Berikut ini adalah ringkasan statistik dari harga beras dan cabai merah di Jawa Timur selama tahun 2021–2023:

summary(data)
##  Kota/Kabupaten       Tanggal          Harga Beras (Rp/kg)
##  Length:1368        Length:1368        Min.   :10490      
##  Class :character   Class :character   1st Qu.:11689      
##  Mode  :character   Mode  :character   Median :12022      
##                                        Mean   :12014      
##                                        3rd Qu.:12333      
##                                        Max.   :13622      
##  Harga Cabai Merah (Rp/kg) Curah Hujan (mm/bulan) Tingkat Inflasi (%)
##  Min.   : 4637             Min.   :  0.00         Min.   :-0.3353    
##  1st Qu.:24501             1st Qu.: 70.44         1st Qu.: 0.1693    
##  Median :30110             Median :120.19         Median : 0.3038    
##  Mean   :29987             Mean   :128.59         Mean   : 0.3044    
##  3rd Qu.:35282             3rd Qu.:187.59         3rd Qu.: 0.4364    
##  Max.   :60822             Max.   :357.60         Max.   : 1.0852
mean(data$`Harga Beras (Rp/kg)`)
## [1] 12014.17
mean(data$`Harga Cabai Merah (Rp/kg)`)
## [1] 29986.71
sd(data$`Harga Beras (Rp/kg)`)
## [1] 481.508
sd(data$`Harga Cabai Merah (Rp/kg)`)
## [1] 8097.827

Plot dibawah ini merupakan distribusi harga cabai merah per kota/kabupaten di Jawa Timur

#boxplot cabai merah
ggplot(data, aes(x = `Kota/Kabupaten`, y = `Harga Cabai Merah (Rp/kg)`, fill = `Kota/Kabupaten`)) +
  geom_boxplot(color = "black") +
  theme_minimal() +
  labs(
    title = "Distribusi Harga Cabai Merah per Kota/Kabupaten",
    x = "Kota/Kabupaten",
    y = "Harga Cabai Merah (Rp/kg)"
  ) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "none"
  )

data_filtered <- data %>%
  filter(`Kota/Kabupaten` %in% c("Kabupaten Pamekasan", "Kabupaten Bondowoso")) %>%
  filter(Tanggal >= "2021-01" & Tanggal <= "2023-12")
data_filtered$Tanggal <- as.Date(paste0(data_filtered$Tanggal, "-01"))

Visualisasi berikut memperlihatkan perkembangan harga cabai merah dan beras di Kabupaten Pamekasan dan Bondowoso.

ggplot(data_filtered, aes(x = Tanggal, y = `Harga Cabai Merah (Rp/kg)`, color = `Kota/Kabupaten`)) +
  geom_line(size = 1) +
  stat_peaks(geom = "point", span = 15, color = "steelblue3", size = 2) +
  stat_peaks(geom = "label", span = 15, color = "steelblue3", angle = 0,
             hjust = -0.1, x.label.fmt = "%d/%m/%y") +
  stat_peaks(geom = "rug", span = 15, color = "blue", sides = "b") +
  labs(title = "Harga Cabai Merah (2021–2023)",
       x = "Tanggal", y = "Harga (Rp/kg)") +
  theme_minimal()

ggplot(data_filtered, aes(x = Tanggal, y = `Harga Beras (Rp/kg)`, color = `Kota/Kabupaten`)) +
  geom_line(size = 1) +
  stat_peaks(geom = "point", span = 15, color = "steelblue3", size = 2) +
  stat_peaks(geom = "label", span = 15, color = "steelblue3", angle = 0,
             hjust = -0.1, x.label.fmt = "%d/%m/%y") +
  stat_peaks(geom = "rug", span = 15, color = "blue", sides = "b") +
  labs(title = "Harga Beras (2021–2023)",
       x = "Tanggal", y = "Harga (Rp/kg)") +
  theme_minimal()

Visualisasi berikut merupakan hubungan antara Harga Cabai Merah dan Tingkat Inflasi

ggplot(data, aes(x = `Harga Cabai Merah (Rp/kg)`, y = `Tingkat Inflasi (%)`, color = `Kota/Kabupaten`)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 1) +
  labs(title = "Hubungan antara Harga Cabai Merah dan Tingkat Inflasi",
       x = "Harga Cabai Merah (Rp/kg)",
       y = "Tingkat Inflasi (%)") +
  theme_minimal() +
  theme(legend.position = "bottom")
## `geom_smooth()` using formula = 'y ~ x'

Visualisasi berikut menunjukkan hubungan antara harga beras dan curah hujan

ggplot(data, aes(x = `Harga Cabai Merah (Rp/kg)`, y = `Curah Hujan (mm/bulan)`, color = `Kota/Kabupaten`)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 1) +
  labs(title = "Hubungan antara Curah Hujan dan Harga Cabai Merah",
       x = "Harga Cabai Merah (Rp/kg)",
       y = "Curah Hujan (mm/bulan)") +
  theme_minimal() +
  theme(legend.position = "bottom")
## `geom_smooth()` using formula = 'y ~ x'