PS 5: Preliminary results
Luke Heller & Valerie Larsen
2026-03-05
Last updated: 2026-03-23
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Knit directory: dickinson_power/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 38132bb | maggiedouglas | 2026-03-11 | add student draft results |
| html | 38132bb | maggiedouglas | 2026-03-11 | add student draft results |
Data preparation
Libraries
library(tidyverse)
library(DT)Annual electricity data
annual_kwh <- read.csv("./output/kwh_annual_2026-03-04.csv")
reshall_s_annual <- annual_kwh %>%
filter(type == "Res Hall - S")
reshall_s_annual$NAME <- replace_values(reshall_s_annual$NAME, "135 N. College St. 139 N. College St." ~ "135-139 N. College St.", "49 S. College St.51 S. College St." ~ "49-51 S. College St.") # update names of meters on N. College St. and S. College St. to be more concise
str(reshall_s_annual)
kwh_dollar <- .08138507
kwh_ghg <- .30082405
annual_transform <- reshall_s_annual %>%
group_by(NAME) %>%
summarize(meter, days_perc, kwh_corr, sqft, occupants) %>%
mutate(kwh_corr = round(kwh_corr, digits = 0),
cost = paste("$", round((kwh_corr*kwh_dollar), digits = 0)),
ghg_emis = round((kwh_corr*kwh_ghg/1000), digits = 1),
kwh_sqft_annual = round((kwh_corr/sqft), digits = 1),
kwh_occupants_annual = round((kwh_corr/occupants), digits = 0))
annual_order <- annual_transform[, c("meter", "NAME", "days_perc", "kwh_corr", "sqft", "occupants", "kwh_sqft_annual", "kwh_occupants_annual", "cost", "ghg_emis")]
reshall_s_annual_final <- annual_order %>%
arrange(desc(kwh_corr))
str(reshall_s_annual_final)Daily electricity data
daily_kwh <- read.csv("./output/kwh_daily_2026-03-04.csv")
reshall_s_daily <- daily_kwh %>%
filter(type == "Res Hall - S") %>%
mutate(date = ymd(date),
month = month(date, label = TRUE),
day = wday(date, label = TRUE),
kwh_sqft_annual = round((kwh/sqft*365), digits = 1), # adjust daily values to per year equivalent
kwh_occupants_annual = round((kwh/occupants*365), digits = 0)) # adjust daily values to per year equivalent
reshall_s_daily$NAME <- replace_values(reshall_s_daily$NAME, "135 N. College St. 139 N. College St." ~ "135-139 N. College St.", "49 S. College St.51 S. College St." ~ "49-51 S. College St.") # update names of meters on N. College St. and S. College St. to be more concise
str(reshall_s_daily)Building type summary
Descriptive table
datatable(reshall_s_annual_final, filter = "top",
rownames = FALSE,
colnames = c("Meter", "Building", "Days of data (%)", "kWh", "sqft", "Occupants", "kWh per sqft", "kWh per person", "Cost - $", "CO2e - MT"),
caption = "Table 1. Descriptive statistics for annual electricity use, estimated financial cost, and estimated greenhouse gas emissions of the small residence halls.")Electricity use over the year
group_small <- reshall_s_daily %>%
group_by(NAME, month) %>%
summarize(total_kwh = sum(kwh))`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by NAME and month.
ℹ Output is grouped by NAME.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(NAME, month))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.ggplot(group_small, aes(x = month, y = reorder(NAME, total_kwh), fill = total_kwh)) +
geom_tile() +
scale_fill_gradient(low = "white", high = rgb(255, 64, 192, maxColorValue = 255)) +
labs(title = "Total Electricity Use by Month in Fiscal Year 2025") + theme_bw() + labs(x = "", y = "")
| Version | Author | Date |
|---|---|---|
| 38132bb | maggiedouglas | 2026-03-11 |
Electricity intensity
reshall_s_daily_academic_year <- reshall_s_daily %>%
filter(period %in% c("Spring", "Fall")) # filter data to only during the academic year when most students are on campus
ggplot(reshall_s_daily_academic_year,
aes(x = NAME, y = kwh_sqft_annual)) +
annotate("rect", xmin = -Inf, xmax = Inf, ymin = 8.2, ymax = 36.1, color = "lightgray", alpha = 0.3) + geom_hline(yintercept = 13.6, linetype = "dashed", color = "white") +
geom_boxplot(fill = rgb(255, 64, 192, maxColorValue = 255)) +
coord_flip() +
theme_bw() +
labs(title = "Electricity Intensity by Building", x = "", y = "Electricity Intensity (kWh/sqft/year)")
| Version | Author | Date |
|---|---|---|
| 38132bb | maggiedouglas | 2026-03-11 |
ggplot(reshall_s_daily,
aes(x = type, y = kwh_sqft_annual)) +
annotate("rect", xmin = -Inf, xmax = Inf, ymin = 8.2, ymax = 36.1, color = "lightgray", alpha = 0.3) + geom_hline(yintercept = 13.6, linetype = "dashed", color = "white") +
geom_boxplot(fill = rgb(255, 64, 192, maxColorValue = 255)) +
coord_flip() +
facet_grid(period ~ .) +
theme_bw() +
labs(title = "Electricity Intensity by Season", x = "", y = "Electricity Intensity (kWh/sqft/year)")
| Version | Author | Date |
|---|---|---|
| 38132bb | maggiedouglas | 2026-03-11 |
Partner contributions
Valerie did most of the work with the annual electricity dataset and created the descriptive table. Luke primarily focused on the daily electricity dataset, created the heat map and the box plots, troubleshooted the final code, and submitted the final problem set.
Other than the unusual name for the buildings located on N. and S. College St., neither of us noticed any major underlying issues in the data. The new data has uncovered new electricity use patterns which we did not notice previously and might be interested in investigating further later in the semester.
sessionInfo()R version 4.5.2 (2025-10-31)
Platform: x86_64-apple-darwin20
Running under: macOS Ventura 13.7.8
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
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time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DT_0.34.0 lubridate_1.9.5 forcats_1.0.1 stringr_1.6.0
[5] dplyr_1.2.0 purrr_1.2.1 readr_2.2.0 tidyr_1.3.2
[9] tibble_3.3.1 ggplot2_4.0.2 tidyverse_2.0.0 workflowr_1.7.2
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[49] farver_2.1.2 htmltools_0.5.9 labeling_0.4.3 rmarkdown_2.30
[53] compiler_4.5.2 getPass_0.2-4 S7_0.2.1