 1084
## 6 614 55 441 193 female 1303
## 7 801 56 357 214 female 1428
## 8 396 59 61 80 female 596
## 9 864 65 1618 352 female 2899
## 10 845 64 1935 414 female 3258
## 11 404 97 33 47 female 581
## 12 781 47 1906 452 female 3186
## 13 457 103 136 108 female 804
## 14 1029 71 244 189 female 1533
## 15 1047 90 653 298 female 2088
## 16 552 91 185 158 female 986
## 17 718 104 583 304 female 1709
## 18 495 114 65 74 female 748
## 19 382 77 230 147 female 836
## 20 1090 59 313 177 female 1639
## 21 497 591 153 291 male 1532
## 22 839 942 302 365 male 2448
## 23 798 1308 668 584 male 3358
## 24 892 842 287 395 male 2416
## 25 1585 781 2476 1740 male 6582
## 26 755 764 428 438 male 2385
## 27 388 655 153 233 male 1429
## 28 617 879 757 719 male 2972
## 29 248 438 22 65 male 773
## 30 1641 440 6471 2063 male 10615
## 31 1180 1243 768 813 male 4004
## 32 619 684 99 204 male 1606
## 33 253 422 15 48 male 738
## 34 661 739 71 188 male 1659
## 35 1981 869 1489 1032 male 5371
## 36 1746 746 2662 1594 male 6748
## 37 1865 915 5184 1767 male 9731
## 38 238 522 29 75 male 864
## 39 1199 1095 261 344 male 2899
## 40 1524 964 1739 1410 male 56374. Membuat visualisasi boxplot untuk membandingkan pengeluaran total antara pria dan wanita
ggplot(household_data, aes(x = gender, y = total_expenditure, fill = gender)) +
geom_boxplot() +
labs(title = "Perbandingan Pengeluaran Total Berdasarkan Gender",
x = "Gender", y = "Total Pengeluaran (HKD)") +
theme_minimal()
5. Membuat visualisasi scatter plot untuk setiap kategori pengeluaran berdasarkan total pengeluaran dan gender
* Scatter plot untuk pengeluaran housing
ggplot(household_data, aes(x = total_expenditure, y = housing, color = gender)) +
geom_point() +
labs(title = "Hubungan Total Pengeluaran dan Pengeluaran Housing Berdasarkan Gender",
x = "Total Pengeluaran", y = "Pengeluaran Housing (HKD)") +
theme_minimal()
* Scatter plot untuk pengeluaran food
ggplot(household_data, aes(x = total_expenditure, y = food, color = gender)) +
geom_point() +
labs(title = "Hubungan Total Pengeluaran dan Pengeluaran Food Berdasarkan Gender",
x = "Total Pengeluaran", y = "Pengeluaran Food (HKD)") +
theme_minimal()
* Scatter plot untuk pengeluaran goods
ggplot(household_data, aes(x = total_expenditure, y = goods, color = gender)) +
geom_point() +
labs(title = "Hubungan Total Pengeluaran dan Pengeluaran Goods Berdasarkan Gender",
x = "Total Pengeluaran", y = "Pengeluaran Goods (HKD)") +
theme_minimal()
* Scatter plot untuk pengeluaran services
ggplot(household_data, aes(x = total_expenditure, y = services, color = gender)) +
geom_point() +
labs(title = "Hubungan Total Pengeluaran dan Pengeluaran Services Berdasarkan Gender",
x = "Total Pengeluaran", y = "Pengeluaran Services (HKD)") +
theme_minimal()