Analisis Hubungan Jumlah Pengunjung dan Konsumsi Energi Perpustakaan
Data Mining C Kelompok 5_Diffa 081_Nadia 173_Khairunnisa 186_Nawal 190
2025-09-14
knitr::opts_chunk$set(echo = TRUE)
library(readr)
library(tidyverse)
library(lubridate)
library(zoo)
library(broom)
library(scales)
library(ggplot2)1. Filter and Synchronize the Data
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wifi <- read_csv("C:/Users/Muhammad Nisar/Downloads/wifi.csv")## Rows: 1048575 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): time, Event Time, Uni, Building, Floor
## dbl (2): Associated Client Count, Authenticated Client Count
##
## 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.head(wifi)## # A tibble: 6 × 7
## time `Event Time` Associated Client Co…¹ Authenticated Client…² Uni
## <chr> <chr> <dbl> <dbl> <chr>
## 1 2/1/2020 0:02 Sat Feb 01 … 184 182 Lanc…
## 2 2/1/2020 0:02 Sat Feb 01 … 6 6 Lanc…
## 3 2/1/2020 0:02 Sat Feb 01 … 18 18 Lanc…
## 4 2/1/2020 0:02 Sat Feb 01 … 23 23 Lanc…
## 5 2/1/2020 0:02 Sat Feb 01 … 45 45 Lanc…
## 6 2/1/2020 0:02 Sat Feb 01 … 16 16 Lanc…
## # ℹ abbreviated names: ¹`Associated Client Count`,
## # ²`Authenticated Client Count`
## # ℹ 2 more variables: Building <chr>, Floor <chr>str(wifi)## spc_tbl_ [1,048,575 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ time : chr [1:1048575] "2/1/2020 0:02" "2/1/2020 0:02" "2/1/2020 0:02" "2/1/2020 0:02" ...
## $ Event Time : chr [1:1048575] "Sat Feb 01 00:02:12 UTC 2020" "Sat Feb 01 00:02:12 UTC 2020" "Sat Feb 01 00:02:12 UTC 2020" "Sat Feb 01 00:02:12 UTC 2020" ...
## $ Associated Client Count : num [1:1048575] 184 6 18 23 45 16 32 6 73 5 ...
## $ Authenticated Client Count: num [1:1048575] 182 6 18 23 45 16 32 6 73 5 ...
## $ Uni : chr [1:1048575] "Lancaster University" "Lancaster University" "Lancaster University" "Lancaster University" ...
## $ Building : chr [1:1048575] "Graduate College" "Management School" "SW hse 32-33" "SW hse 29" ...
## $ Floor : chr [1:1048575] "A Floor" "C Floor" "D Floor" "B floor" ...
## - attr(*, "spec")=
## .. cols(
## .. time = col_character(),
## .. `Event Time` = col_character(),
## .. `Associated Client Count` = col_double(),
## .. `Authenticated Client Count` = col_double(),
## .. Uni = col_character(),
## .. Building = col_character(),
## .. Floor = col_character()
## .. )
## - attr(*, "problems")=<externalptr>lib1 <- read_csv("C:/Users/Muhammad Nisar/Downloads/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/Muhammad Nisar/Downloads/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/Muhammad Nisar/Downloads/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.head(lib1); head(lib2); head(lib3)## # 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 NA
## 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 8## # 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/M11R2056 2129016 KWh 2129016 NA
## 2 2020-01-01 00:10:00 MC065-L01/M11R2056 2129034 KWh 2129034 18
## 3 2020-01-01 00:20:00 MC065-L01/M11R2056 2129054 KWh 2129054 20
## 4 2020-01-01 00:30:00 MC065-L01/M11R2056 2129071 KWh 2129071 17
## 5 2020-01-01 00:40:00 MC065-L01/M11R2056 2129086 KWh 2129086 15
## 6 2020-01-01 00:50:00 MC065-L01/M11R2056 2129103 KWh 2129103 17## # 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/M13R2064 6914209 KWh 6914209 NA
## 2 2020-01-01 00:10:00 MC065-L01/M13R2064 6914266 KWh 6914266 57
## 3 2020-01-01 00:20:00 MC065-L01/M13R2064 6914310 KWh 6914310 44
## 4 2020-01-01 00:30:00 MC065-L01/M13R2064 6914376 KWh 6914376 66
## 5 2020-01-01 00:40:00 MC065-L01/M13R2064 6914439 KWh 6914439 63
## 6 2020-01-01 00:50:00 MC065-L01/M13R2064 6914474 KWh 6914474 35str(lib1); str(lib2); str(lib3)## spc_tbl_ [18,864 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ts : POSIXct[1:18864], format: "2020-01-01 00:00:00" "2020-01-01 00:10:00" ...
## $ name : chr [1:18864] "MC065-L01/M9R2048" "MC065-L01/M9R2048" "MC065-L01/M9R2048" "MC065-L01/M9R2048" ...
## $ reading : num [1:18864] 1489442 1489449 1489456 1489464 1489471 ...
## $ units : chr [1:18864] "KWh" "KWh" "KWh" "KWh" ...
## $ cumulative: num [1:18864] 1489442 1489449 1489456 1489464 1489471 ...
## $ rate : num [1:18864] NA 7 7 8 7 8 7 8 7 8 ...
## - attr(*, "spec")=
## .. cols(
## .. ts = col_datetime(format = ""),
## .. name = col_character(),
## .. reading = col_double(),
## .. units = col_character(),
## .. cumulative = col_double(),
## .. rate = col_double()
## .. )
## - attr(*, "problems")=<externalptr>## spc_tbl_ [18,864 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ts : POSIXct[1:18864], format: "2020-01-01 00:00:00" "2020-01-01 00:10:00" ...
## $ name : chr [1:18864] "MC065-L01/M11R2056" "MC065-L01/M11R2056" "MC065-L01/M11R2056" "MC065-L01/M11R2056" ...
## $ reading : num [1:18864] 2129016 2129034 2129054 2129071 2129086 ...
## $ units : chr [1:18864] "KWh" "KWh" "KWh" "KWh" ...
## $ cumulative: num [1:18864] 2129016 2129034 2129054 2129071 2129086 ...
## $ rate : num [1:18864] NA 18 20 17 15 17 17 17 17 17 ...
## - attr(*, "spec")=
## .. cols(
## .. ts = col_datetime(format = ""),
## .. name = col_character(),
## .. reading = col_double(),
## .. units = col_character(),
## .. cumulative = col_double(),
## .. rate = col_double()
## .. )
## - attr(*, "problems")=<externalptr>## spc_tbl_ [18,864 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ts : POSIXct[1:18864], format: "2020-01-01 00:00:00" "2020-01-01 00:10:00" ...
## $ name : chr [1:18864] "MC065-L01/M13R2064" "MC065-L01/M13R2064" "MC065-L01/M13R2064" "MC065-L01/M13R2064" ...
## $ reading : num [1:18864] 6914209 6914266 6914310 6914376 6914439 ...
## $ units : chr [1:18864] "KWh" "KWh" "KWh" "KWh" ...
## $ cumulative: num [1:18864] 6914209 6914266 6914310 6914376 6914439 ...
## $ rate : num [1:18864] NA 57 44 66 63 35 47 55 34 41 ...
## - attr(*, "spec")=
## .. cols(
## .. ts = col_datetime(format = ""),
## .. name = col_character(),
## .. reading = col_double(),
## .. units = col_character(),
## .. cumulative = col_double(),
## .. rate = col_double()
## .. )
## - attr(*, "problems")=<externalptr>#1a. Filter: gunakan hanya data WiFi dari Library building
wifi_lib <- wifi %>%
filter(Building == "Library")
#1b. Standardisasi waktu ke interval 10 menit
wifi_lib <- wifi_lib %>%
mutate(
time = mdy_hm(time), # ubah dari character ke POSIXct
time = floor_date(time, unit = "10 minutes") # bulatkan ke 10 menit
)
str(wifi_lib$time)## POSIXct[1:15948], format: "2020-02-01 00:00:00" "2020-02-01 00:00:00" "2020-02-01 00:00:00" ...head(wifi_lib$time, 10)## [1] "2020-02-01 00:00:00 UTC" "2020-02-01 00:00:00 UTC"
## [3] "2020-02-01 00:00:00 UTC" "2020-02-01 00:00:00 UTC"
## [5] "2020-02-01 00:00:00 UTC" "2020-02-01 00:00:00 UTC"
## [7] "2020-02-01 00:00:00 UTC" "2020-02-01 00:00:00 UTC"
## [9] "2020-02-01 00:10:00 UTC" "2020-02-01 00:10:00 UTC"# 1c. Resample WiFi data: rata-rata jumlah client tiap 10 menit
colnames(wifi_lib)## [1] "time" "Event Time"
## [3] "Associated Client Count" "Authenticated Client Count"
## [5] "Uni" "Building"
## [7] "Floor"wifi_10min <- wifi_lib %>%
group_by(time) %>%
summarise(occupancy = mean(`Associated Client Count`, na.rm = TRUE)) %>%
arrange(time)
head(wifi_10min, 10)## # A tibble: 10 × 2
## time occupancy
## <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 10.5
## 7 2020-02-01 01:00:00 9.25
## 8 2020-02-01 01:10:00 8.67
## 9 2020-02-01 01:20:00 8.5
## 10 2020-02-01 01:30:00 8.38#1d. Gabungkan library meters → total electricity per 10 menit
# memastikan timestamp di tiap dataset (lib1, lib2, lib3) diparse dulu
colnames(lib1)## [1] "ts" "name" "reading" "units" "cumulative"
## [6] "rate"head(lib1, 5)## # A tibble: 5 × 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 NA
## 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 7lib1 <- lib1 %>%
mutate(time = floor_date(ts, "10 minutes"))
lib2 <- lib2 %>%
mutate(time = floor_date(ts, "10 minutes"))
lib3 <- lib3 %>%
mutate(time = floor_date(ts, "10 minutes"))
#Gabung total Energy
energy_10min <- lib1 %>%
select(time, rate1 = rate) %>%
full_join(lib2 %>% select(time, rate2 = rate), by = "time") %>%
full_join(lib3 %>% select(time, rate3 = rate), by = "time") %>%
mutate(total_energy = rowSums(across(starts_with("rate")), na.rm = TRUE)) %>%
arrange(time)
head(energy_10min, 10)## # A tibble: 10 × 5
## time rate1 rate2 rate3 total_energy
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-01 00:00:00 NA NA NA 0
## 2 2020-01-01 00:10:00 7 18 57 82
## 3 2020-01-01 00:20:00 7 20 44 71
## 4 2020-01-01 00:30:00 8 17 66 91
## 5 2020-01-01 00:40:00 7 15 63 85
## 6 2020-01-01 00:50:00 8 17 35 60
## 7 2020-01-01 01:00:00 7 17 47 71
## 8 2020-01-01 01:10:00 8 17 55 80
## 9 2020-01-01 01:20:00 7 17 34 58
## 10 2020-01-01 01:30:00 8 17 41 66##2. Data Cleaning
#2a. Isi Missing Values rata-rata 144 observasi pertama
fill_mean <- function(x) {
avg <- mean(head(x, 144), na.rm = TRUE)
zoo::na.fill(x, fill = avg)
}
energy_10min <- energy_10min %>%
mutate(across(starts_with("rate"), fill_mean),
total_energy = rowSums(across(starts_with("rate")), na.rm = TRUE))
#2b. Standarisasi
wifi_10min <- wifi_10min %>%
distinct(time, .keep_all = TRUE)
energy_10min <- energy_10min %>%
distinct(time, .keep_all = TRUE)
#2c. Dokumentasi Cleaning
cat("Cleaning Steps:\n")## Cleaning Steps:cat("1. Missing energy readings diisi dengan mean dari 144 observasi pertama.\n")## 1. Missing energy readings diisi dengan mean dari 144 observasi pertama.cat("2. Timestamp dibulatkan ke 10 menit (wifi & energy).\n")## 2. Timestamp dibulatkan ke 10 menit (wifi & energy).cat("3. Duplicate records dihapus.\n")## 3. Duplicate records dihapus.data_final <- wifi_10min %>%
inner_join(energy_10min %>% select(time, total_energy), by = "time")
head(data_final, 10)## # A tibble: 10 × 3
## time occupancy total_energy
## <dttm> <dbl> <dbl>
## 1 2020-02-01 00:00:00 22.8 64.8
## 2 2020-02-01 00:10:00 20.6 97
## 3 2020-02-01 00:20:00 17.2 96
## 4 2020-02-01 00:30:00 15.4 100
## 5 2020-02-01 00:40:00 12.4 89
## 6 2020-02-01 00:50:00 10.5 91
## 7 2020-02-01 01:00:00 9.25 90
## 8 2020-02-01 01:10:00 8.67 92
## 9 2020-02-01 01:20:00 8.5 87
## 10 2020-02-01 01:30:00 8.38 83##3. Visualisasi
## 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.
## `geom_smooth()` using formula = 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: data_final$occupancy and data_final$total_energy
## t = 79.016, df = 2053, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8563791 0.8778089
## sample estimates:
## cor
## 0.8674959
##4. Analisis
#4a. Identifikasi peak hours of occupancy
# Peak hours occupancy
peak_hours <- data_final %>%
mutate(hour = hour(time)) %>%
group_by(hour) %>%
summarise(avg_occ = mean(occupancy, na.rm = TRUE), .groups = "drop") %>%
arrange(desc(avg_occ))
peak_hours %>% head(5) # top (5) jam tertinggi## # A tibble: 5 × 2
## hour avg_occ
## <int> <dbl>
## 1 15 362.
## 2 14 355.
## 3 16 334.
## 4 13 330.
## 5 12 298.ggplot(peak_hours, aes(x = hour, y = avg_occ)) +
geom_col(fill = "violet") +
labs(title = "Average Occupancy by Hour",
x = "Hour of Day", y = "Average Occupancy") +
theme_minimal()
#4b. Examine whether occupancy significantly influences energy consumption
# Scatter occupancy vs energy + regresi
ggplot(data_final, aes(x = occupancy, y = total_energy)) +
geom_point(alpha = 0.3, color = "green") +
geom_smooth(method = "lm", se = TRUE, color = "darkgreen") +
labs(title = "Relationship between Occupancy and Energy Consumption",
x = "Occupancy", y = "Total Energy") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
#Uji Signifikan
model <- lm(total_energy ~ occupancy, data = data_final)
summary(model)##
## Call:
## lm(formula = total_energy ~ occupancy, data = data_final)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57.455 -15.955 1.376 16.868 50.975
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.039e+02 6.677e-01 155.60 <2e-16 ***
## occupancy 2.626e-01 3.324e-03 79.02 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.36 on 2053 degrees of freedom
## Multiple R-squared: 0.7525, Adjusted R-squared: 0.7524
## F-statistic: 6244 on 1 and 2053 DF, p-value: < 2.2e-16#4c. Highlight cases where energy consumption does not align with occupancy
colnames(data_final)## [1] "time" "occupancy" "total_energy"# Deteksi anomali: energi tinggi, occupancy rendah
threshold_occ <- quantile(data_final$occupancy, 0.25, na.rm = TRUE) # 25% occupancy terendah
threshold_energy <- quantile(data_final$total_energy, 0.75, na.rm = TRUE) # 25% energi tertinggi
anomalies <- data_final %>%
filter(occupancy < threshold_occ, total_energy > threshold_energy)
head(anomalies, 10)## # A tibble: 0 × 3
## # ℹ 3 variables: time <dttm>, occupancy <dbl>, total_energy <dbl>ggplot(data_final, aes(x = occupancy, y = total_energy)) +
geom_point(alpha = 0.3, color = "gray") +
geom_point(data = anomalies, aes(x = occupancy, y = total_energy),
color = "darkred", size = 2) +
labs(title = "Cases where Energy vs Occupancy (Anomalies Highlighted)",
x = "Occupancy", y = "Total Energy") +
theme_minimal()
#Lebih lanjut
# Hitung threshold
threshold_occ <- quantile(data_final$occupancy, 0.25, na.rm = TRUE)
threshold_energy <- quantile(data_final$total_energy, 0.75, na.rm = TRUE)
# Tandai anomali
data_final <- data_final %>%
mutate(anomaly = ifelse(occupancy < threshold_occ & total_energy > threshold_energy, "Anomaly", "Normal"))
# Plot dengan highlight
ggplot(data_final, aes(x = occupancy, y = total_energy, color = anomaly)) +
geom_point(alpha = 0.6) +
scale_color_manual(values = c("Normal" = "chocolate", "Anomaly" = "red")) +
labs(title = "Cases where Energy Vs Occupancy (Anomalies Highlighted)",
x = "Occupancy", y = "Total Energy") +
theme_minimal()
##5. Weekday vs Weekend
#5a. Separate weekdays vs weekends
library(dplyr)
library(lubridate)
data_final <- data_final %>%
mutate(day_type = if_else(wday(time, week_start = 1) <= 5, "Weekday", "Weekend"))
#5b. Visualisasi Ulanh
library(ggplot2)
# Scatter plot dibandingkan
ggplot(data_final, aes(x = occupancy, y = total_energy, color = day_type)) +
geom_point(alpha = 0.3) +
scale_color_manual(values = c("Weekday" = "orange", "Weekend" = "darkblue")) +
labs(title = "Occupancy vs Energy: Weekday vs Weekend",
x = "Occupancy", y = "Total Energy", color = "Day Type") +
theme_minimal()
# Daily profiles (average overlay)
avg_profile_daytype <- data_final %>%
mutate(tod = hour(time) + minute(time)/60) %>%
group_by(day_type, tod) %>%
summarise(avg_occ = mean(occupancy, na.rm = TRUE),
avg_energy = mean(total_energy, na.rm = TRUE), .groups = "drop")
ggplot(avg_profile_daytype, aes(x = tod)) +
geom_line(aes(y = avg_occ, color = day_type), size = 1) +
labs(title = "Average Daily Profile: Occupancy (Weekday vs Weekend)",
x = "Hour of Day", y = "Occupancy", color = "Day Type") +
theme_minimal()
ggplot(avg_profile_daytype, aes(x = tod)) +
geom_line(aes(y = avg_energy, color = day_type), size = 1) +
labs(title = "Average Daily Profile: Energy (Weekday vs Weekend)",
x = "Hour of Day", y = "Total Energy", color = "Day Type") +
theme_minimal()
# Gabungan Occupancy & Energy (Weekday vs Weekend)
# Pastikan ada kolom time of day & day_type
library(lubridate)
data_final <- data_final %>%
mutate(
day_type = if_else(wday(time, week_start = 1) <= 5, "Weekday", "Weekend"),
tod = hour(time) + minute(time) / 60
)
avg_profile_bytype <- data_final %>%
group_by(day_type, tod) %>%
summarise(
avg_occ = mean(occupancy, na.rm = TRUE),
avg_energy = mean(total_energy, na.rm = TRUE),
.groups = "drop"
)
ggplot(avg_profile_bytype, aes(x = tod)) +
geom_line(aes(y = avg_occ, color = paste(day_type, "Occupancy")), size = 1.2) +
geom_line(aes(y = avg_energy, color = paste(day_type, "Energy")), size = 1.2) +
scale_color_manual(
values = c(
"Weekday Occupancy" = "blue",
"Weekday Energy" = "darkorange",
"Weekend Occupancy" = "darkblue",
"Weekend Energy" = "darkred"
)
) +
labs(
title = "Average Daily Profiles: Occupancy vs Energy (Weekday vs Weekend)",
x = "Hour of Day", y = "Value", color = "Legend"
) +
theme_minimal()
# Ringkasan
summary_stats <- data_final %>%
group_by(day_type) %>%
summarise(
avg_occ = mean(occupancy, na.rm = TRUE),
avg_energy = mean(total_energy, na.rm = TRUE),
median_occ = median(occupancy, na.rm = TRUE),
median_energy = median(total_energy, na.rm = TRUE),
.groups = "drop"
)
print(summary_stats)## # A tibble: 2 × 5
## day_type avg_occ avg_energy median_occ median_energy
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Weekday 169. 146. 118. 158.
## 2 Weekend 80.0 130. 47.1 129