`geom_col` is changing the limits for a variable

tidyverse
1

I want to plot the minimum wage for each state and fill by year in a bar chart. My data can be found here.
load the data with read_excel()

bzip <- read_excel("mw_state_monthly.xlsx")
bzip %>% 
    transmute(date = gsub('m', '',`Monthly Date`), date = ym(date), state = `State Abbreviation`, year = year(date),  mw = pmax(`Monthly Federal Minimum`, `Monthly State Minimum`)) %>% 
    distinct() %>% 
    filter(date > ymd(20081231)) %>% 
    ggplot(data = ., aes(mw, state, fill = year)) + geom_col()

and the resulting plot fails to render the limits for the minimum wage values:

image
image1536×995 66.3 KB

2

The year variable is being treated as a continuous value, so the fill color is a gradient rather than distinct colors. You can fix that by making year a factor. You can also set geom_col(position = "dodge") so that the bars do not stack but are side by side. However, with 13 years of data, I think there are too many years to distinguish easily by color and with 51 states, the plot will have too many bars.

3

I was hoping to create something like this from wikipedia

MRE data:

bzip <- structure(list(year = c(2009, 2009, 2010, 2011, 2012, 2013, 2014, 
2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 
2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 
2018, 2019, 2020, 2021, 2022, 2009, 2010, 2011, 2012, 2013, 2014, 
2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2010, 2011, 
2012, 2013, 2014, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 
2022, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 
2019, 2020, 2021, 2022, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 
2016, 2017, 2018, 2019, 2019, 2020, 2020, 2021, 2021, 2022, 2022, 
2009, 2009, 2010, 2011, 2012, 2013, 2014, 2014, 2015, 2015, 2016, 
2016, 2017, 2017, 2018, 2018, 2019, 2019, 2020, 2020, 2021, 2021, 
2022, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2014, 2015, 
2015, 2016, 2017, 2018, 2019, 2019, 2020, 2021, 2022, 2009, 2009, 
2010, 2011, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 
2020, 2021, 2021, 2022, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 
2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2010, 
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 
2022, 2022, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 
2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 
2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 
2010, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 
2020, 2020, 2021, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 
2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 
2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 
2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 
2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2010, 
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 
2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2015, 2016, 
2016, 2017, 2017, 2018, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 
2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 
2021, 2022, 2009, 2010, 2011, 2012, 2013, 2014, 2014, 2015, 2016, 
2017, 2018, 2019, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 2011, 
2012, 2013, 2014, 2014, 2015, 2015, 2016, 2016, 2017, 2018, 2019, 
2020, 2021, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 
2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 2011, 
2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 
2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 
2019, 2020, 2021, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 
2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 
2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 
2018, 2019, 2020, 2021, 2022, 2009, 2010, 2011, 2012, 2013, 2014, 
2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2019, 2020, 
2021, 2022, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 
2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 2010, 2011, 2012, 
2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2020, 2021, 2021, 
2022, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 
2017, 2018, 2019, 2020, 2021, 2022, 2009, 2010, 2011, 2012, 2013, 
2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 
2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 
2021, 2022, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2016, 
2017, 2017, 2018, 2018, 2019, 2019, 2020, 2020, 2021, 2021, 2022, 
2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 
2018, 2019, 2020, 2021, 2022, 2009, 2010, 2011, 2012, 2013, 2014, 
2015, 2016, 2017, 2018, 2019, 2020, 2020, 2021, 2022, 2009, 2009, 
2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 
2021, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 
2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 2011, 2012, 
2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 
2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 
2020, 2021, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 
2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 2011, 
2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2021, 
2022, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 
2019, 2020, 2021, 2022, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 
2016, 2017, 2018, 2019, 2020, 2021, 2022, 2009, 2009, 2010, 2011, 
2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 
2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 
2020, 2021, 2022, 2009, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 
2016, 2017, 2018, 2019, 2020, 2021, 2022), state = c("AK", "AK", 
"AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", 
"AK", "AK", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", 
"AL", "AL", "AL", "AL", "AL", "AL", "AR", "AR", "AR", "AR", "AR", 
"AR", "AR", "AR", "AR", "AR", "AR", "AR", "AR", "AR", "AR", "AZ", 
"AZ", "AZ", "AZ", "AZ", "AZ", "AZ", "AZ", "AZ", "AZ", "AZ", "AZ", 
"AZ", "AZ", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", 
"CA", "CA", "CA", "CA", "CA", "CA", "CO", "CO", "CO", "CO", "CO", 
"CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO", "CT", "CT", 
"CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", "CT", 
"CT", "CT", "CT", "CT", "CT", "DC", "DC", "DC", "DC", "DC", "DC", 
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", "DC", 
"DC", "DC", "DC", "DC", "DC", "DC", "DC", "DE", "DE", "DE", "DE", 
"DE", "DE", "DE", "DE", "DE", "DE", "DE", "DE", "DE", "DE", "DE", 
"DE", "DE", "DE", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", 
"FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "FL", "GA", 
"GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", "GA", 
"GA", "GA", "GA", "HI", "HI", "HI", "HI", "HI", "HI", "HI", "HI", 
"HI", "HI", "HI", "HI", "HI", "HI", "HI", "IA", "IA", "IA", "IA", 
"IA", "IA", "IA", "IA", "IA", "IA", "IA", "IA", "IA", "IA", "ID", 
"ID", "ID", "ID", "ID", "ID", "ID", "ID", "ID", "ID", "ID", "ID", 
"ID", "ID", "ID", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", 
"IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IL", "IN", "IN", 
"IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", 
"IN", "IN", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "KS", 
"KS", "KS", "KS", "KS", "KS", "KS", "KY", "KY", "KY", "KY", "KY", 
"KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "LA", 
"LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", "LA", 
"LA", "LA", "LA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", "MA", 
"MA", "MA", "MA", "MA", "MA", "MA", "MD", "MD", "MD", "MD", "MD", 
"MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", "MD", 
"MD", "MD", "MD", "ME", "ME", "ME", "ME", "ME", "ME", "ME", "ME", 
"ME", "ME", "ME", "ME", "ME", "ME", "ME", "MI", "MI", "MI", "MI", 
"MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", 
"MI", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", 
"MN", "MN", "MN", "MN", "MN", "MN", "MN", "MN", "MO", "MO", "MO", 
"MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", "MO", 
"MO", "MS", "MS", "MS", "MS", "MS", "MS", "MS", "MS", "MS", "MS", 
"MS", "MS", "MS", "MS", "MS", "MT", "MT", "MT", "MT", "MT", "MT", 
"MT", "MT", "MT", "MT", "MT", "MT", "MT", "MT", "MT", "NC", "NC", 
"NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", "NC", 
"NC", "NC", "ND", "ND", "ND", "ND", "ND", "ND", "ND", "ND", "ND", 
"ND", "ND", "ND", "ND", "ND", "ND", "NE", "NE", "NE", "NE", "NE", 
"NE", "NE", "NE", "NE", "NE", "NE", "NE", "NE", "NE", "NE", "NH", 
"NH", "NH", "NH", "NH", "NH", "NH", "NH", "NH", "NH", "NH", "NH", 
"NH", "NH", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", 
"NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NJ", "NM", "NM", "NM", "NM", 
"NM", "NM", "NM", "NM", "NM", "NM", "NM", "NM", "NM", "NM", "NV", 
"NV", "NV", "NV", "NV", "NV", "NV", "NV", "NV", "NV", "NV", "NV", 
"NV", "NV", "NV", "NV", "NV", "NV", "NV", "NY", "NY", "NY", "NY", 
"NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", 
"OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", "OH", 
"OH", "OH", "OH", "OK", "OK", "OK", "OK", "OK", "OK", "OK", "OK", 
"OK", "OK", "OK", "OK", "OK", "OK", "OK", "OR", "OR", "OR", "OR", 
"OR", "OR", "OR", "OR", "OR", "OR", "OR", "OR", "OR", "OR", "OR", 
"OR", "OR", "OR", "OR", "OR", "OR", "PA", "PA", "PA", "PA", "PA", 
"PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "PA", "RI", 
"RI", "RI", "RI", "RI", "RI", "RI", "RI", "RI", "RI", "RI", "RI", 
"RI", "RI", "RI", "SC", "SC", "SC", "SC", "SC", "SC", "SC", "SC", 
"SC", "SC", "SC", "SC", "SC", "SC", "SC", "SD", "SD", "SD", "SD", 
"SD", "SD", "SD", "SD", "SD", "SD", "SD", "SD", "SD", "SD", "SD", 
"TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", "TN", 
"TN", "TN", "TN", "TN", "TX", "TX", "TX", "TX", "TX", "TX", "TX", 
"TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "UT", "UT", "UT", 
"UT", "UT", "UT", "UT", "UT", "UT", "UT", "UT", "UT", "UT", "UT", 
"UT", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", 
"VA", "VA", "VA", "VA", "VA", "VA", "VT", "VT", "VT", "VT", "VT", 
"VT", "VT", "VT", "VT", "VT", "VT", "VT", "VT", "VT", "WA", "WA", 
"WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WA", 
"WA", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", "WI", 
"WI", "WI", "WI", "WI", "WI", "WV", "WV", "WV", "WV", "WV", "WV", 
"WV", "WV", "WV", "WV", "WV", "WV", "WV", "WV", "WY", "WY", "WY", 
"WY", "WY", "WY", "WY", "WY", "WY", "WY", "WY", "WY", "WY", "WY", 
"WY"), mw = c(7.15000009536743, 7.25, 7.75, 7.75, 7.75, 7.75, 
7.75, 8.75, 9.75, 9.80000019073486, 9.84000015258789, 9.89000034332275, 
10.1899995803833, 10.3400001525879, 10.3400001525879, 6.55000019073486, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.5, 8, 8.5, 8.5, 9.25, 10, 11, 11, 7.25, 7.25, 7.34999990463257, 
7.65000009536743, 7.80000019073486, 7.90000009536743, 8.05000019073486, 
8.05000019073486, 10, 10.5, 11, 12, 12.1499996185303, 12.8000001907349, 
8, 8, 8, 8, 8, 8, 9, 9, 10, 10.5, 11, 12, 13, 14, 15, 7.28000020980835, 
7.25, 7.3600001335144, 7.6399998664856, 7.78000020980835, 8, 
8.22999954223633, 8.3100004196167, 9.30000019073486, 10.1999998092651, 
11.1000003814697, 12, 12.3199996948242, 12.5600004196167, 8, 
8.25, 8.25, 8.25, 8.25, 8.69999980926514, 9.14999961853027, 9.60000038146973, 
10.1000003814697, 10.1000003814697, 10.1000003814697, 11, 11, 
12, 12, 13, 13, 14, 7.55000019073486, 8.25, 8.25, 8.25, 8.25, 
8.25, 8.25, 9.5, 9.5, 10.5, 10.5, 11.5, 11.5, 12.5, 12.5, 13.25, 
13.25, 14, 14, 15, 15, 15.1999998092651, 15.1999998092651, 16.1000003814697, 
7.15000009536743, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.75, 7.75, 
8.25, 8.25, 8.25, 8.25, 8.75, 9.25, 9.25, 9.25, 10.5, 7.21000003814697, 
7.25, 7.25, 7.25, 7.30999994277954, 7.67000007629395, 7.78999996185303, 
7.92999982833862, 8.05000019073486, 8.05000019073486, 8.10000038146973, 
8.25, 8.46000003814697, 8.5600004196167, 8.5600004196167, 10, 
10, 11, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 7.75, 8.5, 9.25, 10.1000003814697, 10.1000003814697, 
10.1000003814697, 10.1000003814697, 10.1000003814697, 12, 7.25, 
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7.25, 7.25, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.75, 8, 8, 8.25, 
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11, 12, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 6.55000019073486, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 6.55000019073486, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 8, 8, 8, 8, 8, 8, 9, 10, 11, 11, 12, 12.75, 
13.5, 14.25, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 8, 8.25, 8.25, 8.75, 8.75, 9.25, 9.25, 10.1000003814697, 
10.1000003814697, 11, 11.75, 12.5, 7.25, 7.5, 7.5, 7.5, 7.5, 
7.5, 7.5, 7.5, 7.5, 9, 10, 11, 12, 12.1499996185303, 12.75, 7.40000009536743, 
7.40000009536743, 7.40000009536743, 7.40000009536743, 7.40000009536743, 
7.40000009536743, 8.14999961853027, 8.14999961853027, 8.5, 8.89999961853027, 
9.25, 9.25, 9.44999980926514, 9.64999961853027, 9.64999961853027, 
9.86999988555908, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 8, 8, 9, 9, 9.5, 9.5, 9.64999961853027, 9.85999965667725, 
10, 10.0799999237061, 10.3299999237061, 7.05000019073486, 7.25, 
7.25, 7.25, 7.25, 7.34999990463257, 7.5, 7.65000009536743, 7.65000009536743, 
7.69999980926514, 7.84999990463257, 8.60000038146973, 9.44999980926514, 
10.3000001907349, 11.1499996185303, 6.55000019073486, 7.25, 7.25, 
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7.25, 6.90000009536743, 7.25, 7.25, 7.34999990463257, 7.65000009536743, 
7.80000019073486, 7.90000009536743, 8.05000019073486, 8.05000019073486, 
8.14999961853027, 8.30000019073486, 8.5, 8.64999961853027, 8.75, 
9.19999980926514, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 
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8.4399995803833, 8.60000038146973, 8.85000038146973, 10, 11, 
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9, 10.5, 11.5, 6.84999990463257, 7.55000019073486, 7.55000019073486, 
8.25, 8.25, 8.25, 8.25, 8.25, 8.25, 8.25, 8.25, 8.25, 8.25, 8.25, 
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8.10000038146973, 8.10000038146973, 8.14999961853027, 8.30000019073486, 
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7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 6.55000019073486, 7.25, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 6.55000019073486, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 9.5, 11, 8.0600004196167, 
8.0600004196167, 8.14999961853027, 8.46000003814697, 8.60000038146973, 
8.72999954223633, 9.14999961853027, 9.60000038146973, 10, 10.5, 
10.7799997329712, 10.960000038147, 11.75, 12.5500001907349, 8.55000019073486, 
8.55000019073486, 8.67000007629395, 9.03999996185303, 9.1899995803833, 
9.31999969482422, 9.47000026702881, 9.47000026702881, 11, 11.5, 
12, 13.5, 13.960000038147, 14.4899997711182, 6.55000019073486, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 8, 8.75, 
8.75, 8.75, 8.75, 8.75, 8.75, 8.75, 6.55000019073486, 7.25, 7.25, 
7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 7.25, 
7.25)), row.names = c(NA, -795L), class = c("tbl_df", "tbl", 
"data.frame"))

if I use year as a factor, the whole bar gets the color of the last year(2022), which defeats the purpose of the fill aesthetic.

4

It looks like the plot you want to replicate has the minimum wage for the first year and then increments for the following years. Is that what you want? I think the data frame would have to be recalculated to achieve that but I can't work on it just now.

5

Using the data you just posted, maybe this is close to what you want.

library(dplyr)
library(ggplot2)
bzip <- bzip |> group_by(state) |> mutate(mw_min = min(mw)) 
bzip <- bzip |> group_by(state) |> mutate(mw_lag = lag(mw), 
                                  mw_adj = ifelse(mw == mw_min & year == 2009, mw, mw - mw_lag))

bzip |> filter(mw_adj > 0) |> 
  ggplot(aes(mw_adj, state, fill = factor(year))) + 
  geom_col(color = "black", position = position_stack(reverse = TRUE))
6

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