How to perform a differences in differences regression in R?

I have a data frame containing the change in spending over last week for 50 states (in percents), then the date of reopening, and a boolean column of whether the state is reopened or no.

# A tibble: 1,377 x 5
# Groups:   state [51]
   state statefips day_month_year R2to1 changeVsLastWeekSpend
   <chr> <chr>     <date>         <lgl>                 <dbl>
 1 AK    02        2020-01-12     FALSE              NA      
 2 AK    02        2020-01-19     FALSE              -0.0219 
 3 AK    02        2020-01-26     FALSE               0.0262 
 4 AK    02        2020-02-02     FALSE              -0.00165
 5 AK    02        2020-02-09     FALSE               0.0271 
 6 AK    02        2020-02-16     FALSE               0.0258 
 7 AK    02        2020-02-23     FALSE              -0.0409 
 8 AK    02        2020-03-01     FALSE              -0.0517 
 9 AK    02        2020-03-08     FALSE               0.0976 
10 AK    02        2020-03-15     FALSE              -0.0160 

I would like to perform a DID regression on the data, but I am unsure if it is even possible, since each state reopened at a different time. If it were possible, how would you do it in R?

I was thinking of the following regression (should I use fixed effects, within?):

plm(data = filter(Affinity_State_Weekly.csv.p,WeekAfterReopening2to1 > -4 & WeekAfterReopening2to1 < 4, R2to1True == TRUE) ,
changeVsLastWeekSpend  ~ R2to1, model = "within")

But I am unsure if the output is truly a DID regression. I am sorry if this question is easy, but I am a novice at R and econometrics.