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| File | Version | Author | Date | Message |
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| html | 40c81af | maggiedouglas | 2026-02-14 | Build site. |
| Rmd | f2835df | maggiedouglas | 2026-02-14 | adjust gitignore and improve data wrangling and main meter case study |
| html | f2835df | maggiedouglas | 2026-02-14 | adjust gitignore and improve data wrangling and main meter case study |
| Rmd | c2d2f6e | maggiedouglas | 2026-02-07 | project set up and preliminary analyses |
dollars_kwh <- 0.0813
co2_kg_kwh <- 0.299511787library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(DT)
kwh <- read.csv("./data/kwh_by_building.csv", strip.white = TRUE)
key <- read.csv("./data/building_list_FY25.csv", strip.white = TRUE)
kwh_daily <- read.csv("./data/FY25 PPL Electricity Data.csv", strip.white = TRUE)str(kwh)'data.frame': 152 obs. of 4 variables:
$ NAME : chr "100 S. West St." "100 S. West St." "100 S. West St." "100 S. West St." ...
$ meter_origin: chr "100 S West St" "102 S West St *Apt 1" "102 S West St *Apt 2" "102 S West St *Apt 3" ...
$ kwh_tot : int 12628 4609 4109 2070 7284 5457 3365 6181 5231 3948 ...
$ kwh_count : int 365 365 365 365 365 365 365 365 365 365 ...str(key)'data.frame': 133 obs. of 11 variables:
$ type : chr "Res Hall - M" "Other" "Res Hall - M" "Res Hall - U" ...
$ banner_code: chr "1428" "1824" "1720" "1667" ...
$ NAME : chr "100 S. West St." "119 W. High St. (1, 2, 3)" "133 N. College St." "133 W. High St. (2, 5)" ...
$ occupant : chr "Students" "Visiting Faculty" "Students" "Students" ...
$ address : chr "100 S. West St." "119 W. High St." "133 N. College St." "133 W. High St" ...
$ date_constr: int NA NA 1890 NA NA 1910 1910 NA NA NA ...
$ date_acqd : int NA 2012 2004 NA 2001 1989 1987 NA 2022 NA ...
$ date_reno : int NA NA 2019 NA NA 2019 2019 NA NA NA ...
$ sqft : int 7190 2240 2272 NA 1900 1476 1728 5825 2864 NA ...
$ main_meter : int 0 0 0 0 0 0 0 0 0 0 ...
$ rental : int 1 0 0 1 0 0 0 1 1 1 ...str(kwh_daily)'data.frame': 55138 obs. of 6 variables:
$ account_number: num 1e+09 1e+09 1e+09 1e+09 1e+09 ...
$ meter_origin : chr "152 W Louther St *Apt 2" "152 W Louther St *Apt 2" "152 W Louther St *Apt 2" "152 W Louther St *Apt 2" ...
$ meter_number : int 300056642 300056642 300056642 300056642 300056642 300056642 300056642 300056642 300056642 300056642 ...
$ date : chr "7/18/24" "7/19/24" "7/20/24" "7/21/24" ...
$ total_kwh : num 28 26.4 26.5 26.3 27.2 ...
$ ave_temp : int 78 76 78 81 75 78 80 79 76 76 ...comb <- kwh %>%
left_join(key, by = "NAME", relationship = "many-to-one")
# generate key for building info
comb_key <- comb %>%
select(type, NAME, main_meter, date_constr, date_acqd, date_reno, sqft) %>%
unique()
# generate key to connect meters to building names
meter_key <- comb %>%
select(meter_origin, NAME)res_hall_S <- filter(comb, type == "Res Hall - S")agg <- res_hall_S %>%
group_by(NAME, type) %>%
summarize(kwh_tot = sum(kwh_tot, na.rm = TRUE),
sqft = mean(sqft, na.rm = TRUE))`summarise()` has grouped output by 'NAME'. You can override using the
`.groups` argument.# different choices for rounding are okay, but need to implement round() successfully
trans <- agg %>%
mutate(kwh_sqft = round(kwh_tot/sqft,digits = 2),
dollars = round(kwh_tot*dollars_kwh, digits = 0),
ghg_kgCO2 = round(kwh_tot*co2_kg_kwh, digits = 0)) %>%
arrange(-kwh_tot) %>%
select(-type)datatable(trans,
filter = 'top',
rownames = FALSE)buildings <- filter(key, type == "Res Hall - S") %>%
arrange(main_meter, -sqft) %>%
select(NAME, sqft, main_meter)
datatable(buildings,
filter = 'top',
rownames = FALSE)agg_tot <- comb %>%
group_by(type, NAME) %>%
summarize(kwh_tot = sum(kwh_tot, na.rm = TRUE),
sqft = mean(sqft, na.rm = TRUE)) %>%
mutate(kwh_sqft = kwh_tot/sqft) %>%
filter(is.finite(kwh_sqft))`summarise()` has grouped output by 'type'. You can override using the
`.groups` argument.write.csv(agg_tot, "./output/annual_kwh.csv", row.names = F)
trans_tot <- agg_tot %>%
mutate(kwh_sqft = round(kwh_tot/sqft,digits = 2),
dollars = round(kwh_tot*dollars_kwh, digits = 0),
ghg_kgCO2 = round(kwh_tot*co2_kg_kwh, digits = 0)) %>%
arrange(type, -kwh_tot) # Generate daily kwh data at the building level + join to building info
comb_daily <- kwh_daily %>%
left_join(meter_key, by = "meter_origin", relationship = "many-to-one") %>%
group_by(NAME, date) %>%
summarize(kwh = sum(total_kwh, na.rm = TRUE),
ave_temp = mean(ave_temp, na.rm = TRUE)) %>%
filter(NAME != "") %>%
left_join(comb_key, by = "NAME") %>%
mutate(kwh_sqft = kwh/sqft,
dollars = kwh*dollars_kwh,
co2_kg = kwh*co2_kg_kwh)`summarise()` has grouped output by 'NAME'. You can override using the
`.groups` argument.comb_clean <- comb_daily %>%
select(type, NAME, date, ave_temp, kwh, kwh_sqft) %>%
filter(is.finite(kwh_sqft))
write.csv(comb_daily, "./output/daily_kwh.csv", row.names = F)
main_meter <- filter(kwh_daily, meter_origin == "W High St (37 Big Buildings)") %>%
mutate(date = mdy(date),
month = month(date, label = TRUE),
day = wday(date, label = TRUE),
dollars = total_kwh*dollars_kwh,
co2_kg = total_kwh*co2_kg_kwh,)
# Check which buildings are and are not represented in the electricity data
comb_check <- kwh %>%
full_join(key, by = "NAME", relationship = "many-to-one") %>%
select(NAME, address, type, meter_origin, kwh_tot, sqft, main_meter, rental) %>%
arrange(type, main_meter, NAME, -kwh_tot) %>%
mutate(diagnosis = ifelse(main_meter == 1, "main meter",
ifelse((is.na(kwh_tot) & main_meter == 0), "missing electric",
ifelse((kwh_tot > 0 & main_meter == 1), "double",
ifelse(is.na(type), "missing building", "")))))
write.csv(comb_check, "./output/building_check.csv", row.names = FALSE)
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] DT_0.33 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.8 utf8_1.2.4 generics_0.1.3 stringi_1.8.3
[5] hms_1.1.3 digest_0.6.37 magrittr_2.0.3 timechange_0.3.0
[9] evaluate_0.23 grid_4.3.2 fastmap_1.1.1 rprojroot_2.0.4
[13] jsonlite_1.8.8 processx_3.8.3 whisker_0.4.1 ps_1.7.5
[17] promises_1.2.1 httr_1.4.7 fansi_1.0.6 crosstalk_1.2.1
[21] scales_1.3.0 jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3
[25] ellipsis_0.3.2 munsell_0.5.0 withr_3.0.0 cachem_1.0.8
[29] yaml_2.3.8 tools_4.3.2 tzdb_0.4.0 colorspace_2.1-0
[33] httpuv_1.6.13 vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4
[37] git2r_0.33.0 htmlwidgets_1.6.4 fs_1.6.3 pkgconfig_2.0.3
[41] callr_3.7.3 pillar_1.9.0 bslib_0.6.1 later_1.3.2
[45] gtable_0.3.4 glue_1.7.0 Rcpp_1.1.0 xfun_0.41
[49] tidyselect_1.2.0 rstudioapi_0.16.0 knitr_1.45 htmltools_0.5.7
[53] rmarkdown_2.25 compiler_4.3.2 getPass_0.2-4