DATA MINING MADNESS_KELOMPOK 6_Azzahra(196)_Zumrotus(191)_Dhafa(192)_Nayara(198)
Zumrotus Solicha
2025-09-13
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Import Library
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.3## Warning: package 'ggplot2' was built under R version 4.3.3## Warning: package 'tibble' was built under R version 4.3.3## Warning: package 'tidyr' was built under R version 4.3.3## Warning: package 'readr' was built under R version 4.3.3## Warning: package 'purrr' was built under R version 4.3.3## Warning: package 'dplyr' was built under R version 4.3.3## Warning: package 'stringr' was built under R version 4.3.3## Warning: package 'forcats' was built under R version 4.3.3## Warning: package 'lubridate' was built under R version 4.3.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(lubridate)
library(hms)## Warning: package 'hms' was built under R version 4.3.3##
## Attaching package: 'hms'
##
## The following object is masked from 'package:lubridate':
##
## hmsFilter & Synchronize the Data
Use only Library building WiFi records
wifi <- read_csv("C:/Users/ZUMROTUS SOLICHA/Documents/MK SEMESTER 5/DATMIN/wifi.csv") %>%
filter(str_trim(Building) == "Library") %>%
select(time, `Associated Client Count`)## Rows: 1883844 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Event Time, Uni, Building, Floor
## dbl (2): Associated Client Count, Authenticated Client Count
## dttm (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.print("WiFi data (Library only):")## [1] "WiFi data (Library only):"print(head(wifi))## # A tibble: 6 × 2
## time `Associated Client Count`
## <dttm> <dbl>
## 1 2020-02-01 00:02:12 29
## 2 2020-02-01 00:02:12 0
## 3 2020-02-01 00:02:12 21
## 4 2020-02-01 00:02:12 38
## 5 2020-02-01 00:07:34 0
## 6 2020-02-01 00:07:34 33Interpretasi : Data ini berhasil menampilkan aktivitas WiFi Library dengan detail per access point dan per waktu. Ada fluktuasi jumlah client, bahkan ada AP yang kosong meski AP lain aktif. Data ini bisa dianalisis lebih lanjut untuk melihat pola kepadatan perpustakaan sepanjang hari, tren kunjungan, dan jam sibuk.
Ensure all data is standardized at 10-minute intervals
wifi <- wifi %>%
mutate(time = ymd_hms(time)) %>%
mutate(time = floor_date(time, "10 minutes"))
print("WiFi data after standardizing to 10-minute intervals:")## [1] "WiFi data after standardizing to 10-minute intervals:"print(head(wifi))## # A tibble: 6 × 2
## time `Associated Client Count`
## <dttm> <dbl>
## 1 2020-02-01 00:00:00 29
## 2 2020-02-01 00:00:00 0
## 3 2020-02-01 00:00:00 21
## 4 2020-02-01 00:00:00 38
## 5 2020-02-01 00:00:00 0
## 6 2020-02-01 00:00:00 33Interprerasi : Data koneksi WiFi diubah menjadi interval 10 menit dengan menghitung rata-rata jumlah perangkat terhubung untuk menyamakan skala waktu dengan data energi agar bisa dibandingkan langsung.
Resample WiFi data (mean of Associated Client Count)
wifi <- wifi %>%
group_by(time) %>%
summarise(mean_client_count = mean(`Associated Client Count`, na.rm = TRUE)) %>%
ungroup()
print("WiFi resampled (mean client count per 10 minutes):")## [1] "WiFi resampled (mean client count per 10 minutes):"print(head(wifi))## # A tibble: 6 × 2
## time mean_client_count
## <dttm> <dbl>
## 1 2020-02-01 00:00:00 22.8
## 2 2020-02-01 00:10:00 20.6
## 3 2020-02-01 00:20:00 17.2
## 4 2020-02-01 00:30:00 15.4
## 5 2020-02-01 00:40:00 12.4
## 6 2020-02-01 00:50:00 10.5Interpretasi : Data resampling menunjukkan bahwa pada awal tanggal 1 Februari 2020 (pukul 00.10–01.00), jumlah rata-rata pengguna WiFi di Library menurun dari sekitar 20 client menjadi hanya 9 client dalam waktu kurang dari satu jam. Hal ini mengindikasikan tren penurunan aktivitas pengguna WiFi pada malam hari.
Aggregate library meters into total electricity rate per 10 minutes
library(readr)
lib1 <- read_csv("C:/Users/ZUMROTUS SOLICHA/Documents/MK SEMESTER 5/DATMIN/library1.csv")## Rows: 18864 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): name, units
## dbl (3): reading, cumulative, rate
## dttm (1): ts
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.lib2 <- read_csv("C:/Users/ZUMROTUS SOLICHA/Documents/MK SEMESTER 5/DATMIN/library2.csv")## Rows: 18864 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): name, units
## dbl (3): reading, cumulative, rate
## dttm (1): ts
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.lib3 <- read_csv("C:/Users/ZUMROTUS SOLICHA/Documents/MK SEMESTER 5/DATMIN/library3.csv")## Rows: 18864 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): name, units
## dbl (3): reading, cumulative, rate
## dttm (1): ts
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.print("Struktur timestamp dari masing-masing listrik:")## [1] "Struktur timestamp dari masing-masing listrik:"print(str(lib1$ts))## POSIXct[1:18864], format: "2020-01-01 00:00:00" "2020-01-01 00:10:00" "2020-01-01 00:20:00" ...
## NULLprint(str(lib2$ts))## POSIXct[1:18864], format: "2020-01-01 00:00:00" "2020-01-01 00:10:00" "2020-01-01 00:20:00" ...
## NULLprint(str(lib3$ts))## POSIXct[1:18864], format: "2020-01-01 00:00:00" "2020-01-01 00:10:00" "2020-01-01 00:20:00" ...
## NULLInterpretasi : Ketiga dataset listrik (lib1, lib2, lib3) memiliki struktur waktu yang konsisten: format POSIXct, interval 10 menit, panjang data sama (18.864 observasi). Hal ini menunjukkan bahwa data siap untuk digabungkan/diakumulasikan menjadi total konsumsi listrik perpustakaan per 10 menit.
Data Cleaning
Address missing electricity readings
impute_with_first144 <- function(df, col_name) {
mean_val <- mean(df[[col_name]][1:144], na.rm = TRUE)
df[[col_name]][is.na(df[[col_name]])] <- mean_val
return(df)
}
lib1 <- impute_with_first144(lib1, "rate")
lib2 <- impute_with_first144(lib2, "rate")
lib3 <- impute_with_first144(lib3, "rate")
print("Contoh data listrik setelah imputasi missing value:")## [1] "Contoh data listrik setelah imputasi missing value:"print(head(lib1))## # A tibble: 6 × 6
## ts name reading units cumulative rate
## <dttm> <chr> <dbl> <chr> <dbl> <dbl>
## 1 2020-01-01 00:00:00 MC065-L01/M9R2048 1489442 KWh 1489442 7.38
## 2 2020-01-01 00:10:00 MC065-L01/M9R2048 1489449 KWh 1489449 7
## 3 2020-01-01 00:20:00 MC065-L01/M9R2048 1489456 KWh 1489456 7
## 4 2020-01-01 00:30:00 MC065-L01/M9R2048 1489464 KWh 1489464 8
## 5 2020-01-01 00:40:00 MC065-L01/M9R2048 1489471 KWh 1489471 7
## 6 2020-01-01 00:50:00 MC065-L01/M9R2048 1489479 KWh 1489479 8Interpretasi : Setelah dilakukan imputasi dengan rata-rata 144 data pertama, dataset listrik kini bersih dari missing value dan siap digunakan untuk analisis lebih lanjut (misalnya forecasting, pola harian, dsb.).
Standardize timestamps and remove duplicates
library(dplyr)
electricity <- full_join(lib1, lib2, by = "ts", suffix = c("_1", "_2")) %>%
full_join(lib3, by = "ts") %>%
distinct()
# Hitung total electricity
electricity <- electricity %>%
mutate(total_electricity = rate_1 + rate_2 + rate)
print("Data listrik gabungan + total:")## [1] "Data listrik gabungan + total:"print(head(electricity))## # A tibble: 6 × 17
## ts name_1 reading_1 units_1 cumulative_1 rate_1 name_2
## <dttm> <chr> <dbl> <chr> <dbl> <dbl> <chr>
## 1 2020-01-01 00:00:00 MC065-L01/M9… 1489442 KWh 1489442 7.38 MC065…
## 2 2020-01-01 00:10:00 MC065-L01/M9… 1489449 KWh 1489449 7 MC065…
## 3 2020-01-01 00:20:00 MC065-L01/M9… 1489456 KWh 1489456 7 MC065…
## 4 2020-01-01 00:30:00 MC065-L01/M9… 1489464 KWh 1489464 8 MC065…
## 5 2020-01-01 00:40:00 MC065-L01/M9… 1489471 KWh 1489471 7 MC065…
## 6 2020-01-01 00:50:00 MC065-L01/M9… 1489479 KWh 1489479 8 MC065…
## # ℹ 10 more variables: reading_2 <dbl>, units_2 <chr>, cumulative_2 <dbl>,
## # rate_2 <dbl>, name <chr>, reading <dbl>, units <chr>, cumulative <dbl>,
## # rate <dbl>, total_electricity <dbl>Interpretasi : Menghasilkan dataset lengkap yang menggabungkan ketiga sumber data listrik menjadi satu tabel, dengan tambahan kolom total konsumsi (total_electricity). Data ini siap dipakai untuk analisis lebih lanjut seperti tren penggunaan listrik, forecasting, maupun perbandingan antar sumber energi.
Visualization of Occupancy–Energy Relationship
Gabungkan wifi & listrik
library(dplyr)
wifi <- wifi %>% rename(ts = time)
df <- inner_join(wifi, electricity, by = "ts")
print("Data gabungan WiFi dan listrik:")## [1] "Data gabungan WiFi dan listrik:"print(head(df))## # A tibble: 6 × 18
## ts mean_client_count name_1 reading_1 units_1 cumulative_1
## <dttm> <dbl> <chr> <dbl> <chr> <dbl>
## 1 2020-02-01 00:00:00 22.8 MC065-L0… 1558154 KWh 1558154
## 2 2020-02-01 00:10:00 20.6 MC065-L0… 1558167 KWh 1558167
## 3 2020-02-01 00:20:00 17.2 MC065-L0… 1558180 KWh 1558180
## 4 2020-02-01 00:30:00 15.4 MC065-L0… 1558191 KWh 1558191
## 5 2020-02-01 00:40:00 12.4 MC065-L0… 1558202 KWh 1558202
## 6 2020-02-01 00:50:00 10.5 MC065-L0… 1558213 KWh 1558213
## # ℹ 12 more variables: rate_1 <dbl>, name_2 <chr>, reading_2 <dbl>,
## # units_2 <chr>, cumulative_2 <dbl>, rate_2 <dbl>, name <chr>, reading <dbl>,
## # units <chr>, cumulative <dbl>, rate <dbl>, total_electricity <dbl>Interpretasi : Data hasil penggabungan WiFi dan listrik menunjukkan jumlah rata-rata pengguna WiFi pada setiap waktu tertentu beserta data konsumsi listrik yang terkait. Dengan data ini, kita dapat melihat bagaimana tingkat okupansi (jumlah pengguna) berkorelasi dengan penggunaan energi pada waktu yang sama.
Time Series Plots
print("Plot Time Series Occupancy vs Electricity")## [1] "Plot Time Series Occupancy vs Electricity"library(ggplot2)
ggplot(df, aes(x = ts)) +
geom_line(aes(y = mean_client_count, color = "Occupancy")) +
geom_line(aes(y = total_electricity, color = "Electricity")) +
labs(title = "Time Series: Occupancy vs Electricity",
x = "Time", y = "Value", color = "Legend") +
theme_minimal()
Interpretasi : Pola keduanya bergerak searah, saat okupansi meningkat,
konsumsi energi juga cenderung naik. Ada periode tertentu energi tetap
tinggi meskipun okupansi menurun, menandakan adanya konsumsi dasar
(baseline) seperti AC dan server
Scatter Plot
print("Scatter Plot Occupancy vs Electricity")## [1] "Scatter Plot Occupancy vs Electricity"library(ggplot2)
ggplot(df, aes(x = mean_client_count, y = total_electricity)) +
geom_point(alpha = 0.5, color = "blue") +
labs(title = "Scatterplot: Occupancy vs Electricity",
x = "Mean Client Count", y = "Total Electricity") +
theme_minimal()
Interpretasi : Scatter plot menunjukkan hubungan positif antara okupansi
dan konsumsi energi. Saat jumlah pengguna meningkat, konsumsi energi
juga naik, namun tidak secara linear. Setelah mencapai 150–200 pengguna,
konsumsi energi melambat dan cenderung stabil di kisaran 180–220 kWh,
menandakan adanya efek saturasi karena penambahan pengguna baru tidak
lagi meningkatkan konsumsi energi dan fasilitas sudah berjalan pada
kapasitas optimal
Daily Profiles (24h) + average line
library(dplyr)
library(lubridate)
library(ggplot2)
df <- df %>%
mutate(date = as_date(ts),
time_of_day = as_hms(ts))
print("Plot Daily Profile of Occupancy")## [1] "Plot Daily Profile of Occupancy"ggplot(df, aes(x = time_of_day, y = mean_client_count, group = date)) +
geom_line(alpha = 0.2, color = "blue") +
stat_summary(fun = mean, geom = "line", aes(group = 1),
color = "red", linewidth = 1.2) +
labs(title = "Daily Profile of Occupancy",
x = "Time of Day", y = "Mean Client Count") +
theme_minimal()
Interpretasi : Grafik menunjukkan jumlah pengunjung meningkat sejak
pagi, memuncak siang hingga sore, lalu menurun di malam hari. Konsumsi
listrik mengikuti pola ini, namun tetap stabil di malam hari,
mengindikasikan adanya beban dasar seperti lampu, AC, dan server yang
terus menyala meski pengunjung berkurang
Analysis
Identify peak hours of occupancy
peak_hours <- df %>%
mutate(hour = hour(ts)) %>%
group_by(hour) %>%
summarise(avg_occupancy = mean(mean_client_count, na.rm = TRUE)) %>%
arrange(desc(avg_occupancy))
print("Peak hours of occupancy:")## [1] "Peak hours of occupancy:"print(peak_hours)## # A tibble: 24 × 2
## hour avg_occupancy
## <int> <dbl>
## 1 15 370.
## 2 14 357.
## 3 16 346.
## 4 13 331.
## 5 12 300.
## 6 17 292.
## 7 11 260.
## 8 18 233.
## 9 19 193.
## 10 10 183.
## # ℹ 14 more rowsInterpretasi : Hasil analisis menunjukkan bahwa tingkat okupansi tertinggi terjadi pada pukul 15.00 dengan rata-rata sekitar 370 pengguna tersambung, diikuti pukul 14.00 dan 16.00. Ini mengindikasikan bahwa aktivitas pengguna mencapai puncaknya pada pertengahan hingga sore hari.
Correlation
correlation <- cor(df$mean_client_count, df$total_electricity, use = "complete.obs")
print("Korelasi Occupancy vs Electricity:")## [1] "Korelasi Occupancy vs Electricity:"print(correlation)## [1] 0.8782117Interpretasi : Terdapat korelasi sangat kuat antara okupansi dan konsumsi energi, menandakan hubungan yang signifikan.
Highlight anomaly cases
print("Kasus di mana listrik tinggi tapi occupancy rendah:")## [1] "Kasus di mana listrik tinggi tapi occupancy rendah:"df %>%
filter(
mean_client_count < quantile(mean_client_count, 0.25, na.rm = TRUE) &
total_electricity > quantile(total_electricity, 0.75, na.rm = TRUE)
) %>%
head()## # A tibble: 0 × 20
## # ℹ 20 variables: ts <dttm>, mean_client_count <dbl>, name_1 <chr>,
## # reading_1 <dbl>, units_1 <chr>, cumulative_1 <dbl>, rate_1 <dbl>,
## # name_2 <chr>, reading_2 <dbl>, units_2 <chr>, cumulative_2 <dbl>,
## # rate_2 <dbl>, name <chr>, reading <dbl>, units <chr>, cumulative <dbl>,
## # rate <dbl>, total_electricity <dbl>, date <date>, time_of_day <time>Interpretasi : Hasil ini menunjukkan bahwa tidak ditemukan pola anomali signifikan, sehingga konsumsi listrik umumnya sejalan dengan tingkat okupansi. Dengan kata lain, saat sedikit pengguna yang terhubung WiFi, pemakaian listrik juga tidak tinggi.
Weekend vs Weekday Comparison
library(lubridate)
library(dplyr)
df <- df %>%
mutate(
weekday = wday(ts, label = TRUE),
is_weekend = ifelse(weekday %in% c("Sat", "Sun"), "Weekend", "Weekday")
)
head(df)## # A tibble: 6 × 22
## ts mean_client_count name_1 reading_1 units_1 cumulative_1
## <dttm> <dbl> <chr> <dbl> <chr> <dbl>
## 1 2020-02-01 00:00:00 22.8 MC065-L0… 1558154 KWh 1558154
## 2 2020-02-01 00:10:00 20.6 MC065-L0… 1558167 KWh 1558167
## 3 2020-02-01 00:20:00 17.2 MC065-L0… 1558180 KWh 1558180
## 4 2020-02-01 00:30:00 15.4 MC065-L0… 1558191 KWh 1558191
## 5 2020-02-01 00:40:00 12.4 MC065-L0… 1558202 KWh 1558202
## 6 2020-02-01 00:50:00 10.5 MC065-L0… 1558213 KWh 1558213
## # ℹ 16 more variables: rate_1 <dbl>, name_2 <chr>, reading_2 <dbl>,
## # units_2 <chr>, cumulative_2 <dbl>, rate_2 <dbl>, name <chr>, reading <dbl>,
## # units <chr>, cumulative <dbl>, rate <dbl>, total_electricity <dbl>,
## # date <date>, time_of_day <time>, weekday <ord>, is_weekend <chr>Interpretasi :
Occupancy Weekday vs Weekend
library(dplyr)
library(lubridate)
library(ggplot2)
# Tambahkan kolom hari dan tipe hari
df <- df %>%
mutate(
day_of_week = wday(ts, week_start = 1) - 1, # 0 = Senin, 6 = Minggu
day_type = ifelse(day_of_week < 5, "Weekday", "Weekend")
)
# Pisahkan ke dua DataFrame
weekday_df <- df %>% filter(day_type == "Weekday")
weekend_df <- df %>% filter(day_type == "Weekend")
# Fungsi plot time series
plot_time_series <- function(data, title) {
ggplot(data, aes(x = ts)) +
geom_line(aes(y = mean_client_count, color = "Occupancy (WiFi)"), alpha = 0.7) +
geom_line(aes(y = total_electricity, color = "Total Energy"), alpha = 0.7) +
labs(
title = paste(title, ": Time Series"),
x = "Time",
y = "Value",
color = "Legend"
) +
theme_minimal()
}
# Plot untuk Weekday dan Weekend
plot_time_series(weekday_df, "Weekday")
plot_time_series(weekend_df, "Weekend")
Interpretasi : Grafik menunjukkan bahwa pada hari kerja (weekday), pola
okupansi dan konsumsi energi berulang secara teratur dengan nilai yang
tinggi, sedangkan pada akhir pekan (weekend) okupansi lebih rendah dan
energi cenderung menurun meski tetap digunakan. Hal ini menandakan
keterkaitan erat antara tingkat okupansi dan konsumsi energi
bangunan.
Electricity Weekday vs Weekend
library(ggplot2)
# Scatter plot untuk Weekday
ggplot(weekday_df, aes(x = mean_client_count, y = total_electricity)) +
geom_point(alpha = 0.4, color = "blue") +
labs(
title = "Weekday: Occupancy vs Energy",
x = "Occupancy (WiFi Clients)",
y = "Total Energy (kWh)"
) +
theme_minimal()
# Scatter plot untuk Weekend
ggplot(weekend_df, aes(x = mean_client_count, y = total_electricity)) +
geom_point(alpha = 0.4, color = "red") +
labs(
title = "Weekend: Occupancy vs Energy",
x = "Occupancy (WiFi Clients)",
y = "Total Energy (kWh)"
) +
theme_minimal()
Interpretasi : Grafik menunjukkan bahwa peningkatan jumlah okupansi
(WiFi Clients) cenderung meningkatkan konsumsi energi total (kWh),
meskipun hubungan tersebut melandai pada tingkat okupansi tinggi. Hal
ini mengindikasikan adanya titik jenuh di mana tambahan okupansi tidak
lagi signifikan menambah konsumsi energi.
Scatter Weekday vs Weekend
library(dplyr)
library(ggplot2)
library(lubridate)
library(tidyr)
# Pastikan kolom bantu ada
df <- df %>%
mutate(
time_of_day = format(ts, "%H:%M"),
date = as.Date(ts),
weekday = wday(ts, label = TRUE, abbr = TRUE),
is_weekend = ifelse(weekday %in% c("Sat","Sun"), "weekend", "weekday")
)
weekday_df <- df %>% filter(is_weekend == "weekday")
weekend_df <- df %>% filter(is_weekend == "weekend")
plot_daily_profile <- function(data, value_col, title, color){
if(nrow(data) == 0){
message(paste("⚠️ Tidak ada data untuk", title))
return(NULL)
}
# Hitung rata-rata per jam per hari
profile <- data %>%
group_by(date, time_of_day) %>%
summarise(value = mean(.data[[value_col]], na.rm = TRUE), .groups = "drop") %>%
tidyr::pivot_wider(names_from = date, values_from = value)
# Ubah ke long
if(ncol(profile) <= 2){
# Kalau hanya ada 1 tanggal
profile_long <- data %>%
group_by(time_of_day) %>%
summarise(value = mean(.data[[value_col]], na.rm = TRUE), .groups = "drop") %>%
mutate(date = unique(data$date)[1])
} else {
profile_long <- profile %>%
tidyr::pivot_longer(-time_of_day, names_to = "date", values_to = "value")
}
# Hitung rata-rata semua hari
avg_profile <- profile_long %>%
group_by(time_of_day) %>%
summarise(mean_value = mean(value, na.rm = TRUE), .groups = "drop")
# Plot
ggplot() +
geom_line(data = profile_long, aes(x = time_of_day, y = value, group = date),
color = color, alpha = 0.3) +
geom_line(data = avg_profile, aes(x = time_of_day, y = mean_value),
color = "black", linewidth = 1) +
labs(title = paste(title, "-", value_col),
x = "Time of Day", y = value_col) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
}
# === Weekday ===
plot_daily_profile(weekday_df, "mean_client_count", "Weekday Daily Profile", "blue")## Warning: Removed 61 rows containing missing values or values outside the scale range
## (`geom_line()`).## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
plot_daily_profile(weekday_df, "total_electricity", "Weekday Daily Profile", "red")## Warning: Removed 61 rows containing missing values or values outside the scale range
## (`geom_line()`).## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
# === Weekend ===
plot_daily_profile(weekend_df, "mean_client_count", "Weekend Daily Profile", "blue")## ⚠️ Tidak ada data untuk Weekend Daily Profile## NULLplot_daily_profile(weekend_df, "total_electricity", "Weekend Daily Profile", "red")## ⚠️ Tidak ada data untuk Weekend Daily Profile## NULLInterpretasi : Profil harian menunjukkan bahwa konsumsi listrik total dan jumlah klien WiFi sama-sama meningkat pada jam kerja, dengan puncak sekitar tengah hari hingga sore. Tren ini mengindikasikan bahwa aktivitas pengguna sangat memengaruhi kebutuhan energi di hari kerja.
# Cek jumlah baris weekend_df
cat("Jumlah baris weekend_df:", nrow(weekend_df), "\n")## Jumlah baris weekend_df: 0# Hitung rata-rata weekday
weekday_avg <- colMeans(
weekday_df[, c("mean_client_count", "total_electricity")],
na.rm = TRUE
)
# Hitung rata-rata weekend
weekend_avg <- colMeans(
weekend_df[, c("mean_client_count", "total_electricity")],
na.rm = TRUE
)
# Cetak hasil
cat("Rata-rata Weekday:\n")## Rata-rata Weekday:print(weekday_avg)## mean_client_count total_electricity
## 147.9898 143.1979cat("\nRata-rata Weekend:\n")##
## Rata-rata Weekend:print(weekend_avg)## mean_client_count total_electricity
## NaN NaNInterpretasi : Hasil menunjukkan bahwa rata-rata okupansi (147,99) dan konsumsi listrik (143,20 kWh) pada hari kerja lebih tinggi, sedangkan pada akhir pekan datanya kosong/tidak tersedia. Hal ini mengindikasikan aktivitas utama pengguna lebih dominan terjadi pada hari kerja.
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.