Data wrangling for Class Project
Maggie Douglas
04 March, 2026
Last updated: 2026-03-04
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Knit directory: dickinson_power/
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Ignored: output/kwh_daily_2026-03-04.csv
Ignored: output/kwh_daily_20260225.csv
Ignored: output/kwh_daily_20260226.csv
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 2a86883 | maggiedouglas | 2026-03-04 | Build site. |
| html | e50511f | maggiedouglas | 2026-03-04 | attempt to update website |
| html | 5f7e5dd | maggiedouglas | 2026-03-04 | Build site. |
| Rmd | bfe7b73 | maggiedouglas | 2026-03-04 | fix data wrangling! |
| html | bfe7b73 | maggiedouglas | 2026-03-04 | fix data wrangling! |
| Rmd | d04b276 | maggiedouglas | 2026-03-04 | fix data wrangling! |
| html | d04b276 | maggiedouglas | 2026-03-04 | fix data wrangling! |
| Rmd | 1ea78ae | maggiedouglas | 2026-02-27 | update summary code |
| html | 1ea78ae | maggiedouglas | 2026-02-27 | update summary code |
| Rmd | e752845 | maggiedouglas | 2026-02-26 | adjust data processing + building summary |
| html | e752845 | maggiedouglas | 2026-02-26 | adjust data processing + building summary |
| Rmd | 08cd7e1 | maggiedouglas | 2026-02-25 | update wrangling script |
| html | 08cd7e1 | maggiedouglas | 2026-02-25 | update wrangling script |
| Rmd | 8c6712f | maggiedouglas | 2026-02-24 | update script to fix issues |
| html | 8c6712f | maggiedouglas | 2026-02-24 | update script to fix issues |
| Rmd | 2fef649 | maggiedouglas | 2026-02-24 | updated to integrate individual meter data |
| html | 2fef649 | maggiedouglas | 2026-02-24 | updated to integrate individual meter data |
| Rmd | a379c87 | maggiedouglas | 2026-02-23 | fixed issue with East College |
| html | a379c87 | maggiedouglas | 2026-02-23 | fixed issue with East College |
| Rmd | 1e465a5 | maggiedouglas | 2026-02-23 | updated data and building case study with new info |
| html | 1e465a5 | maggiedouglas | 2026-02-23 | updated data and building case study with new info |
| html | 10507be | maggiedouglas | 2026-02-14 | Build site. |
| html | 661b13b | maggiedouglas | 2026-02-14 | Build site. |
| Rmd | dfaee9a | maggiedouglas | 2026-02-14 | Integrate occupancy data |
| html | dfaee9a | maggiedouglas | 2026-02-14 | Integrate occupancy data |
| 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 |
Purpose
This code is meant to match PPL data to building information and reorganize it to generate electricity data by building (or combinations of buildings for those metered together).
The main steps involved are:
- Read in the PPL electricity data, building information data, and a
key to connect them by name
- Create and store an annual summary of the electricity data by meter
- Restructure the building data to generate a single row for the Main Meter and Weis Meter buildings
- Join PPL electricity data (daily and annual) to building information using a key that connects the two by building name
- Aggregate electricity and square footage data by building and calculate electricity use per square foot, estimated cost, and estimated associated GHG emissions
- Inspect the data using a table
- Export the cleaned and reorganized data for downstream analyses
Load libraries + data
library(tidyverse) # load 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(RColorBrewer)# load electricity data + reformat date
kwh <- read.csv("./data/FY25 PPL Electricity Data.csv", strip.white = T) %>%
mutate(date = mdy(date)) %>%
complete(nesting(account_number, meter_origin, meter_number), date)
kwh_sub <- read.csv("./output/kwh_main_daily.csv", strip.white = T) %>%
mutate(date = ymd(date))
# store temp data for later
temp <- select(kwh, date, ave_temp) %>%
unique()
# load building data + clean up building names
buildings <- read.csv("./keys/fy25_building_list_updated.csv",
strip.white = T) %>%
mutate(NAME = str_remove_all(NAME, "/"),
NAME = str_replace_all(NAME, " "," "),
NAME = str_replace_all(NAME, " "," "))
# load keys to link datasets
key <- read.csv("./keys/meter_building_key.csv", strip.white = T) %>%
mutate(NAME = str_remove_all(NAME, "/"),
NAME = str_replace_all(NAME, " "," "),
NAME = str_replace_all(NAME, " "," "))
key_sub <- read.csv("./keys/submeter_building_key.csv", strip.white = T) %>%
mutate(NAME = str_remove_all(NAME, "/"),
NAME = str_replace_all(NAME, " "," "),
NAME = str_replace_all(NAME, " "," "))
key_occ <- read.csv("./keys/occupancy_key.csv",
strip.white = T) %>%
mutate(NAME = str_remove_all(NAME, "/"),
NAME = str_replace_all(NAME, " "," "),
NAME = str_replace_all(NAME, " "," "))
# load occupancy data + generate mean for AY
occupants <- read.csv("./data/housing_counts.csv",
strip.white = T) %>%
mutate(sem_yr = paste(semester, year)) %>% # create semester column
filter(sem_yr %in% c("Fall 2024", "Spring 2025")) %>% # filter to FY25
group_by(building_name) %>%
summarize(occupants = mean(occupants, na.rm = T)) %>% # generate mean occupants across semester
left_join(key_occ, by = "building_name") %>% # join to building names
group_by(NAME) %>%
summarize(occupants = sum(occupants, na.rm = T)) # sum across buildings with multiple rows
# store annual totals for kWh
kwh_annual <- kwh %>%
filter(!is.na(total_kwh)) %>%
group_by(meter_origin) %>%
summarize(kwh = sum(total_kwh, na.rm = T),
days_perc = (n()/365)*100)
kwh_sub_annual <- kwh_sub %>%
filter(!is.na(kwh)) %>%
group_by(building) %>%
summarize(kwh = sum(kwh, na.rm = T),
days_perc = (n()/365)*100)Check key data
str(kwh)tibble [56,210 × 6] (S3: tbl_df/tbl/data.frame)
$ account_number: num [1:56210] 2.38e+08 2.38e+08 2.38e+08 2.38e+08 2.38e+08 ...
$ meter_origin : chr [1:56210] "131 S College St" "131 S College St" "131 S College St" "131 S College St" ...
$ meter_number : int [1:56210] 300087452 300087452 300087452 300087452 300087452 300087452 300087452 300087452 300087452 300087452 ...
$ date : Date[1:56210], format: "2024-07-01" "2024-07-02" ...
$ total_kwh : num [1:56210] 77 70.6 85.7 100.3 106.6 ...
$ ave_temp : int [1:56210] 69 73 78 81 84 86 84 84 86 87 ...str(buildings)'data.frame': 135 obs. of 14 variables:
$ TYPE : chr "Academic" "Academic" "Academic" "Academic" ...
$ type_new : chr "Academic" "Academic" "Academic" "Academic" ...
$ banner_code: chr "1110" "1540" "1035" "1810" ...
$ NAME : chr "162-164 Dickinson Ave." "46 S. West St." "57 S. College" "Green Valley Sanctuary" ...
$ occupant : chr "DEAL Archeology Labs" "Music office/rehearsal space" "Education Dept. Offices" "Research Facility" ...
$ address : chr "162-164 Dickinson Ave." "46 S. West St." "57 S. College St." "" ...
$ date_constr: chr "" "" "" "" ...
$ date_acqd : int 1998 1982 1979 1966 NA NA NA 1950 NA NA ...
$ date_reno : int 2010 NA NA NA 1997 2009 1940 2002 2001 NA ...
$ sqft : int 2500 1775 4576 2500 4000 29133 33692 11039 22000 112800 ...
$ rental : int 0 0 0 0 1 0 0 0 0 0 ...
$ main_meter : int 0 0 0 0 0 1 1 1 1 1 ...
$ main_disagg: int 0 0 0 0 0 1 1 1 1 1 ...
$ weis_meter : int 0 0 0 0 0 0 0 0 0 0 ...str(occupants)tibble [66 × 2] (S3: tbl_df/tbl/data.frame)
$ NAME : chr [1:66] "100 S. West St." "133 N. College St." "133 W. High St. (2, 5)" "135 Cedar St." ...
$ occupants: num [1:66] 19 5 4.5 4 8 20 7 8 4 6 ...Restructure building data
- Generate total square footage for buildings on the Main Meter and fill in values for other variables
- Similar operation for buildings on the Weis Meter
- Filter building info to those with individual meters and then add back in the Main Meter and Weis aggregated information
# Generate lookup table for each meter status
buildings_main <- buildings %>%
filter(main_meter == 1) %>% # filter to main meter buildings
summarize(sqft = sum(sqft, na.rm = T)) %>% # sum sqft for these buildings
mutate(meter = "Main Meter - Total",
NAME = "Main Meter",
type_new = "Main Meter") %>%
select(type_new, NAME, sqft, meter)
buildings_weis <- buildings %>%
filter(weis_meter == 1) %>% # filter to weis buildings
summarize(sqft = sum(sqft, na.rm = T)) %>%
mutate(meter = "Weis Meter - Total",
NAME = "Weis Meter",
type_new = "Weis Meter") %>%
select(type_new, NAME, sqft, meter)
buildings_agg <- rbind(buildings_main, buildings_weis)
buildings_individual <- buildings %>%
filter(main_meter == 0 & weis_meter == 0) %>% # keep only buildings on individual meters
mutate(meter = "Individual") %>%
select(meter, NAME, type_new, occupant, address, date_constr, date_acqd, date_reno, sqft, rental) %>% # select relevant columns
# rbind(buildings_main, buildings_weis) %>%
select(type_new, NAME, sqft, meter)
buildings_submeter <- buildings %>%
filter(main_disagg ==1) %>% # keep only buildings on individual meters
mutate(meter = "Submeter") %>%
select(meter, NAME, type_new, occupant, address, date_constr, date_acqd, date_reno, sqft, rental) %>% # select relevant columns
# rbind(buildings_main, buildings_weis) %>%
select(type_new, NAME, sqft, meter)
# Store summary of building meter status
buildings_sum <- buildings %>%
mutate(meter = ifelse(weis_meter == 1, "Weis Meter",
ifelse(main_meter == 1 & main_disagg == 0, "Main Meter",
ifelse(main_disagg == 1, "Submeter", "Individual"))))Wrangle datasets
Annual totals
# generate annual summary for individually metered buildings
joined_individual <- kwh_annual %>%
left_join(key, by = "meter_origin") %>% # join to key to match meters to building names
right_join(buildings_individual, by = "NAME", relationship = "many-to-one") %>% # join to building info by name
group_by(type_new, NAME, days_perc, meter) %>% # group by building
summarize(kwh = sum(kwh, na.rm = T), # sum kwh by building
sqft = mean(sqft, na.rm = T)) %>% # take mean to preserve sqft data
mutate(type = type_new) %>%
left_join(occupants, by = "NAME") %>%
ungroup() %>%
mutate_all(~ifelse(is.nan(.), NA, .)) %>%
select(type, meter, NAME, days_perc, kwh, sqft, occupants)`summarise()` has grouped output by 'type_new', 'NAME', 'days_perc'. You can
override using the `.groups` argument.# generate annual summary for submetered buildings
joined_sub <- kwh_sub_annual %>%
left_join(key_sub, by = "building") %>%
left_join(buildings_submeter, by = "NAME", relationship = "many-to-one") %>% # join to building info by name
filter(building != "CHW_Base") %>% # remove duplicate CHW value - not sure what this is?
group_by(type_new, NAME, days_perc, meter) %>% # group by building
summarize(kwh = sum(kwh, na.rm = T), # sum kwh by building
sqft = mean(sqft, na.rm = T)) %>% # preserve sqft as is
mutate(type = type_new) %>%
# filter(!is.na(type_new)) %>% # filter out those with NA for type
left_join(occupants, by = "NAME") %>%
ungroup() %>%
mutate_all(~ifelse(is.nan(.), NA, .)) %>%
select(type, meter, NAME, days_perc, kwh, sqft, occupants)`summarise()` has grouped output by 'type_new', 'NAME', 'days_perc'. You can
override using the `.groups` argument.# generate annual summary for main and weis meters
joined_agg <- kwh_annual %>%
left_join(key, by = "meter_origin") %>%
right_join(buildings_agg, by = "NAME") %>%
group_by(type_new, NAME, days_perc, meter) %>% # group by building
summarize(kwh = sum(kwh, na.rm = T), # sum kwh by building
sqft = mean(sqft, na.rm = T)) %>% # preserve sqft as is
mutate(type = type_new) %>%
# filter(!is.na(type_new)) %>% # filter out those with NA for type
left_join(occupants, by = "NAME") %>%
ungroup() %>%
mutate_all(~ifelse(is.nan(.), NA, .)) %>%
select(type, meter, NAME, days_perc, kwh, sqft, occupants)`summarise()` has grouped output by 'type_new', 'NAME', 'days_perc'. You can
override using the `.groups` argument.# combine into one data frame
joined_full <- rbind(joined_individual, joined_sub, joined_agg) %>%
filter(kwh != 0) %>%
mutate(kwh_corr = kwh/(days_perc/100)) %>% # estimate kwh for all days in the year
arrange(type, meter) %>%
select(type, meter, NAME, days_perc, kwh, kwh_corr, sqft, occupants)
# store lookup for data coverage
data_cov <- joined_full %>%
select(NAME, days_perc)Daily data
# generate daily summary for individually metered buildings
daily_individual <- kwh %>%
left_join(key, by = "meter_origin") %>% # join to key to match meters to building names
right_join(buildings_individual, by = "NAME", relationship = "many-to-many") %>% # join to building info by name
group_by(type_new, NAME, date, meter) %>% # group by building
summarize(kwh = sum(total_kwh, na.rm = T), # sum kwh by building
sqft = mean(sqft, na.rm = T)) %>%
mutate(type = type_new) %>%
ungroup() %>%
mutate_all(~ifelse(is.nan(.), NA, .)) %>% # convert NaN to NA
select(type, meter, date, NAME, kwh, sqft) %>%
complete(nesting(type, meter, NAME), date) %>% # restore NA values for missing date-building combos
filter(!is.na(date)) # remove rows without value for date`summarise()` has grouped output by 'type_new', 'NAME', 'date'. You can
override using the `.groups` argument.# generate daily summary for submetered buildings
daily_sub <- kwh_sub %>%
left_join(key_sub, by = "building") %>%
left_join(buildings_submeter, by = "NAME", relationship = "many-to-one") %>% # join to building info by name
filter(building != "CHW_Base") %>% # remove duplicate CHW value - not sure what this is?
group_by(type_new, NAME, date, meter) %>% # group by building
summarize(kwh = sum(kwh, na.rm = T),
sqft = mean(sqft, na.rm = T)) %>%
mutate(type = type_new) %>%
filter(!is.na(type_new)) %>%
ungroup() %>%
select(type, meter, date, NAME, kwh, sqft) %>%
mutate_all(~ifelse(is.nan(.), NA, .)) %>% # convert NaN to NA
mutate(kwh = ifelse(kwh == 0, NA, kwh)) %>% # restore NA values for missing date-building combos
filter(!is.na(date)) # remove rows without value for date`summarise()` has grouped output by 'type_new', 'NAME', 'date'. You can
override using the `.groups` argument.# generate daily summary for main and weis meters
daily_agg <- kwh %>%
left_join(key, by = "meter_origin") %>%
right_join(buildings_agg, by = "NAME") %>%
group_by(type_new, NAME, date, meter) %>% # group by building
summarize(kwh = sum(total_kwh, na.rm = T), # sum kwh by building
sqft = mean(sqft, na.rm = T)) %>% # preserve sqft as is
mutate(type = type_new) %>%
ungroup() %>%
mutate_all(~ifelse(is.nan(.), NA, .)) %>%
select(type, meter, date, NAME, kwh, sqft)`summarise()` has grouped output by 'type_new', 'NAME', 'date'. You can
override using the `.groups` argument.# generate complete daily dataset
daily_full <- rbind(daily_individual, daily_agg, daily_sub) %>%
mutate(date = as.Date(date)) %>%
arrange(type, meter, NAME, date) %>%
select(type, meter, NAME, date, kwh, sqft) %>%
left_join(occupants, by = "NAME") %>%
left_join(data_cov, by = "NAME") %>%
merge(temp, by = "date") %>% # for some reason the join by date is not fully working... duplicating rows
filter(!is.na(ave_temp)) %>% # remove duplicate rows
mutate(period = ifelse((date > ymd("2024-08-28") & date < ymd("2024-12-21")), "Fall",
ifelse((date > ymd("2025-01-07") & date < ymd("2025-05-18")), "Spring",
ifelse(date > ymd("2024-12-21") & date < ymd("2025-01-07"), "Winter", "Summer")))) %>%
select(type, meter, NAME, days_perc, sqft, occupants, period, date, kwh, ave_temp) %>%
arrange(NAME)Data availability by type
buildings_sum$meter <- factor(buildings_sum$meter,
levels = c("Weis Meter", "Main Meter",
"Submeter", "Individual"))
pal <- c("lightgrey","darkgrey","#CAB2D6","#6A3D9A")
buildings_graph <- buildings_sum %>%
filter(!is.na(meter) & !type_new %in% c("Res Hall - U","Non-building","Production","Mixed"))
ggplot(buildings_graph,
aes(x = reorder(type_new, sqft, FUN = "sum"), y = sqft/1000, fill = meter)) +
geom_col(position = "stack") +
scale_fill_manual(values = pal) +
theme_bw() +
labs(x = "", y = "Square Footage (1000)", fill = "Meter status") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
| Version | Author | Date |
|---|---|---|
| bfe7b73 | maggiedouglas | 2026-03-04 |
| d04b276 | maggiedouglas | 2026-03-04 |
| 1ea78ae | maggiedouglas | 2026-02-27 |
| e752845 | maggiedouglas | 2026-02-26 |
| 08cd7e1 | maggiedouglas | 2026-02-25 |
| 8c6712f | maggiedouglas | 2026-02-24 |
| 2fef649 | maggiedouglas | 2026-02-24 |
| a379c87 | maggiedouglas | 2026-02-23 |
| 1e465a5 | maggiedouglas | 2026-02-23 |
ggplot(buildings_graph,
aes(x = reorder(type_new, sqft, FUN = "sum"), fill = meter)) +
geom_bar(position = "stack") +
scale_fill_manual(values = pal) +
theme_bw() +
labs(x = "", y = "Number of Buildings", fill = "Meter status") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
Practice boxplot
daily_test <- daily_full %>%
filter(NAME %in% c("Main Meter","Weis Meter")) %>%
mutate(kwh_sqft = (kwh/sqft*365)) # adjust daily values to per year equivalent
ggplot(daily_test,
aes(x = type, y = kwh_sqft, fill = type)) +
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() +
coord_flip() +
facet_grid(period ~ .) +
theme_bw() +
theme(legend.position = "none") +
labs(x = "", y = "kWh per sqft per year",
title = "Electricity Intensity by Building Type")
ggplot(daily_test,
aes(x = type, y = kwh_sqft, fill = period)) +
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() +
coord_flip() +
theme_bw() +
theme(legend.position = "none") +
labs(x = "", y = "kWh per sqft per year",
title = "Electricity Intensity by Building Type")
Export for downstream analyses
today <- today("EST") %>%
str_remove(" UTC")
write.csv(daily_full, paste0("./output/kwh_daily_", today, ".csv"), row.names = F)
write.csv(joined_full, paste0("./output/kwh_annual_", today, ".csv"), row.names = F)
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] RColorBrewer_1.1-3 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
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] workflowr_1.7.1 jsonlite_1.8.8 whisker_0.4.1 promises_1.2.1
[17] fansi_1.0.6 scales_1.3.0 jquerylib_0.1.4 cli_3.6.2
[21] rlang_1.1.3 munsell_0.5.0 withr_3.0.0 cachem_1.0.8
[25] yaml_2.3.8 tools_4.3.2 tzdb_0.4.0 colorspace_2.1-0
[29] httpuv_1.6.13 vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4
[33] git2r_0.33.0 fs_1.6.3 pkgconfig_2.0.3 pillar_1.9.0
[37] bslib_0.6.1 later_1.3.2 gtable_0.3.4 glue_1.7.0
[41] Rcpp_1.1.0 highr_0.10 xfun_0.41 tidyselect_1.2.0
[45] rstudioapi_0.16.0 knitr_1.45 farver_2.1.1 htmltools_0.5.7
[49] labeling_0.4.3 rmarkdown_2.25 compiler_4.3.2