#analisis data untuk penugasan forkas 19
#Inflasi Bulanan Menurut Kabupaten/Kota (M-to-M) (Persen)
library(tidyverse)
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library(janitor)
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## chisq.test, fisher.test
library(readxl)
inflasi1 <- read_excel("D:/STIS/SEMESTER 2/forkas.xlsx")
inflasi <- inflasi1
#ambil header bulan
inflasi <- inflasi[-1,]
#rename kolom pertama jadi kabupaten
colnames(inflasi)[1] <- "kabupaten"
#hapus baris kosong
inflasi <- inflasi %>% filter(!is.na(kabupaten))
#ke format long
inflasi2 <- inflasi %>%
pivot_longer(cols = -kabupaten,
names_to = "bulan",
values_to = "inflasi") %>%mutate(inflasi = as.numeric(inflasi))
inflasi2
## # A tibble: 1,800 × 3
## kabupaten bulan inflasi
## <chr> <chr> <dbl>
## 1 KOTA MEULABOH Januari -0.44
## 2 KOTA MEULABOH Februari -0.72
## 3 KOTA MEULABOH Maret 1.88
## 4 KOTA MEULABOH April 1.75
## 5 KOTA MEULABOH Mei -0.18
## 6 KOTA MEULABOH Juni 0.07
## 7 KOTA MEULABOH Juli 0.39
## 8 KOTA MEULABOH Agustus 0.55
## 9 KOTA MEULABOH September 0.19
## 10 KOTA MEULABOH Oktober 0.14
## # ℹ 1,790 more rows
#analisis statistik deskriptif
inflasi2 %>%
summarise(
average = mean(inflasi, na.rm = TRUE),
median = median(inflasi, na.rm = TRUE),
sd = sd(inflasi, na.rm = TRUE),
min = min(inflasi, na.rm = TRUE),
max = max(inflasi, na.rm = TRUE)
)
## # A tibble: 1 × 5
## average median sd min max
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.261 0.2 0.860 -2.47 6.94
#analisis trend per bulan
inflasi_bulan <- inflasi2 %>%
group_by(bulan) %>%
summarise(rata_inflasi = mean(inflasi, na.rm = TRUE)) %>%
mutate(bulan = factor(bulan, levels = c(
"Januari", "Februari", "Maret", "April", "Mei", "Juni",
"Juli", "Agustus", "September", "Oktober", "November", "Desember"))) %>%
arrange(bulan)
inflasi_bulan
## # A tibble: 12 × 2
## bulan rata_inflasi
## <fct> <dbl>
## 1 Januari -0.654
## 2 Februari -0.403
## 3 Maret 1.73
## 4 April 1.05
## 5 Mei -0.400
## 6 Juni 0.167
## 7 Juli 0.504
## 8 Agustus -0.124
## 9 September 0.191
## 10 Oktober 0.150
## 11 November 0.0759
## 12 Desember 0.839
#analisis perwilayah
inflasi_kab <- inflasi2 %>%
group_by(kabupaten) %>%
summarise(rata_inflasi = mean(inflasi, na.rm = TRUE)) %>%
arrange(desc(rata_inflasi))
inflasi_kab
## # A tibble: 150 × 2
## kabupaten rata_inflasi
## <chr> <dbl>
## 1 KOTA GUNUNGSITOLI 0.879
## 2 KAB ACEH TAMIANG 0.586
## 3 KAB PASAMAN BARAT 0.58
## 4 KAB TOLI TOLI 0.508
## 5 TEMBILAHAN 0.505
## 6 KOTA BANDA ACEH 0.501
## 7 KAB NABIRE 0.481
## 8 KOTA SIBOLGA 0.462
## 9 KOTA MEULABOH 0.458
## 10 KOTA LHOKSEUMAWE 0.456
## # ℹ 140 more rows
#nilai ekstrem
inflasi2 %>%
filter(inflasi == max(inflasi, na.rm = TRUE) |
inflasi == min(inflasi, na.rm = TRUE))
## # A tibble: 2 × 3
## kabupaten bulan inflasi
## <chr> <chr> <dbl>
## 1 KOTA GUNUNGSITOLI Desember 6.94
## 2 KAB GORONTALO Mei -2.47
#grafik
ggplot(inflasi_bulan, aes(x = bulan, y = rata_inflasi, group = 1)) +
geom_line() +
geom_point() +
geom_text(aes(label = round(rata_inflasi, 2)), vjust = -1, size = 2.5) +
labs(
title = "Trend Inflasi Bulanan (M-to-M)",
x = "Bulan",
y = "Rata-rata Inflasi (%)"
) +
theme_light()
#top 10 inflasi tertinggi
top10 <- inflasi_kab %>%
slice_max(rata_inflasi, n = 10)
ggplot(top10, aes(x = reorder(kabupaten, rata_inflasi), y = rata_inflasi)) +
geom_col(fill = "darkorange") +
geom_text(aes(label = round(rata_inflasi, 2)),
hjust = -0.1, size = 3) +
coord_flip() +
labs(
title = "Top 10 Kabupaten/Kota Inflasi Tertinggi (%)",
x = "Kabupaten/Kota",
y = "Rata-rata Inflasi (%)"
) +
theme_light()
#top 10 inflasi terendah
bottom10 <- inflasi_kab %>%
slice_min(rata_inflasi, n = 10)
ggplot(bottom10, aes(x = reorder(kabupaten, rata_inflasi), y = rata_inflasi)) +
geom_col(fill = "gold") +
geom_text(aes(label = round(rata_inflasi, 2)),
hjust = -0.1, size = 3) +
coord_flip() +
labs(
title = "Top 10 Inflasi Terendah",
x = "Kabupaten/Kota",
y = "Rata-rata Inflasi (%)"
) +
theme_light()
#histogram
ggplot(inflasi2, aes(x = inflasi)) +
geom_histogram(bins = 30,
fill = "darkorange3",
color = "white") +
labs(
title = "Distribusi Inflasi",
x = "Inflasi (%)",
y = "Frekuensi"
) +
theme_light()
#boxplot
ggplot(inflasi2, aes(y = inflasi)) +
geom_boxplot(fill = "cornflowerblue") +
labs(title = "Boxplot Inflasi") +
theme_light()
#heatmap
inflasi2$bulan <- factor(inflasi2$bulan, levels = c(
"Januari","Februari","Maret","April","Mei","Juni",
"Juli","Agustus","September","Oktober","November","Desember"
))
top10_kab <- inflasi2 %>%
group_by(kabupaten) %>%
summarise(rata_inflasi = mean(inflasi, na.rm = TRUE)) %>%
slice_max(rata_inflasi, n = 10)
data_top10 <- inflasi2 %>%
filter(kabupaten %in% top10_kab$kabupaten)
ggplot(data_top10, aes(x = bulan, y = kabupaten, fill = inflasi)) +
geom_tile(color = "white") +
scale_fill_gradient2(
low = "dodgerblue",
mid = "navajowhite",
high = "orangered",
midpoint = 0
) +
labs(
title = "Heatmap Inflasi (Top 10 Kabupaten/Kota)",
x = "Bulan",
y = "Kabupaten/Kota",
fill = "Inflasi (%)"
) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 6),
plot.title = element_text(face = "bold"))
