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State_template_2024.rmd
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---
title: "Environmental Enforcement Watch"
header-includes:
- \usepackage{draftwatermark}
output:
html_paged:
number_sections: false
css: ["custom_current.css"]
params:
full_name: "Iowa"
state: "IA"
---
\SetWatermarkText{DRAFT}
```{r setup, include=FALSE}
library(reticulate)
library(RSQLite)
library(rlist)
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()
target_year <- '2023'
target_year_int <- strtoi(target_year)
state <- params$state
full_name <- params$full_name
output_loc <- paste('Output/',state,"/",sep="")
dir.create(output_loc)
conn <- dbConnect( RSQLite::SQLite(), "leg_info.db")
senators = dbGetQuery( conn, "select * from legislators where cd_state=?",
params=c( state ))
dbDisconnect( conn )
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
```
```{r echo=FALSE, include=FALSE}
virtualenv_python('edgienv')
u <- import( 'Region' )
region <- u$Region(db='region_cds.db', type='State', state=state,
programs=c('CAA', 'CWA', 'RCRA'),
base_year=target_year_int)
target_year_int <- strtoi( target_year )
violations <- region$get_events( 'violations', 'All' )
CAAviolations <- region$get_events( 'violations', 'CAA' )
CWAviolations <- region$get_events( 'violations', 'CWA' )
RCRAviolations <- region$get_events( 'violations', 'RCRA' )
USAinspectionsper1000_All <- region$get_per_1000( 'inspections', 'USA' )
inspectionsper1000_state <- region$get_per_1000( 'inspections', 'State' )
USAviolationsper1000_All <- region$get_per_1000( 'violations', 'USA' )
violationsper1000_state <- region$get_per_1000( 'violations', 'State' )
inflation <- region$get_inflation()
CAAenforcement_clean <- region$get_events( 'enforcements', 'CAA' )
CWAenforcement_clean <- region$get_events( 'enforcements', 'CWA' )
RCRAenforcement_clean <- region$get_events( 'enforcements', 'RCRA' )
enforcement <- region$get_events( 'enforcements', 'All' )
CWArecurring <- region$get_recurring_violations( 'CWA' )
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())
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")
```
```{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))
}
sen1_name <- senators[1, "name"]
sen2_name <- senators[2, "name"]
sen1_since <- senators[1, "since_date"]
sen2_since <- senators[2, "since_date"]
map_src <- paste('State_maps/',state,'_map.png',sep='')
this_state <- params$state
```
<div class="title"><span style="padding-left: 150px">
Congressional Report Card
</span></div>
<hr style="height:3px;border-width:0; margin-left:15px; margin-right:15px;color: #b9b9b9;background-color: #b9b9b9; margin-top: 0em; margin-bottom: 0em">
##### `r full_name`; seats held by `r sen1_name` since `r sen1_since` and `r sen2_name` since `r sen2_since`
<div class="container">
<img src="`r map_src`" alt="Snow" style="width:100%;">
<div class="circle-map1">`r region$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 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 adequately funded and 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. 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.
\pagebreak
<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 2</span></h4>
# Grading the District on Data from `r target_year_int-4` through `r 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 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 strtoi(target_year)-4` through `r target_year`.</h3>
<br />
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
grade <- function(score) {
return (case_when(score >= 40 ~ 'F',
score >= 30 ~ 'D',
score >= 20 ~ 'C',
score >= 10 ~ 'B',
TRUE ~ 'A'))
}
metric_colors <- c("darkblue", "#008080", "orange")
# ranked_t$order <- c("b","a","c","e","d","f","h","g","i")
ranked_t$order <- c("b","a","c","b","a","c","b","a","c")
state_rank_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("Ranking Among All States, 2017-2021"))+
scale_y_continuous(expand=c(0,0),limits=c(0,50))+
theme_meg()
```
```{r echo=FALSE, fig.height=3, out.width= "95%", out.extra='style="float:center; padding:0px"'}
state_rank_bar
```
<h3>As an example, a Violations ranking of `r ranked$CWA_Viol_Rank` for CWA means that this state has more violations per facility than `r ranked$CWA_Viol_Rank` of all states in the United States. From these rankings we can assign letter grades to states--the top 10, those states with more violations than 80% of all states, would get an F; the states scoring between 30 and 40 get a D; between 20 and 30 get a C; between 10 and 20 get a B; and the 10 with least violations per facility get an A. With that grading scheme, `r params$full_name` is given the following grades:</h3>
<ul>
<li><h3>CAA Violations per Facility - `r grade(ranked$CAA_Viol_Rank)`</h3></li>
<li><h3>CAA Violations per Inspection - `r grade(ranked$CAA_Insp_Rank)`</h3></li>
<li><h3>CAA Violations per Enforcement - `r grade(ranked$CAA_Enf_Rank)`</h3></li>
<li><h3>CWA Violations per Facility - `r grade(ranked$CWA_Viol_Rank)`</h3></li>
<li><h3>CWA Violations per Inspection - `r grade(ranked$CWA_Insp_Rank)`</h3></li>
<li><h3>CWA Violations per Enforcement - `r grade(ranked$CWA_Enf_Rank)`</h3></li>
<li><h3>RCRA Violations per Facility - `r grade(ranked$RCRA_Viol_Rank)`</h3></li>
<li><h3>RCRA Violations per Inspection - `r grade(ranked$RCRA_Insp_Rank)`</h3></li>
<li><h3>RCRA Violations per Enforcement - `r grade(ranked$RCRA_Enf_Rank)`</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 the willingness to call fouls.</li>
</ul>
*see data limitations page for metric calculations
\pagebreak
```{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='State',data_type='inspections',
y_field='Count',program='CWA',states=[r.this_state,])
cwa_viol_years = create_df(db='region_cds.db',
target_year=r.target_year_int,region_type='State',
data_type='violations',
y_field='Count',program='CWA',states=[r.this_state,])
cwa_enf_count_years = create_df(db='region_cds.db',
target_year=r.target_year_int,region_type='State',
data_type='enforcements',
y_field='Count',program='CWA',states=[r.this_state,])
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='State',
data_type='enforcements',
y_field='Amount',program='CWA',states=[r.this_state,])
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.
```
```{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 ($1000)", x="Year", title="CWA - Enforcement Fines Per Facility")
```
```{r echo=FALSE}
which_president <- function(which_year) {
case_when(
which_year < 2009 ~ "Bush",
which_year < 2017 & which_year > 2008 ~ "Obama",
which_year < 2021 & which_year > 2016 ~ "Trump",
which_year > 2020 ~ "Biden"
)
}
##getting %difference CWA violations
CWAviolations <- CWAviolations %>%
mutate(President = which_president(Year))
vCWAchange <- data.frame("BushObamaAverage" =mean(CWAviolations[1:16,2]),
"Trump Average" =mean(CWAviolations[17:20,2])) # SH
vCWAchange$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$Amount,2)
#add presidental years
enforcement <- enforcement %>%
mutate(President = which_president(Year))
actionchange <- data.frame("BushObamaTrumpAverage" =mean(enforcement[1:20,3]),
"Trump Average" =mean(enforcement[21:23,3]))
actionchange$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$PercentDifference>=0, "increase", "decrease")
```
<br />
<hr style="height:3px;border-width:0;margin-left:15px; margin-right:15px;color: #C1E0D7;background-color: #C1E0D7; margin-top: 0em; margin-bottom: 0em">
<h4>Report Generated on `r format(Sys.time(), '%B %d, %Y')`<span style="padding-left:400px">Page 3</span></h4>
# Clean Water Act - Inspections, Violations, Enforcements Since 2000
<h3>These graphs show the changes in numbers of inspections, violations and enforcement actions
per facility for the U.S., the state of `r params$state`,
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, fig.height = 5, fig.width = 8, fig.align = "center"}
(graph1 | graph2) / (graph3 | graph4)
```
<h3>Clean Water Act Violations*: <span style="color:#569b5e">Violations during the four Trump administration years were `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
\pagebreak
<hr style="height:3px;border-width:0;color: #C1E0D7;background-color: #C1E0D7; margin-top: 2em">
<h4>Report Generated on `r format(Sys.time(), '%B %d, %Y')`<span style="padding-left:400px">Page 3</span></h4>
```{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
CWArecurring <- CWArecurring %>%
select(State,Facilities, Percent)
names(CWArecurring)[1] <- "Region"
CWArecurring[3] <-round(CWArecurring[3],0)
```
```{r echo=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]),
"BidenAverage" =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 echo=FALSE}
##getting %difference effluent violations
##So, we're going to do the first 3 years of Biden and Trump so average
# [17:20,2] Trump [17:19] first three years
# [21:23,2] Biden first three years
effluentviolations <- CWAviolations
effluentchange <- data.frame("Trump Average" =mean(effluentviolations[17:19,2]),
"Biden Average" =mean(effluentviolations[21:23,2]))
effluentchange$PercentDifference <- as.numeric(100*(effluentchange[1,2]-effluentchange[1,1])/
effluentchange[1,1])
effluentchange[,1:3] <-round(effluentchange[,1:3],2)
president_colors <- c("#450d54", "#22908d", "#e56d13", "#0EAD5D")
#then simple plot
effluentgraph <- ggplot(effluentviolations, mapping=aes(x=factor(Year),
y=Count, fill=President, group=1))+
geom_bar(stat="identity", alpha=.6)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'), guide=guide_legend(breaks=c("Bush","Obama","Trump","Biden")))+
labs(y=" # Violations", x="Year", title="Clean Water Act Violations\n", subtitle ="Mostly complete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(effluentviolations$Count+(.2*max(effluentviolations$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012,2016,2020))+
theme_meg()+
theme(axis.text.x=element_text(size=9),
axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5, size=10, color="darkgreen"),
plot.title =
element_text(lineheight = .1, size=10))
##Inspections % change and graph
##So, we're going to do the first 3 years of Obama and Trump so average
# [17:20,2] Trump [17:19] first three years
# [21:23,2] Biden first three years
inspectionchange <- data.frame("Trump Average" =mean(inspections[17:19,2]),
"Biden Average" =mean(inspections[21:23,2]))
inspectionchange$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)
#then simple plot
#first add in the Presidential Years
inspections$President <- as.character("Bush")
inspections[9:16,3] <- as.character("Obama")
inspections[17:20,3] <- as.character("Trump")
inspections[21:23,3] <- as.character("Biden")
inspectiongraph <- ggplot(inspections, mapping=aes(x=factor(Year),
y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=president_colors, breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y="Inspections", x="Year", title="Facility Inspections-CAA,CWA,RCRA\n", subtitle = "Potentially incomplete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(inspections$Count+(.2*max(inspections$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012,2016,2020))+
theme_meg()+
theme(axis.text.x=element_text(size=9),
axis.title.x = element_blank(), plot.subtitle = element_text(size = 10, color = "dark gray", hjust=.5), plot.title =
element_text(lineheight = .1, size=10))
#enforcement
enforcement$Amount <- round(enforcement$Amount,0)
enforcementchange <- data.frame("Trump Average Count" =mean(enforcement[17:20,3]),
"Biden Average Count" =mean(enforcement[21:23,3]),
"Trump Average Fine" =mean(enforcement[17:20,2]),
"Biden Average Fine" =mean(enforcement[21:23,2]))
enforcementchange$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$PercentDifferenceFine)
#adjust for inflation
enforcement <- merge(enforcement, inflation, by='Year', all.x=TRUE)
# enforcement <- cbind(enforcement, inflation)
enforcement$inflationadjust <- as.numeric(enforcement$Amount*enforcement$base)
##make a new amount column /1000
enforcement$Amountinthousands <- ((enforcement$inflationadjust)/1000)
enforcement$Amountinthousands <-round(enforcement$Amountinthousands,2)
#add presidental years
enforcement$President <- as.character("Bush")
enforcement[9:16,4] <- as.character("Obama")
enforcement[17:20,4] <- as.character("Trump")
enforcement[21:23,4] <- as.character("Biden")
#plot change in enforcement
enforcementactiongraph <- ggplot(enforcement, mapping=aes(x=factor(Year),y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'), guide=guide_legend(breaks=c("Bush","Obama","Trump","Biden")))+
labs(y="# Actions", x="Year", title="Enforcement Actions-CAA,CWA,RCRA\n", subtitle = "Potentially incomplete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(effluentviolations$Count+(.2*max(effluentviolations$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012,2016,2020))+
geom_text(aes(label=Count, family="Georgia", fontface="bold"), position = position_dodge(0.9), vjust = -0.1, size=2)+
theme_meg()+
theme(axis.text.x=element_text(size=9),
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'), guide=guide_legend(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))+
theme_meg()+
theme(axis.text.x=element_text(size=9),
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 3 years of the Trump administration to the first 3 years of the Biden administration, there has been 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 CWArecurring[1,2]` facilities, representing `r CWArecurring[1,3]`% of all regulated facilities in `r 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
# effluentgraph
```
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/master/AllPrograms.ipynb).
\pagebreak
<hr style="height:3px;border-width:0;color: #C1E0D7;background-color: #C1E0D7; margin-top: 2em">
<h4>Report Generated on `r format(Sys.time(), '%B %d, %Y')`<span style="padding-left:400px">Page 4</span></h4>
# This State in Comparison
```{r, 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$Region <- as.factor("State")
inspectionsper1000 <- rbind(USAinspectionsper1000_All, inspectionsper1000_state)
inspectionsper1000$Inspectionsper1000 <- as.numeric(inspectionsper1000$Inspectionsper1000)
inspectionsper1000[2] <-round(inspectionsper1000[2],0)
#Now let's visualize this
inspect1000bar <- ggplot(inspectionsper1000, aes(fill=Region, y=Inspectionsper1000, x=Program, alpha=Program))+
geom_bar(position="dodge", stat="identity")+
scale_fill_manual(values=c("#333333", "#d43a69"))+
labs(y="Inspections per 1000 Facilities\n", x="")+
scale_alpha_manual(values=c(.6,1,.6), guide='none')+
geom_text(aes(label=Inspectionsper1000, family="Georgia"), position = position_dodge(0.9), vjust = -0.1, size=4)+
ggtitle(paste("Inspections per 1000 Facilities in",target_year," "))+
scale_y_continuous(expand=c(0,0),limits=c(0,1200))+geom_hline(yintercept=1000, linetype="dashed", color="black")+geom_text(aes(label="Average of 1 inspection per facility",family="Georgia"),x=2,y=1050,color="black",size=4)+
theme_meg()
#comparing violations
#let's combine the violations data at national, state, CD level
names(USAviolationsper1000_All)[2] <- "violationsper1000"
USAviolationsper1000_All$Region <- as.factor("National")
names(violationsper1000_state)[2] <- "violationsper1000"
violationsper1000_state$Region <- as.factor("State")
violationsper1000 <- rbind(USAviolationsper1000_All, violationsper1000_state)
violationsper1000$violationsper1000 <- as.numeric(violationsper1000$violationsper1000)
violationsper1000[2] <-round(violationsper1000[2],0)
#Now let's visualize this
violations1000bar <- ggplot(violationsper1000, aes(fill=Region, y=violationsper1000, x=Program, alpha=Program))+
geom_bar(position="dodge", stat="identity", show.legend=FALSE)+
scale_fill_manual(values=c("#333333", "#d43a69"))+
scale_alpha_manual(values=c(.3,1,.3), guide='none')+
labs(y="Violations per 1000 Facilities", x="")+
geom_text(aes(label=violationsper1000, family="Georgia"), position = position_dodge(0.9), vjust = -0.1, size=4)+
ggtitle(paste("Violations per 1000 Facilities in",target_year," "))+
scale_y_continuous(expand=c(0,0),limits=c(0,max(violationsper1000$violationsper1000+(.2*max(violationsper1000$violationsper1000)))))+
theme_meg()+
theme(legend.position = c(0.8, 0.5))
inspect1000bar / violations1000bar
knitr::opts_chunk$set(fig.width = 10,fig.height = 6)
```
<br />
These two charts show how inspections and violations in this state compare to the national average 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 Jupyter Notebooks which pull data from ECHO at the state and congressional district level, click [here](https://github.com/edgi-govdata-archiving/ECHO-Cross-Program/blob/main/ECHO-Cross-Programs.ipynb).
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
<hr style="height:3px;border-width:0;color: #C1E0D7;background-color: #C1E0D7; margin-top: 2em">
<h4>Report Generated on `r format(Sys.time(), '%B %d, %Y')`<span style="padding-left:400px">Page 5</span></h4>
<h1><span style="margin-bottom:0px; padding-bottom:0px">Recent Non-Compliance in this State</span></h1>
<font size="2">These figures show the ten facilities in this state with the worst history of environmental compliance based on their number of noncompliant quarters in the past 3 years (not necessarily consecutive).</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(11:1000),] # 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(11:1000),] # 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(11:1000),] # 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
<hr style="height:3px;border-width:0;color: #C1E0D7;background-color: #C1E0D7; margin-top: 2em">
<h4>Report Generated on `r format(Sys.time(), '%B %d, %Y')`<span style="padding-left:400px">Page 6</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 recent 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$get_active_facilities('CAA')` facilities currently reporting under the CAA in this state.</h2>
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCAAviolations <- FALSE
nrows_df <- nrow( CAAviolations )
if ( nrows_df > 0 ) {
hasCAAviolations <- TRUE
#clean up data
CAAviolations <- CAAviolations %>%
mutate(President = which_president(Year))
vCAAgraph <- ggplot(CAAviolations, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.3)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y=" # Violations", x="Year", title="CAA Violations\n", subtitle ="Very incomplete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CAAviolations$Count+(.2*max(CAAviolations$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012, 2016, 2020))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5))
}
```
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCAAinspections <- FALSE
nrows_df <- nrow( CAAinspections )
if ( nrows_df > 0 ) {
hasCAAinspections <- TRUE
#clean up data
CAAinspections <- CAAinspections %>%
mutate(President = which_president(Year))
iCAAgraph <- ggplot(CAAinspections, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y=" # Inspections", x="Year", title="CAA Inspections\n", subtitle ="Potentially incomplete data")+
#ggtitle("CAA Inspections")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CAAinspections$Count+(.2*max(CAAinspections$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012, 2016, 2020))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5))
}
```
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCAAenforcement <- FALSE
nrows_df <- nrow( CAAenforcement_clean )
if ( nrows_df > 0 ) {
hasCAAenforcement <- TRUE
#clean up data
CAAenforcement <- CAAenforcement_clean
CAAenforcement <- CAAenforcement %>%
mutate(President = which_president(Year))
#adjust for inflation
CAAenforcement <- merge(CAAenforcement, inflation, by.x='Year', all.x=TRUE)
CAAenforcement$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))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(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))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5), plot.caption = element_text(size=8))
}
```
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
(iCAAgraph | vCAAgraph) / (eCAAgraph | fineCAAgraph)
```
These figures show patterns of CAA inspections, violations, enforcement actions and fines in this state since 2001 based on available EPA data (see page 10). The bars are colored by the president in office that year. Figure transparency illustrates data reliability: the more transparent, the more uncertain the data. Data on CAA violations is particularly unreliable as emissions are often not directly monitored but are estimates. Inspection, enforcement, and fine data can be unreliable because state reporting to ECHO may be incomplete.For access to the Jupyter Notebook which pulls data from ECHO at the state and congressional district level, click [here](https://colab.research.google.com/github/edgi-govdata-archiving/ECHO-Cross-Program/blob/master/AllPrograms.ipynb).
\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 Water Act
<div class="text-box-info">
The [Clean Water Act (CWA)](https://www.epa.gov/laws-regulations/summary-clean-water-act) establishes quality standards for surface waters. In this report, we focus on CWA’s National Pollutant Discharge Elimination System (NPDES) which permits facilities to discharge certain kinds and amounts of pollutants. Unlike the CAA, under the CWA effluent (waste emissions) is directly measured and routinely reported electronically to ECHO. CWA violations are automatically triggered if data is not submitted and if contaminant levels in effluent exceed the permitted amount. Such CWA violations can lead to inspections. [More info on CWA](https://docs.google.com/presentation/d/1g6ZN3B5jvs3F1VAigiUtNNezjXdJnzuELfo9Deo9Y2w/edit?usp=sharing)
</div>
<h2>There are `r region$get_active_facilities('CWA') ` facilities currently reporting under the
CWA in this state.</h2>
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCWAviolations <- FALSE
nrows_df <- nrow( CWAviolations )
if ( nrows_df > 0 ) {
hasCWAviolations <- TRUE
#clean up data
# data cleaned elsewhere
#plot
vCWAgraph <- ggplot(CWAviolations, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE)+
scale_fill_manual(values=president_colors,breaks=c('Bush', 'Obama', 'Trump', 'Biden'))+
labs(y=" # Violations", x="Year", title="CWA Violations\n", subtitle ="Mostly complete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CWAviolations$Count+(.2*max(CWAviolations$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012,2016,2020))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5))
}
```
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCWAinspections <- FALSE
nrows_df <- nrow( CWAinspections )
if ( nrows_df > 0 ) {
hasCWAinspections <- TRUE
#clean up data
names(CWAinspections)[1] <- "Year"
CWAinspections <- CWAinspections %>%
mutate(President = which_president(Year))
iCWAgraph <- ggplot(CWAinspections, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+