Grouping by name, how do I set the names that don't have values otherwise to 0?

Question

So, I have some data I'm working with where I'm analyzing the cost of solar panels placed in different neighborhoods.

SOL2010<-mainframe%>%
  filter(year==2010)%>%
  group_by(Name)%>%
  summarize(sum(DECLARED_VALUATION))

So, when I run this, I can make some nice choropleths eventually, but here's my problem. My filters limit my eventual dataframe to only the neighborhoods that actually placed solar panels. And this changes with each year; When I try to do some tmaps stuff, it sets anything without a name as "missing" rather than 0, which is what I would want.

My problem seems to be that there are some neighborhoods that have never had any solar panel projects, and so those neighborhoods are NA'ed, instead of being 0.

Ideally, I'd be able to do something where I set the sum(Declared_Valuation) of any neighborhood that doesn't exist as 0, but this would require me putting the missing neighborhoods back into the original dataframe. From my original dataframe, I could get a list of the neighborhood as factors; would that help me programatically get the missing neighborhoods into my dataframes?

You could left_join on a table containing a column with the unique `Name`s and then replace the NAs with zeros? I'm not sure, but this `.drop=FALSE` might also work: https://stackoverflow.com/a/54794922/ — Frank, Mar 11 '19 at 19:12
Hot damn, that's a much better idea than I was coming up with. Thank you so much. — OfTheAzureSky, Mar 11 '19 at 20:21
Cool, glad it helps :) Fine if I close with a link to https://stackoverflow.com/q/22523131 ? — Frank, Mar 11 '19 at 20:25

Grouping by name, how do I set the names that don't have values otherwise to 0?

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