PS 5: Preliminary results
Claire Raesly and Liv Kacar
2026-03-04
Last updated: 2026-03-12
Checks: 6 1
Knit directory: dickinson_power/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d9d0396 | maggiedouglas | 2026-03-12 | update scripts to show Lindsey options |
| html | d9d0396 | maggiedouglas | 2026-03-12 | update scripts to show Lindsey options |
| Rmd | 0a9f663 | maggiedouglas | 2026-03-11 | added alternate versions for stacked bar graphs - large Res Halls |
| html | 0a9f663 | maggiedouglas | 2026-03-11 | added alternate versions for stacked bar graphs - large Res Halls |
| 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)
library(paletteer)
is_outlier <- function(x) {
return(x < quantile(x, 0.25, na.rm = T) - 1.5 * IQR(x, na.rm = T) | x > quantile(x, 0.75, na.rm = T) + 1.5 * IQR(x, na.rm = T))
}Annual electricity data
annual <- read.csv("./output/kwh_annual_2026-03-12.csv")
str(annual)
cost_conversion <- 0.08138507
ghg_conversion <- 0.30082405 / 1000
annual_large_res <- annual %>%
filter(type == "Res Hall - L") %>%
mutate("Cost - $" = round(kwh_corr * cost_conversion, digits = 0),
"CO2e - MT" = round(kwh_corr * ghg_conversion, digits = 0),
"kWh per sqft" = round(kwh_corr/sqft, digits = 1),
"kWh per person" = round(kwh_corr/occupants, digits = 0),
"Days of data (%)" = round(days_perc, digits = 0),
"kWh" = round(kwh, digits = 0),
"Corrected kWh" = round(kwh_corr, digits = 0),
"Meter" = meter,
"Building" = NAME) %>%
arrange(desc(kwh_corr)) %>%
select("Meter", "Building", "Days of data (%)", "kWh", "Corrected kWh", "sqft", "kWh per sqft", "kWh per person", "Cost - $", "CO2e - MT")
str(annual_large_res)
summary(annual_large_res)Daily electricity data
daily <- read.csv("./output/kwh_daily_2026-03-12.csv")
str(daily)
daily_large_res <- daily %>%
filter(type == "Res Hall - L") %>%
mutate(date = ymd(date),
month = month(date, label = TRUE),
day = wday(date, label = TRUE)) %>%
mutate(kwh_tot_yr = kwh*365,
kwh_sqft = kwh_tot_yr/sqft,
kwh_person = kwh_tot_yr/occupants) %>%
group_by(NAME) %>%
mutate(kwh_out = is_outlier(kwh)) %>%
filter(!date %in% c(ymd("2024-09-02"),ymd("2024-09-06")))
outs <- filter(daily_large_res, kwh_out == TRUE)
str(daily_large_res)
summary(daily_large_res)Building type summary
Descriptive table
datatable(annual_large_res, rownames = FALSE,
filter = "none",
class = "compact",
options = list(pageLength = 19, autoWidth = TRUE, dom = 't'),
caption = "Table 1. Large Residence Halls summary of electricity use and associated cost and greenhouse gas emissions in fiscal year 2025. Corrected kWh reflects an estimate of annual electricity use including days with missing data. Occupancy reflects average student occupancy during the academic year.")Electricity use over the year
Option 1 - stacked bar
ggplot(daily_large_res, aes(x= month, y= kwh/(10^3), fill = NAME)) +
geom_col(position = "stack") +
theme_bw() +
scale_fill_paletteer_d("ggthemes::Tableau_20")+
labs(x = "",
y = "Electricity use (1000 kWh)",
fill = "Building",
title = "Electricity Use by Building by Month",
subtitle = "Fiscal Year 2025")
Option 2 - faceted, Y axis constant
ggplot(daily_large_res, aes(x= month, y= kwh/(10^3), fill = NAME)) +
geom_col(position = "stack") +
facet_wrap(. ~ NAME) +
scale_fill_paletteer_d("ggthemes::Tableau_20")+
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme(legend.position = "none") +
labs(x = "",
y = "Electricity use (1000 kWh)",
title = "Electricity Use by Building by Month",
subtitle = "Fiscal Year 2025")
Option 3 - faceted, Y axis varies
ggplot(daily_large_res, aes(x= month, y= kwh/(10^3), fill = NAME)) +
geom_col(position = "stack") +
facet_wrap(. ~ NAME, scales = "free_y") +
scale_fill_paletteer_d("ggthemes::Tableau_20")+
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme(legend.position = "none") +
labs(x = "",
y = "Electricity use (1000 kWh)",
title = "Electricity Use by Building by Month",
subtitle = "Fiscal Year 2025")
Electricity intensity
Option 1 - all days included
ggplot(daily_large_res, aes(fct_reorder(NAME, kwh_sqft, median), y = kwh_sqft,
fill = NAME)) +
annotate("rect", xmin=-Inf, xmax=Inf, ymin=8.2, ymax=36.1,
color="lightgrey", alpha= 0.3) +
geom_hline(yintercept=13.6, linetype="dashed", color="white") +
geom_boxplot() +
# ylim(0,50) +
theme_bw() +
theme(legend.position = "none") +
scale_fill_paletteer_d("ggthemes::Tableau_20") +
labs(x = "",
y = "Electricity Intensity (kWh/sqft/year)",
title = "Electricity Intensity by Square Foot (kWh/sqft/year)") +
coord_flip()
Option 2 - only Academic Year
daily_ay <- filter(daily_large_res, period %in% c("Spring","Fall"))
ggplot(daily_ay, aes(fct_reorder(NAME, kwh_sqft, median), y = kwh_sqft,
fill = NAME)) +
annotate("rect", xmin=-Inf, xmax=Inf, ymin=8.2, ymax=36.1,
color="lightgrey", alpha= 0.3) +
geom_hline(yintercept=13.6, linetype="dashed", color="white") +
geom_boxplot() +
# ylim(0,50) +
theme_bw() +
theme(legend.position = "none") +
scale_fill_paletteer_d("ggthemes::Tableau_20") +
labs(x = "",
y = "Electricity Intensity (kWh/sqft/year)",
title = "Electricity Intensity by Square Foot (kWh/sqft/year)") +
coord_flip()
| Version | Author | Date |
|---|---|---|
| d9d0396 | maggiedouglas | 2026-03-12 |
Option 3 - faceted by time of year
daily_large_res$period <- factor(daily_large_res$period,
levels = c("Fall","Spring","Winter","Summer"),
labels = c("Fall Semester","Spring Semester",
"Winter Break","Summer Break"))
ggplot(daily_large_res, aes(fct_reorder(NAME, kwh_sqft, median), y = kwh_sqft,
fill = NAME)) +
annotate("rect", xmin=-Inf, xmax=Inf, ymin=8.2, ymax=36.1,
color="lightgrey", alpha= 0.3) +
geom_hline(yintercept=13.6, linetype="dashed", color="white") +
geom_boxplot() +
facet_wrap(. ~ period) +
# ylim(0,50) +
theme_bw() +
theme(legend.position = "none") +
scale_fill_paletteer_d("ggthemes::Tableau_20") +
labs(x = "",
y = "Electricity Intensity (kWh/sqft/year)",
title = "Electricity Intensity by Square Foot (kWh/sqft/year)") +
coord_flip()
| Version | Author | Date |
|---|---|---|
| d9d0396 | maggiedouglas | 2026-03-12 |
Partner contributions
Claire:
- Worked on the annual electricity data
- Made the summary datatable
- Wrote the partner contributions
Liv:
- Worked on the daily electricity data
- Made the graphs
Data Issues:
There are periods of time when a building meter goes offline, leading to a lot of NA values, then a very high value when the building comes back online. For now, we are adjusting the x-axis limit of the graph to better show the difference between buildings. Note that this means we have removed a lot of the outliers. Other than that, there were no issues.
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:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] paletteer_1.7.0 DT_0.34.0 lubridate_1.9.5 forcats_1.0.1
[5] stringr_1.6.0 dplyr_1.2.0 purrr_1.2.1 readr_2.2.0
[9] tidyr_1.3.2 tibble_3.3.1 ggplot2_4.0.2 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] sass_0.4.10 generics_0.1.4 prismatic_1.1.2 stringi_1.8.7
[5] hms_1.1.4 digest_0.6.39 magrittr_2.0.4 timechange_0.4.0
[9] evaluate_1.0.5 grid_4.5.2 RColorBrewer_1.1-3 fastmap_1.2.0
[13] rprojroot_2.1.1 workflowr_1.7.2 jsonlite_2.0.0 whisker_0.4.1
[17] rematch2_2.1.2 promises_1.5.0 crosstalk_1.2.2 scales_1.4.0
[21] jquerylib_0.1.4 cli_3.6.5 rlang_1.1.7 withr_3.0.2
[25] cachem_1.1.0 yaml_2.3.12 otel_0.2.0 tools_4.5.2
[29] tzdb_0.5.0 httpuv_1.6.16 vctrs_0.7.1 R6_2.6.1
[33] lifecycle_1.0.5 git2r_0.36.2 htmlwidgets_1.6.4 fs_1.6.7
[37] pkgconfig_2.0.3 pillar_1.11.1 bslib_0.10.0 later_1.4.8
[41] gtable_0.3.6 glue_1.8.0 Rcpp_1.1.1 xfun_0.56
[45] tidyselect_1.2.1 rstudioapi_0.18.0 knitr_1.51 farver_2.1.2
[49] htmltools_0.5.9 labeling_0.4.3 rmarkdown_2.30 compiler_4.5.2
[53] S7_0.2.1