Is there a method(s) to tag “many-to-many、one-to-many and many-to-one” relationship, like in the example below?
<code>df1 <- data.frame(
id1 = c(1, 1, 3, 4),
value1 = c("A", "B", "C", "D")
)
df2 <- data.frame(
id2 = c(1, 1, 3, 3, 4),
value2 = c("X", "Y", "Z1", "Z2", "W")
)
# Performing a left join to tag the many-to-many relationship and adding a tag variable
result <- df1 %>%
left_join(df2, by = c("id1" = "id2")) %>%
group_by(id1) %>%
mutate(
tag = if_else(n() > 1, "many-to-many", "one-to-many")
) %>%
ungroup()
result
# A tibble: 7 × 5
id1 value1 id2 value2 tag
<dbl> <chr> <dbl> <chr> <chr>
1 1 A 1 X many-to-many
2 1 A 1 Y many-to-many
3 1 B 1 X many-to-many
4 1 B 1 Y many-to-many
5 3 C 3 Z1 many-to-many
6 3 C 3 Z2 many-to-many
7 4 D 4 W one-to-many
</code>
<code>df1 <- data.frame(
id1 = c(1, 1, 3, 4),
value1 = c("A", "B", "C", "D")
)
df2 <- data.frame(
id2 = c(1, 1, 3, 3, 4),
value2 = c("X", "Y", "Z1", "Z2", "W")
)
# Performing a left join to tag the many-to-many relationship and adding a tag variable
result <- df1 %>%
left_join(df2, by = c("id1" = "id2")) %>%
group_by(id1) %>%
mutate(
tag = if_else(n() > 1, "many-to-many", "one-to-many")
) %>%
ungroup()
result
# A tibble: 7 × 5
id1 value1 id2 value2 tag
<dbl> <chr> <dbl> <chr> <chr>
1 1 A 1 X many-to-many
2 1 A 1 Y many-to-many
3 1 B 1 X many-to-many
4 1 B 1 Y many-to-many
5 3 C 3 Z1 many-to-many
6 3 C 3 Z2 many-to-many
7 4 D 4 W one-to-many
</code>
df1 <- data.frame(
id1 = c(1, 1, 3, 4),
value1 = c("A", "B", "C", "D")
)
df2 <- data.frame(
id2 = c(1, 1, 3, 3, 4),
value2 = c("X", "Y", "Z1", "Z2", "W")
)
# Performing a left join to tag the many-to-many relationship and adding a tag variable
result <- df1 %>%
left_join(df2, by = c("id1" = "id2")) %>%
group_by(id1) %>%
mutate(
tag = if_else(n() > 1, "many-to-many", "one-to-many")
) %>%
ungroup()
result
# A tibble: 7 × 5
id1 value1 id2 value2 tag
<dbl> <chr> <dbl> <chr> <chr>
1 1 A 1 X many-to-many
2 1 A 1 Y many-to-many
3 1 B 1 X many-to-many
4 1 B 1 Y many-to-many
5 3 C 3 Z1 many-to-many
6 3 C 3 Z2 many-to-many
7 4 D 4 W one-to-many
In the example above, I have tagged the “many-to-many、one-to-many” but not many-to-one. How can I do that?
1
<code>count_df <- left_join(
count(df1, id1), count(df2, id2),
by = c("id1" = "id2"),
relationship = "many-to-many"
)
one_or_many <- function(x) if_else(x == 1L, "one", "many")
df1 |>
left_join(df2, by = c("id1" = "id2"), relationship = "many-to-many") |>
left_join(count_df, by = "id1") |>
mutate(
tag = sprintf("%s-to-%s", one_or_many(n.x), one_or_many(n.y)),
n.x = NULL, n.y = NULL
)
# id1 value1 value2 tag
# 1 1 A X many-to-many
# 2 1 A Y many-to-many
# 3 1 B X many-to-many
# 4 1 B Y many-to-many
# 5 3 C Z1 one-to-many
# 6 3 C Z2 one-to-many
# 7 4 D W one-to-one
</code>
<code>count_df <- left_join(
count(df1, id1), count(df2, id2),
by = c("id1" = "id2"),
relationship = "many-to-many"
)
one_or_many <- function(x) if_else(x == 1L, "one", "many")
df1 |>
left_join(df2, by = c("id1" = "id2"), relationship = "many-to-many") |>
left_join(count_df, by = "id1") |>
mutate(
tag = sprintf("%s-to-%s", one_or_many(n.x), one_or_many(n.y)),
n.x = NULL, n.y = NULL
)
# id1 value1 value2 tag
# 1 1 A X many-to-many
# 2 1 A Y many-to-many
# 3 1 B X many-to-many
# 4 1 B Y many-to-many
# 5 3 C Z1 one-to-many
# 6 3 C Z2 one-to-many
# 7 4 D W one-to-one
</code>
count_df <- left_join(
count(df1, id1), count(df2, id2),
by = c("id1" = "id2"),
relationship = "many-to-many"
)
one_or_many <- function(x) if_else(x == 1L, "one", "many")
df1 |>
left_join(df2, by = c("id1" = "id2"), relationship = "many-to-many") |>
left_join(count_df, by = "id1") |>
mutate(
tag = sprintf("%s-to-%s", one_or_many(n.x), one_or_many(n.y)),
n.x = NULL, n.y = NULL
)
# id1 value1 value2 tag
# 1 1 A X many-to-many
# 2 1 A Y many-to-many
# 3 1 B X many-to-many
# 4 1 B Y many-to-many
# 5 3 C Z1 one-to-many
# 6 3 C Z2 one-to-many
# 7 4 D W one-to-one
<code># adding rows to both of the dataframes, to demonstrate the # many-to-one join
df1 <- df1 |> add_row(id1 = c(5, 5), value1 = c("E", "F"))
df2 <- df2 |> add_row(id2 = 5, value2 = "Z3")
# check if there is more than one unique value
# in each group.
# note: this approach doesn't work if you have identical rows, I think!
f <- (x) unique(x) %>% {ifelse(length(.) > 1, "many", "one")}
full_join(df1, df2, by = c("id1" = "id2")) |>
mutate(tag = paste0(f(value1), "-to-", f(value2)), .by = id1)
</code>
<code># adding rows to both of the dataframes, to demonstrate the # many-to-one join
df1 <- df1 |> add_row(id1 = c(5, 5), value1 = c("E", "F"))
df2 <- df2 |> add_row(id2 = 5, value2 = "Z3")
# check if there is more than one unique value
# in each group.
# note: this approach doesn't work if you have identical rows, I think!
f <- (x) unique(x) %>% {ifelse(length(.) > 1, "many", "one")}
full_join(df1, df2, by = c("id1" = "id2")) |>
mutate(tag = paste0(f(value1), "-to-", f(value2)), .by = id1)
</code>
# adding rows to both of the dataframes, to demonstrate the # many-to-one join
df1 <- df1 |> add_row(id1 = c(5, 5), value1 = c("E", "F"))
df2 <- df2 |> add_row(id2 = 5, value2 = "Z3")
# check if there is more than one unique value
# in each group.
# note: this approach doesn't work if you have identical rows, I think!
f <- (x) unique(x) %>% {ifelse(length(.) > 1, "many", "one")}
full_join(df1, df2, by = c("id1" = "id2")) |>
mutate(tag = paste0(f(value1), "-to-", f(value2)), .by = id1)
Output:
<code> id1 value1 value2 tag
1 1 A X many-to-many
2 1 A Y many-to-many
3 1 B X many-to-many
4 1 B Y many-to-many
5 3 C Z1 one-to-many
6 3 C Z2 one-to-many
7 4 D W one-to-one
8 5 E Z3 many-to-one
9 5 F Z3 many-to-one
</code>
<code> id1 value1 value2 tag
1 1 A X many-to-many
2 1 A Y many-to-many
3 1 B X many-to-many
4 1 B Y many-to-many
5 3 C Z1 one-to-many
6 3 C Z2 one-to-many
7 4 D W one-to-one
8 5 E Z3 many-to-one
9 5 F Z3 many-to-one
</code>
id1 value1 value2 tag
1 1 A X many-to-many
2 1 A Y many-to-many
3 1 B X many-to-many
4 1 B Y many-to-many
5 3 C Z1 one-to-many
6 3 C Z2 one-to-many
7 4 D W one-to-one
8 5 E Z3 many-to-one
9 5 F Z3 many-to-one
When you say “many-to-one”, you mean that one row in df2 matches several rows in df1, correct? You can identify these cases in df2 before joining and then take it into account when you generate the tag variable:
<code># this counts how often each value of id2 appears in id1.
df2$m2o <- sapply(df2$id2, function(x) sum(df1$id1 == 1))
# and the same the other way.
df1$o2m <- sapply(df1$id1, function(x) sum(df2$id2 == 1))
result <- df1 %>%
left_join(df2, by = c(id1 = "id2")) %>%
group_by(id1) %>%
mutate(
tag = case_when(m2o > 1 & o2m > 1 ~ "many-to-many",
m2o > 1 ~ "many-to-one",
o2m > 1 ~ "one-to-many",
m2o == 1 & o2m == 1 ~ "one-to-one",
TRUE ~ "no-match")
) %>%
ungroup()
</code>
<code># this counts how often each value of id2 appears in id1.
df2$m2o <- sapply(df2$id2, function(x) sum(df1$id1 == 1))
# and the same the other way.
df1$o2m <- sapply(df1$id1, function(x) sum(df2$id2 == 1))
result <- df1 %>%
left_join(df2, by = c(id1 = "id2")) %>%
group_by(id1) %>%
mutate(
tag = case_when(m2o > 1 & o2m > 1 ~ "many-to-many",
m2o > 1 ~ "many-to-one",
o2m > 1 ~ "one-to-many",
m2o == 1 & o2m == 1 ~ "one-to-one",
TRUE ~ "no-match")
) %>%
ungroup()
</code>
# this counts how often each value of id2 appears in id1.
df2$m2o <- sapply(df2$id2, function(x) sum(df1$id1 == 1))
# and the same the other way.
df1$o2m <- sapply(df1$id1, function(x) sum(df2$id2 == 1))
result <- df1 %>%
left_join(df2, by = c(id1 = "id2")) %>%
group_by(id1) %>%
mutate(
tag = case_when(m2o > 1 & o2m > 1 ~ "many-to-many",
m2o > 1 ~ "many-to-one",
o2m > 1 ~ "one-to-many",
m2o == 1 & o2m == 1 ~ "one-to-one",
TRUE ~ "no-match")
) %>%
ungroup()