[STA1262] Tugas Praktikum
Yulianti Nurdzanah G1401221011
2024-03-03
Input Data
ss <- read.csv("C:\\Users\\yulia\\Downloads\\2023 Maret JABAR - SUSENAS KOR Rumah Tangga (2).csv", header = TRUE, sep=",")
str(ss)## 'data.frame': 25890 obs. of 199 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ URUT : int 500001 500002 500003 500004 500005 500006 500007 500008 500009 500010 ...
## $ PSU : int 12448 31373 12092 31135 33988 34062 33428 18431 18089 114 ...
## $ SSU : int 123442 311039 119908 308689 336798 337531 331261 182888 179477 1020 ...
## $ WI1 : int 12435 31360 12079 31122 33975 34049 33415 18418 18076 101 ...
## $ WI2 : int 123427 311024 119893 308674 336783 337516 331246 182873 179462 1005 ...
## $ R101 : int 32 32 32 32 32 32 32 32 32 32 ...
## $ R102 : int 7 72 6 72 77 77 75 11 10 1 ...
## $ R105 : int 2 1 2 1 1 1 1 2 2 1 ...
## $ NUINFORT : int 2 1 2 2 1 1 1 2 2 2 ...
## $ R1701 : int 5 1 5 5 5 5 1 1 1 5 ...
## $ R1702 : int 5 1 5 5 5 5 1 1 5 5 ...
## $ R1703 : int 5 1 5 5 5 5 1 5 5 5 ...
## $ R1704 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1705 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1706 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1707 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1708 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ NUINFORT1 : int 2 1 2 2 1 1 1 2 2 2 ...
## $ R1801 : int 1 1 1 2 1 1 1 2 1 1 ...
## $ R1802 : int 1 1 1 1 3 1 3 1 1 1 ...
## $ R1803 : int 5 1 1 1 0 1 0 5 1 5 ...
## $ R1804 : int 110 35 96 300 84 300 100 42 54 80 ...
## $ R1805 : int 5 5 5 5 1 1 5 5 5 1 ...
## $ R1806 : int 2 3 2 2 2 2 2 2 2 1 ...
## $ R1807 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R1808 : int 4 6 2 2 2 2 2 2 2 2 ...
## $ R1809A : int 2 1 1 1 1 1 1 1 1 1 ...
## $ R1809B : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R1809C : int 1 4 1 1 1 1 1 1 3 1 ...
## $ R1809D : int 98 0 20 98 98 98 98 98 0 8 ...
## $ R1809E : int 7 0 7 7 7 7 7 7 0 7 ...
## $ R1810A : int 4 4 5 2 1 1 4 7 4 5 ...
## $ R1810B : int 2 2 1 0 0 0 2 2 1 1 ...
## $ R1811A : int 2 1 1 2 1 1 1 2 1 1 ...
## $ R1811B : int 998 0 0 10 0 0 0 998 0 0 ...
## $ R1812 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1813A : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1813B : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1813C : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1813D : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1813E : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1814A : int 4 4 5 5 4 3 4 7 4 5 ...
## $ R1814B : int 2 2 1 2 2 0 2 2 1 1 ...
## $ R1815A : int 1 1 1 1 1 1 1 1 1 2 ...
## $ R1815B : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R1815C : int 1 1 5 1 1 1 5 1 1 1 ...
## $ R1816 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R1816B1 : int 1 1 1 2 1 3 3 1 1 1 ...
## $ R1816B2 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ R1816B3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R1817 : int 4 4 4 4 2 3 4 4 4 4 ...
## $ R1901A : int 1 5 5 1 5 5 5 5 5 5 ...
## $ R1901B : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901C : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901D : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901E : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901F : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901G : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901H : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901I : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901J : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2001A : int 5 5 5 5 1 1 5 5 5 5 ...
## $ R2001B : int 1 5 1 1 1 1 5 1 1 5 ...
## $ R2001C : int 5 5 5 5 5 1 5 5 5 5 ...
## $ R2001D : int 5 5 5 5 5 1 5 5 5 5 ...
## $ R2001E : int 5 5 5 5 5 1 5 5 5 5 ...
## $ R2001F : int 1 5 5 5 1 1 5 5 5 5 ...
## $ R2001G : int 5 5 5 5 5 1 5 1 5 5 ...
## $ R2001H : int 5 5 1 1 1 1 1 1 1 1 ...
## $ R2001I : int 5 5 5 5 1 1 5 5 5 5 ...
## $ R2001J : int 5 5 5 5 1 5 5 5 5 5 ...
## $ R2001K : int 5 5 5 5 1 1 5 5 5 5 ...
## $ R2001L : int 5 5 5 5 1 1 5 5 5 5 ...
## $ R2001M : int 1 1 1 1 5 1 5 5 1 5 ...
## $ R2002_A : chr "A" "A" "A" "A" ...
## $ R2002_B : chr "" "" "" "" ...
## $ R2002_C : chr "" "" "" "" ...
## $ R2002_D : chr "" "" "" "" ...
## $ R2101A : int 2 1 1 1 1 4 2 1 1 1 ...
## $ R2101B : int 0 2 1 1 2 0 0 4 1 1 ...
## $ R2101C : int 2 0 0 0 0 0 3 0 0 0 ...
## $ R2201A2 : int 1 5 5 5 5 5 5 5 5 5 ...
## $ R2201A3 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ R2201B2 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2201B3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R2201C2 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2201C3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R2201D2 : int 5 5 5 5 1 5 5 5 5 5 ...
## $ R2201D3 : int 0 0 0 0 1 0 0 0 0 0 ...
## $ R2201E2 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2201E3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R2201F2 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2201F3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R2202 : int 2 1 5 5 5 5 5 2 2 5 ...
## $ R2203 : int 1 5 5 5 5 5 5 5 5 5 ...
## $ R2204A : int 1 0 0 0 0 0 0 0 0 0 ...
## $ R2204B : int 4 0 0 0 0 0 0 0 0 0 ...
## $ R2204C_A : chr "A" "" "" "" ...
## [list output truncated]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, unionAirminum<-data.frame(kodeM=c(1,2,3,4,5,6,7,8,9,10,11), Airminum = c("Air Kemasan Bermerek", "Air Isi Ulang", "Leding","Sumur Bor/Pompa","Sumur Terlindung","Sumur tak Terlindung","Mata Air Terlindung","Mata Air Tak Terlindung","Air Permukaan", "Air Hujan","Lainnya"))
data<- left_join(ss, Airminum, by = c("R1810A"= "kodeM"))
str(data)## 'data.frame': 25890 obs. of 200 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ URUT : int 500001 500002 500003 500004 500005 500006 500007 500008 500009 500010 ...
## $ PSU : int 12448 31373 12092 31135 33988 34062 33428 18431 18089 114 ...
## $ SSU : int 123442 311039 119908 308689 336798 337531 331261 182888 179477 1020 ...
## $ WI1 : int 12435 31360 12079 31122 33975 34049 33415 18418 18076 101 ...
## $ WI2 : int 123427 311024 119893 308674 336783 337516 331246 182873 179462 1005 ...
## $ R101 : int 32 32 32 32 32 32 32 32 32 32 ...
## $ R102 : int 7 72 6 72 77 77 75 11 10 1 ...
## $ R105 : int 2 1 2 1 1 1 1 2 2 1 ...
## $ NUINFORT : int 2 1 2 2 1 1 1 2 2 2 ...
## $ R1701 : int 5 1 5 5 5 5 1 1 1 5 ...
## $ R1702 : int 5 1 5 5 5 5 1 1 5 5 ...
## $ R1703 : int 5 1 5 5 5 5 1 5 5 5 ...
## $ R1704 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1705 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1706 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1707 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1708 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ NUINFORT1 : int 2 1 2 2 1 1 1 2 2 2 ...
## $ R1801 : int 1 1 1 2 1 1 1 2 1 1 ...
## $ R1802 : int 1 1 1 1 3 1 3 1 1 1 ...
## $ R1803 : int 5 1 1 1 0 1 0 5 1 5 ...
## $ R1804 : int 110 35 96 300 84 300 100 42 54 80 ...
## $ R1805 : int 5 5 5 5 1 1 5 5 5 1 ...
## $ R1806 : int 2 3 2 2 2 2 2 2 2 1 ...
## $ R1807 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R1808 : int 4 6 2 2 2 2 2 2 2 2 ...
## $ R1809A : int 2 1 1 1 1 1 1 1 1 1 ...
## $ R1809B : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R1809C : int 1 4 1 1 1 1 1 1 3 1 ...
## $ R1809D : int 98 0 20 98 98 98 98 98 0 8 ...
## $ R1809E : int 7 0 7 7 7 7 7 7 0 7 ...
## $ R1810A : num 4 4 5 2 1 1 4 7 4 5 ...
## $ R1810B : int 2 2 1 0 0 0 2 2 1 1 ...
## $ R1811A : int 2 1 1 2 1 1 1 2 1 1 ...
## $ R1811B : int 998 0 0 10 0 0 0 998 0 0 ...
## $ R1812 : int 5 5 5 5 5 5 1 5 5 5 ...
## $ R1813A : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1813B : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1813C : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1813D : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1813E : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1814A : int 4 4 5 5 4 3 4 7 4 5 ...
## $ R1814B : int 2 2 1 2 2 0 2 2 1 1 ...
## $ R1815A : int 1 1 1 1 1 1 1 1 1 2 ...
## $ R1815B : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R1815C : int 1 1 5 1 1 1 5 1 1 1 ...
## $ R1816 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R1816B1 : int 1 1 1 2 1 3 3 1 1 1 ...
## $ R1816B2 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ R1816B3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R1817 : int 4 4 4 4 2 3 4 4 4 4 ...
## $ R1901A : int 1 5 5 1 5 5 5 5 5 5 ...
## $ R1901B : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901C : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901D : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901E : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901F : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901G : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901H : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901I : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R1901J : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2001A : int 5 5 5 5 1 1 5 5 5 5 ...
## $ R2001B : int 1 5 1 1 1 1 5 1 1 5 ...
## $ R2001C : int 5 5 5 5 5 1 5 5 5 5 ...
## $ R2001D : int 5 5 5 5 5 1 5 5 5 5 ...
## $ R2001E : int 5 5 5 5 5 1 5 5 5 5 ...
## $ R2001F : int 1 5 5 5 1 1 5 5 5 5 ...
## $ R2001G : int 5 5 5 5 5 1 5 1 5 5 ...
## $ R2001H : int 5 5 1 1 1 1 1 1 1 1 ...
## $ R2001I : int 5 5 5 5 1 1 5 5 5 5 ...
## $ R2001J : int 5 5 5 5 1 5 5 5 5 5 ...
## $ R2001K : int 5 5 5 5 1 1 5 5 5 5 ...
## $ R2001L : int 5 5 5 5 1 1 5 5 5 5 ...
## $ R2001M : int 1 1 1 1 5 1 5 5 1 5 ...
## $ R2002_A : chr "A" "A" "A" "A" ...
## $ R2002_B : chr "" "" "" "" ...
## $ R2002_C : chr "" "" "" "" ...
## $ R2002_D : chr "" "" "" "" ...
## $ R2101A : int 2 1 1 1 1 4 2 1 1 1 ...
## $ R2101B : int 0 2 1 1 2 0 0 4 1 1 ...
## $ R2101C : int 2 0 0 0 0 0 3 0 0 0 ...
## $ R2201A2 : int 1 5 5 5 5 5 5 5 5 5 ...
## $ R2201A3 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ R2201B2 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2201B3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R2201C2 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2201C3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R2201D2 : int 5 5 5 5 1 5 5 5 5 5 ...
## $ R2201D3 : int 0 0 0 0 1 0 0 0 0 0 ...
## $ R2201E2 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2201E3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R2201F2 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R2201F3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R2202 : int 2 1 5 5 5 5 5 2 2 5 ...
## $ R2203 : int 1 5 5 5 5 5 5 5 5 5 ...
## $ R2204A : int 1 0 0 0 0 0 0 0 0 0 ...
## $ R2204B : int 4 0 0 0 0 0 0 0 0 0 ...
## $ R2204C_A : chr "A" "" "" "" ...
## [list output truncated]Packages
library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ ggplot2 3.4.4 ✔ stringr 1.5.0
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggridges)
library(GGally) ## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(plotly)##
## Attaching package: 'plotly'
##
## The following object is masked from 'package:ggplot2':
##
## last_plot
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following object is masked from 'package:graphics':
##
## layoutlibrary(ggmosaic)## Warning: package 'ggmosaic' was built under R version 4.3.3##
## Attaching package: 'ggmosaic'
##
## The following object is masked from 'package:GGally':
##
## happylibrary(treemap)## Warning: package 'treemap' was built under R version 4.3.3library(treemapify)## Warning: package 'treemapify' was built under R version 4.3.3library(ggridges)
library(dplyr)Visualisasi Besaran
Bar Chart
data %>%
count(Airminum) %>%
ggplot()+
geom_col(aes(x=fct_reorder(as.factor(Airminum),n),y=n), fill="lightpink",
width=0.4) +
geom_text(aes(x = fct_reorder(as.factor(Airminum), n), y = n, label = n),
position = position_stack(vjust = 0.7), color = "black", size = 3) +
scale_y_continuous(expand = c(0,0))+
coord_flip() +
ggtitle("Sumber Utama Air Minum") +
xlab("") +
ylab("Jumlah") +
theme_classic() +
theme(plot.title = element_text(hjust = .5, size = 10))
Lollipop Chart
data %>%
count(Airminum) %>%
arrange(desc(n)) %>%
ggplot() +
geom_segment(aes(x = fct_reorder(as.factor(Airminum), n), xend = fct_reorder(as.factor(Airminum), n), y = 0, yend = n), color = "black") +
geom_point(aes(x = fct_reorder(as.factor(Airminum), n), y = n), color = "darkred", size = 2) +
scale_y_continuous(expand = c(0, 0)) +
coord_flip() +
ggtitle("Sumber Utama Air Minum") +
xlab("") +
ylab("Jumlah") +
theme_light() +
theme(plot.title = element_text(hjust = 0.5))
Interpretasi visualisasi besaran (bar chart & lollipop chart)
Berdasarkan data susenas yang divisualisasikan dalam lollipop chart diatas, sumber air utama yang paling banyak digunakan rumah tangga untuk minum adalah air isi ulang, diikuti oleh sumur bor/pompa, sumur terlindung, mata air terlindung, air kemasan bermerek, leding, mata air tak terlindung, sumur tak terlindung, air permukaan, dan lainnya. Namun, tidak ada satupun penduduk yang menggunakan air hujan sebagai sumber utama air minum.
Visualisasi Sebaran
Histogram
ss1 <- read.csv("C:\\Users\\yulia\\Downloads\\2023 Maret JABAR - SUSENAS KOR INDIVIDU PART1 (1).csv", header = TRUE, sep=",")
str(ss1)## 'data.frame': 84688 obs. of 183 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ URUT : int 500001 500001 500001 500001 500002 500002 500003 500003 500003 500004 ...
## $ PSU : int 12448 12448 12448 12448 31373 31373 12092 12092 12092 31135 ...
## $ SSU : int 123442 123442 123442 123442 311039 311039 119908 119908 119908 308689 ...
## $ WI1 : int 12435 12435 12435 12435 31360 31360 12079 12079 12079 31122 ...
## $ WI2 : int 123427 123427 123427 123427 311024 311024 119893 119893 119893 308674 ...
## $ R101 : int 32 32 32 32 32 32 32 32 32 32 ...
## $ R102 : int 7 7 7 7 72 72 6 6 6 72 ...
## $ R105 : int 2 2 2 2 1 1 2 2 2 1 ...
## $ R401 : int 1 2 3 4 1 2 1 2 3 1 ...
## $ R403 : int 1 3 6 6 1 3 1 2 3 1 ...
## $ R404 : int 4 2 1 1 3 1 2 2 1 2 ...
## $ R405 : int 2 2 2 1 2 1 1 2 2 1 ...
## $ R407 : int 68 46 16 6 62 38 54 41 10 40 ...
## $ R408 : int 0 5 0 0 0 0 1 1 0 1 ...
## $ R409 : int 20 22 0 0 16 0 27 18 0 16 ...
## $ R406A : int 5 2 23 17 6 28 10 12 21 26 ...
## $ R406B : int 4 10 1 8 12 7 11 11 4 3 ...
## $ R406C : int 1954 1976 2007 2016 1960 1984 1968 1981 2012 1982 ...
## $ R410 : int 2 2 2 2 1 1 2 2 2 2 ...
## $ R501 : int 1 1 0 0 2 0 1 1 0 1 ...
## $ R502 : int 1 2 3 4 1 1 1 1 1 1 ...
## $ R503 : int 0 1 2 2 0 1 0 0 2 0 ...
## $ R504 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R506 : int 2 2 2 1 2 1 1 2 2 1 ...
## $ R507 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R508 : int 5 2 2 2 2 1 5 5 1 1 ...
## $ R509 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R601 : int 32 32 32 32 32 32 32 32 32 32 ...
## $ R602 : int 7 7 7 7 72 72 6 6 6 72 ...
## $ R603 : int 32 32 32 32 32 32 32 32 32 32 ...
## $ R604 : int 7 7 7 7 72 72 6 6 6 72 ...
## $ R605 : int 0 0 0 3 0 0 0 0 4 0 ...
## $ R606 : int 0 0 0 1 0 0 0 0 0 0 ...
## $ R607 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R608 : int 1 1 1 1 5 5 1 1 1 1 ...
## $ R609 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R610 : int 3 3 3 2 3 3 3 3 2 3 ...
## $ R611 : int 0 0 0 1 0 0 0 0 1 0 ...
## $ R612 : int 3 19 15 3 3 3 13 8 3 8 ...
## $ R613 : int 8 8 1 1 8 8 8 8 5 8 ...
## $ R614 : int 3 19 8 25 3 3 13 8 25 8 ...
## $ R615 : int 0 0 5 5 0 0 0 0 5 0 ...
## $ R616 : int 0 0 5 5 0 0 0 0 5 0 ...
## $ R617 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R618 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R619 : int 0 0 3 1 0 0 0 0 2 0 ...
## $ R620 : int 0 0 0 0 0 0 0 0 3 0 ...
## $ R621 : int 0 0 0 0 0 0 0 0 4 0 ...
## $ R701 : int 1 1 5 5 5 1 1 5 5 1 ...
## $ R702 : int 1 5 5 5 5 5 1 5 5 1 ...
## $ R703_A : chr "" "" "" "" ...
## $ R703_B : chr "" "" "" "" ...
## $ R703_C : chr "C" "C" "C" "" ...
## $ R703_D : chr "D" "D" "D" "" ...
## $ R703_X : chr "" "" "" "" ...
## $ R704 : int 3 3 3 0 3 1 1 3 2 1 ...
## $ R705 : int 5 5 5 0 5 0 0 5 5 0 ...
## $ R706 : int 0 0 0 0 0 11 12 0 0 13 ...
## $ R707 : int 0 0 0 0 0 5 1 0 0 4 ...
## $ R708 : int 0 0 0 0 0 48 50 0 0 48 ...
## $ R709 : int 0 0 0 0 0 48 50 0 0 97 ...
## $ R801 : int 5 1 1 5 1 1 1 1 1 1 ...
## $ R802 : int 5 1 1 5 1 1 1 1 1 1 ...
## $ R807_A : chr "" "" "" "" ...
## $ R807_B : chr "" "" "B" "" ...
## $ R807_C : chr "" "" "" "" ...
## $ R807_X : chr "X" "X" "" "X" ...
## $ R808 : int 5 1 1 5 5 5 1 1 1 1 ...
## $ R809_A : chr "" "" "" "" ...
## $ R809_B : chr "" "" "B" "" ...
## $ R809_C : chr "" "" "" "" ...
## $ R809_D : chr "" "D" "D" "" ...
## $ R809_E : chr "" "" "" "" ...
## $ R810_A : chr "" "A" "A" "" ...
## $ R810_B : chr "" "" "" "" ...
## $ R810_C : chr "" "" "" "" ...
## $ R810_D : chr "" "" "D" "" ...
## $ R810_E : chr "" "E" "" "" ...
## $ R810_F : logi NA FALSE FALSE NA NA NA ...
## $ R811_A : chr "" "A" "" "" ...
## $ R811_B : chr "" "B" "" "" ...
## $ R811_C : chr "" "" "" "" ...
## $ R811_D : chr "" "D" "D" "" ...
## $ R811_E : chr "" "" "" "" ...
## $ R811_F : logi NA NA NA NA NA NA ...
## $ R811_G : chr "" "" "" "" ...
## $ R811_H : chr "" "" "H" "" ...
## $ R811_I : chr "" "" "" "" ...
## $ R811_J : chr "" "J" "J" "" ...
## $ R811_K : chr "" "" "" "" ...
## $ R811_L : chr "" "" "" "" ...
## $ R812 : int 5 5 1 5 5 5 5 5 5 5 ...
## $ R901 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R902 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R903 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R904 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R905 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R906 : int 0 0 0 0 0 0 0 0 0 0 ...
## [list output truncated]library(dplyr)
SP<-data.frame(kodeP=c(1,2,3,4), SP = c("Belum Kawin", "Kawin","Cerai Hidup","Cerai Mati"))
dataK<- left_join(ss1, SP, by = c("R404"= "kodeP"))
str(dataK)## 'data.frame': 84688 obs. of 184 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ URUT : int 500001 500001 500001 500001 500002 500002 500003 500003 500003 500004 ...
## $ PSU : int 12448 12448 12448 12448 31373 31373 12092 12092 12092 31135 ...
## $ SSU : int 123442 123442 123442 123442 311039 311039 119908 119908 119908 308689 ...
## $ WI1 : int 12435 12435 12435 12435 31360 31360 12079 12079 12079 31122 ...
## $ WI2 : int 123427 123427 123427 123427 311024 311024 119893 119893 119893 308674 ...
## $ R101 : int 32 32 32 32 32 32 32 32 32 32 ...
## $ R102 : int 7 7 7 7 72 72 6 6 6 72 ...
## $ R105 : int 2 2 2 2 1 1 2 2 2 1 ...
## $ R401 : int 1 2 3 4 1 2 1 2 3 1 ...
## $ R403 : int 1 3 6 6 1 3 1 2 3 1 ...
## $ R404 : num 4 2 1 1 3 1 2 2 1 2 ...
## $ R405 : int 2 2 2 1 2 1 1 2 2 1 ...
## $ R407 : int 68 46 16 6 62 38 54 41 10 40 ...
## $ R408 : int 0 5 0 0 0 0 1 1 0 1 ...
## $ R409 : int 20 22 0 0 16 0 27 18 0 16 ...
## $ R406A : int 5 2 23 17 6 28 10 12 21 26 ...
## $ R406B : int 4 10 1 8 12 7 11 11 4 3 ...
## $ R406C : int 1954 1976 2007 2016 1960 1984 1968 1981 2012 1982 ...
## $ R410 : int 2 2 2 2 1 1 2 2 2 2 ...
## $ R501 : int 1 1 0 0 2 0 1 1 0 1 ...
## $ R502 : int 1 2 3 4 1 1 1 1 1 1 ...
## $ R503 : int 0 1 2 2 0 1 0 0 2 0 ...
## $ R504 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R506 : int 2 2 2 1 2 1 1 2 2 1 ...
## $ R507 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R508 : int 5 2 2 2 2 1 5 5 1 1 ...
## $ R509 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R601 : int 32 32 32 32 32 32 32 32 32 32 ...
## $ R602 : int 7 7 7 7 72 72 6 6 6 72 ...
## $ R603 : int 32 32 32 32 32 32 32 32 32 32 ...
## $ R604 : int 7 7 7 7 72 72 6 6 6 72 ...
## $ R605 : int 0 0 0 3 0 0 0 0 4 0 ...
## $ R606 : int 0 0 0 1 0 0 0 0 0 0 ...
## $ R607 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R608 : int 1 1 1 1 5 5 1 1 1 1 ...
## $ R609 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R610 : int 3 3 3 2 3 3 3 3 2 3 ...
## $ R611 : int 0 0 0 1 0 0 0 0 1 0 ...
## $ R612 : int 3 19 15 3 3 3 13 8 3 8 ...
## $ R613 : int 8 8 1 1 8 8 8 8 5 8 ...
## $ R614 : int 3 19 8 25 3 3 13 8 25 8 ...
## $ R615 : int 0 0 5 5 0 0 0 0 5 0 ...
## $ R616 : int 0 0 5 5 0 0 0 0 5 0 ...
## $ R617 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R618 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R619 : int 0 0 3 1 0 0 0 0 2 0 ...
## $ R620 : int 0 0 0 0 0 0 0 0 3 0 ...
## $ R621 : int 0 0 0 0 0 0 0 0 4 0 ...
## $ R701 : int 1 1 5 5 5 1 1 5 5 1 ...
## $ R702 : int 1 5 5 5 5 5 1 5 5 1 ...
## $ R703_A : chr "" "" "" "" ...
## $ R703_B : chr "" "" "" "" ...
## $ R703_C : chr "C" "C" "C" "" ...
## $ R703_D : chr "D" "D" "D" "" ...
## $ R703_X : chr "" "" "" "" ...
## $ R704 : int 3 3 3 0 3 1 1 3 2 1 ...
## $ R705 : int 5 5 5 0 5 0 0 5 5 0 ...
## $ R706 : int 0 0 0 0 0 11 12 0 0 13 ...
## $ R707 : int 0 0 0 0 0 5 1 0 0 4 ...
## $ R708 : int 0 0 0 0 0 48 50 0 0 48 ...
## $ R709 : int 0 0 0 0 0 48 50 0 0 97 ...
## $ R801 : int 5 1 1 5 1 1 1 1 1 1 ...
## $ R802 : int 5 1 1 5 1 1 1 1 1 1 ...
## $ R807_A : chr "" "" "" "" ...
## $ R807_B : chr "" "" "B" "" ...
## $ R807_C : chr "" "" "" "" ...
## $ R807_X : chr "X" "X" "" "X" ...
## $ R808 : int 5 1 1 5 5 5 1 1 1 1 ...
## $ R809_A : chr "" "" "" "" ...
## $ R809_B : chr "" "" "B" "" ...
## $ R809_C : chr "" "" "" "" ...
## $ R809_D : chr "" "D" "D" "" ...
## $ R809_E : chr "" "" "" "" ...
## $ R810_A : chr "" "A" "A" "" ...
## $ R810_B : chr "" "" "" "" ...
## $ R810_C : chr "" "" "" "" ...
## $ R810_D : chr "" "" "D" "" ...
## $ R810_E : chr "" "E" "" "" ...
## $ R810_F : logi NA FALSE FALSE NA NA NA ...
## $ R811_A : chr "" "A" "" "" ...
## $ R811_B : chr "" "B" "" "" ...
## $ R811_C : chr "" "" "" "" ...
## $ R811_D : chr "" "D" "D" "" ...
## $ R811_E : chr "" "" "" "" ...
## $ R811_F : logi NA NA NA NA NA NA ...
## $ R811_G : chr "" "" "" "" ...
## $ R811_H : chr "" "" "H" "" ...
## $ R811_I : chr "" "" "" "" ...
## $ R811_J : chr "" "J" "J" "" ...
## $ R811_K : chr "" "" "" "" ...
## $ R811_L : chr "" "" "" "" ...
## $ R812 : int 5 5 1 5 5 5 5 5 5 5 ...
## $ R901 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R902 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R903 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R904 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ R905 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ R906 : int 0 0 0 0 0 0 0 0 0 0 ...
## [list output truncated]ggplot(ss1)+
geom_histogram(aes(x=R407),fill="darkred", color="khaki", alpha=0.8)+
labs(title="Histogram Sebaran Umur Penduduk Jawa Barat Tahun 2023")+
xlab("Umur")+
ylab("Jumlah")+
xlim(0,100)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2 rows containing missing values (`geom_bar()`).
Density Plot
ggplot(ss1)+
geom_density(aes(x=R407,fill= "Umur" ),color="darkred", alpha=0.8)+
labs(title="Density Plot Sebaran Umur Penduduk Jawa Barat 2023")+
xlab("Umur")+
ylab("Jumlah")+
xlim(1,100)## Warning: Removed 909 rows containing non-finite values (`stat_density()`).
Interpretasi visualisasi sebaran (histogram & density plot)
Distribusi umur di Jawa Barat yang diwakili oleh histogram dan density plot tersebut menunjukkan adanya dua puncak utama (yang paling banyak tersebar). Puncak pertama yaitu kelompok usia muda (rentang 0-25 tahun), sedangkan puncak kedua yaitu kelompok usia menengah (35-50 tahun).
Visualisasi Komposisi
Pie-Chart
data2 <- data.frame(
SP = c("Belum Kawin", "Kawin","Cerai Hidup","Cerai Mati"),
count = table(cut(dataK$R404, breaks=c(0,1,2,3,4), labels=c("Belum Kawin", "Kawin","Cerai Hidup","Cerai Mati")))
)ggplot(data2, aes(x = "", y = count.Freq , fill = SP)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0) +
scale_fill_brewer(palette = "Set3") +
theme_void() +
theme(legend.position = "right") +
labs(title = "Komposisi Status Perkawinan Penduduk Jawa Barat Tahun 2023", fill = "Status Perkawinan") +
geom_text(aes(label = count.Freq), position = position_stack(vjust = 0.5))
Tree Map
treemap(dataK,
index=c("SP"),
vSize="R404",
draw=TRUE,
title="Status Perkawinan di Jawa Barat Tahun 2023",
fontsize.title=20,
fontsize.labels=12,
fontcolor.labels="black")
Interpretasi pie-chart dan tree map
Dari grafik tersebut dapat diinterpretasikan bahwa mayoritas penduduk Jawa Barat adalah orang yang sudah menikah (kawin) dan diposisi kedua adalah belum kawin. di posisi terakhir ada cerai hidup. Ini menunjukkanbahwa tingkat perceraian hidup di Jawa Barat masih tergolong rendah.