PS05_Prelim_results
Liam and Charlotte
2026-03-04
Last updated: 2026-03-25
Checks: 7 0
Knit directory: dickinson_power/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 4ab0c63 | maggiedouglas | 2026-03-23 | Build site. |
| Rmd | 38132bb | maggiedouglas | 2026-03-11 | add student draft results |
| html | 38132bb | maggiedouglas | 2026-03-11 | add student draft results |
Data Preparation
Conversion Factors
dol_per_kWh <- 0.081385
MTCO2_per_kWh <- 0.00030082405Libraries
library(tidyverse)
library(DT)Annual Electricity Data
annual_kwh.df<-read.csv('./output/kwh_annual_2026-03-04.csv', strip.white=TRUE) #load data
str(annual_kwh.df) #check data
med_res_annual_kwh<-annual_kwh.df %>% #wrangle for building group annually
filter(type=="Res Hall - M")
transform_med<-med_res_annual_kwh%>%
mutate(
cost=round(kwh_corr*dol_per_kWh, digits=0),
GHG_emis=round(kwh_corr*MTCO2_per_kWh, digits=0),
kWh_per_sqft=round(kwh_corr/sqft, digits=1),
kWh_per_person= round(kwh_corr/occupants, digits=0)
)
building_results<-transform_med %>%
select(NAME, kwh_corr, kWh_per_sqft, cost, GHG_emis, kWh_per_person, meter, days_perc) %>%
arrange(desc(kwh_corr))Daily Electricity Data
med_res_daily_kwh <- read.csv("./output/kwh_daily_2026-03-04.csv")
str(med_res_daily_kwh)
med_res_daily_kwh <- med_res_daily_kwh %>%
filter(type == "Res Hall - M") %>%
mutate(date = ymd(date),
month = month(date, label= TRUE),
day = wday(date, label = TRUE)) %>%
mutate(kwh_sqft_year = kwh/sqft*365, kwh_person_year = kwh/occupants*365) %>%
arrange(desc(kwh))Building Type Summary
Descriptive Table
datatable(building_results,
filter='top',
rownames=FALSE,
colnames = c("Building", "kWh", "kWh per sqft","Cost-$", "CO2e - MT", "kWh per person", "Meter", "Days of data (%)"),
caption='Table 1. Annual Electricity Use Indicators by Medium Residence Hall in Fiscal Year 2025')Electrcity Use Over the Year ’
ggplot() +
annotate("rect", xmin = -Inf, xmax = Inf, ymin = 7.4, ymax = 14.3, color = "lightgray", alpha = 0.3) +
geom_hline(yintercept = 10.3, linetype = "dashed", color = "white") +
geom_boxplot(data = med_res_daily_kwh, aes(x = reorder(NAME, kwh_sqft_year, FUN = median), y = kwh_sqft_year, fill = NAME), show.legend = FALSE) +
coord_flip() +
labs(
x = "",
y = "Electricity Intensity (kWh/sqft/year)",
title = "Electricity Intensity in Medium Residential Halls \nin the Fiscal Year 2025",
subtitle = "Buildings ordered by median energy intensity") +
theme_bw()
| Version | Author | Date |
|---|---|---|
| 38132bb | maggiedouglas | 2026-03-11 |
Electricity Intensity
ggplot(med_res_daily_kwh , aes(x=month, y=kwh, fill=NAME))+
geom_bar(position="stack", stat="identity")+
theme_bw()+
labs(x="",
y="Electricity Use (kWh)",
fill= "Building",
title= "Monthly Electricity Use by Medium Residence Hall in Fiscal Year 2025")
| Version | Author | Date |
|---|---|---|
| 38132bb | maggiedouglas | 2026-03-11 |
Partner Contributions
Charlotte prepared the annual electricity data for this problem set while Liam prepared the daily electricity data. For the building type summary, Charlotte adapted a table she had made last lab to create the summarizing table for annual electricity use indicators by building, Liam created the ranked box plot of daily electricity intensity by building, and Charlotte created the stacked bar graph to show electricity use by month. The data seems to have no issues or problems, all the data is individually metered and clear. There is no missing data. There is occupancy data for all buildings and 100% of the days in the 2025 Fiscal Year were measured.
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] 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
loaded via a namespace (and not attached):
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[5] digest_0.6.39 magrittr_2.0.4 timechange_0.4.0 evaluate_1.0.5
[9] grid_4.5.2 RColorBrewer_1.1-3 fastmap_1.2.0 rprojroot_2.1.1
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[33] lifecycle_1.0.5 git2r_0.36.2 htmlwidgets_1.6.4 fs_1.6.7
[37] pkgconfig_2.0.3 callr_3.7.6 pillar_1.11.1 bslib_0.10.0
[41] later_1.4.8 gtable_0.3.6 glue_1.8.0 Rcpp_1.1.1
[45] xfun_0.56 tidyselect_1.2.1 rstudioapi_0.18.0 knitr_1.51
[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