library(dplyr) # Helps manipulate data

# Let's create example datasets
records <- data.frame(
  user_id = c(1, 2, 3, 4, 5),
  location = c("A", "B", "C", "C", "D")
)
#   user_id location
# 1       1        A
# 2       2        B
# 3       3        C
# 4       4        C
# 5       5        D

transactions <- data.frame(
  user_id = c(1, 1, 1, 2, 3, 3, 3, 4, 5, 5),
  amt = c(50, 30, 20, 25, 42, 67, 43, 82, 90, 9)
)
#    user_id amt
# 1        1  50
# 2        1  30
# 3        1  20
# 4        2  25
# 5        3  42
# 6        3  67
# 7        3  43
# 8        4  82
# 9        5  90
# 10       5   9

census <- data.frame(
  locale = c("A", "B", "E"),
  poverty_rate = c("0.19", "0.15", "0.25")
)
#   locale poverty_rate
# 1      A         0.19
# 2      B         0.15
# 3      E         0.25

# A few types of matches
# - inner join
# - full join (also know as outer join)
# - left join
# - right join

# inner join:
# - Return only matched in both datasets
# - Notice in "by" we use the name of merging variable in the
#   first data.frame first and the name of the second data.frame
inner_join(
  records,
  census,
  by = c("location" = "locale")
)
#   user_id location poverty_rate
# 1       1        A         0.19
# 2       2        B         0.15

# full join:
# - Return matched and unmatched cases
full_join(
  records,
  census,
  by = c("location" = "locale")
)
#   user_id location poverty_rate
# 1       1        A         0.19
# 2       2        B         0.15
# 3       3        C         <NA>
# 4       4        C         <NA>
# 5       5        D         <NA>
# 6      NA        E         0.25

# left join:
# - Return matched cases and return unmatched cases from the first data.frame
left_join(
  records,
  census,
  by = c("location" = "locale")
)
#   user_id location poverty_rate
# 1       1        A         0.19
# 2       2        B         0.15
# 3       3        C         <NA>
# 4       4        C         <NA>
# 5       5        D         <NA>

# right join:
# - Return matched cases and unmatched cases from the second data.frame
right_join(
  records,
  census,
  by = c("location" = "locale")
)
#   user_id location poverty_rate
# 1       1        A         0.19
# 2       2        B         0.15
# 3      NA        E         0.25