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CD_template_2024.rmd
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---
title: "Environmental Enforcement Watch"
output:
html_paged:
number_sections: false
css: ["custom_current.css"]
params:
cd_state: "WA01"
full_name: "Washington's 1st Congressional District"
state: "WA"
district: "01"
---
```{r setup, include=FALSE}
# cd_state: "AK00"
# full_name: "Alaska's Congressional District"
# state: "AK"
# district: "00"
# cd_state: "AL05"
# full_name: "Alabama's 5th Congressional District"
# state: "AL"
# district: "05"
library(reticulate)
library(rlist)
library(data.table)
library(RSQLite)
library(tidyverse)
library(patchwork)
library(here)
library(ggplot2)
library(tidyr)
library(dplyr)
library(shiny)
library(extrafont)
library(viridis)
library(grid)
library(gridExtra)
library(kableExtra)
library(ggrepel)
library(stringr)
library(reshape)
loadfonts()
CD <- substr(params$cd_state, 3, 4)
target_year <- "2023"
full_name <- params$full_name
# States with a single congressional district: The leg_info.db
# has the district as "00", per
# 'https://theunitedstates.io/congress-legislators/legislators-current.json'.
# The regions.db has the district as 1, per EPA's ECHO_EXPORTER.
# So if the CD variable is "00", we will use a Region_CD variable
# when getting the region data.
Region_CD <- CD
# if (Region_CD == "00") Region_CD <- "01"
output_loc <- paste("Output/", params$cd_state, "/", sep = "")
dir.create(output_loc)
conn <- dbConnect(RSQLite::SQLite(), "leg_info.db")
legislator = dbGetQuery(conn, "select * from legislators where cd_state=?",
params=c(params$cd_state))
leg_info <- list(name=legislator$name, party=legislator$party,
sen_class=legislator$sen_class,
since_date=legislator$since_date, since_year=legislator$since_year,
official_url=legislator$official_url,
govtrack_url=legislator$govtrack_url,
wikipedia_url=legislator$wikipedia_url,
opensecrets_url=legislator$opensecrets_url,
twitter=legislator$twitter,
facebook=legislator$facebook)
committee_membership = dbGetQuery(conn,
"select * from committee_members where bioguide_id = ?",
params=c(legislator$bioguide_id))
committees <- list()
if (nrow(committee_membership) > 0) {
for (row in 1:nrow(committee_membership)) {
committee_id <- committee_membership[ row, "committee_id" ]
committee <- dbGetQuery(conn,
"select * from committees where committee_id = ?",
params=c(committee_id))
if (committee$include == 0)
next;
subcommittee_id <- committee_membership[ row, "subcommittee_id" ]
if (is.null(subcommittee_id))
next;
if (nchar(subcommittee_id) == 0) {
committees[[committee_id]] <- list(committee=committee)
} else {
subcommittee <- dbGetQuery(conn,
"select *,? as rank from sub_committees where committee_id=? and subcommittee_id=?",
params=c(committee_membership[row,"rank"],committee_id,subcommittee_id))
committees[[committee_id]] <- list.append(committees[[committee_id]],
subcommittee)
}
}
}
dbDisconnect(conn)
# knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
```
```{r include=FALSE}
knitr::opts_chunk$set(engine.path=list(
python='/mnt/c/Users/steve/workspace/EDGI/EEW-ReportCard-Data/venv/bin/python'))
# library(reticulate)
# virtualenv_create('venv')
# py_install('pandas', envname='/mnt/c/Users/steve/workspace/EDGI/EEW-ReportCard-Data/venv')
# py_install('geopandas', envname='/mnt/c/Users/steve/workspace/EDGI/EEW-ReportCard-Data/venv')
# py_install('requests', envname='/mnt/c/Users/steve/workspace/EDGI/EEW-ReportCard-Data/venv')
# py_install('ECHO_modules', envname='/mnt/c/Users/steve/workspace/EDGI/EEW-ReportCard-Data/venv')
target_year_int <- strtoi(target_year)
u <- import('Region')
region <- u$Region(db='region_cds.db', type='Congressional District',
value=Region_CD, state=params$state, programs=c('CAA', 'CWA', 'RCRA'),
base_year=target_year_int)
USAinspectionsper1000_All <- region$get_per_1000('inspections', 'USA')
inspectionsper1000_state <- region$get_per_1000('inspections', 'State')
inspectionsper1000_cd <- region$get_per_1000('inspections', 'CD')
USAviolationsper1000_All <- region$get_per_1000('violations', 'USA')
violationsper1000_state <- region$get_per_1000('violations', 'State')
violationsper1000_cd <- region$get_per_1000('violations', 'CD')
inflation <- region$get_inflation()
# CWAper1000 <- region$get_cwa_per_1000(target_year_int)
violations <- region$get_events('violations', 'All')
CAAviolations <- region$get_events('violations', 'CAA')
CWAviolations <- region$get_events('violations', 'CWA')
RCRAviolations <- region$get_events('violations', 'RCRA')
enforcement <- region$get_events('enforcements', 'All')
CAAenforcement_clean <- region$get_events('enforcements', 'CAA')
CWAenforcement_clean <- region$get_events('enforcements', 'CWA')
RCRAenforcement_clean <- region$get_events('enforcements', 'RCRA')
CAArecurring <- region$get_recurring_violations('CAA')
CWArecurring <- region$get_recurring_violations('CWA')
RCRArecurring <- region$get_recurring_violations('RCRA')
inspections <- region$get_events('inspections', 'All')
CAAinspections <- region$get_events('inspections', 'CAA')
CWAinspections <- region$get_events('inspections', 'CWA')
RCRAinspections <- region$get_events('inspections', 'RCRA')
CAAbadactors <- region$get_non_compliants('CAA')
CWAbadactors <- region$get_non_compliants('CWA')
RCRAbadactors <- region$get_non_compliants('RCRA')
ranked <- round(region$get_ranked() * 100.)
ranked_t <- data.table::transpose(ranked)
ranked_t["Program"] <- c("CAA", "CAA", "CAA", "CWA", "CWA","CWA","RCRA", "RCRA","RCRA")
ranked_t["Metric"] <- c("Viol/Insp", "Violations", "Viol/Enf", "Viol/Insp", "Violations", "Viol/Enf", "Viol/Insp", "Violations", "Viol/Enf")
this_state <- params$state
```
```{python echo=FALSE}
import warnings
warnings.filterwarnings("ignore")
from create_df import create_df
cwa_insp_years = create_df(db='region_cds.db', target_year=r.target_year_int,
region_type='Congressional District', data_type='inspections',
y_field='Count',program='CWA', states=[r.this_state,],
region_selected=r.Region_CD)
cwa_viol_years = create_df(db='region_cds.db', target_year=r.target_year_int,
region_type='Congressional District',data_type='violations',
y_field='Count',program='CWA',states=[r.this_state,],
region_selected=r.Region_CD)
cwa_enf_count_years = create_df(db='region_cds.db', target_year=r.target_year_int,
region_type='Congressional District',data_type='enforcements',
y_field='Count',program='CWA',states=[r.this_state,],
region_selected=r.Region_CD)
cwa_enf_count_years = cwa_enf_count_years.drop('Amount', axis=1)
cwa_enf_dollar_years = create_df(db='region_cds.db', target_year=r.target_year_int,
region_type='Congressional District',data_type='enforcements',
y_field='Amount',program='CWA',states=[r.this_state,],
region_selected=r.Region_CD)
cwa_enf_dollar_years = cwa_enf_dollar_years.drop('Count', axis=1)
cwa_enf_dollar_years['USA'] = cwa_enf_dollar_years['USA']/1000.
cwa_enf_dollar_years[r.this_state] = cwa_enf_dollar_years[r.this_state]/1000.
cwa_enf_dollar_years[r.Region_CD] = cwa_enf_dollar_years[r.Region_CD]/1000.
```
```{r echo=FALSE, include=FALSE}
cwa_insp_years <- reshape::melt(py$cwa_insp_years, id=c("Year"))
cwa_viol_years <- reshape::melt(py$cwa_viol_years, id=c("Year"))
cwa_enf_count_years <- reshape::melt(py$cwa_enf_count_years, id=c("Year"))
cwa_enf_dollar_years <- reshape::melt(py$cwa_enf_dollar_years, id=c("Year"))
```
```{r echo=FALSE, include=FALSE}
# Color selection
region_colors <- c("darkblue", "#008080", "orange", "#46B8DA")
graph1 <- ggplot(cwa_insp_years, aes(x = Year, y = value, color = variable)) +
geom_line() +
scale_color_manual(values = region_colors) +
guides(color = guide_legend(title = "Regions")) +
labs(y="# Inspections", x="Year", title="CWA - Inspections Per Facility")
graph2 <- ggplot(cwa_viol_years, aes(x = Year, y = value, color = variable)) +
geom_line() +
scale_color_manual(values = region_colors) +
guides(color = guide_legend(title = "Regions")) +
labs(y="# Violations", x="Year", title="CWA - Violations Per Facility")
graph3 <- ggplot(cwa_enf_count_years, aes(x = Year, y = value, color = variable)) +
geom_line() +
scale_color_manual(values = region_colors) +
guides(color = guide_legend(title = "Regions")) +
labs(y="# Enforcements", x="Year", title="CWA - Enforcement Actions Per Facility")
graph4 <- ggplot(cwa_enf_dollar_years, aes(x = Year, y = value, color = variable)) +
geom_line() +
scale_color_manual(values = region_colors) +
guides(color = guide_legend(title = "Regions")) +
labs(y="Fines ($1000)", x="Year", title="CWA - Enforcement Fines Per Facility")
```
```{r echo=FALSE, include=FALSE}
which_president <- function(which_year) {
case_when(
which_year < 2009 ~ "Bush",
which_year < 2017 & which_year > 2008 ~ "Obama",
which_year < 2021 & which_year > 2016 ~ "Trump",
which_year > 2020 ~ "Biden"
)
}
##getting %difference CWA violations
CWAviolations <- CWAviolations %>%
mutate(President = which_president(Year))
vCWAchange <- data.frame("PreBidenAverage" =mean(CWAviolations[1:20,2]),
"Biden Average" =mean(CWAviolations[21:23,2])) # SH
vCWAchange$PercentDifference <- as.numeric(100*(vCWAchange[1,2]-vCWAchange[1,1])/
vCWAchange[1,1])
vCWAchange$PercentDifference <-round(vCWAchange$PercentDifference,0)
vCWAchange$Extra <- ifelse((vCWAchange$PercentDifference>=100)|(vCWAchange$PercentDifference<=-100), "much", "")
vCWAchange$Change <- ifelse(vCWAchange$PercentDifference<0, "better", "worse")
vCWAchange$Positive <- abs(vCWAchange$PercentDifference)
vCWAchange$Value <- ifelse(vCWAchange$PercentDifference>=0, "increase", "decrease")
#enforcement % difference
enforcement$Amount <- round(enforcement$Amount,2)
#add presidental years
enforcement <- enforcement %>%
mutate(President = which_president(Year))
actionchange <- data.frame("PreBidenAverage" =mean(enforcement[1:20,3]),
"Biden Average" =mean(enforcement[21:23,3]))
actionchange$PercentDifference <- as.numeric(100*(actionchange[1,2]-actionchange[1,1])/
actionchange[1,1])
actionchange$PercentDifference <-round(actionchange$PercentDifference,0)
actionchange$Extra <- ifelse((actionchange$PercentDifference>=100)|(actionchange$PercentDifference<=-100), "much", "")
actionchange$Change <- ifelse(actionchange$PercentDifference<0, "worse", "better")
actionchange$Positive <- abs(actionchange$PercentDifference)
actionchange$Value <- ifelse(actionchange$PercentDifference>=0, "increase", "decrease")
```
```{r readData, echo=FALSE}
names(USAinspectionsper1000_All)[1] <- "Program"
names(USAviolationsper1000_All)[1] <- "Program"
names(inflation)[2] <- "base"
#set up a theme default to reduce code block later
theme_meg <- function () {
theme_bw(base_family="Georgia") %+replace%
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.border = element_blank(),
axis.line = element_line(colour = "black"), axis.title.x=element_text(face = "bold", size = 12),
axis.title.y=element_text(face = "bold", size = 10, angle=90), plot.title=element_text(face = "bold", size = 14, hjust =.5),
axis.text.x=element_text(face = "bold", size = 14), axis.text.y=element_text(face = "bold", size = 8))
}
if (CD == '00') {
map_src <- paste('State_maps/map_',params$state,'.png',sep='')
} else {
map_src <- paste('CD_maps/map_',params$state,CD,'.png',sep='')
}
```
<div class="title"><span style="padding-left: 100px">
Congressional Report Card</span></div>
<hr style="height:3px;border-width:0; margin-left:15px; margin-right:15px;color: #b9b9b9;background-color: #b9b9b9; margin-top: .5em; margin-bottom: 0em">
##### `r full_name`; seat held by `r leg_info$name` since `r leg_info$since_year`
<div class="container">
<img src="`r map_src`" alt="Snow" style="width:100%;">
<div class="circle-map1">`r region$get_active_facilities('CAA')`</div>
<div class="circle-map2">`r region$get_active_facilities('CWA')`</div>
<div class="circle-map3">`r region$get_active_facilities('RCRA')`</div>
<div class="facility1">facilities</div>
<div class="facility2">facilities</div>
<div class="facility3">facilities</div>
<div class="rectangle">Operating Facilities</div>
<div class="circle-caa">CAA</div>
<div class="circle-cwa">CWA</div>
<div class="circle-rcra">RCRA</div>
<div class="bottom-left">Map shows `r full_name` with number of regulated facilities per program.</div>
</div>
<a href="https://www.environmentalenforcementwatch.org/"><img src="logosandimages/eew_logo.png" style="float: left; width: 9%; margin-left: 30%"></a>
<a href="https://envirodatagov.org/"><img src="logosandimages/edgi_logo.png" style="float: right; width: 15%; margin-right: 30%" ></a>
\pagebreak
<h4>Report Generated on `r format(Sys.time(), '%B %d, %Y')`<span style="padding-left:400px">Page 1</span></h4>
# Introduction
## Why report cards on compliance with and enforcement of environmental protection laws?
The Environmental Protection Agency (EPA) is charged by Congress to enforce laws that protect people from air pollution, water pollution and hazardous waste. **Without effective enforcement, these laws are meaningless**. Based on data from EPA’s Enforcement and Compliance History Online (ECHO) database this report card reviews violations, inspections and enforcement actions under three laws: Clean Air Act (CAA), Clean Water Act (CWA) and Resource Conservation and Recovery Act (RCRA) for this Congressional District or State since 2001. Report cards like this one are becoming available on the [EEW website](https://www.environmentalenforcementwatch.org) for all House Representatives and Senators. The [EEW website](https://www.environmentalenforcementwatch.org) also has a summary analysis of enforcement trends and data issues for all geographies covered by the House Energy and Commerce and Senate Environment and Public Works Committees. The report cards contain data from *both* state environmental agencies and the EPA. If the states are enforcing the above laws, it is because the EPA has delegated that authority to them. The EPA must ensure that states are doing their job. Congress must ensure that the EPA is doing its job. And the public must have accurate data from states and the EPA in order to understand if national environmental laws are being properly enforced. For the first time, EEW Congressional Report Cards give members of Congress and their constituents the chance to evaluate whether the EPA is fulfilling its mandate in their district. Congress can strengthen EPA enforcement by increasing its budget, passing more effective laws, requiring better data collection, and holding the EPA accountable when it fails to protect people.
## What is a "regulated facility"?
<a ><img src="logosandimages/facilities2.png" style="float: center; width: 70%; margin-left: 15%" ></a>
<br />
A regulated facility in this report is a facility that reports air or water emissions under the Clean Air Act or Clean Water Act, or a facility that generates, transports, or disposes of hazardous waste under the Resource Conservation and Recovery Act. Regulated facilities can be large-scale e.g. oil refineries, or small-scale e.g. dry cleaners.
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# Grading the District on Data from `r target_year_int-4` through `target_year`
<h3>This graph shows how this district compares by its percentile with other U.S. congressional districts on three metrics: number of violations, number of violations per inspection, and number of violations per enforcement action. These metrics are used on the data from each of the three EPA programs--the Clean Water Act (CWA), the Clean Air Act (CAA) and the Resource Conservation and Recovery Act (RCRA). The data used is for the past five years, `r target_year_int-4` through `r target_year`.</h3>
<br />
```{r echo=FALSE, include=FALSE, fig.height = 5, fig.width = 8, fig.align = "center"}
grade <- function(score) {
return (case_when(score >= 80 ~ 'F',
score >= 60 ~ 'D',
score >= 40 ~ 'C',
score >= 20 ~ 'B',
TRUE ~ 'A'))
}
metric_colors <- c("darkblue", "#008080", "orange")
ranked_t$order <- c("b","a","c","b","a","c","b","a","c")
cd_percent_bar <- ggplot(ranked_t, aes(fill=order, y=V1, x=Program, alpha=Program))+
geom_bar(position="dodge", stat="identity")+
scale_fill_manual(values=metric_colors, labels = c("Violations/Facility","Violations/Inspections","Violations/Enforcements"))+
labs(y="Percentiles Among Districts\n", x="")+scale_alpha_manual(values=c(1,1,1), guide='none')+
geom_text(aes(label=V1, family="Georgia"), position = position_dodge(0.9), vjust = -0.1, size=5)+
ggtitle(paste("Percentile Ranking Among All Districts, 2019-2023"))+
scale_y_continuous(expand=c(0,0),limits=c(0,100))+
theme_meg()
```
```{r echo=FALSE, fig.height=3, out.width= "95%", out.extra='style="float:center; padding:0px"'}
cd_percent_bar
```
<h3>As an example, a Violations per Facility score of `r ranked$CWA_Viol_Pct` for CWA violations means that this district has more violations than `r ranked$CWA_Viol_Pct`% of all districts in the United States. From these scores we can assign letter grades to districts--the top 20%, those districts with more violations than 80% of all districts, would get an F; the districts scoring between 60% and 80% get a D; between 40% and 60% get a C; between 20% and 40% get a B; and less than 20% get an A. As such, `r params$full_name` receives the following grades:</h3>
<ul>
<li><h3>CAA Violations per Facility - `r grade(ranked$CAA_Viol_Pct)`</h3></li>
<li><h3>CAA Violations per Inspection - `r grade(ranked$CAA_Insp_Pct)`</h3></li>
<li><h3>CAA Violations per Enforcement - `r grade(ranked$CAA_Enf_Pct)`</h3></li>
<li><h3>CWA Violations per Facility - `r grade(ranked$CWA_Viol_Pct)`</h3></li>
<li><h3>CWA Violations per Inspection - `r grade(ranked$CWA_Insp_Pct)`</h3></li>
<li><h3>CWA Violations per Enforcement - `r grade(ranked$CWA_Enf_Pct)`</h3></li>
<li><h3>RCRA Violations per Facility - `r grade(ranked$RCRA_Viol_Pct)`</h3></li>
<li><h3>RCRA Violations per Inspection - `r grade(ranked$RCRA_Insp_Pct)`</h3></li>
<li><h3>RCRA Violations per Enforcement - `r grade(ranked$RCRA_Enf_Pct)`</h3></li>
</ul>
<h3>Rationale for grading using these metrics:</h3>
<ul>
<li>More <strong>violations per active facility</strong> are worse.</li>
<li>More <strong>inspections</strong> mean more problems will be found, which is good. Dividing violations by inspections indicates the strength of the inspecting</li>
<li>More <strong>enforcements</strong> when violations are found disincentivizes violating. Dividing violations by enforcements indicates EPA's willingness to penalize.</li>
</ul>
*see data limitations page for metric calculations
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# Clean Water Act - Inspections, Violations, Enforcements Since 2001
<h3>These graphs show the changes in numbers of inspections, violations and enforcement actions
per facility for the U.S., the state of `r params$state`, and congressional district `r Region_CD`,
under the Clean Water Act (CWA)*. </h3>
*(The current number of active
facilities is used for the calculations for all graphs, as the historical data for facility counts
was not available. The graphs therefore give trends rather than faithful statistics.)
<br />
```{r echo=FALSE, fig.height=3, out.width= "95%", out.extra='style="float:center; padding:0px"'}
graph1 | graph2
knitr::opts_chunk$set(fig.width = 10,fig.height = 6)
```
```{r echo=FALSE, fig.height=3, out.width= "95%", out.extra='style="float:center; padding:0px"'}
graph3 | graph4
knitr::opts_chunk$set(fig.width = 10,fig.height = 6)
```
<h3>Clean Water Act Violations*: <span style="color:#569b5e">Violations during the Biden administration `r vCWAchange[1,4]` `r vCWAchange[1,5]`</span> than the average over the previous 20 years, representing a <span style="color:#569b5e">`r vCWAchange[1,6]`% `r vCWAchange[1,7]`</span> in violations</h3>
<br />
<h3>Enforcement actions under Clean Air Act, Clean Water Act, and the Resource Conservation and Recovery Act*: <span style="color:#569b5e">`r actionchange[1,4]` `r actionchange[1,5]`</span> than the average over the previous 20 years, representing a <span style="color:#569b5e">`r actionchange[1,6]`% `r actionchange[1,7]`</span> in enforcement actions</h3>
<p>When comparing the graphs, note that the vertical axes may have considerably different scales.</p>
<br />
*see data limitations page for metric calculations
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```{r echo=FALSE}
#CWA
##first let's fix up the data
#ok so We'll put the % in the title for the state, and try to figure out the same for the state/CD
CWArecurring2 <- CWArecurring[-is.na(CWArecurring$CD), ]
CWArecurring2[5] <-round(CWArecurring2[5],1)
CWArecurring <- CWArecurring %>%
select(CD,Facilities, Percent)
names(CWArecurring)[1] <- "Region"
CWArecurring[1,1] <- as.character("Rest of State")
CWArecurring[2,1] <- as.character(paste(params$state,CD))
CWArecurring[2,4] <- as.character(paste(params$state,CD))
CWArecurring$RecurringViolators <- (as.numeric(CWArecurring[1,2]-CWArecurring[2,2]))
CWArecurring[2,5] <- as.numeric(CWArecurring[2,2])
CWArecurring$Proportional <- (as.numeric((100/(CWArecurring[1,2]))*CWArecurring[1,5]))
CWArecurring[2,6] <- as.numeric(100-CWArecurring[1,6])
CWArecurring[3] <-round(CWArecurring[3],0)
#we don't currently utilize the recurring # or % under the CAA or RCRA b/c of the unreliability of the data. We do call that data from the regions.db, however. If and when this is needed, you can use the code above from CWA to replicate the process for these other acts
```
```{r echo=FALSE}
##getting %difference effluent violations
#[9:12,2] Obama
# [17:20,2] Trump [17:19] first three years
# [21:23,2] Biden first three years
effluentviolations <- CWAviolations # SH
effluentchange <- data.frame("Trump Average" =mean(effluentviolations[17:19,2]),
"Biden Average" =mean(effluentviolations[21:23,2])) # SH
effluentchange$PercentDifference <- as.numeric(100*(effluentchange[1,2]-effluentchange[1,1])/
effluentchange[1,1])
effluentchange[,1:3] <-round(effluentchange[,1:3],2)
#then simple plot
president_colors <- c("#450d54", "#22908d", "#e56d13", "#0EAD5D")
effluentgraph <- ggplot(effluentviolations, mapping=aes(x=factor(Year),
y=Count, fill=President, group=1))+
geom_bar(stat="identity", alpha=.6)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'), guide=guide_legend(breaks=c("Bush","Obama","Trump","Biden")))+
labs(y=" # Violations", x="Year", title="Clean Water Act Violations\n", subtitle ="Mostly complete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(effluentviolations$Count+(.2*max(effluentviolations$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012,2016,2020,2024))+
theme_meg()+
theme(axis.text.x=element_text(size=9),
axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5, size=10, color="darkgreen"),
plot.title = element_text(lineheight = .1, size=10))
##Inspections % change and graph
#[9:12,2] Obama
# [17:20,2] Trump [17:19] first three years
# [21:23,2] Biden first three years
inspectionchange <- data.frame("Trump Average" =mean(inspections[17:19,2]),
"Biden Average" =mean(inspections[21:23,2]))
inspectionchange$PercentDifference <- as.numeric(100*(inspectionchange[1,2]-inspectionchange[1,1])/
inspectionchange[1,1])
inspectionchange[,1:3] <-round(inspectionchange[,1:3],0)
#syntax for bullet points
inspectionchange$Change <- ifelse(inspectionchange$PercentDifference>=0, "increase", "decrease")
inspectionchange$Positive <- abs(inspectionchange$PercentDifference)
#first add in the Presidential Years
inspections <- inspections %>%
mutate(President = which_president(Year))
#then simple plot
inspectiongraph <- ggplot(inspections, mapping=aes(x=factor(Year),
y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=president_colors, breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y="Inspections", x="Year", title="Facility Inspections-CAA,CWA,RCRA\n", subtitle = "Potentially incomplete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(inspections$Count+(.2*max(inspections$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012,2016,2020,2024))+
theme_meg()+
theme(axis.text.x=element_text(size=9),
axis.title.x = element_blank(), plot.subtitle = element_text(size = 10, color = "dark gray", hjust=.5), plot.title =
element_text(lineheight = .1, size=10))
# axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5, size=9), plot.title =
# element_text(lineheight = .1, size=10)) # SH
#enforcement
#[9:12,2] Obama
# [17:20,2] Trump [17:19] first three years
# [21:23,2] Biden first three years
enforcementchange <- data.frame("Trump Average Count" =mean(enforcement[17:19,3]),
"Biden Average Count" =mean(enforcement[21:23,3]),
"Trump Average Fine" =mean(enforcement[17:19,2]),
"Biden Average Fine" =mean(enforcement[21:23,2])) # SH
enforcementchange$PercentDifferenceCount <- as.numeric(100*(enforcementchange[1,2]-enforcementchange[1,1])/
enforcementchange[1,1])
enforcementchange$PercentDifferenceFine <- as.numeric(100*(enforcementchange[1,4]-enforcementchange[1,3])/
enforcementchange[1,3])
enforcementchange[,1:6] <-round(enforcementchange[,1:6],0)
#syntax for bullet points
enforcementchange$CountChange <- ifelse(enforcementchange$PercentDifferenceCount>=0, "increase", "decrease")
enforcementchange$PositiveCount <- abs(enforcementchange$PercentDifferenceCount)
enforcementchange$FineChange <- ifelse(enforcementchange$PercentDifferenceFine>=0, "increase", "decrease")
enforcementchange$PositiveFine <- abs(enforcementchange$PercentDifferenceFine)
#adjust for inflation
enforcement <- merge(enforcement, inflation, by='Year', all.x=TRUE)
enforcement$inflationadjust <- as.numeric(enforcement$Amount*enforcement$base)
##make a new amount column /1000
enforcement$Amountinthousands <- ((enforcement$inflationadjust)/1000)
enforcement$Amountinthousands <-round(enforcement$Amountinthousands,2)
#plot change in enforcement
enforcementactiongraph <- ggplot(enforcement, mapping=aes(x=factor(Year),y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=president_colors, breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y="# Actions", x="Year", title="Enforcement Actions-CAA,CWA,RCRA\n", subtitle = "Potentially incomplete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(effluentviolations$Count+(.2*max(effluentviolations$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012,2016,2020,2024))+
geom_text(aes(label=Count, family="Georgia", fontface="bold"), position = position_dodge(0.9), vjust = -0.1, size=3)+
theme_meg()+
theme(axis.text.x=element_text(size=9),
axis.title.x = element_blank(), plot.subtitle = element_text(size = 10, color = "dark gray", hjust=.5), plot.title =
element_text(lineheight = .1, size=10))
# axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5, size=9), plot.title =
# element_text(lineheight = .1, size=10))
enforcementfinegraph <- enforcement %>% ggplot()+
geom_bar(aes(x=factor(Year),y=Amountinthousands , fill=President, group=1),stat="identity", show.legend=FALSE, alpha=.6)+
scale_fill_manual(values=president_colors, breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y="Fines ($1000s)", x="Year", caption="Values adjusted for inflation. Note, the y-axis reports values in 1000's. A value of $2000\nrepresents $2,000,000 (2 million dollars).", title="Enforcement Fines-CAA,CWA,RCRA\n", subtitle="Potentially incomplete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(enforcement$Amountinthousands+(.2*max(enforcement$Amountinthousands)))))+
scale_x_discrete(breaks=c(2004,2008,2012,2016,2020,2024))+
theme_meg()+
theme(axis.text.x=element_text(size=9),
axis.title.x = element_blank(), plot.subtitle = element_text(size = 10, color = "dark gray", hjust=.5), plot.title =
element_text(lineheight = .1, size=10))
# axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5, size=9), plot.caption = element_text(size=10), plot.title =
# element_text(lineheight = .1, size=10))
```
# Highlights for `r full_name`
<h3>Comparing the first three years of the Trump administration to those of the Biden administration, there was a <span style="color:#569b5e">`r inspectionchange[1,5]`% `r inspectionchange[1,4]` in inspections, `r enforcementchange[1,10]`% `r enforcementchange[1,9]` in fines</span>, and a <span style="color:#569b5e">`r enforcementchange[1,8]`% `r enforcementchange[1,7]` in enforcement actions.</span></h3>
<br />
<h3>Under the Clean Water Act, the law whose regulation is best documented by available EPA data, <span style="color:#569b5e">`r CWArecurring2[1,2]` facilities, representing `r CWArecurring2[1,5]`% of all regulated facilities in `r params$cd_state`,</span> were in violation for <span style="color:#569b5e">at least 9 months of the last 3 years.</span></h3>
```{r echo=FALSE, fig.height=6, out.width= "95%", out.extra='style="float:center; padding:0px"'}
effluentgraph/inspectiongraph/enforcementactiongraph/enforcementfinegraph
```
The reliability of data in figures throughout this report is indicated by the figure subtitle and degree of transparency. See the data limitations page (Page 10) to view the transparency-coding table and access state and congressional district data [here](https://colab.research.google.com/github/edgi-govdata-archiving/ECHO-Cross-Program/blob/main/ECHO-Cross-Programs.ipynb).
<p>When comparing the graphs, note that the vertical axes may have considerably different scales.</p>
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# This District in Comparison
```{r echo=FALSE, include=FALSE, fig.height = 6, fig.width = 8, fig.align = "left"}
#comparing inspections
#let's combine the inspections data at national, state, CD level
names(USAinspectionsper1000_All)[2] <- "Inspectionsper1000"
USAinspectionsper1000_All$Region <- as.factor("National")
names(inspectionsper1000_state)[2] <- "Inspectionsper1000"
inspectionsper1000_state$Region <- as.factor("State")
names(inspectionsper1000_cd)[2] <- "Inspectionsper1000"
inspectionsper1000_cd$Region <- as.factor("Congressional District")
inspectionsper1000 <- rbind(USAinspectionsper1000_All, inspectionsper1000_state, inspectionsper1000_cd)
inspectionsper1000$Inspectionsper1000 <- as.numeric(inspectionsper1000$Inspectionsper1000)
inspectionsper1000[2] <-round(inspectionsper1000[2],0)
#Now let's visualize this
inspect1000bar <- ggplot(inspectionsper1000, aes(fill=Region, y=Inspectionsper1000, x=Program, alpha=Program))+
geom_bar(position="dodge", stat="identity")+
scale_fill_manual(values=region_colors)+
labs(y="Inspections per 1000 Facilities\n", x="")+
scale_alpha_manual(values=c(.6,1,.6), guide='none')+
geom_text(aes(label=Inspectionsper1000, family="Georgia"), position = position_dodge(0.9), vjust = -0.1, size=4)+
ggtitle(paste("Inspections per 1000 Facilities in",target_year," "))+
scale_y_continuous(expand=c(0,0),limits=c(0,1200))+geom_hline(yintercept=1000, linetype="dashed", color="black")+geom_text(aes(label="Average of 1 inspection per facility",family="Georgia"),x=2,y=1050,color="black",size=4)+
theme_meg()
#comparing violations
#let's combine the violations data at national, state, CD level
names(USAviolationsper1000_All)[2] <- "violationsper1000"
USAviolationsper1000_All$Region <- as.factor("National")
names(violationsper1000_state)[2] <- "violationsper1000"
violationsper1000_state$Region <- as.factor("State")
names(violationsper1000_cd)[2] <- "violationsper1000"
violationsper1000_cd$Region <- as.factor("Congressional District")
violationsper1000 <- rbind(USAviolationsper1000_All, violationsper1000_state, violationsper1000_cd)
violationsper1000$violationsper1000 <- as.numeric(violationsper1000$violationsper1000)
violationsper1000[2] <-round(violationsper1000[2],0)
#Now let's visualize this
violations1000bar <- ggplot(violationsper1000, aes(fill=Region, y=violationsper1000, x=Program, alpha=Program))+
geom_bar(position="dodge", stat="identity", show.legend=FALSE)+
scale_fill_manual(values=region_colors)+
scale_alpha_manual(values=c(.3,1,.3), guide='none')+
labs(y="Violations per 1000 Facilities", x="")+
geom_text(aes(label=violationsper1000, family="Georgia"), position = position_dodge(0.9), vjust = -0.1, size=4)+
ggtitle(paste("Violations per 1000 Facilities in",target_year," "))+
scale_y_continuous(expand=c(0,0),limits=c(0,max(violationsper1000$violationsper1000+(.2*max(violationsper1000$violationsper1000)))))+
theme_meg()+
theme(legend.position = c(0.8, 0.5))
```
```{r echo=FALSE, out.width= "95%", out.extra='style="float:center; padding:0px"'}
inspect1000bar / violations1000bar
knitr::opts_chunk$set(fig.width = 10,fig.height = 6)
```
<br />
These two charts show how inspections and violations in this district compare to the national and state averages per 1000 facilities in `r target_year`. We use data from `r target_year` as it was the most recent full year and the ECHO database only reports *currently* active facilities. To enable comparison across locations with a differing number of active facilities, we standardize the comparison to a value per 1000 facilities, proportionally adjusting the data if there are more or less than 1000 facilities in a district or state.
For access to the software which pulls data from ECHO, see the Github repository [here](https://github.com/edgi-govdata-archiving/EEW-ReportCard-Data).
The reliability of data in figures throughout this report is indicated by the figure subtitle and degree of transparency. Figure transparency illustrates data reliability: the more transparent, the more uncertain the data. See the data limitations page (Page 10) to view the transparency-coding table
\pagebreak
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<h1><span style="margin-bottom:0px; padding-bottom:0px">Recent Non-Compliance in this District</span></h1>
<font size="2">These figures show the ten facilities in this district with the worst environmental compliance based on the number of noncompliant quarters in the past 3 years (not necessarily consecutive). In some districts, for some programs, the number may be fewer than ten.
The EPA data shows records for the past 12 months for CAA and RCRA, and 13 months for CWA. The facilities shown with equal number of quarters in violation are selected at random. We only have room for ten in the graph, so we note when there are more facilities with the same number of quarters in violation.</font>
```{r echo=FALSE, out.extra='style="float: left; width: 50%; height: 30%; margin-right: 5%"'}
#call in data and clean up
nrows_df <- nrow(CAAbadactors)
make_extras_string <- function(badactors) {
extras_string = ""
if (nrows_df > 10) {
last_fac_count = badactors[10,]$noncomp_count
extras_count = 0
for (i in 10:nrows_df) {
if (badactors[i,]$noncomp_count < last_fac_count) {
break
}
extras_count <- extras_count + 1
}
if (extras_count > 0) {
noun <- "facilities"
if (extras_count == 1) noun = "facility"
extras_string <- sprintf("%d additional %s with %d quarters in violation",
extras_count, noun, last_fac_count);
}
}
}
extras_string <- make_extras_string(CAAbadactors)
if (nrows_df > 0) {
CAAbadactors <- CAAbadactors[-c(11:100),] # SH
CAAbadactors$Facility <- CAAbadactors$fac_name # SH
CAAbadactors$Facility <- strtrim(CAAbadactors$Facility, 30)
bar_width <- 0.8
if (nrows_df < 4) {
bar_width <- 0.2
}
#Let's try the CAA plot
if (nrows_df == 1) {
CAAfacilities <- CAAbadactors %>%
mutate(Facility = fct_reorder(Facility, noncomp_count)) %>%
ggplot(aes(x=Facility, y=noncomp_count))+
geom_col(width = 0.1)+
scale_fill_viridis(discrete = TRUE, option="B")+
labs(y="Quarters in Violation", x="", title="CAA Violators")+
scale_y_continuous(expand=c(0,0), breaks=c(0,3,6,9,12), limits=c(0,13))+
# scale_x_discrete(expand=c(0,0))+
theme_meg()+
theme(axis.text.y=element_text(size=14, face = "bold", color="black"), axis.title.x = element_text(size=14), axis.text.x=element_text(size=14,face = "bold"), plot.title=element_text(size=16))+
coord_flip()
} else {
CAAfacilities <- CAAbadactors %>%
mutate(Facility = fct_reorder(Facility, noncomp_count)) %>%
ggplot(mapping=aes(x=Facility, y=noncomp_count, fill=Facility, group=1))+
geom_bar(stat="identity", show.legend = FALSE, width=bar_width)+
scale_fill_viridis(discrete = TRUE, option="B")+
labs(y="Quarters in Violation", x="", title="CAA Violators")+
scale_y_continuous(expand=c(0,0), breaks=c(0,3,6,9,12), limits=c(0,13))+
scale_x_discrete(expand=c(0,0))+
theme_meg()+
theme(axis.text.y=element_text(size=14, face = "bold", color="black"), axis.title.x = element_text(size=14), axis.text.x=element_text(size=14,face = "bold"), plot.title=element_text(size=16))+
coord_flip()
}
CAAfacilities
}
```
<br />
<p style="text-align: right;"><font size="3">**ECHO reports for facilities:**</font></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[1,7]) `](`r na.omit(CAAbadactors[1,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[2,7]) `](`r na.omit(CAAbadactors[2,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[3,7]) `](`r na.omit(CAAbadactors[3,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[4,7]) `](`r na.omit(CAAbadactors[4,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[5,7]) `](`r na.omit(CAAbadactors[5,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[6,7]) `](`r na.omit(CAAbadactors[6,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[7,7]) `](`r na.omit(CAAbadactors[7,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[8,7]) `](`r na.omit(CAAbadactors[8,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[9,7]) `](`r na.omit(CAAbadactors[9,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CAAbadactors[10,7]) `](`r na.omit(CAAbadactors[10,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">`r extras_string `</font>
<br />
<br />
```{r echo=FALSE, out.extra='style="float: left; width: 50%; height: 30%; margin-right: 5%; margin-top:1.5%"'}
#call in data and clean up
nrows_df <- nrow(RCRAbadactors)
extras_string <- make_extras_string(RCRAbadactors)
if (nrows_df > 0) {
RCRAbadactors <- RCRAbadactors[-c(11:100),] # SH -
RCRAbadactors$Facility <- RCRAbadactors$fac_name # SH -
RCRAbadactors$Facility <- strtrim(RCRAbadactors$Facility, 30)
bar_width <- 0.8
if (nrows_df < 4) {
bar_width <- 0.2
}
#Let's try the RCRA plot
if (nrows_df == 1) {
RCRAfacilities <- RCRAbadactors %>%
mutate(Facility = fct_reorder(Facility, noncomp_count)) %>%
ggplot(aes(x=Facility, y=noncomp_count))+
geom_col(width = 0.1)+
scale_fill_viridis(discrete = TRUE, option="B")+
labs(y="Quarters in Violation", x="", title="RCRA Violators")+
scale_y_continuous(expand=c(0,0), breaks=c(0,3,6,9,12), limits=c(0,13))+
# scale_x_discrete(expand=c(0,0))+
theme_meg()+
theme(axis.text.y=element_text(size=14, face = "bold", color="black"), axis.title.x = element_text(size=14), axis.text.x=element_text(size=14,face = "bold"), plot.title=element_text(size=16))+
coord_flip()
} else {
RCRAfacilities <- RCRAbadactors %>%
mutate(Facility = fct_reorder(Facility, noncomp_count)) %>%
ggplot(mapping=aes(x=Facility, y=noncomp_count, fill=Facility, group=1))+
geom_bar(stat="identity", show.legend = FALSE, width=bar_width)+
scale_fill_viridis(discrete = TRUE, option="B")+
labs(y="Quarters in Violation", x="", title="RCRA Violators")+
scale_y_continuous(expand=c(0,0), breaks=c(0,3,6,9,12), limits=c(0,13))+
scale_x_discrete(expand=c(0,0))+
theme_meg()+
theme(axis.text.y=element_text(size=14, face = "bold", color="black"), axis.title.x = element_text(size=14), axis.text.x=element_text(size=14,face = "bold"), plot.title=element_text(size=16))+
coord_flip()
}
RCRAfacilities
}
```
<p style="text-align: right;"><font size="3">**ECHO reports for facilities:**</font></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[1,7]) `](`r na.omit(RCRAbadactors[1,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[2,7]) `](`r na.omit(RCRAbadactors[2,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[3,7]) `](`r na.omit(RCRAbadactors[3,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[4,7]) `](`r na.omit(RCRAbadactors[4,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[5,7]) `](`r na.omit(RCRAbadactors[5,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[6,7]) `](`r na.omit(RCRAbadactors[6,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[7,7]) `](`r na.omit(RCRAbadactors[7,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[8,7]) `](`r na.omit(RCRAbadactors[8,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[9,7]) `](`r na.omit(RCRAbadactors[9,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(RCRAbadactors[10,7]) `](`r na.omit(RCRAbadactors[10,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">`r extras_string `</font><br /></p>
<br />
<br />
```{r echo=FALSE, out.extra='style="float: left; width: 50%; height: 30%; margin-right: 5%; margin-top:1.5%"'}
#call in data and clean up
nrows_df <- nrow(CWAbadactors)
extras_string <- make_extras_string(CWAbadactors)
if (nrows_df > 0) {
CWAbadactors <- CWAbadactors[-c(11:100),] # SH -
CWAbadactors$Facility <- CWAbadactors$fac_name # SH -
CWAbadactors$Facility <- strtrim(CWAbadactors$Facility, 30)
bar_width <- 0.8
if (nrows_df < 4) {
bar_width <- 0.2
}
#Let's try the CWA plot
if (nrows_df == 1) {
CWAfacilities <- CWAbadactors %>%
mutate(Facility = fct_reorder(Facility, noncomp_count)) %>%
ggplot(aes(x=Facility, y=noncomp_count))+
geom_col(width = 0.1)+
scale_fill_viridis(discrete = TRUE, option="B")+
labs(y="Quarters in Violation", x="", title="CWA Violators")+
scale_y_continuous(expand=c(0,0), breaks=c(0,3,6,9,13), limits=c(0,14))+
# scale_x_discrete(expand=c(0,0))+
theme_meg()+
theme(axis.text.y=element_text(size=14, face = "bold", color="black"), axis.title.x = element_text(size=14), axis.text.x=element_text(size=14,face = "bold"), plot.title=element_text(size=16))+
coord_flip()
} else {
CWAfacilities <- CWAbadactors %>%
mutate(Facility = fct_reorder(Facility, noncomp_count)) %>%
ggplot(mapping=aes(x=Facility, y=noncomp_count, fill=Facility, group=1))+
geom_bar(stat="identity", show.legend = FALSE, width=bar_width)+
scale_fill_viridis(discrete = TRUE, option="B")+
labs(y="Quarters in Violation", x="", title="CWA Violators")+
scale_y_continuous(expand=c(0,0), breaks=c(0,3,6,9,13), limits=c(0,14))+
scale_x_discrete(expand=c(0,0))+
theme_meg()+
theme(axis.text.y=element_text(size=14, face = "bold", color="black"), axis.title.x = element_text(size=14), axis.text.x=element_text(size=14,face = "bold"), plot.title=element_text(size=16))+
coord_flip()
}
CWAfacilities
}
```
<p style="text-align: right;"><font size="3">**ECHO reports for facilities:**</font></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[1,7]) `](`r na.omit(CWAbadactors[1,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[2,7]) `](`r na.omit(CWAbadactors[2,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[3,7]) `](`r na.omit(CWAbadactors[3,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[4,7]) `](`r na.omit(CWAbadactors[4,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[5,7]) `](`r na.omit(CWAbadactors[5,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[6,7]) `](`r na.omit(CWAbadactors[6,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[7,7]) `](`r na.omit(CWAbadactors[7,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[8,7]) `](`r na.omit(CWAbadactors[8,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[9,7]) `](`r na.omit(CWAbadactors[9,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">[`r na.omit(CWAbadactors[10,7]) `](`r na.omit(CWAbadactors[10,4]) `)</font><br /></p>
<p style="text-align: right;"><font size="2">`r extras_string `</font><br /></p>
\pagebreak
<br />
<hr style="height:3px;border-width:0;margin-left:15px; margin-right:15px;color: #C1E0D7;background-color: #C1E0D7; margin-top: 0em; margin-bottom: 0em">
<h4>Report Generated on `r format(Sys.time(), '%B %d, %Y')`<span style="padding-left:400px">Page 7</span></h4>
# Clean Air Act
<div class="text-box-info">
The [Clean Air Act (CAA)](https://www.epa.gov/laws-regulations/summary-clean-air-act) regulates air emissions from mobile sources, such as cars, and stationary sources, such as refineries and power plants. *Please note, in this report we are only utilizing data from stationary air emission sources.* For the CAA, violations are most commonly recognized via inspections. Infrequent inspection usually results in fewer identified violations. If CAA violations have decreased, make sure to check whether inspections have also decreased, as cuts in inspections are likely related to drops in CAA violations. Unless thorough inspections are occurring regularly, fewer violations does not necessarily mean air quality has improved.
[More info on CAA](https://docs.google.com/presentation/d/1MzRQf3QeJqmYMSttdk0ghrXeOmOZgOV_MTynl-ri6Ik/edit?usp=sharing)
</div>
<h2>There are `r region$get_active_facilities('CAA')` facilities currently reporting under the CAA in this district.</h2>
```{r echo=FALSE, include=FALSE, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCAAviolations <- FALSE
nrows_df <- nrow(CAAviolations)
if (nrows_df > 0) {
hasCAAviolations <- TRUE
#clean up data
CAAviolations <- CAAviolations %>%
mutate(President = which_president(Year))
vCAAgraph <- ggplot(CAAviolations, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y=" # Violations", x="Year", title="CAA Violations\n", subtitle ="Very incomplete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CAAviolations$Count+(.2*max(CAAviolations$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012, 2016, 2020,2024))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(size = 10, color = "red", hjust=.5))
}
```
```{r echo=FALSE, include=FALSE, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCAAinspections <- FALSE
nrows_df <- nrow(CAAinspections)
if (nrows_df > 0) {
hasCAAinspections <- TRUE
#clean up data
CAAinspections <- CAAinspections %>%
mutate(President = which_president(Year))
iCAAgraph <- ggplot(CAAinspections, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y=" # Inspections", x="Year", title="CAA Inspections\n", subtitle ="Potentially incomplete data")+
#ggtitle("CAA Inspections")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CAAinspections$Count+(.2*max(CAAinspections$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012, 2016, 2020,2024))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(size = 10, color = "dark gray", hjust=.5))
}
```
```{r echo=FALSE, include=FALSE, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCAAenforcement <- FALSE
nrows_df <- nrow(CAAenforcement_clean)
if (nrows_df > 0) {
hasCAAenforcement <- TRUE
#clean up data
CAAenforcement <- CAAenforcement_clean
CAAenforcement <- CAAenforcement %>%
mutate(President = which_president(Year))
#adjust for inflation
CAAenforcement <- merge(CAAenforcement, inflation, by='Year', all.x=TRUE)
CAAenforcement$inflationadjust <- as.numeric(CAAenforcement$Amount*CAAenforcement$base)
##make a new amount column /1000
CAAenforcement$Amountinthousands <- ((CAAenforcement$inflationadjust)/1000)
CAAenforcement$Amountinthousands <-round(CAAenforcement$Amountinthousands,2)
eCAAgraph <- ggplot(CAAenforcement, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y=" # Actions", x="Year", title="CAA Enforcement Actions\n", subtitle ="Potentially incomplete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CAAenforcement$Count+(.2*max(CAAenforcement$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012, 2016, 2020,2024))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(size = 10, color = "dark gray", hjust=.5))
fineCAAgraph <- ggplot(CAAenforcement, mapping=aes(x=factor(Year), y=Amountinthousands, fill=President, group=1))+
geom_bar(stat="identity", alpha=.6)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'), guide=guide_legend(breaks=c("Bush","Obama","Trump","Biden")))+
labs(y=" Fines ($1000s)", x="Year", title="CAA Fines\n", subtitle ="Potentially incomplete data", caption="Values adjusted for inflation. Note, the y-axis reports values in\n1000's. A value of $2000 represents $2,000,000 (2 million dollars).")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CAAenforcement$Amountinthousands+(.2*max(CAAenforcement$Amountinthousands)))))+
scale_x_discrete(breaks=c(2004,2008,2012, 2016, 2020,2024))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(size = 10, color = "dark gray", hjust=.5))
# axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5), plot.caption = element_text(size=8))
}
```
```{r echo=FALSE, out.width= "95%", out.extra='style="float:center; padding:0px"'}
no_violations <- ""
if ( hasCAAviolations ) {
(iCAAgraph | vCAAgraph) / (eCAAgraph | fineCAAgraph)
} else {
no_violations <- "<h3><i>[There is no violation data for CAA in this district.]</i></h3>"
(iCAAgraph) / (eCAAgraph | fineCAAgraph)
}
knitr::opts_chunk$set(fig.width = 10,fig.height = 6)
```