Overview
Joining data tables with joyn is particularly convenient
as it allows you to analyze/be aware of the quality of the merging.
This vignette explores dplyr-like join functions available in
joyn. Their major objective is to let you employ a syntax
you are supposedly already familiar with - the dplyr one -
while at the same time benefiting of the additional tools that
joyn offers. That is, obtaining additional information and
verification of the joining.
There are four types of dplyr-like join functions in
joyn:
Left joins:
joyn::left_join()Right joins:
joyn::right_join()Full joins:
joyn::full_join()Inner joins:
joyn::inner_join()
Each of them is a wrapper that works in a similar way as the
corresponding dplyr function.
library(joyn)
#>
#> Attaching package: 'joyn'
#> The following object is masked from 'package:base':
#>
#> merge
library(data.table)
#> Warning: package 'data.table' was built under R version 4.3.3Rationale
x1 <- data.table(id = c(1L, 1L, 2L, 3L, NA_integer_),
t = c(1L, 2L, 1L, 2L, NA_integer_),
x = 11:15)
y1 <- data.table(id = c(1,2, 4),
y = c(11L, 15L, 16))Suppose you want to perform a simple left join
between tables x1 and y1.
With joyn you have two possibilities:
using the
joyn()function, specifyingkeep = "left"using the
joyn::left_join()function
In addition, you could use dplyr::left_join() or base R
merging functions.
Consider these three options:
# Option 1
joyn(x = x1,
y = y1,
keep = "left",
match_type = "m:1")
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 2 40%
#> 2 y 1 20%
#> 3 x & y 2 40%
#> 4 total 5 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id and y
#> id t x y .joyn
#> <num> <int> <int> <num> <fctr>
#> 1: 1 1 11 11 x & y
#> 2: 1 2 12 11 x & y
#> 3: 2 1 13 15 x & y
#> 4: 3 2 14 NA x
#> 5: NA NA 15 NA x
# Option 2
joyn::left_join(x = x1,
y = y1,
relationship = "many-to-one")
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 2 40%
#> 2 y 1 20%
#> 3 x & y 2 40%
#> 4 total 5 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id and y
#> ⚠ Warning: joyn does not currently allow inequality joins, so keep = NULL will
#> retain only keys in x
#> id t x y .joyn
#> <num> <int> <int> <num> <fctr>
#> 1: 1 1 11 11 x & y
#> 2: 1 2 12 11 x & y
#> 3: 2 1 13 15 x & y
#> 4: 3 2 14 NA x
#> 5: NA NA 15 NA x
# Option 3
dplyr::left_join(x = x1,
y = y1,
relationship = "many-to-one")
#> Joining with `by = join_by(id)`
#> id t x y
#> <num> <int> <int> <num>
#> 1: 1 1 11 11
#> 2: 1 2 12 11
#> 3: 2 1 13 15
#> 4: 3 2 14 NA
#> 5: NA NA 15 NAComparing the results, the same returning data table is produced.
However, joyn::left_join() allows you to enjoy both the
intuitive syntax from dplyr and the additional tools from
joyn. These include additional options to customize how the
join is performed, the availability of the joyn report, messages
informing you on time of execution and the status of the join as well as
the execution of various checks during the merging. (For additional
information on each of these joyn’s features, please take a
look at all the other articles in this website.)
Some examples
1. Left join
ℹ️ Left joins return in the output table all rows from
x, i.e., the left table, and only matching rows from
y, i.e., the right table.
# Data tables to be joined
df1 <- data.frame(id = c(1L, 1L, 2L, 3L, NA_integer_, NA_integer_),
t = c(1L, 2L, 1L, 2L, NA_integer_, 4L),
x = 11:16)
df2 <- data.frame(id = c(1,2, 4, NA_integer_, 8),
y = c(11L, 15L, 16, 17L, 18L),
t = c(13:17))Example usage of some of the joyn’s additional
options:
Updating NAs in left table
Using the update_NAs argument from joyn you
can update the values that are NA in the t variable in the left
table with the actual values from the matching column t in the
right one
left_join(x = df1,
y = df2,
relationship = "many-to-one",
by = "id",
update_NAs = TRUE)
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 1 16.7%
#> 2 x & y 4 66.7%
#> 3 NA updated 1 16.7%
#> 4 total 6 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id, y, and t
#> id t.x x y t.y .joyn
#> 1 1 1 11 11 13 x & y
#> 2 1 2 12 11 13 x & y
#> 3 2 1 13 15 14 x & y
#> 4 3 2 14 NA NA x
#> 5 NA 16 15 17 16 NA updated
#> 6 NA 4 16 17 16 x & ySpecifying which variables to keep from the right table after the join
left_join(x = df1,
y = df2,
relationship = "many-to-one",
by = "id",
y_vars_to_keep = "y")
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 1 16.7%
#> 2 y 2 33.3%
#> 3 x & y 3 50%
#> 4 total 6 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> id t x y .joyn
#> 1 1 1 11 11 x & y
#> 2 1 2 12 11 x & y
#> 3 2 1 13 15 x & y
#> 4 3 2 14 NA x
#> 5 NA NA 15 17 x & y
#> 6 NA 4 16 17 x & y2. Right join
ℹ️ Right joins return in the output table matching rows from
x, i.e., the left table, and all rows from y,
i.e., the right table.
Example usage of some of the joyn’s additional
options:
Specifying a name for the reporting variable
right_join(x = df1,
y = df2,
relationship = "many-to-one",
by = "id",
reportvar = "right.joyn")
#>
#> ── JOYn Report ──
#>
#> right.joyn n percent
#> 1 x 1 14.3%
#> 2 y 2 28.6%
#> 3 x & y 4 57.1%
#> 4 total 7 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable right.joyn
#> ℹ Note: Removing key variables id from id, y, and t
#> id t.x x y t.y right.joyn
#> 1 1 1 11 11 13 x & y
#> 2 1 2 12 11 13 x & y
#> 3 2 1 13 15 14 x & y
#> 4 4 NA NA 16 15 y
#> 5 8 NA NA 18 17 y
#> 6 NA NA 15 17 16 x & y
#> 7 NA 4 16 17 16 x & yUpdating values in common variables
By setting update_values = TRUE, all values in x (both
NAs and not) will be updated with the actual values of variables in y
with the same name as the ones in x. You can then see the status of the
update in the reporting variable.
right_join(x = df1,
y = df2,
relationship = "many-to-one",
by = "id",
reportvar = "right.joyn")
#>
#> ── JOYn Report ──
#>
#> right.joyn n percent
#> 1 x 1 14.3%
#> 2 y 2 28.6%
#> 3 x & y 4 57.1%
#> 4 total 7 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable right.joyn
#> ℹ Note: Removing key variables id from id, y, and t
#> id t.x x y t.y right.joyn
#> 1 1 1 11 11 13 x & y
#> 2 1 2 12 11 13 x & y
#> 3 2 1 13 15 14 x & y
#> 4 4 NA NA 16 15 y
#> 5 8 NA NA 18 17 y
#> 6 NA NA 15 17 16 x & y
#> 7 NA 4 16 17 16 x & y3. Full join
ℹ️ Full joins return in the output table all rows, both matching and
non matching rows from x, i.e., the left table, and
y, i.e., the right table.
full_join(x = x1,
y = y1,
relationship = "many-to-one",
keep = TRUE)
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 2 33.3%
#> 2 y 1 16.7%
#> 3 x & y 3 50%
#> 4 total 6 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id.y, id, and y
#> id t x id.y y .joyn
#> <num> <int> <int> <num> <num> <fctr>
#> 1: 1 1 11 1 11 x & y
#> 2: 1 2 12 1 11 x & y
#> 3: 2 1 13 2 15 x & y
#> 4: 3 2 14 NA NA x
#> 5: 4 NA NA 4 16 y
#> 6: NA NA 15 NA NA x4. Inner join
ℹ️ Inner joins return in the output table only rows that match
between x, i.e., the left table, and y, i.e.,
the right table.
Simple inner join
inner_join(x = df1,
y = df2,
relationship = "many-to-one",
by = "id")
#>
#> ── JOYn Report ──
#>
#> .joyn n percent
#> 1 x 1 20%
#> 2 y 2 40%
#> 3 x & y 2 40%
#> 4 total 5 100%
#> ────────────────────────────────────────────────────────── End of JOYn report ──
#> ℹ Note: Joyn's report available in variable .joyn
#> ℹ Note: Removing key variables id from id, y, and t
#> id t.x x y t.y .joyn
#> 1 1 1 11 11 13 x & y
#> 2 1 2 12 11 13 x & y
#> 3 2 1 13 15 14 x & y
#> 4 NA NA 15 17 16 x & y
#> 5 NA 4 16 17 16 x & y