Dokumen ini merupakan e-book eksploratif berbasis R Markdown untuk menganalisis tren perekonomian Indonesia selama 10 tahun terakhir. Data diambil dari sumber terbuka seperti BPS, World Bank, atau file Excel yang tersedia.
Tujuan: - Memahami tren pertumbuhan ekonomi Indonesia - Menjelaskan faktor-faktor penting seperti inflasi, pengangguran, PDB - Memberikan contoh analisis berbasis data
library(readxl)
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
library(ggplot2)
library(corrplot)
## corrplot 0.95 loaded
library(summarytools)
data_pdb <- read_excel("C:/Users/cpcit/Downloads/data_ekonomi_indonesia_2019_2024.xlsx", sheet = "PDB")
data_inflasi <- read_excel("C:/Users/cpcit/Downloads/data_ekonomi_indonesia_2019_2024.xlsx", sheet = "Inflasi")
data_ekspor <- read_excel("C:/Users/cpcit/Downloads/data_ekonomi_indonesia_2019_2024.xlsx", sheet = "Ekspor_Impor")
dfSummary(data_pdb)
## Data Frame Summary
## data_pdb
## Dimensions: 6 x 4
## Duplicates: 0
##
## ------------------------------------------------------------------------------------------------------------------------------
## No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
## ---- ------------------------------------- ------------------------------ ---------------------- -------- ---------- ---------
## 1 Tahun Mean (sd) : 2021.5 (1.9) 2019 : 1 (16.7%) III 6 0
## [numeric] min < med < max: 2020 : 1 (16.7%) III (100.0%) (0.0%)
## 2019 < 2021.5 < 2024 2021 : 1 (16.7%) III
## IQR (CV) : 2.5 (0) 2022 : 1 (16.7%) III
## 2023 : 1 (16.7%) III
## 2024 : 1 (16.7%) III
##
## 2 PDB Harga Berlaku (Rp Triliun) Mean (sd) : 18612.7 (2949.6) 15434.20 : 1 (16.7%) III 6 0
## [numeric] min < med < max: 15833.90 : 1 (16.7%) III (100.0%) (0.0%)
## 15434.2 < 18279.6 < 22139 16970.80 : 1 (16.7%) III
## IQR (CV) : 5061.5 (0.2) 19588.40 : 1 (16.7%) III
## 21710.00 : 1 (16.7%) III
## 22139.00 : 1 (16.7%) III
##
## 3 PDB Harga Konstan 2010 (Rp Triliun) Mean (sd) : 12901.1 (2071.6) 10722.40 : 1 (16.7%) III 6 0
## [numeric] min < med < max: 10949.60 : 1 (16.7%) III (100.0%) (0.0%)
## 10722.4 < 12649.6 < 15590 11710.40 : 1 (16.7%) III
## IQR (CV) : 3391.6 (0.2) 13588.80 : 1 (16.7%) III
## 14845.60 : 1 (16.7%) III
## 15590.00 : 1 (16.7%) III
##
## 4 Pertumbuhan PDB (%) Mean (sd) : 3.7 (2.9) -2.07 : 1 (16.7%) III 6 0
## [numeric] min < med < max: 3.69 : 1 (16.7%) III (100.0%) (0.0%)
## -2.1 < 5 < 5.3 5.02 : 1 (16.7%) III
## IQR (CV) : 1.2 (0.8) 5.03 : 1 (16.7%) III
## 5.31 : 2 (33.3%) IIIIII
## ------------------------------------------------------------------------------------------------------------------------------
data_pdb %>%
summarise(
Rata_PDB_Berlaku = mean(`PDB Harga Berlaku (Rp Triliun)`),
Rata_PDB_Konstan = mean(`PDB Harga Konstan 2010 (Rp Triliun)`),
Rata_Pertumbuhan = mean(`Pertumbuhan PDB (%)`)
)
## # A tibble: 1 × 3
## Rata_PDB_Berlaku Rata_PDB_Konstan Rata_Pertumbuhan
## <dbl> <dbl> <dbl>
## 1 18613. 12901. 3.72
ggplot(data_pdb, aes(x = Tahun, y = `PDB Harga Berlaku (Rp Triliun)`)) +
geom_line(color = "blue") +
geom_point() +
labs(title = "Tren PDB Harga Berlaku Indonesia", y = "PDB (Triliun Rupiah)", x = "Tahun")
ggplot(data_pdb, aes(x = Tahun, y = `Pertumbuhan PDB (%)`)) +
geom_line(color = "darkorange") +
geom_point() +
labs(title = "Pertumbuhan PDB Tahunan", y = "%", x = "Tahun")
m <- cor(data_pdb %>% select(-Tahun), use = "complete.obs")
corrplot(m, method = "circle")
data_pdb %>%
filter(Tahun %in% c(2020, 2021)) %>%
select(Tahun, `PDB Harga Berlaku (Rp Triliun)`, `Pertumbuhan PDB (%)`)
## # A tibble: 2 × 3
## Tahun `PDB Harga Berlaku (Rp Triliun)` `Pertumbuhan PDB (%)`
## <dbl> <dbl> <dbl>
## 1 2020 15434. -2.07
## 2 2021 16971. 3.69