Data wrangling for Class Project

workflowr

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Last updated: 2026-02-24

Checks: 6 1

Knit directory: dickinson_power/

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Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/FY25 Main Meter Data.xlsx
    Ignored:    data/building_list_FY25_updated.xlsx
    Ignored:    data/graph_data_life_exp.csv
    Ignored:    output/.DS_Store
    Ignored:    output/building_check.csv
    Ignored:    output/building_check.xlsx
    Ignored:    output/kwh_annual.csv
    Ignored:    output/kwh_daily.csv
    Ignored:    output/kwh_main_annual.csv
    Ignored:    output/kwh_main_daily.csv

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    Modified:   analysis/data_wrangling_final.Rmd
    Modified:   keys/housing_counts.csv

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/data_wrangling_final.Rmd) and HTML (docs/data_wrangling_final.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
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

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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   3.5.0     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ 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 errors

library(DT) # library to create tables
library(scales) # library to format dollars

Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

# load electricity data
kwh <- read.csv("./data/FY25 PPL Electricity Data.csv", strip.white = T) # load PPL data
kwh_sub <- read.csv("./output/kwh_main_daily.csv", strip.white = T)

# load building data + occupancy
buildings <- read.csv("./keys/fy25_building_list_updated.csv", strip.white = T) # load building information
occupants <- read.csv("./keys/housing_counts.csv", strip.white = T) # load occupancy info

# load keys to link datasets
key <- read.csv("./keys/meter_building_key.csv", strip.white = T) # load key to link meters to buildings
sub_key <- read.csv("./keys/submeter_building_key.csv", strip.white = T) # key to link submeter names to buildings

# filter to AY24/25 and average across the year
occu_agg <- occupants %>%
  mutate(sem_yr = paste(semester, year)) %>%
  filter(sem_yr %in% c("Fall 2024", "Spring 2025")) %>%
  group_by(NAME, sem_yr) %>%
  summarize(occupants = sum(occupants, na.rm = T),
            n = n()) %>%
  group_by(NAME) %>%
  summarize(occupants = mean(occupants, na.rm = T))

`summarise()` has grouped output by 'NAME'. You can override using the
`.groups` argument.

# 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)

# store conversion factors
dollars_kwh <- 0.08138507
co2_kg_kwh <- 0.30082405

Check data

str(kwh)

'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 ...

str(buildings)

'data.frame':   136 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 2019 ...
 $ sqft       : int  2500 1775 4576 2500 4000 29133 33692 11039 22000 28050 ...
 $ 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 0 ...
 $ weis_meter : int  0 0 0 0 0 0 0 0 0 0 ...

str(key)

'data.frame':   202 obs. of  2 variables:
 $ meter_origin: chr  "100 S College St" "100 S West St" "101 S College St" "102 S West St *Apt 1" ...
 $ NAME        : chr  "Drayer Hall" "100 S. West St." "Landis House" "100 S. West St." ...

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

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",
         occupant = "Mixed",
         address = "Many different",
         date_constr = NA,
         date_acqd = NA,
         date_reno = NA,
         rental = 0)

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",
         occupant = "Mixed",
         address = "Many different",
         date_constr = NA,
         date_acqd = NA,
         date_reno = NA,
         rental = 0)

# Generate clean building lookup table
buildings_clean <- buildings %>%
  filter(main_disagg == 1 | weis_meter == 0) %>% # keep only buildings on individual meters
  mutate(meter = ifelse(weis_meter == 1, "Weis Meter", 
                               ifelse(main_meter == 1, "Main 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)

# 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", "Individual")))

Wrangle datasets

Annual totals

joined <- kwh_annual %>%
  left_join(key, by = "meter_origin") %>% # join to key to match meters to building names
  full_join(buildings_clean, by = "NAME", relationship = "many-to-one") %>% # join to building info by name
  filter(!meter %in% c("Main Meter", "Weis Meter")) %>% # filter out those on main or Weis meter (not disagg or total)
  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
  filter(!is.na(type_new)) %>% # filter out those with NA for type
  left_join(occu_agg, by = "NAME") %>%
  mutate(kwh_sqft = kwh/sqft, # calculate kwh per sqft
         kwh_person = kwh/occupants,
         dollars = kwh*dollars_kwh,
         ghg_kgCO2 = kwh*co2_kg_kwh,
         type = type_new) %>% 
  ungroup() %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) %>%
  select(type, meter, NAME, days_perc, kwh, sqft, kwh_sqft, occupants, kwh_person, dollars, ghg_kgCO2)

`summarise()` has grouped output by 'type_new', 'NAME', 'days_perc'. You can
override using the `.groups` argument.

joined_sub <- kwh_sub_annual %>%
  left_join(sub_key, by = "building") %>%
  left_join(buildings_clean, 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
  filter(!is.na(type_new)) %>% # filter out those with NA for type
  left_join(occu_agg, by = "NAME") %>%
  mutate(kwh_sqft = kwh/sqft, # calculate kwh per sqft
         kwh_person = kwh/occupants,
         dollars = kwh*dollars_kwh,
         ghg_kgCO2 = kwh*co2_kg_kwh,
         type = type_new) %>% 
  ungroup() %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) %>%
  select(type, meter, NAME, days_perc, kwh, sqft, kwh_sqft, occupants, kwh_person, dollars, ghg_kgCO2)

`summarise()` has grouped output by 'type_new', 'NAME', 'days_perc'. You can
override using the `.groups` argument.

joined_full <- rbind(joined, joined_sub)

# generate summary by building category
joined_cat <- joined_full %>%
  group_by(type) %>%
  summarize(n = n(),
            kwh = sum(kwh),
            dollars = sum(dollars),
            ghg_kgCO2 = sum(ghg_kgCO2),
            sqft = sum(sqft, na.rm = T),
            med_kwh_sqft = median(kwh_sqft, na.rm = T),
            kwh_sqft_25 = quantile(kwh_sqft, .25, na.rm = T),
            kwh_sqft_75 = quantile(kwh_sqft, .75, na.rm = T)) %>%
  arrange(-kwh)

Daily data

joined_daily <- kwh %>%
  left_join(key, by = "meter_origin") %>% # join to key to match meters to building names
  full_join(buildings_clean, by = "NAME", relationship = "many-to-many") %>% # join to building info by name
  filter(!is.na(meter_origin)) %>%
  group_by(type_new, NAME, date) %>%
  summarize(kwh = sum(total_kwh, na.rm = T),
            sqft = mean(sqft, na.rm = T),
            ave_temp = mean(ave_temp, na.rm = T)) %>%
  left_join(occu_agg, by = "NAME") %>%
  filter(!is.na(type_new)) %>%
  mutate(type = type_new) %>% 
  ungroup() %>%
  select(type, NAME, date, ave_temp, kwh, sqft, occupants) %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) # convert NaN to NA

Warning in left_join(., key, by = "meter_origin"): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 14881 of `x` matches multiple rows in `y`.
ℹ Row 39 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

`summarise()` has grouped output by 'type_new', 'NAME'. You can override using
the `.groups` argument.

joined_daily_sub <- kwh_sub %>%
  left_join(sub_key, by = "building") %>% 
  full_join(buildings_clean, by = "NAME", relationship = "many-to-many") %>% # join to building info by name
  group_by(type_new, NAME, date) %>%
  summarize(kwh = sum(kwh, na.rm = T),
            sqft = mean(sqft, na.rm = T),
            ave_temp = mean(ave_temp, na.rm = T)) %>%
  left_join(occu_agg, by = "NAME") %>%
  filter(!is.na(type_new)) %>%
  mutate(type = type_new) %>% 
  ungroup() %>%
  select(type, NAME, date, ave_temp, kwh, sqft, occupants) %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) # convert NaN to NA

`summarise()` has grouped output by 'type_new', 'NAME'. You can override using
the `.groups` argument.

joined_daily_full <- rbind(joined_daily, joined_daily_sub)

Summary table

joined_pretty_cat <- joined_cat %>%
  mutate(n = ifelse(n == 1, "-", n),
         kwh = round(kwh, digits = 0),
         dollars = paste("$",round(dollars, digits = 0)),
         ghg_MTCO2 = round(ghg_kgCO2/1000, digits = 0),
         sqft = round(sqft, digits = 0),
         med_kwh_sqft = round(med_kwh_sqft, digits = 1),
         kwh_sqft_25 = ifelse(n == 1, "-", round(kwh_sqft_25, digits = 1)),
         kwh_sqft_75 = ifelse(n == 1, "-", round(kwh_sqft_75, digits = 1))) %>%
    select(type, n, kwh, dollars, ghg_MTCO2, sqft, med_kwh_sqft, kwh_sqft_25, kwh_sqft_75) %>% 
  arrange(desc(kwh))

datatable(joined_pretty_cat,
          filter = 'top',
          rownames = FALSE,
          colnames = c("Building\ntype","Buildings\nwith data", "kWh", "Est. cost", 
                       "CO2e\n(MT)", "Square\nfootage", "Median\nkWh\nper sqft",
                       "25th Perc.", "75th Perc."),
          caption = "Table 1. Descriptive statistics for annual electricity use by building type.")

Summary graph by meter status

ggplot(filter(buildings_sum, !is.na(meter) & !type_new %in% c("Res Hall - U","Non-building","Production")),
       aes(x = reorder(type_new, sqft, FUN = "sum"), y = sqft/1000, fill = meter)) +
  geom_col(position = "stack") +
  theme_bw() +
  labs(x = "", y = "Square footage (1000)", fill = "Meter status")

Past versions of "graph meter-1.png"

Version	Author	Date
2fef649	maggiedouglas	2026-02-24
a379c87	maggiedouglas	2026-02-23
1e465a5	maggiedouglas	2026-02-23

Export for downstream analyses

write.csv(joined_daily_full, "./output/kwh_daily.csv", row.names = F)
write.csv(joined_full, "./output/kwh_annual.csv", row.names = F)

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.7.2

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] scales_1.3.0    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_1.3.0     tibble_3.2.1    ggplot2_3.5.0   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_0.6.34     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       crosstalk_1.2.1   jquerylib_0.1.4   cli_3.6.2        
[21] rlang_1.1.3       ellipsis_0.3.2    munsell_0.5.0     withr_3.0.0      
[25] cachem_1.0.8      yaml_2.3.8        tools_4.3.2       tzdb_0.4.0       
[29] colorspace_2.1-0  httpuv_1.6.13     vctrs_0.6.5       R6_2.5.1         
[33] lifecycle_1.0.4   git2r_0.33.0      htmlwidgets_1.6.4 fs_1.6.3         
[37] pkgconfig_2.0.3   pillar_1.9.0      bslib_0.6.1       later_1.3.2      
[41] gtable_0.3.4      glue_1.7.0        Rcpp_1.0.12       highr_0.10       
[45] xfun_0.41         tidyselect_1.2.0  rstudioapi_0.15.0 knitr_1.45       
[49] farver_2.1.1      htmltools_0.5.7   labeling_0.4.3    rmarkdown_2.25   
[53] compiler_4.3.2