PS 5: Preliminary results
Luke Heller & Valerie Larsen
2026-03-05
Last updated: 2026-03-25
Checks: 7 0
Knit directory: dickinson_power/
This reproducible R Markdown analysis was created with workflowr (version 1.7.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20260107) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 474aa52. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: analysis/.DS_Store
Ignored: analysis_to-fix/.DS_Store
Ignored: data/.DS_Store
Ignored: data/FY25 Main Meter Data.xlsx
Ignored: data/building_list_FY25_updated.xlsx
Ignored: data/graph_data_life_exp.csv
Ignored: data/housing_counts.csv
Ignored: keys/.DS_Store
Ignored: output/annual_kwh.csv
Ignored: output/building_check.csv
Ignored: output/building_check.xlsx
Ignored: output/daily_kwh.csv
Ignored: output/kwh_academic_2026-03-16.csv
Ignored: output/kwh_academic_2026-03-17.csv
Ignored: output/kwh_academic_2026-03-18.csv
Ignored: output/kwh_academic_2026-03-22.csv
Ignored: output/kwh_academic_2026-03-23.csv
Ignored: output/kwh_academic_2026-03-25.csv
Ignored: output/kwh_annual.csv
Ignored: output/kwh_annual_2026-03-04.csv
Ignored: output/kwh_annual_2026-03-12.csv
Ignored: output/kwh_annual_2026-03-16.csv
Ignored: output/kwh_annual_2026-03-17.csv
Ignored: output/kwh_annual_2026-03-18.csv
Ignored: output/kwh_annual_2026-03-22.csv
Ignored: output/kwh_annual_2026-03-23.csv
Ignored: output/kwh_annual_2026-03-25.csv
Ignored: output/kwh_annual_20260225.csv
Ignored: output/kwh_annual_20260226.csv
Ignored: output/kwh_daily.csv
Ignored: output/kwh_daily_2026-03-04.csv
Ignored: output/kwh_daily_2026-03-12.csv
Ignored: output/kwh_daily_2026-03-16.csv
Ignored: output/kwh_daily_2026-03-17.csv
Ignored: output/kwh_daily_2026-03-18.csv
Ignored: output/kwh_daily_2026-03-22.csv
Ignored: output/kwh_daily_2026-03-23.csv
Ignored: output/kwh_daily_2026-03-25.csv
Ignored: output/kwh_daily_20260225.csv
Ignored: output/kwh_daily_20260226.csv
Ignored: output/kwh_main_annual.csv
Ignored: output/kwh_main_daily.csv
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown
(analysis/PS05_prelim_results_Res_Hall_S.Rmd) and HTML
(docs/PS05_prelim_results_Res_Hall_S.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| 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
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:
[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):
[1] sass_0.4.10 generics_0.1.4 stringi_1.8.7 hms_1.1.4
[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
[13] jsonlite_2.0.0 processx_3.8.6 whisker_0.4.1 ps_1.9.1
[17] promises_1.5.0 httr_1.4.8 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 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