Combine migration in and out data by different common columns - r

Question

I have two datasets, one for migration inflow to county A from other counties and other for migration outflow from county A to other counties. In order to combine the two data sets as:
Desired output:
Key County State FIPS Inflow Outflow FiscalYear Year
510012012 Accomack County VA 51001 NA 27 2011 - 2012 2012
160012012 Ada County ID 16001 12 18 2011 - 2012 2012
80012012 Adams County CO 8001 22 39 2011 - 2012 2012
80012011 Adams County CO 8001 42 31 2010 - 2011 2011
450032012 Aiken County SC 45003 NA 21 2011 - 2012 2012
120012012 Alachua County FL 12001 433 NA 2011 - 2012 2012
Part of the problem is that the common columns have unequal number of rows. Another problem is that different counties can belong to the same state as shown in the dummy data below. So, what I am thinking is to have a unique key (Key) for both destination and origin county by concatenating FIPS (unique for each county) and Year. That way I can combine the counties with their respective states and the rest of the associated columns values in a single row.
How can I combine the two into one dataset in such a way that I don't have to hardcode each and every common county and state name and FIPS and Year? Missing values would be NA.
My original migration outflow data has 517 observations and migration inflow has 441, thus different number of counties in each dataset.
Sample data:
# People moving out of county A to other counties
inflow_df = structure(list(Origin_FIPS = c(12001L, 8001L, 16001L, 12001L,
8001L, 16001L), Origin_StateName = c("FL", "CO", "ID", "FL",
"CO", "ID"), Origin_Place = c("Alachua County", "Adams County",
"Ada County", "Alachua County", "Adams County", "Ada County"),
InIndividuals = c(433L, 30L, 16L, 381L, 42L, 21L), FiscalYear = c("2011 - 2012",
"2011 - 2012", "2011 - 2012", "2010 - 2011", "2010 - 2011",
"2010 - 2011"), Year = c(2012L, 2012L, 2012L, 2011L, 2011L,
2011L), Key = c(120012012L, 80012012L, 160012012L, 120012011L,
80012011L, 160012011L)), class = "data.frame", row.names = c(NA,
-6L))
# People moving in county A from other counties
outflow_df = structure(list(Dest_FIPS = c(51001L, 16001L, 8001L, 8001L, 45003L
), Dest_StateName = c("VA", "ID", "CO", "CO", "SC"), Dest_Place = c("Accomack County",
"Ada County", "Adams County", "Adams County", "Aiken County"),
OutIndividuals = c(27L, 16L, 39L, 31L, 21L), FiscalYear = c("2011 - 2012",
"2011 - 2012", "2011 - 2012", "2010 - 2011", "2011 - 2012"
), Year = c(2012L, 2012L, 2012L, 2011L, 2012L), Key = c(510012012L,
160012012L, 80012012L, 80012011L, 450032012L)), class = "data.frame", row.names = c(NA,
-5L))

Answer 1

Perhaps this helps
library(dplyr)
library(tidyr)
library(stringr)
library(data.table)
bind_rows(lst(Inflow_df, Outflow_df), .id = 'datname') %>%
pivot_longer(cols = contains("_"), names_to = ".value",
names_pattern = ".*_([^_]+$)") %>%
mutate(Key = str_c(County, Year), rn = rowid(Key, datname)) %>%
pivot_wider(names_from = datname, values_from = Individuals) %>%
arrange(rn) %>%
select(-rn)