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wilayah<-c("Provinsi Bengkulu", "Bengkulu Selatan", "Rejang Lebong", "Bengkulu Utara", "Kaur",
"Seluma", "Muko-muko", "Lebong", "Kepahiang", "Bengkulu Tengah", "Kota Bengkulu")
indeks_pembangunan_manusia<-c(74.3, 74.06, 74.43, 72.27, 70.92, 70.27, 73, 72.95, 71.38, 70.81, 83.38)
indeks_pembangunan_manusia
## [1] 74.30 74.06 74.43 72.27 70.92 70.27 73.00 72.95 71.38 70.81 83.38
pengeluaran_perkapita<-c(11172, 10657, 10848, 11188, 9365, 8949, 11075, 12012, 10044, 10114, 14924)
pengeluaran_perkapita
## [1] 11172 10657 10848 11188 9365 8949 11075 12012 10044 10114 14924
wilayah_faktor <- factor(wilayah)
wilayah_faktor
## [1] Provinsi Bengkulu Bengkulu Selatan Rejang Lebong Bengkulu Utara
## [5] Kaur Seluma Muko-muko Lebong
## [9] Kepahiang Bengkulu Tengah Kota Bengkulu
## 11 Levels: Bengkulu Selatan Bengkulu Tengah Bengkulu Utara Kaur ... Seluma
data_list_ipm_pp <- list(
Wilayah = wilayah,
Indeks_Pembangunan_Manusia = indeks_pembangunan_manusia,
Pengeluaran_Perkapita = pengeluaran_perkapita)
data_list_ipm_pp
## $Wilayah
## [1] "Provinsi Bengkulu" "Bengkulu Selatan" "Rejang Lebong"
## [4] "Bengkulu Utara" "Kaur" "Seluma"
## [7] "Muko-muko" "Lebong" "Kepahiang"
## [10] "Bengkulu Tengah" "Kota Bengkulu"
##
## $Indeks_Pembangunan_Manusia
## [1] 74.30 74.06 74.43 72.27 70.92 70.27 73.00 72.95 71.38 70.81 83.38
##
## $Pengeluaran_Perkapita
## [1] 11172 10657 10848 11188 9365 8949 11075 12012 10044 10114 14924
x <- indeks_pembangunan_manusia
y <- pengeluaran_perkapita
cor(x,y)
## [1] 0.9247782
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
# Data frame contoh
df <- data.frame(x,y)
# Scatter plot dengan garis regresi
ggplot(df, aes(x = x, y = y)) +
geom_point(color = "darkred") + # Scatter plot
geom_smooth(method = "lm", color = "blue") + # Garis regresi
labs(
x = "Indeks Pembangunan Manusia tahun 2023",
y = "Pengeluaran Per Kapita tahun 2023",
title = "Scatter Plot Indeks Pembangunan Manusia vs Pengeluaran Per Kapita"
)
## `geom_smooth()` using formula = 'y ~ x'
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