library(sf)
## Warning: package 'sf' was built under R version 4.4.3
## Linking to GEOS 3.13.0, GDAL 3.10.1, PROJ 9.5.1; sf_use_s2() is TRUE
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.3
##
## 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
peta <- st_read("E:/z DRIVE E/KULIAH/SEMESTER 5/Statistika Lingkungan/Week 4/Kecamatan_Semarang.geojson")
## Reading layer `Kecamatan_Semarang' from data source
## `E:\z DRIVE E\KULIAH\SEMESTER 5\Statistika Lingkungan\Week 4\Kecamatan_Semarang.geojson'
## using driver `GeoJSON'
## Simple feature collection with 32 features and 10 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: 110.2673 ymin: -7.114464 xmax: 110.5089 ymax: -6.931992
## Geodetic CRS: WGS 84
peta
## Simple feature collection with 32 features and 10 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: 110.2673 ymin: -7.114464 xmax: 110.5089 ymax: -6.931992
## Geodetic CRS: WGS 84
## First 10 features:
## id X.id X.relations admin_level boundary
## 1 relation/8110792 relation/8110792 <NA> 6 administrative
## 2 relation/8132993 relation/8132993 <NA> 6 administrative
## 3 relation/8147917 relation/8147917 <NA> 6 administrative
## 4 relation/8178535 relation/8178535 <NA> 6 administrative
## 5 relation/8201927 relation/8201927 <NA> 6 administrative
## 6 relation/8201936 relation/8201936 <NA> 6 administrative
## 7 relation/8222496 relation/8222496 <NA> 6 administrative
## 8 relation/8244965 relation/8244965 <NA> 6 administrative
## 9 relation/8350713 relation/8350713 <NA> 6 administrative
## 10 relation/8350849 relation/8350849 <NA> 6 administrative
## is_in.city is_in.province name source type
## 1 Semarang Jawa Tengah Gayamsari HOT_InAWARESurvey_2018 boundary
## 2 Semarang Jawa Tengah Semarang Selatan HOT_InAWARESurvey_2018 boundary
## 3 Semarang Jawa Tengah Gajah Mungkur HOT_InAWARESurvey_2018 boundary
## 4 Semarang Jawa Tengah Candisari HOT_InAWARESurvey_2018 boundary
## 5 Semarang Jawa Tengah Semarang Timur HOT_InAWARESurvey_2018 boundary
## 6 Semarang Jawa Tengah Semarang Utara HOT_InAWARESurvey_2018 boundary
## 7 Semarang Jawa Tengah Semarang Tengah HOT_InAWARESurvey_2018 boundary
## 8 Semarang Jawa Tengah Semarang Barat HOT_InAWARESurey_2018 boundary
## 9 Semarang Jawa Tengah Genuk HOT_InAWARESurvey_2018 boundary
## 10 Semarang Jawa Tengah Pedurungan HOT_InAWARESurvey_2018 boundary
## geometry
## 1 POLYGON ((110.4404 -6.95149...
## 2 POLYGON ((110.432 -6.994293...
## 3 POLYGON ((110.3874 -7.01746...
## 4 POLYGON ((110.4161 -7.00192...
## 5 POLYGON ((110.4316 -6.98417...
## 6 POLYGON ((110.4308 -6.96088...
## 7 POLYGON ((110.4026 -6.97739...
## 8 POLYGON ((110.3797 -7.00813...
## 9 POLYGON ((110.5048 -6.96815...
## 10 POLYGON ((110.4553 -6.96618...
set.seed(100)
data <- data.frame(
Kecamatan = peta$name, # kolom nama kecamatan
PM25 = sample(20:100, length(peta$name), replace = TRUE)
)
data
## Kecamatan PM25
## 1 Gayamsari 93
## 2 Semarang Selatan 97
## 3 Gajah Mungkur 42
## 4 Candisari 89
## 5 Semarang Timur 23
## 6 Semarang Utara 74
## 7 Semarang Tengah 89
## 8 Semarang Barat 26
## 9 Genuk 26
## 10 Pedurungan 74
## 11 Tugu 62
## 12 Gunung Pati 80
## 13 Tembalang 31
## 14 Mijen 70
## 15 Banyumanik 91
## 16 Ngaliyan 37
## 17 <NA> 44
## 18 <NA> 21
## 19 <NA> 70
## 20 <NA> 87
## 21 <NA> 87
## 22 <NA> 71
## 23 <NA> 67
## 24 <NA> 51
## 25 <NA> 58
## 26 <NA> 35
## 27 <NA> 94
## 28 <NA> 85
## 29 <NA> 89
## 30 <NA> 64
## 31 <NA> 49
## 32 <NA> 49
# Gabungkan data dengan peta
peta_data <- left_join(peta, data, by=c("name"="Kecamatan"))
## Warning in sf_column %in% names(g): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 17 of `x` matches multiple rows in `y`.
## ℹ Row 17 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
peta_data
## Simple feature collection with 272 features and 11 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: 110.2673 ymin: -7.114464 xmax: 110.5089 ymax: -6.931992
## Geodetic CRS: WGS 84
## First 10 features:
## id X.id X.relations admin_level boundary
## 1 relation/8110792 relation/8110792 <NA> 6 administrative
## 2 relation/8132993 relation/8132993 <NA> 6 administrative
## 3 relation/8147917 relation/8147917 <NA> 6 administrative
## 4 relation/8178535 relation/8178535 <NA> 6 administrative
## 5 relation/8201927 relation/8201927 <NA> 6 administrative
## 6 relation/8201936 relation/8201936 <NA> 6 administrative
## 7 relation/8222496 relation/8222496 <NA> 6 administrative
## 8 relation/8244965 relation/8244965 <NA> 6 administrative
## 9 relation/8350713 relation/8350713 <NA> 6 administrative
## 10 relation/8350849 relation/8350849 <NA> 6 administrative
## is_in.city is_in.province name source type
## 1 Semarang Jawa Tengah Gayamsari HOT_InAWARESurvey_2018 boundary
## 2 Semarang Jawa Tengah Semarang Selatan HOT_InAWARESurvey_2018 boundary
## 3 Semarang Jawa Tengah Gajah Mungkur HOT_InAWARESurvey_2018 boundary
## 4 Semarang Jawa Tengah Candisari HOT_InAWARESurvey_2018 boundary
## 5 Semarang Jawa Tengah Semarang Timur HOT_InAWARESurvey_2018 boundary
## 6 Semarang Jawa Tengah Semarang Utara HOT_InAWARESurvey_2018 boundary
## 7 Semarang Jawa Tengah Semarang Tengah HOT_InAWARESurvey_2018 boundary
## 8 Semarang Jawa Tengah Semarang Barat HOT_InAWARESurey_2018 boundary
## 9 Semarang Jawa Tengah Genuk HOT_InAWARESurvey_2018 boundary
## 10 Semarang Jawa Tengah Pedurungan HOT_InAWARESurvey_2018 boundary
## PM25 geometry
## 1 93 POLYGON ((110.4404 -6.95149...
## 2 97 POLYGON ((110.432 -6.994293...
## 3 42 POLYGON ((110.3874 -7.01746...
## 4 89 POLYGON ((110.4161 -7.00192...
## 5 23 POLYGON ((110.4316 -6.98417...
## 6 74 POLYGON ((110.4308 -6.96088...
## 7 89 POLYGON ((110.4026 -6.97739...
## 8 26 POLYGON ((110.3797 -7.00813...
## 9 26 POLYGON ((110.5048 -6.96815...
## 10 74 POLYGON ((110.4553 -6.96618...
# Plot chloropleth
ggplot(peta_data) +
geom_sf(aes(fill=PM25), color="white") +
geom_sf_text(aes(label = name), size = 2, color = "white") +
scale_fill_gradient(low="tomato", high="navy", name="PM2.5") +
theme_minimal() +
labs(title="Choropleth Map PM2.5 - Kota Semarang")
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning: Removed 256 rows containing missing values or values outside the scale range
## (`geom_text()`).
set.seed(42)
# Hari dan jam
hari <- c("Senin","Selasa","Rabu","Kamis","Jumat","Sabtu","Minggu")
jam <- 0:23
# Buat grid data
data_heat <- expand.grid(Hari = hari, Jam = jam)
# Fungsi untuk simulasi realistis
data_heat <- data_heat %>%
rowwise() %>%
mutate(PM25 = {
base <- 30
# Tambah polusi saat jam sibuk
if (Jam %in% 7:9 | Jam %in% 17:19) base <- base + 40
# Tambah polusi khusus hari kerja
if (Hari %in% c("Senin","Selasa","Rabu","Kamis","Jumat")) base <- base + 10
# Sedikit lebih rendah saat malam/dini hari
if (Jam %in% 0:5) base <- base - 15
# Akhir pekan lebih bersih
if (Hari %in% c("Sabtu","Minggu")) base <- base - 10
# Variasi acak
round(rnorm(1, mean = base, sd = 10))
}) %>%
ungroup()
data_heat
## # A tibble: 168 × 3
## Hari Jam PM25
## <fct> <int> <dbl>
## 1 Senin 0 39
## 2 Selasa 0 19
## 3 Rabu 0 29
## 4 Kamis 0 31
## 5 Jumat 0 29
## 6 Sabtu 0 4
## 7 Minggu 0 20
## 8 Senin 1 24
## 9 Selasa 1 45
## 10 Rabu 1 24
## # ℹ 158 more rows
# Plot heatmap
ggplot(data_heat, aes(x=Jam, y=Hari, fill=PM25)) +
geom_tile(color="white") +
scale_fill_gradient(low="lightyellow", high="darkred") +
theme_minimal() +
labs(
title="Heatmap PM2.5 per Jam dan Hari",
x="Jam", y="Hari"
)
set.seed(42)
data_violin <- data.frame(
Sungai = rep(c("Banjir Kanal Barat", "Banjir Kanal Timur", "Kali Garang"), each=50),
pH = c(
rnorm(50, 6.8, 0.3), # Banjir Kanal Barat
rnorm(50, 7.2, 0.4), # Banjir Kanal Timur
rnorm(50, 6.5, 0.2) # Kali Garang
),
Waktu = rep(seq.POSIXt(from = as.POSIXct("2025-09-01 00:00"),
by = "hour", length.out = 50), 3)
)
head(data_violin, 10)
## Sungai pH Waktu
## 1 Banjir Kanal Barat 7.211288 2025-09-01 00:00:00
## 2 Banjir Kanal Barat 6.630591 2025-09-01 01:00:00
## 3 Banjir Kanal Barat 6.908939 2025-09-01 02:00:00
## 4 Banjir Kanal Barat 6.989859 2025-09-01 03:00:00
## 5 Banjir Kanal Barat 6.921280 2025-09-01 04:00:00
## 6 Banjir Kanal Barat 6.768163 2025-09-01 05:00:00
## 7 Banjir Kanal Barat 7.253457 2025-09-01 06:00:00
## 8 Banjir Kanal Barat 6.771602 2025-09-01 07:00:00
## 9 Banjir Kanal Barat 7.405527 2025-09-01 08:00:00
## 10 Banjir Kanal Barat 6.781186 2025-09-01 09:00:00
ggplot(data_violin, aes(x=Sungai, y=pH, fill=Sungai)) +
geom_violin(trim=FALSE, alpha=0.6) +
geom_boxplot(width=0.1, fill="white") +
theme_minimal() +
labs(title="Sebaran pH Air Sungai di Semarang",
subtitle="Violin plot menunjukkan distribusi lengkap",
x="", y="pH")
library(ggridges)
## Warning: package 'ggridges' was built under R version 4.4.3
set.seed(42)
data_ridge <- data.frame(
Tahun = rep(2015:2024, each=12),
Bulan = rep(month.abb, times=10),
CurahHujan = rnorm(120, mean=200, sd=50)
)
data_ridge
## Tahun Bulan CurahHujan
## 1 2015 Jan 268.54792
## 2 2015 Feb 171.76509
## 3 2015 Mar 218.15642
## 4 2015 Apr 231.64313
## 5 2015 May 220.21342
## 6 2015 Jun 194.69377
## 7 2015 Jul 275.57610
## 8 2015 Aug 195.26705
## 9 2015 Sep 300.92119
## 10 2015 Oct 196.86430
## 11 2015 Nov 265.24348
## 12 2015 Dec 314.33227
## 13 2016 Jan 130.55696
## 14 2016 Feb 186.06056
## 15 2016 Mar 193.33393
## 16 2016 Apr 231.79752
## 17 2016 May 185.78735
## 18 2016 Jun 67.17723
## 19 2016 Jul 77.97665
## 20 2016 Aug 266.00567
## 21 2016 Sep 184.66807
## 22 2016 Oct 110.93458
## 23 2016 Nov 191.40413
## 24 2016 Dec 260.73373
## 25 2017 Jan 294.75967
## 26 2017 Feb 178.47654
## 27 2017 Mar 187.13653
## 28 2017 Apr 111.84185
## 29 2017 May 223.00487
## 30 2017 Jun 168.00026
## 31 2017 Jul 222.77251
## 32 2017 Aug 235.24187
## 33 2017 Sep 251.75518
## 34 2017 Oct 169.55368
## 35 2017 Nov 225.24776
## 36 2017 Dec 114.14957
## 37 2018 Jan 160.77705
## 38 2018 Feb 157.45462
## 39 2018 Mar 79.28962
## 40 2018 Apr 201.80613
## 41 2018 May 210.29993
## 42 2018 Jun 181.94714
## 43 2018 Jul 237.90816
## 44 2018 Aug 163.66476
## 45 2018 Sep 131.58595
## 46 2018 Oct 221.64090
## 47 2018 Nov 159.43034
## 48 2018 Dec 272.20506
## 49 2019 Jan 178.42769
## 50 2019 Feb 232.78239
## 51 2019 Mar 216.09626
## 52 2019 Apr 160.80805
## 53 2019 May 278.78638
## 54 2019 Jun 232.14497
## 55 2019 Jul 204.48803
## 56 2019 Aug 213.82754
## 57 2019 Sep 233.96444
## 58 2019 Oct 204.49164
## 59 2019 Nov 50.34550
## 60 2019 Dec 214.24415
## 61 2020 Jan 181.63827
## 62 2020 Feb 209.26153
## 63 2020 Mar 229.09119
## 64 2020 Apr 269.98684
## 65 2020 May 163.63540
## 66 2020 Jun 265.12713
## 67 2020 Jul 216.79241
## 68 2020 Aug 251.92530
## 69 2020 Sep 246.03643
## 70 2020 Oct 236.04391
## 71 2020 Nov 147.84405
## 72 2020 Dec 195.49068
## 73 2021 Jan 231.17591
## 74 2021 Feb 152.32383
## 75 2021 Mar 172.85856
## 76 2021 Apr 229.04982
## 77 2021 May 238.40894
## 78 2021 Jun 223.18838
## 79 2021 Jul 155.71119
## 80 2021 Aug 145.01096
## 81 2021 Sep 275.63535
## 82 2021 Oct 212.89607
## 83 2021 Nov 204.42201
## 84 2021 Dec 193.95517
## 85 2022 Jan 140.28356
## 86 2022 Feb 230.59984
## 87 2022 Mar 189.14301
## 88 2022 Apr 190.86216
## 89 2022 May 246.66732
## 90 2022 Jun 241.08866
## 91 2022 Jul 269.60582
## 92 2022 Aug 176.19130
## 93 2022 Sep 232.51743
## 94 2022 Oct 269.55552
## 95 2022 Nov 144.46056
## 96 2022 Dec 156.96037
## 97 2023 Jan 143.41307
## 98 2023 Feb 127.03930
## 99 2023 Mar 203.99913
## 100 2023 Apr 232.66022
## 101 2023 May 260.04827
## 102 2023 Jun 252.23755
## 103 2023 Jul 149.83957
## 104 2023 Aug 292.42410
## 105 2023 Sep 166.66133
## 106 2023 Oct 205.27569
## 107 2023 Nov 178.88721
## 108 2023 Dec 193.88249
## 109 2024 Jan 209.40965
## 110 2024 Feb 205.95805
## 111 2024 Mar 198.74537
## 112 2024 Apr 205.40364
## 113 2024 May 175.72824
## 114 2024 Jun 174.78914
## 115 2024 Jul 116.94505
## 116 2024 Aug 180.88331
## 117 2024 Sep 174.36749
## 118 2024 Oct 335.09455
## 119 2024 Nov 131.89419
## 120 2024 Dec 206.86281
ggplot(data_ridge, aes(x=CurahHujan, y=factor(Tahun), fill=..x..)) +
geom_density_ridges_gradient(scale=3, rel_min_height=0.01) +
scale_fill_gradient(low="skyblue", high="navy") +
theme_minimal() +
labs(title="Ridgeline Plot Curah Hujan Tahunan",
x="Curah Hujan (mm)", y="Tahun")
## Warning: The dot-dot notation (`..x..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(x)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Picking joint bandwidth of 22.7
library(ggplot2)
library(dplyr)
# Buat data simulasi kualitas udara (time series per jam)
set.seed(123)
n <- 24 # jumlah jam dalam sehari
data <- data.frame(
jam = 0:(n-1),
pm25 = 60 + 20*sin(seq(0, 2*pi, length.out = n)) + rnorm(n, 0, 3)
)
# Visualisasi polar plot
ggplot(data, aes(x = jam, y = pm25)) +
geom_line(color = "red", size = 1.5) + # garis utama
coord_polar() + # ubah jadi polar chart
theme_minimal(base_size = 14) +
labs(title = "Kualitas Udara (PM2.5 per Jam)",
x = NULL, y = NULL) +
theme(
panel.grid.minor = element_blank(),
axis.text.y = element_text(color = "grey40"),
axis.text.x = element_text(color = "grey40"),
plot.title = element_text(hjust = 0.5, face = "bold")
)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Paket yang dibutuhkan
library(ggstream)
## Warning: package 'ggstream' was built under R version 4.4.3
library(ggplot2)
# Simulasi data emisi
sectors <- c("Transportasi", "Industri", "Pertanian", "Rumah Tangga")
years <- 2015:2025
emisi_data <- expand.grid(Tahun = years, Sektor = sectors)
emisi_data$Emisi <- round(runif(nrow(emisi_data), 50, 300), 1)
# Visualisasi stream graph
ggplot(emisi_data, aes(x = Tahun, y = Emisi, fill = Sektor)) +
geom_stream(type = "ridge") +
labs(title = "Stream Graph Emisi Gas Rumah Kaca per Sektor",
x = "Tahun", y = "Emisi (kt CO₂e)") +
theme_minimal(base_size = 14)
# Paket yang dibutuhkan
library(ggplot2)
library(dplyr)
# Simulasi data arah dan intensitas polusi
wind_data <- data.frame(
arah = factor(c("N", "NE", "E", "SE", "S", "SW", "W", "NW"), levels = c("N", "NE", "E", "SE", "S", "SW", "W", "NW")),
intensitas = round(runif(8, 20, 100), 1)
)
# Visualisasi polar plot
ggplot(wind_data, aes(x = arah, y = intensitas, fill = arah)) +
geom_bar(stat = "identity", width = 1) +
coord_polar() +
labs(title = "Polar Plot Intensitas Polusi Berdasarkan Arah Angin",
x = "", y = "Intensitas (µg/m³)") +
theme_minimal(base_size = 14) +
theme(axis.text.x = element_text(size = 12))
