If you want only one plot, do you really need to use facet_wrap? You can make the plot based on a subset of the dataset. For example, run this:
o3_cbsa_annual_2015_std_summary <- data.frame(stringsAsFactors=FALSE,
data_cbsa = c("Asheville, NC", "Asheville, NC", "Asheville, NC",
"Asheville, NC", "Asheville, NC", "Asheville, NC",
"Asheville, NC", "Asheville, NC", "Asheville, NC",
"Asheville, NC", "Asheville, NC", "Asheville, NC",
"Charlotte-Concord-Gastonia, NC-SC",
"Charlotte-Concord-Gastonia, NC-SC", "Charlotte-Concord-Gastonia, NC-SC",
"Charlotte-Concord-Gastonia, NC-SC",
"Charlotte-Concord-Gastonia, NC-SC", "Charlotte-Concord-Gastonia, NC-SC", "Charlotte-Concord-Gastonia, NC-SC",
"Charlotte-Concord-Gastonia, NC-SC",
"Charlotte-Concord-Gastonia, NC-SC", "Charlotte-Concord-Gastonia, NC-SC",
"Charlotte-Concord-Gastonia, NC-SC",
"Charlotte-Concord-Gastonia, NC-SC", "Cullowhee, NC", "Cullowhee, NC",
"Cullowhee, NC", "Cullowhee, NC", "Cullowhee, NC",
"Cullowhee, NC", "Cullowhee, NC", "Cullowhee, NC",
"Cullowhee, NC", "Cullowhee, NC", "Cullowhee, NC",
"Durham-Chapel Hill, NC", "Durham-Chapel Hill, NC",
"Durham-Chapel Hill, NC", "Durham-Chapel Hill, NC",
"Durham-Chapel Hill, NC", "Durham-Chapel Hill, NC",
"Durham-Chapel Hill, NC", "Durham-Chapel Hill, NC",
"Durham-Chapel Hill, NC", "Durham-Chapel Hill, NC",
"Durham-Chapel Hill, NC", "Durham-Chapel Hill, NC", "Fayetteville, NC", "Fayetteville, NC", "Fayetteville, NC",
"Fayetteville, NC", "Fayetteville, NC", "Fayetteville, NC",
"Fayetteville, NC", "Fayetteville, NC", "Fayetteville, NC", "Fayetteville, NC", "Fayetteville, NC",
"Fayetteville, NC", "Greensboro-High Point, NC",
"Greensboro-High Point, NC", "Greensboro-High Point, NC",
"Greensboro-High Point, NC", "Greensboro-High Point, NC",
"Greensboro-High Point, NC", "Greensboro-High Point, NC", "Greensboro-High Point, NC",
"Greensboro-High Point, NC", "Greensboro-High Point, NC",
"Greensboro-High Point, NC", "Greensboro-High Point, NC",
"Greenville, NC", "Greenville, NC", "Greenville, NC",
"Greenville, NC", "Greenville, NC", "Greenville, NC",
"Greenville, NC", "Greenville, NC", "Greenville, NC",
"Greenville, NC", "Greenville, NC", "Greenville, NC",
"Hickory-Lenoir-Morganton, NC", "Hickory-Lenoir-Morganton, NC", "Hickory-Lenoir-Morganton, NC",
"Hickory-Lenoir-Morganton, NC", "Hickory-Lenoir-Morganton, NC",
"Hickory-Lenoir-Morganton, NC", "Hickory-Lenoir-Morganton, NC", "Hickory-Lenoir-Morganton, NC",
"Hickory-Lenoir-Morganton, NC", "Hickory-Lenoir-Morganton, NC",
"Hickory-Lenoir-Morganton, NC",
"Hickory-Lenoir-Morganton, NC", "Kinston, NC", "Kinston, NC", "Kinston, NC",
"Kinston, NC", "Kinston, NC", "Kinston, NC", "Kinston, NC", "Kinston, NC", "Kinston, NC", "Kinston, NC",
"Kinston, NC", "Kinston, NC", "Morehead City, NC",
"Morehead City, NC", "Morehead City, NC", "Morehead City, NC", "Morehead City, NC", "Morehead City, NC",
"Morehead City, NC", "Morehead City, NC", "Morehead City, NC", "Morehead City, NC", "Morehead City, NC",
"Morehead City, NC", "Oxford, NC", "Oxford, NC", "Oxford, NC", "Oxford, NC", "Oxford, NC", "Oxford, NC",
"Oxford, NC", "Oxford, NC", "Oxford, NC", "Oxford, NC",
"Oxford, NC", "Oxford, NC", "Raleigh, NC", "Raleigh, NC",
"Raleigh, NC", "Raleigh, NC", "Raleigh, NC",
"Raleigh, NC", "Raleigh, NC", "Raleigh, NC", "Raleigh, NC",
"Raleigh, NC", "Raleigh, NC", "Raleigh, NC",
"Rocky Mount, NC", "Rocky Mount, NC", "Rocky Mount, NC",
"Rocky Mount, NC", "Rocky Mount, NC", "Rocky Mount, NC",
"Rocky Mount, NC", "Rocky Mount, NC", "Rocky Mount, NC", "Rocky Mount, NC", "Rocky Mount, NC",
"Rocky Mount, NC", "Sanford, NC", "Sanford, NC", "Sanford, NC",
"Sanford, NC", "Sanford, NC", "Wilmington, NC",
"Wilmington, NC", "Wilmington, NC", "Wilmington, NC",
"Wilmington, NC", "Wilmington, NC", "Wilmington, NC",
"Wilmington, NC", "Wilmington, NC", "Wilmington, NC",
"Wilmington, NC", "Wilmington, NC", "Winston-Salem, NC", "Winston-Salem, NC", "Winston-Salem, NC",
"Winston-Salem, NC", "Winston-Salem, NC", "Winston-Salem, NC",
"Winston-Salem, NC", "Winston-Salem, NC",
"Winston-Salem, NC", "Winston-Salem, NC", "Winston-Salem, NC"),
data_year = c(2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
2015, 2016, 2017, 2018, 2007, 2008, 2009, 2010,
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2007,
2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
2018, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
2015, 2016, 2017, 2018, 2007, 2008, 2009, 2010, 2011,
2012, 2013, 2014, 2015, 2016, 2017, 2018, 2007,
2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
2017, 2018, 2007, 2008, 2009, 2010, 2011, 2012, 2013,
2014, 2015, 2016, 2017, 2018, 2007, 2008, 2009, 2010,
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2007,
2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
2017, 2018, 2007, 2008, 2009, 2010, 2011, 2012,
2013, 2014, 2015, 2016, 2017, 2018, 2007, 2008, 2009,
2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,
2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,
2016, 2017, 2018, 2007, 2008, 2009, 2010, 2011, 2012,
2013, 2014, 2015, 2016, 2017, 2018, 2014, 2015, 2016,
2017, 2018, 2007, 2008, 2009, 2010, 2011, 2012,
2013, 2014, 2015, 2016, 2017, 2018, 2007, 2008, 2009,
2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017),
highest_4th_max_cbsa = c(0.077, 0.08, 0.066, 0.074, 0.067, 0.07, 0.064,
0.067, 0.066, 0.067, 0.063, 0.065, 0.096, 0.093,
0.073, 0.082, 0.088, 0.085, 0.067, 0.068, 0.073, 0.074,
0.068, 0.07, 0.08, 0.063, 0.069, 0.068, 0.069,
0.067, 0.068, 0.065, 0.069, 0.064, 0.065, 0.08, 0.078,
0.066, 0.074, 0.072, 0.076, 0.062, 0.065, 0.061, 0.063,
0.061, 0.063, 0.082, 0.075, 0.068, 0.073, 0.076,
0.069, 0.062, 0.066, 0.062, 0.064, 0.063, 0.064, 0.086,
0.084, 0.072, 0.076, 0.076, 0.078, 0.062, 0.065,
0.066, 0.068, 0.065, 0.067, 0.079, 0.077, 0.066, 0.069,
0.074, 0.071, 0.063, 0.063, 0.06, 0.065, 0.061, 0.066,
0.081, 0.076, 0.064, 0.071, 0.067, 0.067, 0.063,
0.064, 0.065, 0.066, 0.063, 0.065, 0.077, 0.074, 0.064,
0.069, 0.069, 0.069, 0.063, 0.065, 0.062, 0.063,
0.062, 0.064, 0.07, 0.059, 0.062, 0.065, 0.063, 0.058,
0.062, 0.062, 0.058, 0.06, 0.057, 0.06, 0.083, 0.081,
0.07, 0.074, 0.072, 0.072, 0.063, 0.065, 0.063, 0.065,
0.065, 0.065, 0.084, 0.078, 0.069, 0.074, 0.078,
0.075, 0.062, 0.064, 0.065, 0.069, 0.066, 0.063, 0.08,
0.075, 0.066, 0.072, 0.072, 0.071, 0.064, 0.062, 0.061,
0.064, 0.061, 0.062, 0.064, 0.06, 0.064, 0.059,
0.06, 0.071, 0.066, 0.06, 0.062, 0.064, 0.064, 0.064,
0.063, 0.057, 0.06, 0.057, 0.062, 0.085, 0.081, 0.068,
0.081, 0.076, 0.079, 0.066, 0.067, 0.07, 0.07, 0.066)
)
library(ggplot2)
ggplot(data = subset(x = o3_cbsa_annual_2015_std_summary,
data_cbsa == 'Asheville, NC'),
mapping = aes(x = data_year,
y = highest_4th_max_cbsa)) +
geom_col(fill = 'red')

Created on 2019-08-16 by the reprex package (v0.3.0)
For your future posts, please provide a smaller dataset sufficient enough to reproduce the problem. Also, please include the library calls.