04-report-template-oyo

Set up

library(tidyverse)
library(janitor)

Import your files

all_outages <- read_rds("data-processed-power/power_outages.rds")

all_outages |> glimpse()

Rows: 526,165
Columns: 14
$ state_event          <chr> "Arkansas", "Arkansas", "Arkansas", "Arkansas", "…
$ datetime_event_began <dttm> 2014-11-24 00:00:00, 2014-11-24 00:00:00, 2014-1…
$ datetime_restoration <dttm> 2014-11-24 00:00:00, 2014-11-24 00:00:00, 2014-1…
$ event_type           <chr> "Fuel Supply Emergency - Coal", "Fuel Supply Emer…
$ fips                 <dbl> 5021, 5035, 5051, 5069, 5093, 5119, 5121, 5121, 5…
$ state                <chr> "Arkansas", "Arkansas", "Arkansas", "Arkansas", "…
$ county               <chr> "Clay", "Crittenden", "Garland", "Jefferson", "Mi…
$ start_time           <dttm> 2014-11-24 09:30:00, 2014-11-24 09:30:00, 2014-1…
$ duration             <dbl> 5.75, 1.25, 0.75, 1.25, 0.75, 1.25, 5.25, 0.75, 6…
$ end_time             <dttm> 2014-11-24 15:15:00, 2014-11-24 10:45:00, 2014-1…
$ min_customers        <dbl> 456, 755, 279, 252, 301, 523, 223, 229, 588, 287,…
$ max_customers        <dbl> 1803, 755, 293, 256, 301, 523, 401, 229, 700, 287…
$ mean_customers       <dbl> 1301.4783, 755.0000, 287.3333, 254.0000, 301.0000…
$ event_category       <chr> "Fuel Supply Issue", "Fuel Supply Issue", "Fuel S…

OYO:

Create parameter list and filter for parameters

# state_list <- 
# 
# state_list

States: Texas

# outages <- all_outages |> 
# 
# outages

Some analysis:

Common cause of outages

# outages |>
#   group_by (event_category) |>
#   summarize(total_events = n()) |>
#   arrange(total_events |> desc())

Cause of longest outages

# outages |>
#   group_by (event_category) |>
#   summarize(avg_outage_length = mean(duration), total_occurences = n()) |>
#   arrange(avg_outage_length |> desc(), total_occurences)