PS 5: Preliminary results
Claire Raesly and Liv Kacar
2026-03-04
Last updated: 2026-03-11
Checks: 6 1
Knit directory: dickinson_power/
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Data preparation
Libraries
library(tidyverse)
library(DT)
library(paletteer)Annual electricity data
annual <- read.csv("./output/kwh_annual_2026-03-04.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 = 2),
"CO2e - MT" = round(kwh_corr * ghg_conversion, digits = 2),
"kWh per sqft" = round(kwh_corr/sqft, digits = 2),
"kWh per person" = round(kwh_corr/occupants, digits = 2),
"Days of data (%)" = round(days_perc, digits = 2),
"kWh" = round(kwh, digits = 2),
kWh_corr = round(kwh_corr, digits = 2),
"Meter" = meter,
"Building" = NAME) %>%
arrange(desc(kwh_corr)) %>%
select("Meter", "Building", "Days of data (%)", "kWh", "kWh_corr", "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_2026-03-04.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)
str(daily_large_res)
summary(daily_large_res)Building type summary
Descriptive table
datatable(annual_large_res, filter = "top", rownames = FALSE, caption = "Large Residence Halls Summary")Electricity use over the year
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()
Electricity intensity
ggplot(daily_large_res, aes(x= month, y= kwh/(10^4), fill = NAME)) +
geom_col(position = "stack") +
theme_bw() +
scale_fill_paletteer_d("ggthemes::Tableau_20")+
labs(x = "",
y = "Electricity use (millions of kWh)",
fill = "Building",
title = "Electricity Use by Building by Month",
subtitle = "Fiscal Year 2025")
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 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.7.8
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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_1.6.0 DT_0.33 lubridate_1.9.3 forcats_1.0.0
[5] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[9] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] sass_0.4.8 utf8_1.2.4 generics_0.1.3 prismatic_1.1.2
[5] stringi_1.8.3 hms_1.1.3 digest_0.6.37 magrittr_2.0.3
[9] timechange_0.3.0 evaluate_0.23 grid_4.3.2 fastmap_1.1.1
[13] rprojroot_2.0.4 workflowr_1.7.1 jsonlite_1.8.8 rematch2_2.1.2
[17] promises_1.2.1 fansi_1.0.6 crosstalk_1.2.1 scales_1.3.0
[21] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3 ellipsis_0.3.2
[25] munsell_0.5.0 withr_3.0.0 cachem_1.0.8 yaml_2.3.8
[29] tools_4.3.2 tzdb_0.4.0 colorspace_2.1-0 httpuv_1.6.13
[33] vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4 git2r_0.33.0
[37] htmlwidgets_1.6.4 fs_1.6.3 pkgconfig_2.0.3 pillar_1.9.0
[41] bslib_0.6.1 later_1.3.2 gtable_0.3.4 glue_1.7.0
[45] Rcpp_1.1.0 highr_0.10 xfun_0.41 tidyselect_1.2.0
[49] rstudioapi_0.16.0 knitr_1.45 farver_2.1.1 htmltools_0.5.7
[53] labeling_0.4.3 rmarkdown_2.25 compiler_4.3.2