data_kor <- read.csv("C:/Users/FAQIH/Downloads/2023 Maret JABAR - SUSENAS KOR INDIVIDU PART1.csv")
head(data_kor)
## X URUT PSU SSU WI1 WI2 R101 R102 R105 R401 R403 R404 R405 R407
## 1 0 500001 12448 123442 12435 123427 32 7 2 1 1 4 2 68
## 2 1 500001 12448 123442 12435 123427 32 7 2 2 3 2 2 46
## 3 2 500001 12448 123442 12435 123427 32 7 2 3 6 1 2 16
## 4 3 500001 12448 123442 12435 123427 32 7 2 4 6 1 1 6
## 5 4 500002 31373 311039 31360 311024 32 72 1 1 1 3 2 62
## 6 5 500002 31373 311039 31360 311024 32 72 1 2 3 1 1 38
## R408 R409 R406A R406B R406C R410 R501 R502 R503 R504 R506 R507 R508 R509 R601
## 1 0 20 5 4 1954 2 1 1 0 1 2 1 5 1 32
## 2 5 22 2 10 1976 2 1 2 1 1 2 1 2 1 32
## 3 0 0 23 1 2007 2 0 3 2 1 2 1 2 1 32
## 4 0 0 17 8 2016 2 0 4 2 1 1 1 2 1 32
## 5 0 16 6 12 1960 1 2 1 0 1 2 1 2 1 32
## 6 0 0 28 7 1984 1 0 1 1 1 1 1 1 1 32
## R602 R603 R604 R605 R606 R607 R608 R609 R610 R611 R612 R613 R614 R615 R616
## 1 7 32 7 0 0 1 1 5 3 0 3 8 3 0 0
## 2 7 32 7 0 0 1 1 5 3 0 19 8 19 0 0
## 3 7 32 7 0 0 1 1 5 3 0 15 1 8 5 5
## 4 7 32 7 3 1 1 1 5 2 1 3 1 25 5 5
## 5 72 32 72 0 0 1 5 5 3 0 3 8 3 0 0
## 6 72 32 72 0 0 1 5 5 3 0 3 8 3 0 0
## R617 R618 R619 R620 R621 R701 R702 R703_A R703_B R703_C R703_D R703_X R704
## 1 0 0 0 0 0 1 1 C D 3
## 2 0 0 0 0 0 1 5 C D 3
## 3 0 0 3 0 0 5 5 C D 3
## 4 0 0 1 0 0 5 5 0
## 5 0 0 0 0 0 5 5 C D 3
## 6 0 0 0 0 0 1 5 A D 1
## R705 R706 R707 R708 R709 R801 R802 R807_A R807_B R807_C R807_X R808 R809_A
## 1 5 0 0 0 0 5 5 X 5
## 2 5 0 0 0 0 1 1 X 1
## 3 5 0 0 0 0 1 1 B 1
## 4 0 0 0 0 0 5 5 X 5
## 5 5 0 0 0 0 1 1 X 5
## 6 0 11 5 48 48 1 1 X 5
## R809_B R809_C R809_D R809_E R810_A R810_B R810_C R810_D R810_E R810_F R811_A
## 1 NA
## 2 D A E FALSE A
## 3 B D A D FALSE
## 4 NA
## 5 NA
## 6 NA
## R811_B R811_C R811_D R811_E R811_F R811_G R811_H R811_I R811_J R811_K R811_L
## 1 NA
## 2 B D NA J
## 3 D NA H J
## 4 NA
## 5 NA
## 6 NA
## R812 R901 R902 R903 R904 R905 R906 R907 R908 R909 R910 R911 R912 R913 R914
## 1 5 5 0 0 0 5 0 0 0 0 0 0 0 0 0
## 2 5 5 0 0 0 5 0 0 0 0 0 0 0 0 0
## 3 1 5 0 0 0 5 0 0 0 0 0 0 0 0 0
## 4 5 5 0 0 0 5 0 0 0 0 0 0 0 0 0
## 5 5 5 0 0 0 5 0 0 0 0 0 0 0 0 0
## 6 5 5 0 0 0 5 0 0 0 0 0 0 0 0 0
## R915 R916 R1001 R1002 R1003 R1004 R1005 R1006 R1007 R1008 R1009 R1010 R1101_A
## 1 0 0 1 4 8 4 8 4 8 4 8 5
## 2 0 0 1 4 8 4 8 4 8 4 8 5
## 3 0 0 1 4 8 4 8 4 8 4 8 5
## 4 0 0 1 4 8 4 8 4 8 4 8 5
## 5 0 0 1 4 8 4 8 4 8 4 8 5 A
## 6 0 0 1 4 8 4 8 4 8 4 8 5 A
## R1101_B R1101_C R1101_D R1101_E R1101_X R1102 R1103 R1104 R1105 R1106 R1107_A
## 1 E 5 0 0 0 0
## 2 X 5 0 0 0 0
## 3 X 1 5 1 5 5
## 4 X 1 5 1 5 5
## 5 5 0 0 0 0
## 6 5 0 0 0 0
## R1107_B R1107_C R1107_D R1107_E R1107_F R1107_G R1107_H R1108 R1109_A R1109_B
## 1 NA 0
## 2 NA 0
## 3 NA 0
## 4 NA 0
## 5 NA 0
## 6 NA 0
## R1109_C R1109_D R1109_E R1109_X R1110_A R1110_B R1110_C R1110_D R1110_E
## 1
## 2
## 3
## 4
## 5
## 6
## R1110_F R1110_G R1110_H R1110_I R1201 R1202_A R1202_B R1202_C R1202_D R1202_E
## 1 NA 5
## 2 NA 5
## 3 NA 5
## 4 NA 5
## 5 NA 5
## 6 NA 5
## R1202_F R1202_G R1203 R1204_A R1204_B R1204_C R1204_D R1204_E R1204_X R1205_A
## 1 NA 0
## 2 NA 0
## 3 NA 0
## 4 NA 0
## 5 NA 0
## 6 NA 0
## R1205_B R1205_C R1205_D R1205_E R1205_F R1205_G R1205_H R1205_I R1206 R1207
## 1 NA 5 5
## 2 NA 5 5
## 3 NA 5 5
## 4 NA 5 5
## 5 NA 5 5
## 6 NA 5 1
## R1208 R1209 FWT
## 1 0 5 454.8891
## 2 0 5 454.8891
## 3 0 5 454.8891
## 4 0 5 454.8891
## 5 0 5 172.3768
## 6 35 1 172.3768
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.2
##
## 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
df_filter <- data_kor %>% select(R101, R102, ,R905, R906, R907, R908, R909, R910, R911, R912, R913, R914, R915, R916)
head(df_filter)
## R101 R102 R905 R906 R907 R908 R909 R910 R911 R912 R913 R914 R915 R916
## 1 32 7 5 0 0 0 0 0 0 0 0 0 0 0
## 2 32 7 5 0 0 0 0 0 0 0 0 0 0 0
## 3 32 7 5 0 0 0 0 0 0 0 0 0 0 0
## 4 32 7 5 0 0 0 0 0 0 0 0 0 0 0
## 5 32 72 5 0 0 0 0 0 0 0 0 0 0 0
## 6 32 72 5 0 0 0 0 0 0 0 0 0 0 0
library(dplyr)
data_kejahatan <- df_filter %>%
group_by(R102) %>%
summarise(
total_perkab_kota = n(),
korban_kehajatan = sum(R905 == 1, na.rm = TRUE),
kejadian_pencurian = sum(R906, na.rm = TRUE),
kejadian_pencurian_dilaporkan = sum(R907, na.rm = TRUE),
kejadian_penganiayaan = sum(R908, na.rm = TRUE),
kejadian_penganiayaan_dilaporkan = sum(R909, na.rm = TRUE),
kejadian_pencurian_kekerasan =sum(R910, na.rm = TRUE),
kejadian_pencurian_kekerasan_dilaporkan = sum(R911, na.rm =TRUE),
kejadian_pelecehan = sum(R912, na.rm = TRUE),
kejadian_pelecehan_dilaporkan = sum(R913, na.rm = TRUE),
kejahatan_lain = sum(R914, na.rm = TRUE),
kejahatan_lainnya_dilaporkan = sum(R915, na.rm = TRUE),
mendapat_bantuan_hukum = sum(R916 == 1, na.rm = TRUE),
)
# Tampilkan hasil
print(data_kejahatan)
## # A tibble: 27 × 14
## R102 total_perkab_kota korban_kehajatan kejadian_pencurian
## <int> <int> <int> <int>
## 1 1 4582 28 28
## 2 2 3475 22 23
## 3 3 3382 17 22
## 4 4 4051 27 26
## 5 5 3882 33 42
## 6 6 3111 9 5
## 7 7 3062 20 27
## 8 8 2764 11 12
## 9 9 3460 22 41
## 10 10 2784 16 19
## # ℹ 17 more rows
## # ℹ 10 more variables: kejadian_pencurian_dilaporkan <int>,
## # kejadian_penganiayaan <int>, kejadian_penganiayaan_dilaporkan <int>,
## # kejadian_pencurian_kekerasan <int>,
## # kejadian_pencurian_kekerasan_dilaporkan <int>, kejadian_pelecehan <int>,
## # kejadian_pelecehan_dilaporkan <int>, kejahatan_lain <int>,
## # kejahatan_lainnya_dilaporkan <int>, mendapat_bantuan_hukum <int>
par(mfrow=c(1,2))
plot(data_kejahatan$total_perkab_kota, data_kejahatan$korban_kehajatan, pch=19)
jumlah_sampel_penduduk <- sum(data_kejahatan$total_perkab_kota, na.rm = TRUE)
jumlah_sampel_penduduk
## [1] 84688
library(dplyr)
jumlah_kasus <- data_kejahatan %>% mutate(jumlah_kasus_dilaporkan = kejadian_pencurian_dilaporkan + kejadian_penganiayaan_dilaporkan + kejadian_pencurian_kekerasan_dilaporkan + kejadian_pelecehan_dilaporkan + kejahatan_lainnya_dilaporkan)
jumlah_kasus
## # A tibble: 27 × 15
## R102 total_perkab_kota korban_kehajatan kejadian_pencurian
## <int> <int> <int> <int>
## 1 1 4582 28 28
## 2 2 3475 22 23
## 3 3 3382 17 22
## 4 4 4051 27 26
## 5 5 3882 33 42
## 6 6 3111 9 5
## 7 7 3062 20 27
## 8 8 2764 11 12
## 9 9 3460 22 41
## 10 10 2784 16 19
## # ℹ 17 more rows
## # ℹ 11 more variables: kejadian_pencurian_dilaporkan <int>,
## # kejadian_penganiayaan <int>, kejadian_penganiayaan_dilaporkan <int>,
## # kejadian_pencurian_kekerasan <int>,
## # kejadian_pencurian_kekerasan_dilaporkan <int>, kejadian_pelecehan <int>,
## # kejadian_pelecehan_dilaporkan <int>, kejahatan_lain <int>,
## # kejahatan_lainnya_dilaporkan <int>, mendapat_bantuan_hukum <int>, …
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.2
library(tidyr)
## Warning: package 'tidyr' was built under R version 4.4.2
library(scales)
## Warning: package 'scales' was built under R version 4.4.2
total_pencurian <- sum(data_kejahatan$kejadian_pencurian_dilaporkan)
total_penganiayaan <- sum(data_kejahatan$kejadian_penganiayaan_dilaporkan)
total_pencurian_kekerasan <- sum(data_kejahatan$kejadian_pencurian_kekerasan_dilaporkan)
total_pelecehan <- sum(data_kejahatan$kejadian_pelecehan_dilaporkan)
total_kejahatan_lain <- sum(data_kejahatan$kejahatan_lainnya_dilaporkan)
total_kasus <- sum(jumlah_kasus$jumlah_kasus_dilaporkan)
# Buat data proporsi
data_proporsi <- data.frame(
Jenis_Kasus_dilaporkan = c("Pencurian", "Penganiayaan", "Pencurian_dengan_kekerasan", "Pelecehan", "Kejahatan_lainnya"),
Persentase = c(total_pencurian / total_kasus * 100,
total_penganiayaan / total_kasus * 100,
total_pencurian_kekerasan / total_kasus * 100,
total_pelecehan / total_kasus * 100,
total_kejahatan_lain / total_kasus * 100)
)
ggplot(data_proporsi, aes(x = reorder(Jenis_Kasus_dilaporkan, -Persentase), y = Persentase, fill = Jenis_Kasus_dilaporkan)) +
geom_bar(stat = "identity", show.legend = FALSE) +
scale_y_continuous(labels = percent_format(scale = 1)) + # Ubah ke format persen
labs(title = "Persentase Kasus Kejahatan yang Dilaporkan di Jawa Barat",
x = "Jenis Kasus",
y = "Persentase (%)") +
theme_minimal()
library(ggplot2)
library(dplyr)
data_aggregat <- data_kejahatan %>%
mutate(Total_Kejadian = kejadian_pencurian + kejadian_penganiayaan +
kejadian_pencurian_kekerasan + kejadian_pelecehan + kejahatan_lain) %>%
arrange(desc(Total_Kejadian))
ggplot(data_aggregat, aes(x = reorder(R102, -Total_Kejadian),
y = Total_Kejadian, fill = Total_Kejadian)) +
geom_bar(stat = "identity", show.legend = FALSE) +
coord_flip() +
labs(title = "Kabupaten/Kota dengan Kasus Kejahatan Tertinggi",
x = "Kode Kabupaten/Kota",
y = "Total Kasus") +
theme_minimal()
