visualisasi data pemilihh
nurrahmania
2026-02-19
df <- read.csv("C:/Users/ACER A314/Documents/DATABASE REKAP DATA PEMILIH.csv",
sep = ";",
skip = 5, # skip 5 baris awal
header = FALSE,
stringsAsFactors = FALSE)
# Lihat dulu isinya
View(df)
head(df)## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14
## 1 NA NA L P L+P L P L+P L P L+P L
## 2 1 ASAKOTA 6 47 41 48 89 1 5 6 27 19 46 10
## 3 2 MPUNDA 10 45 39 37 76 7 9 16 11 8 19 21
## 4 3 RABA 11 54 61 104 165 14 12 26 31 20 51 35
## 5 4 RASANAE BARAT 6 42 25 40 65 4 4 8 27 10 37 6
## 6 5 RASANAE TIMUR 8 30 47 80 127 9 3 12 17 10 27 15
## V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26
## 1 P L+P L P L+P L P L+P L P L+P
## 2 7 17 5 3 8 14 22 36 98 104 202 0,79%
## 3 10 31 4 2 6 22 18 40 104 84 188 0,78%
## 4 16 51 1 8 9 19 45 64 161 205 366 1,25%
## 5 7 13 1 2 3 11 23 34 74 86 160 0,74%
## 6 16 31 9 7 16 27 45 72 124 161 285 2,01%# Hapus baris 1 yang isinya header sisa (NA NA L P L+P dst)
df <- df[-1, ]
# Beri nama kolom yang jelas
colnames(df) <- c("NO", "KELURAHAN", "JML_KEL", "JML_TPS",
"FISIK_L", "FISIK_P", "FISIK_LP",
"INTELEKTUAL_L", "INTELEKTUAL_P", "INTELEKTUAL_LP",
"MENTAL_L", "MENTAL_P", "MENTAL_LP",
"WICARA_L", "WICARA_P", "WICARA_LP",
"RUNGU_L", "RUNGU_P", "RUNGU_LP",
"NETRA_L", "NETRA_P", "NETRA_LP",
"TOTAL_L", "TOTAL_P", "TOTAL_LP",
"PERSEN_DISABILITAS")
# Reset nomor baris
rownames(df) <- NULL
# Ubah kolom angka dari character ke numeric
kolom_angka <- c("NO", "JML_KEL", "JML_TPS",
"FISIK_L", "FISIK_P", "FISIK_LP",
"INTELEKTUAL_L", "INTELEKTUAL_P", "INTELEKTUAL_LP",
"MENTAL_L", "MENTAL_P", "MENTAL_LP",
"WICARA_L", "WICARA_P", "WICARA_LP",
"RUNGU_L", "RUNGU_P", "RUNGU_LP",
"NETRA_L", "NETRA_P", "NETRA_LP",
"TOTAL_L", "TOTAL_P", "TOTAL_LP")
df[kolom_angka] <- lapply(df[kolom_angka], as.numeric)## Warning in lapply(df[kolom_angka], as.numeric): NAs introduced by coercion# Cek hasil akhir
head(df)## NO KELURAHAN JML_KEL JML_TPS FISIK_L FISIK_P FISIK_LP INTELEKTUAL_L
## 1 1 ASAKOTA 6 47 41 48 89 1
## 2 2 MPUNDA 10 45 39 37 76 7
## 3 3 RABA 11 54 61 104 165 14
## 4 4 RASANAE BARAT 6 42 25 40 65 4
## 5 5 RASANAE TIMUR 8 30 47 80 127 9
## 6 NA 41 218 213 309 522 35
## INTELEKTUAL_P INTELEKTUAL_LP MENTAL_L MENTAL_P MENTAL_LP WICARA_L WICARA_P
## 1 5 6 27 19 46 10 7
## 2 9 16 11 8 19 21 10
## 3 12 26 31 20 51 35 16
## 4 4 8 27 10 37 6 7
## 5 3 12 17 10 27 15 16
## 6 33 68 113 67 180 87 56
## WICARA_LP RUNGU_L RUNGU_P RUNGU_LP NETRA_L NETRA_P NETRA_LP TOTAL_L TOTAL_P
## 1 17 5 3 8 14 22 36 98 104
## 2 31 4 2 6 22 18 40 104 84
## 3 51 1 8 9 19 45 64 161 205
## 4 13 1 2 3 11 23 34 74 86
## 5 31 9 7 16 27 45 72 124 161
## 6 143 20 22 42 93 153 246 561 640
## TOTAL_LP PERSEN_DISABILITAS
## 1 202.000 0,79%
## 2 188.000 0,78%
## 3 366.000 1,25%
## 4 160.000 0,74%
## 5 285.000 2,01%
## 6 1.201 1,05%str(df)## 'data.frame': 7 obs. of 26 variables:
## $ NO : num 1 2 3 4 5 NA NA
## $ KELURAHAN : chr "ASAKOTA" "MPUNDA" "RABA" "RASANAE BARAT" ...
## $ JML_KEL : num 6 10 11 6 8 41 NA
## $ JML_TPS : num 47 45 54 42 30 218 NA
## $ FISIK_L : num 41 39 61 25 47 213 NA
## $ FISIK_P : num 48 37 104 40 80 309 NA
## $ FISIK_LP : num 89 76 165 65 127 522 NA
## $ INTELEKTUAL_L : num 1 7 14 4 9 35 NA
## $ INTELEKTUAL_P : num 5 9 12 4 3 33 NA
## $ INTELEKTUAL_LP : num 6 16 26 8 12 68 NA
## $ MENTAL_L : num 27 11 31 27 17 113 NA
## $ MENTAL_P : num 19 8 20 10 10 67 NA
## $ MENTAL_LP : num 46 19 51 37 27 180 NA
## $ WICARA_L : num 10 21 35 6 15 87 NA
## $ WICARA_P : num 7 10 16 7 16 56 NA
## $ WICARA_LP : num 17 31 51 13 31 143 NA
## $ RUNGU_L : num 5 4 1 1 9 20 NA
## $ RUNGU_P : num 3 2 8 2 7 22 NA
## $ RUNGU_LP : num 8 6 9 3 16 42 NA
## $ NETRA_L : num 14 22 19 11 27 93 NA
## $ NETRA_P : num 22 18 45 23 45 153 NA
## $ NETRA_LP : num 36 40 64 34 72 246 NA
## $ TOTAL_L : num 98 104 161 74 124 561 NA
## $ TOTAL_P : num 104 84 205 86 161 640 NA
## $ TOTAL_LP : num 202 188 366 160 285 ...
## $ PERSEN_DISABILITAS: chr "0,79%" "0,78%" "1,25%" "0,74%" ...# Install dan load library yang dibutuhkan
library(ggplot2)
library(tidyr)
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, union# GRAFIK 1: Total Pemilih Disabilitas per Kelurahan
# ============================================
ggplot(df, aes(x = reorder(KELURAHAN, TOTAL_LP), y = TOTAL_LP, fill = KELURAHAN)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Total Pemilih Disabilitas per Kelurahan",
x = "Kelurahan", y = "Jumlah Pemilih") +
theme_minimal() +
theme(legend.position = "none",
plot.title = element_text(face = "bold", size = 14))## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_bar()`).
# ============================================
# GRAFIK 2: Perbandingan L vs P per Jenis Disabilitas
# ============================================
disabilitas_lp <- data.frame(
Jenis = c("Fisik", "Intelektual", "Mental", "Wicara", "Rungu", "Netra"),
Laki_laki = c(sum(df$FISIK_L, na.rm=TRUE), sum(df$INTELEKTUAL_L, na.rm=TRUE),
sum(df$MENTAL_L, na.rm=TRUE), sum(df$WICARA_L, na.rm=TRUE),
sum(df$RUNGU_L, na.rm=TRUE), sum(df$NETRA_L, na.rm=TRUE)),
Perempuan = c(sum(df$FISIK_P, na.rm=TRUE), sum(df$INTELEKTUAL_P, na.rm=TRUE),
sum(df$MENTAL_P, na.rm=TRUE), sum(df$WICARA_P, na.rm=TRUE),
sum(df$RUNGU_P, na.rm=TRUE), sum(df$NETRA_P, na.rm=TRUE))
)
disabilitas_long <- pivot_longer(disabilitas_lp,
cols = c("Laki_laki", "Perempuan"),
names_to = "Gender", values_to = "Jumlah")
ggplot(disabilitas_long, aes(x = Jenis, y = Jumlah, fill = Gender)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = c("Laki_laki" = "#4472C4", "Perempuan" = "#ED7D31"),
labels = c("Laki-laki", "Perempuan")) +
labs(title = "Perbandingan L vs P per Jenis Disabilitas",
x = "Jenis Disabilitas", y = "Jumlah", fill = "Gender") +
theme_minimal() +
theme(plot.title = element_text(face = "bold", size = 14))
# ============================================
# GRAFIK 4: Overview Semua Jenis Disabilitas (Pie Chart)
# ============================================
overview <- data.frame(
Jenis = c("Fisik", "Intelektual", "Mental", "Wicara", "Rungu", "Netra"),
Total = c(sum(df$FISIK_LP, na.rm=TRUE), sum(df$INTELEKTUAL_LP, na.rm=TRUE),
sum(df$MENTAL_LP, na.rm=TRUE), sum(df$WICARA_LP, na.rm=TRUE),
sum(df$RUNGU_LP, na.rm=TRUE), sum(df$NETRA_LP, na.rm=TRUE))
)
overview$Persen <- round(overview$Total / sum(overview$Total) * 100, 1)
overview$Label <- paste0(overview$Jenis, "\n", overview$Persen, "%")
ggplot(overview, aes(x = "", y = Total, fill = Jenis)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y") +
geom_text(aes(label = Label), position = position_stack(vjust = 0.5), size = 3) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Overview Semua Jenis Disabilitas") +
theme_void() +
theme(plot.title = element_text(face = "bold", size = 14, hjust = 0.5),
legend.position = "none")
# Pastikan semua library di-load di chunk pertama
library(ggplot2)
library(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(wordcloud2)
library(RColorBrewer)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithslibrary(dplyr)
plot_data <- df %>%
select(KELURAHAN, FISIK_LP, INTELEKTUAL_LP, MENTAL_LP,
WICARA_LP, RUNGU_LP, NETRA_LP, TOTAL_LP) %>%
mutate(across(-KELURAHAN, as.numeric))
fig <- plot_ly(plot_data, x = ~KELURAHAN, y = ~FISIK_LP,
type = "bar", name = "Fisik",
marker = list(color = "#FF69B4")) %>%
add_trace(y = ~INTELEKTUAL_LP, name = "Intelektual",
marker = list(color = "#FF1493")) %>%
add_trace(y = ~MENTAL_LP, name = "Mental",
marker = list(color = "#DC143C")) %>%
add_trace(y = ~WICARA_LP, name = "Wicara",
marker = list(color = "#C71585")) %>%
add_trace(y = ~RUNGU_LP, name = "Rungu",
marker = list(color = "#8B0000")) %>%
add_trace(y = ~NETRA_LP, name = "Netra",
marker = list(color = "#FFB6C1")) %>%
layout(title = list(text = "<b>Disabilitas per Kelurahan (Interaktif)</b>"),
barmode = "stack",
xaxis = list(title = "Kelurahan", tickangle = -30),
yaxis = list(title = "Jumlah Pemilih"),
legend = list(title = list(text = "Jenis Disabilitas")),
plot_bgcolor = "#fff0f5",
paper_bgcolor = "#fff0f5")
fig## Warning: Ignoring 1 observations## Warning: Ignoring 1 observations
## Warning: Ignoring 1 observations
## Warning: Ignoring 1 observations
## Warning: Ignoring 1 observations
## Warning: Ignoring 1 observations# GRAFIK 3: WORDCLOUD Kelurahan
wc_data <- df %>%
mutate(TOTAL_LP = as.numeric(TOTAL_LP)) %>%
select(KELURAHAN, TOTAL_LP) %>%
filter(!is.na(TOTAL_LP))
wordcloud2(wc_data,
color = rep(c("#FF69B4", "#FF1493", "#DC143C", "#C71585", "#8B0000"),
length.out = nrow(wc_data)),
backgroundColor = "#fff0f5",
size = 1.2,
fontWeight = "bold")# GRAFIK 4: Perbandingan Total L vs P (Donut Chart)
total_gender <- data.frame(
Gender = c("Laki-laki", "Perempuan"),
Total = c(
sum(as.numeric(df$TOTAL_L), na.rm = TRUE),
sum(as.numeric(df$TOTAL_P), na.rm = TRUE)
)
)
total_gender$Persen <- round(total_gender$Total / sum(total_gender$Total) * 100, 1)
total_gender$Label <- paste0(total_gender$Gender, "\n",
total_gender$Total, " (", total_gender$Persen, "%)")
ggplot(total_gender, aes(x = 2, y = Total, fill = Gender)) +
geom_bar(stat = "identity", width = 1, color = "white", linewidth = 1) +
coord_polar("y") +
xlim(0.5, 2.5) +
scale_fill_manual(values = c("Laki-laki" = "#8B0000", "Perempuan" = "#FF69B4")) +
geom_text(aes(label = Label),
position = position_stack(vjust = 0.5),
color = "white", fontface = "bold", size = 4) +
labs(title = "Perbandingan Total Pemilih Disabilitas\nLaki-laki vs Perempuan") +
theme_void() +
theme(plot.title = element_text(face = "bold", size = 14, hjust = 0.5),
legend.position = "none",
plot.background = element_rect(fill = "#fff0f5", color = NA))
Setelah di-paste, klik Knit untuk melihat hasilnya. Kirim screenshot
kalau ada error ya!