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State_template.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)
loadfonts()
target_year <- '2020'
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(type='State', state=state, programs=c('CAA', 'CWA', 'RCRA'))
target_year_int <- strtoi( target_year )
violations <- region$get_events( 'violations', 'All', target_year_int )
CAAviolations <- region$get_events( 'violations', 'CAA', target_year_int )
CWAviolations <- region$get_events( 'violations', 'CWA', target_year_int )
RCRAviolations <- region$get_events( 'violations', 'RCRA', target_year_int )
USAinspectionsper1000_All <- region$get_per_1000( 'inspections', 'USA', target_year_int )
inspectionsper1000_state <- region$get_per_1000( 'inspections', 'State', target_year_int )
USAviolationsper1000_All <- region$get_per_1000( 'violations', 'USA', target_year_int )
violationsper1000_state <- region$get_per_1000( 'violations', 'State', target_year_int )
inflation <- region$get_inflation( target_year_int )
CWAper1000 <- region$get_cwa_per_1000( target_year_int )
CAAenforcement_clean <- region$get_events( 'enforcements', 'CAA', target_year_int )
CWAenforcement_clean <- region$get_events( 'enforcements', 'CWA', target_year_int )
RCRAenforcement_clean <- region$get_events( 'enforcements', 'RCRA', target_year_int )
enforcement <- region$get_events( 'enforcements', 'All', target_year_int )
CWArecurring <- region$get_recurring_violations( 'CWA' )
inspections <- region$get_events( 'inspections', 'All', target_year_int )
CAAinspections <- region$get_events( 'inspections', 'CAA', target_year_int )
CWAinspections <- region$get_events( 'inspections', 'CWA', target_year_int )
RCRAinspections <- region$get_events( 'inspections', 'RCRA', target_year_int )
CAAbadactors <- region$get_non_compliants( 'CAA' )
CWAbadactors <- region$get_non_compliants( 'CWA' )
RCRAbadactors <- region$get_non_compliants( 'RCRA' )
```
```{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 = 10),
axis.title.y=element_text(face = "bold", size = 8, angle=90), plot.title=element_text(face = "bold", size = 12, hjust =.5),
axis.text.x=element_text(face = "bold", size = 12), axis.text.y=element_text(face = "bold", size=8), plot.margin=unit(c(1,1,1,1),"cm"), plot.subtitle=element_text(face="bold",size=8))
}
theme_meg2 <- 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 = 10),
axis.title.y=element_text(face = "bold", size = 7, angle=90), plot.title=element_text(face = "bold", size = 10, hjust =.5),
axis.text.x=element_text(face = "bold", size = 12), axis.text.y=element_text(face = "bold", size = 6))
}
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='')
```
<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 state since 2001. Report cards are available on the [EEW website](https://www.environmentalenforcementwatch.org) for all members of the key congressional committees that oversee the EPA: the House Energy and Commerce Committee and Senate Environment and Public Works Committee. The [EEW website](https://www.environmentalenforcementwatch.org) also has a summary analysis of nationwide enforcement trends and data issues. The report cards contain data from *both* state environmental agencies and EPA. Provided states are enforcing the above laws, it is because EPA has delegated that authority to them. EPA must ensure that states are doing their job. Congress must ensure that EPA is doing its job. And the public must have accurate data from states and 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 EPA is fulfilling its mandate in their state. Congress can strengthen EPA enforcement by increasing its budget, passing more effective laws, requiring better data collection, and holding 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;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 2</span></h4>
# Key Changes in this State under the Trump Administration
```{r echo=FALSE}
##getting %difference CWA violations
CWAviolations <- CWAviolations %>%
mutate(
President = case_when(
Year < 2009 ~ "Bush",
Year < 2017 & Year > 2008 ~ "Obama",
Year > 2016 ~ "Trump"
))
vCWAchange <- data.frame("BushObamaAverage" =mean(CWAviolations[1:16,2]),
"Trump Average" =mean(CWAviolations[17:19,2]))
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$Amount,2)
#add presidental years
enforcement <- enforcement %>%
mutate(
President = case_when(
Year < 2009 ~ "Bush",
Year < 2017 & Year > 2008 ~ "Obama",
Year > 2016 ~ "Trump"
))
actionchange <- data.frame("BushObamaAverage" =mean(enforcement[1:16,3]),
"Trump Average" =mean(enforcement[17:19,3]))
actionchange$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$PercentDifference>=0, "increase", "decrease")
```
<h3>Clean Water Act Violations*: <span style="color:#569b5e">`r vCWAchange[1,4]` `r vCWAchange[1,5]`</span> than the average over the previous 16 years, representing a <span style="color:#569b5e">`r vCWAchange[1,6]`% `r vCWAchange[1,7]`</span> in violations</h3>
<br />
<h3>Enforcement Actions under Clean Air Act, Clean Water Act, and the Resource Conservation and Recovery Act*: <span style="color:#569b5e">`r actionchange[1,4]` `r actionchange[1,5]`</span> than the average over the previous 16 years, representing a <span style="color:#569b5e">`r actionchange[1,6]`% `r actionchange[1,7]`</span> in enforcement actions</h3>
*see data limitations page for metric calculations
<br />
```{r, fig.height = 7, fig.width = 5, fig.align = "left"}
CWAper1000$Num.per.1000 <- as.numeric(round(CWAper1000$Num.per.1000,0))
CWAper10002 <- CWAper1000
CWAper10002$Test <- as.numeric(0)
CWAper10002$Highlight <- ifelse(CWAper10002$CD.State==state, state, "Other")
CWAper1000$Highlight <- ifelse(CWAper1000$CD.State==state, state, "Other")
CWAper1000$Per100 <- as.numeric(CWAper1000$Num.per.1000/10)
#make a dataframe that is per 100
CWAper100 <- CWAper1000[CWAper1000$Num.per.1000 < 12000.,]
outliers <- CWAper1000[CWAper1000$Num.per.1000 >= 12000.,]
#removing NY9 and WA5 and MI7, AK, and IA2 and extra CA9
# CWAper100 <- CWAper100[-c(38,52,29,15,54,8),]
# CWAper100 <- CWAper100[CWAper100$Num.per.100 < 1200.,]
state_dot <- CWAper1000[which(CWAper1000$CD.State==state), ]
dotplot <-ggplot(CWAper100, aes(x=Region, y=Per100, fill=Highlight))+
geom_dotplot(binaxis='y', stackdir='center', stackratio=1.5, binwidth=15,
dotsize=1.5, position=position_dodge(.3))+
geom_hline(yintercept=100, linetype="dashed", color="#e56d13")+
geom_hline(yintercept=0, linetype="dashed", color="#569b5e")+
scale_fill_manual(values=c("#ffffff","#e56d13"))+
labs(title=paste("CWA Violations per 100 Facilities in",target_year," "), x="CD/State", y="")+
geom_text(aes(label="100 violations per\n100 facilities*", family="Georgia"),
x=2.35, y=180, color="#e56d13", size=3)+
geom_text(aes(label="0 violations", family="Georgia"),
x=2.4, y=15, color="#569b5e", size=3)+
theme_meg()+
theme(axis.line.x=element_blank(), axis.title.x=element_blank(),
legend.title = element_blank(), axis.text.x=element_text(size=10, hjust=.6), legend.position = c(0.1,.8), axis.ticks.x=element_blank())+scale_x_discrete(labels=c("Congressional Districts","States"))
ggsave(paste("dotplot_",state,".png",sep=""), dotplot, path=here(output_loc), scale = 2, width=8, height=5, units=c("cm"))
dotplot_loc <- paste(output_loc,"dotplot_",state,".png",sep="")
cd_outliers <- ''
state_outliers <- ''
state_start <- FALSE
cd_start <- FALSE
for ( row in 1:nrow( outliers )) {
state_cd <- outliers[ row, "CD.State" ]
per_100 <- outliers[ row, "Per100" ]
region_type <- outliers[ row, "Region" ]
if (region_type == 'States' ) {
if ( state_start ) {
state_outliers <- paste( state_outliers, ',' )
}
state_start <- TRUE
state_outliers <- paste( state_outliers, state_cd, '(', per_100, ')' )
} else {
if ( cd_start ) {
cd_outliers <- paste( cd_outliers, ',' )
}
cd_start <- TRUE
if ( substr( state_cd, 3, 3 ) == '0' ) {
state_cd <- substr( state_cd, 1, 2 )
}
cd_outliers <- paste( cd_outliers, state_cd, '(', per_100, ')' )
}
}
if ( nchar( trimws( cd_outliers ) < 2 )) {
cd_outliers_txt <- paste( 'Congressional Districts: ', cd_outliers )
}
if ( nchar( trimws( state_outliers ) < 2 )) {
state_outliers_txt <- paste( 'States: ', state_outliers )
}
```
<a ><img src="`r dotplot_loc`" style="float: left; width: 100%; height: 60%; margin-right: 5%" ></a>
*This is an average, it does not mean that every facility has a violation. Some facilities may have many more than 1 or zero.
<br />
<br />
<h3>In `r state`, there were <span style="color:#569b5e">`r state_dot[1,5]`</span> Clean Water Act violations per 100 regulated facilities in 2019.</h3>
<h2> Outliers (not shown): Districts and states with more than 800 violations per 100 installations.</h2>
* <h3> `r cd_outliers_txt` </h3>
* <h3> `r state_outliers_txt` </h3>
\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}
##getting %difference effluent violations
##So, we're going to do the first 3 years of Obama and Trump so average
#[9:11,2] Obama
# [17:19,2] Trump
effluentviolations <- CWAviolations
effluentchange <- data.frame("Obama Average" =mean(effluentviolations[9:12,2]),
"Trump Average" =mean(effluentviolations[17:20,2]))
effluentchange$PercentDifference <- as.numeric(100*(effluentchange[1,2]-effluentchange[1,1])/
effluentchange[1,1])
effluentchange[,1:3] <-round(effluentchange[,1:3],2)
#then simple plot
#first add in the Presidential Years
effluentviolations$President <- as.character("Bush")
effluentviolations[9:12,3] <- as.character("Obama")
effluentviolations[17:20,3] <- as.character("Trump")
effluentgraph <- ggplot(effluentviolations, mapping=aes(x=factor(Year),
y=Count, fill=President, group=1))+
geom_bar(stat="identity")+
scale_fill_manual(values=c("#450d54", "#22908d", "#e56d13"))+
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_meg2()+
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))
##Inspections % change and graph
##So, we're going to do the first 3 years of Obama and Trump so average
#[9:11,2] Obama
# [17:19,2] Trump
inspectionchange <- data.frame("Obama Average" =mean(inspections[9:12,2]),
"Trump Average" =mean(inspections[17:20,2]))
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[9:12,3] <- as.character("Obama")
inspections[17:20,3] <- as.character("Trump")
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=c("#450d54", "#22908d", "#e56d13"))+
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(2004,2008,2012,2016,2019))+
theme_meg2()+
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))
#enforcement
enforcement$Amount <- round(enforcement$Amount,0)
enforcementchange <- data.frame("Obama Average Count" =mean(enforcement[9:12,3]),
"Trump Average Count" =mean(enforcement[17:20,3]),
"Obama Average Fine" =mean(enforcement[9:12,2]),
"Trump Average Fine" =mean(enforcement[17:20,2]))
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 <- 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")
#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=c("#450d54", "#22908d", "#e56d13"))+
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_meg2()+
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=c("#450d54", "#22908d", "#e56d13"))+
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_meg2()+
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 Obama administration to the first 3 years of the Trump 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, 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$Inspectionsper1000)
inspectionsper1000[2] <-round(inspectionsper1000[2],0)
#Now let's visualize this
inspect1000bar <- ggplot(inspectionsper1000, aes(fill=Region, y=Inspectionsper1000, x=Program, alpha=Program))+
geom_bar(position="dodge", stat="identity")+
scale_fill_manual(values=c("#333333", "#d43a69"))+
labs(y="Inspections per 1000 Facilities\n", x="")+
scale_alpha_manual(values=c(.6,1,.6), guide='none')+
geom_text(aes(label=Inspectionsper1000, family="Georgia"), position = position_dodge(0.9), vjust = -0.1, size=4)+
ggtitle("Inspections per 1000 Facilities in 2019")+
scale_y_continuous(expand=c(0,0),limits=c(0,1200))+geom_hline(yintercept=1000, linetype="dashed", color="black")+geom_text(aes(label="Average of 1 inspection per facility",family="Georgia"),x=2,y=1050,color="black",size=4)+
theme_meg()
#comparing violations
#let's combine the violations data at national, state, CD level
names(USAviolationsper1000_All)[2] <- "violationsper1000"
USAviolationsper1000_All$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$violationsper1000)
violationsper1000[2] <-round(violationsper1000[2],0)
#Now let's visualize this
violations1000bar <- ggplot(violationsper1000, aes(fill=Region, y=violationsper1000, x=Program, alpha=Program))+
geom_bar(position="dodge", stat="identity", show.legend=FALSE)+
scale_fill_manual(values=c("#333333", "#d43a69"))+
scale_alpha_manual(values=c(.3,1,.3), guide='none')+
labs(y="Violations per 1000 Facilities", x="")+
geom_text(aes(label=violationsper1000, family="Georgia"), position = position_dodge(0.9), vjust = -0.1, size=4)+
ggtitle("Violations per 1000 Facilities in 2019")+
scale_y_continuous(expand=c(0,0),limits=c(0,max(violationsper1000$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 2019. We use data from 2019 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://colab.research.google.com/github/edgi-govdata-archiving/ECHO-Cross-Program/blob/master/AllPrograms.ipynb). For national data, click [here](https://colab.research.google.com/github/edgi-govdata-archiving/ECHO-Cross-Program/blob/master/ECHO_National.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 )
if ( nrows_df > 0 ) {
CAAbadactors <- CAAbadactors[-c(9:1000),] # 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;">[`r na.omit(CAAbadactors[1,7]) `](`r na.omit(CAAbadactors[1,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CAAbadactors[2,7]) `](`r na.omit(CAAbadactors[2,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CAAbadactors[3,7]) `](`r na.omit(CAAbadactors[3,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CAAbadactors[4,7]) `](`r na.omit(CAAbadactors[4,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CAAbadactors[5,7]) `](`r na.omit(CAAbadactors[5,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CAAbadactors[6,7]) `](`r na.omit(CAAbadactors[6,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CAAbadactors[7,7]) `](`r na.omit(CAAbadactors[7,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CAAbadactors[8,7]) `](`r na.omit(CAAbadactors[8,4]) `)<br /></p>
<br />
<br />
<br />
<br />
```{r echo=FALSE, out.extra='style="float: left; width: 50%; height: 30%; margin-right: 5%"'}
#call in data and clean up
#call in data and clean up
nrows_df <- nrow( RCRAbadactors )
if ( nrows_df > 0 ) {
RCRAbadactors <- RCRAbadactors[-c(9:1000),] # 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;">[`r na.omit(RCRAbadactors[1,7]) `](`r na.omit(RCRAbadactors[1,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(RCRAbadactors[2,7]) `](`r na.omit(RCRAbadactors[2,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(RCRAbadactors[3,7]) `](`r na.omit(RCRAbadactors[3,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(RCRAbadactors[4,7]) `](`r na.omit(RCRAbadactors[4,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(RCRAbadactors[5,7]) `](`r na.omit(RCRAbadactors[5,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(RCRAbadactors[6,7]) `](`r na.omit(RCRAbadactors[6,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(RCRAbadactors[7,7]) `](`r na.omit(RCRAbadactors[7,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(RCRAbadactors[8,7]) `](`r na.omit(RCRAbadactors[8,4]) `)<br /></p>
<br />
<br />
<br />
<br />
<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 )
if ( nrows_df > 0 ) {
CWAbadactors <- CWAbadactors[-c(9:1000),] # 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;">[`r na.omit(CWAbadactors[1,7]) `](`r na.omit(CWAbadactors[1,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CWAbadactors[2,7]) `](`r na.omit(CWAbadactors[2,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CWAbadactors[3,7]) `](`r na.omit(CWAbadactors[3,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CWAbadactors[4,7]) `](`r na.omit(CWAbadactors[4,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CWAbadactors[5,7]) `](`r na.omit(CWAbadactors[5,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CWAbadactors[6,7]) `](`r na.omit(CWAbadactors[6,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CWAbadactors[7,7]) `](`r na.omit(CWAbadactors[7,4]) `)<br /></p>
<p style="text-align: right;">[`r na.omit(CWAbadactors[8,7]) `](`r na.omit(CWAbadactors[8,4]) `)<br /></p>
<br />
<br />
<br />
<br />
\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$get_active_facilities('CAA')` facilities currently reporting under the CAA in this state.</h2>
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCAAviolations <- FALSE
nrows_df <- nrow( CAAviolations )
if ( nrows_df > 0 ) {
hasCAAviolations <- TRUE
#clean up data
CAAviolations <- CAAviolations %>%
mutate(
President = case_when(
Year < 2009 ~ "Bush",
Year < 2017 & Year > 2008 ~ "Obama",
Year > 2016 ~ "Trump"
))
vCAAgraph <- ggplot(CAAviolations, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.3)+
scale_fill_manual(values=c("#450d54", "#22908d", "#e56d13"))+
labs(y=" # Violations", x="Year", title="CAA Violations\n", subtitle ="Very incomplete data")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CAAviolations$Count+(.2*max(CAAviolations$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012, 2016, 2020))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5))
}
```
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCAAinspections <- FALSE
nrows_df <- nrow( CAAinspections )
if ( nrows_df > 0 ) {
hasCAAinspections <- TRUE
#clean up data
CAAinspections <- CAAinspections %>%
mutate(
President = case_when(
Year < 2009 ~ "Bush",
Year < 2017 & Year > 2008 ~ "Obama",
Year > 2016 ~ "Trump"
))
iCAAgraph <- ggplot(CAAinspections, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=c("#450d54", "#22908d", "#e56d13"))+
labs(y=" # Inspections", x="Year", title="CAA Inspections\n", subtitle ="Potentially incomplete data")+
#ggtitle("CAA Inspections")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CAAinspections$Count+(.2*max(CAAinspections$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012, 2016, 2020))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5))
}
```
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCAAenforcement <- FALSE
nrows_df <- nrow( CAAenforcement_clean )
if ( nrows_df > 0 ) {
hasCAAenforcement <- TRUE
#clean up data
CAAenforcement <- CAAenforcement_clean
CAAenforcement <- CAAenforcement %>%
mutate(
President = case_when(
Year < 2009 ~ "Bush",
Year < 2017 & Year > 2008 ~ "Obama",
Year > 2016 ~ "Trump"
))
#adjust for inflation
CAAenforcement <- merge(CAAenforcement, inflation, by.x='Year', by.y='index', all.x=TRUE)
CAAenforcement$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=c("#450d54", "#22908d", "#e56d13"))+
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=c("#450d54", "#22908d", "#e56d13"))+
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 president. 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=c("#450d54", "#22908d", "#e56d13"))+
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$President <- as.character("Bush")
CWAinspections[9:16,3] <- as.character("Obama")
CWAinspections[17:20,3] <- as.character("Trump")
iCWAgraph <- ggplot(CWAinspections, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=c("#450d54", "#22908d", "#e56d13"))+
labs(y=" # Inspections", x="Year", title="CWA Inspections\n", subtitle ="Potentially incomplete data")+
ggtitle("CWA Inspections")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CWAinspections$Count+(.2*max(CWAinspections$Count)))))+
scale_x_discrete(breaks=c(2004,2008,2012,2016, 2020))+
theme_meg()+
theme(axis.text.x=element_text(size=10),
axis.title.x = element_blank(), plot.subtitle = element_text(hjust=.5))
}
```
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasCWAenforcement <- FALSE
nrows_df <- nrow( CWAenforcement_clean )
if ( nrows_df > 0 ) {
hasCWAenforcement <- TRUE
#clean up data
CWAenforcement <- CWAenforcement_clean
CWAenforcement$President <- as.character("Bush")
CWAenforcement[8:16,4] <- as.character("Obama")
CWAenforcement[17:20,4] <- as.character("Trump")
#adjust for inflation
CWAenforcement <- merge(CWAenforcement, inflation, by.x='Year', by.y='index', all.x=TRUE)
CWAenforcement$inflationadjust <- as.numeric(CWAenforcement$Amount*CWAenforcement$base)
##make a new amount column /1000
CWAenforcement$Amountinthousands <- ((CWAenforcement$inflationadjust)/1000)
CWAenforcement$Amountinthousands <-round(CWAenforcement$Amountinthousands,2)
eCWAgraph <- ggplot(CWAenforcement, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.6)+
scale_fill_manual(values=c("#450d54", "#22908d", "#e56d13"))+
labs(y=" # Actions", x="Year", title="CWA Enforcement Actions\n", subtitle="Potentially incomplete data")+
#ggtitle("CWA Enforcement Actions")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CWAenforcement$Count+(.2*max(CWAenforcement$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))
fineCWAgraph <- ggplot(CWAenforcement, mapping=aes(x=factor(Year), y=Amountinthousands, fill=President, group=1))+
geom_bar(stat="identity", alpha=.6)+
scale_fill_manual(values=c("#450d54", "#22908d", "#e56d13"))+
labs(y=" Fines($1000s)", x="Year", title="CWA 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 $2,000,000 (2 million dollars).")+
#ggtitle("CWA Fines")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(CWAenforcement$Amountinthousands+(.2*max(CWAenforcement$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"}
no_enforcement <- ""
if ( hasCWAenforcement ) {
(iCWAgraph | vCWAgraph) / (eCWAgraph | fineCWAgraph)
} else {
no_enforcement <- "<h3><i>[There is no enforcement or fine data for CWA in this state.]</i></h3>"
(iCWAgraph | vCWAgraph)
}
```
`r no_enforcement`
These figures show patterns of Clean Water Act inspections, violations, enforcement actions and fines in this state since 2001 based on available EPA data (see page 10). The bars are colored by president. Figure transparency illustrates data reliability: the more transparent, the more uncertain the data. Data on CWA violations is particularly reliable as effluent violations are automatically reported to EPA.
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).
<br />
<br />
\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 8</span></h4>
# Resource Conservation and Recovery Act
<div class="text-box-info">
The [Resource Conservation and Recovery Act (RCRA)](https://www.epa.gov/laws-regulations/summary-resource-conservation-and-recovery-act) gives EPA the authority to control hazardous waste from "cradle-to-grave", regulating the generation, transportation, treatment, storage, and disposal of hazardous waste. Facilities self-report under RCRA, like the CAA, and violations are most often found after an inspection. If RCRA violations have decreased, make sure to check whether inspections have also decreased as recent cuts in inspections are likely related to drops in RCRA violations. [More info on RCRA](https://docs.google.com/presentation/d/1lV9b_vkcfCbTz8uss1XjXLHy2svpi2tmizb6et1Wfkc/edit?usp=sharing)
</div>
<h2>There are `r region$get_active_facilities('RCRA')` facilities currently reporting under
RCRA in this state.</h2>
```{r, fig.height = 5, fig.width = 8, fig.align = "center"}
hasRCRAviolations <- FALSE
nrows_df <- nrow( RCRAviolations )
if ( nrows_df > 0 ) {
hasRCRAviolations <- TRUE
#clean up data
RCRAviolations$President <- as.character("Bush")
RCRAviolations[9:16,3] <- as.character("Obama")
RCRAviolations[17:20,3] <- as.character("Trump")
vRCRAgraph <- ggplot(RCRAviolations, mapping=aes(x=factor(Year), y=Count, fill=President, group=1))+
geom_bar(stat="identity", show.legend = FALSE, alpha=.3)+
scale_fill_manual(values=c("#450d54", "#22908d", "#e56d13"))+
labs(y=" # Violations", x="Year", title = "RCRA violations\n", subtitle = "Very incomplete data")+
#ggtitle("RCRA violations")+
scale_y_continuous(expand=c(0,0), limits=c(0,max(RCRAviolations$Count+(.2*max(RCRAviolations$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))
}
hasRCRAinspections <- FALSE
nrows_df <- nrow( RCRAinspections )
if ( nrows_df > 0 ) {
hasRCRAinspections <- TRUE
#clean up data